diff --git a/.github/workflow/ci.yml b/.github/workflow/ci.yml new file mode 100644 index 0000000..98a1c32 --- /dev/null +++ b/.github/workflow/ci.yml @@ -0,0 +1,87 @@ +name: Implementation Testing + +on: + push: + branches: + - '*' + pull_request: + branches: + - '*' + +env: + CACHE_NUMBER: 0 # increase to reset cache manually + +jobs: + foundation: + + strategy: + matrix: + python-version: ["3.10"] + defaults: + run: + shell: bash -l {0} + name: linux-64-py${{ matrix.python-version }} + runs-on: ubuntu-latest + steps: + # checkout the code in this repository + - uses: actions/checkout@v4 + with: + path: 'activitysim-sandag-abm3' + + # checkout the main branch of ActivitySim itself + - uses: actions/checkout@v4 + with: + repository: 'ActivitySim/activitysim' + ref: main + path: 'activitysim' + fetch-depth: 0 # get all tags, lets setuptools_scm do its thing + + - name: Setup Mambaforge + uses: conda-incubator/setup-miniconda@v3 + with: + miniforge-variant: Mambaforge + miniforge-version: latest + activate-environment: asim-test + use-mamba: true + python-version: ${{ matrix.python-version }} + + - name: Set cache date for year and month + run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV + + - uses: actions/cache@v4 + with: + path: /usr/share/miniconda3/envs/asim-test + key: linux-64-conda-${{ hashFiles('activitysim/conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }} + id: cache + + - name: Update environment + run: | + mamba env update -n asim-test -f activitysim/conda-environments/github-actions-tests.yml + mamba install --yes \ + "psutil=5.9.5" \ + "pydantic=2.6.1" \ + "pypyr=5.8.0" \ + "pytables=3.6.1" \ + "pytest-cov" \ + "pytest-regressions=2.5.0" \ + "scikit-learn=1.2.2" \ + "sharrow>=2.6.0" \ + "simwrapper=1.8.5" \ + "xarray=2023.2.0" \ + "zarr=2.14.2" \ + "zstandard=0.21.0" + if: steps.cache.outputs.cache-hit != 'true' + + - name: Install activitysim + # installing without dependencies is faster, we trust that all needed dependencies + # are in the conda environment defined above. Also, this avoids pip getting + # confused and reinstalling tables (pytables). + run: | + python -m pip install -e ./activitysim --no-deps + - name: Conda checkup + run: | + mamba info -a + mamba list + - name: Test this implementation + run: | + python -m pytest sandag-abm3-example/test \ No newline at end of file diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..7e902e8 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +output/* \ No newline at end of file diff --git a/README.md b/README.md index 7985ca5..63e1dc3 100644 --- a/README.md +++ b/README.md @@ -1 +1,3 @@ -# sandag-abm3-example \ No newline at end of file +# sandag-abm3-example + +SANDAG ABM3 Example -- Currently in development... \ No newline at end of file diff --git a/configs/common/constants.yaml b/configs/common/constants.yaml new file mode 100644 index 0000000..f1655aa --- /dev/null +++ b/configs/common/constants.yaml @@ -0,0 +1,296 @@ +## ActivitySim +## See full license in LICENSE.txt. + +scenarioYear: 2022 +# set below to 1 for everything to be internal, 0 otherwise +NO_EXTERNAL: 0 + +# set below to be 1 for years before 2021, 0 otherwise +PRE_COVID: 0 + +walk_speed: 3 #mph + +#HHT_NONE: 0 +#HHT_FAMILY_MARRIED: 1 +#HHT_FAMILY_MALE: 2 +#HHT_FAMILY_FEMALE: 3 +#HHT_NONFAMILY_MALE_ALONE: 4 +#HHT_NONFAMILY_MALE_NOTALONE: 5 +#HHT_NONFAMILY_FEMALE_ALONE: 6 +#HHT_NONFAMILY_FEMALE_NOTALONE: 7 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_PREK: 1 +SCHOOL_SEGMENT_GRADE: 2 +SCHOOL_SEGMENT_HIGH: 3 +SCHOOL_SEGMENT_UNIV: 4 + + +INCOME_SEGMENT_LOW: 1 +INCOME_SEGMENT_MED: 2 +INCOME_SEGMENT_HIGH: 3 +INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: 1 +PTYPE_PART: 2 +PTYPE_UNIVERSITY: 3 +PTYPE_NONWORK: 4 +PTYPE_RETIRED: 5 +PTYPE_DRIVING: 6 +PTYPE_SCHOOL: 7 +PTYPE_PRESCHOOL: 8 + +PTYPE_NAME: + PTYPE_FULL: 1 + PTYPE_PART: 2 + PTYPE_UNIVERSITY: 3 + PTYPE_NONWORK: 4 + PTYPE_RETIRED: 5 + PTYPE_DRIVING: 6 + PTYPE_SCHOOL: 7 + PTYPE_PRESCHOOL: 8 +PTYPE_VALUE: + 1: PTYPE_FULL + 2: PTYPE_PART + 3: PTYPE_UNIVERSITY + 4: PTYPE_NONWORK + 5: PTYPE_RETIRED + 6: PTYPE_DRIVING + 7: PTYPE_SCHOOL + 8: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H + +# Defines cuts on psedomsa in landuse file. Replacement for area_type +cbd_threshold: 2 +urban_threshold: 5 +rural_threshold: 5 + +distributed_time_factor_work_mean: 1.0 +distributed_time_factor_work_stddev: 0.7 +distributed_time_factor_nonwork_mean: 1.0 +distributed_time_factor_nonwork_stddev: 0.6 +distributed_time_factor_min: 0.1 +distributed_time_factor_max: 10 + +c_drive: 1.5 + +useAV: 0 +autoIVTFactorAV: 0.75 +autoParkingCostFactorAV: 0.5 +autoCostPerMileFactorAV: 0.7 +autoTerminalTimeFactorAV: 0.65 +minAgeDriveAloneAV: 13 + + +#valueOfTime: 8.00 +costPerMile: 18.29 +costShareSr2: 1.75 +costShareSr3: 2.50 +waitThresh: 10.00 +walkThresh: 1.50 +shortWalk: 0.333 +longWalk: 0.667 +walkSpeed: 3.00 +bikeThresh: 6.00 +bikeSpeed: 7.80 +ebikeSpeed: 10.00 +escooterSpeed: 6.70 +driveSpeed: 25.00 +maxCbdAreaTypeThresh: 2 +indivTour: 1.00000 + +#### [NICK] WHAT ARE THESE? DO WE EVEN NEED THESE? +upperEA: 5 +upperAM: 10 +upperMD: 15 +upperPM: 19 + +# SHOULD IT BE THIS? +# upperEA: 6 +# upperAM: 12 +# upperMD: 25 +# upperPM: 32 + +### Crosswalk between SOC codes and SANDAG occupation classification +soc_occupation_xwalk: + 11: mngt_busi_scic_arts # 11 - Management Occupations + 13: mngt_busi_scic_arts # 13 - Business and Financial Operations Occupations + 15: mngt_busi_scic_arts # 15 - Computer and Mathematical Occupations + 17: mngt_busi_scic_arts # 17 - Architecture and Engineering Occupations + 19: mngt_busi_scic_arts # 19 - Life, Physical, and Social Science Occupations + 21: mngt_busi_scic_arts # 21 - Community and Social Service Occupations + 23: mngt_busi_scic_arts # 23 - Legal Occupations + 25: services # 25 - Educational Instruction and Library Occupations + 27: mngt_busi_scic_arts # 27 - Arts, Design, Entertainment, Sports, and Media Occupations + 29: health # 29 - Healthcare Practitioners and Technical Occupations + 31: health # 31 - Healthcare Support Occupations + 33: services # 33 - Protective Service Occupations + 35: services # 35 - Food Preparation and Serving Related Occupations + 37: services # 37 - Building and Grounds Cleaning and Maintenance Occupations + 39: services # 39 - Personal Care and Service Occupations + 41: sales_office # 41 - Sales and Related Occupations + 43: sales_office # 43 - Office and Administrative Support Occupations + 45: constr_maint # 45 - Farming, Fishing, and Forestry Occupations + 47: constr_maint # 47 - Construction and Extraction Occupations + 49: constr_maint # 49 - Installation, Maintenance, and Repair Occupations + 51: prod_trans_move # 51 - Production Occupations + 53: prod_trans_move # 53 - Transportation and Material Moving Occupations + 55: military # 55 - Military Specific Occupations + 99: # 99 - Non-workers, Code not part of SOC system + +# RIDEHAIL Settings +Taxi_baseFare: 2.20 +Taxi_costPerMile: 2.30 +Taxi_costPerMinute: 0.10 +Taxi_waitTime_mean: + 1: 5.5 + 2: 9.5 + 3: 13.3 + 4: 17.3 + 5: 26.5 +#### [NICK] Updated all SD numbers from sandag_abm.properties. Were all 0 before +Taxi_waitTime_sd: + 1: 6.4 + 2: 6.4 + 3: 6.4 + 4: 6.4 + 5: 6.4 +TNC_single_baseFare: 2.20 +TNC_single_costPerMile: 1.33 +TNC_single_costPerMinute: 0.24 +TNC_single_costMinimum: 7.20 +TNC_single_waitTime_mean: + 1: 3.0 + 2: 6.3 + 3: 8.4 + 4: 8.5 + 5: 10.3 +TNC_single_waitTime_sd: + 1: 4.1 + 2: 4.1 + 3: 4.1 + 4: 4.1 + 5: 4.1 +TNC_shared_baseFare: 2.20 +TNC_shared_costPerMile: 0.53 +TNC_shared_costPerMinute: 0.10 +TNC_shared_costMinimum: 3.00 +TNC_shared_IVTFactor: 1.5 +TNC_shared_waitTime_mean: + 1: 5.0 + 2: 8.0 + 3: 11.0 + 4: 15.0 + 5: 15.0 +TNC_shared_waitTime_sd: + 1: 4.1 + 2: 4.1 + 3: 4.1 + 4: 4.1 + 5: 4.1 +min_waitTime: 0 +max_waitTime: 50 + +ivt_lrt_multiplier: 0.65 +ivt_brt_multiplier: 0.95 +ivt_cmr_multiplier: 0.65 +ivt_ltd_multiplier: 1.0 +ivt_cost_multiplier: 0.6 +# line-haul mode constants; note commuter rail is based on CMRIVTT. Also currently hyperloop is not applied because we do not skim hyperloop IVTT +eb_equiv_asc: 10 +brt_equiv_asc: -5 +lrt_equiv_asc: -20 +hyp_equiv_asc: -20 +pnr_lrt_equiv_asc: -20 +# +walktimeshort_multiplier: 2 +walktimelong_multiplier: 10 +biketimeshort_multiplier: 4 +biketimelong_multiplier: 20 +short_i_wait_multiplier: 2 +long_i_wait_multiplier: 1 +wacc_multiplier: 2 +wegr_multiplier: 2 +waux_multiplier: 2 +dtim_multiplier: 2 +xwait_multiplier: 2 +dacc_ratio: 0 +xfers_wlk_multiplier: 10 +xfers_drv_multiplier: 20 +drvtrn_distpen_0_multiplier: 270 +drvtrn_distpen_max: 15 +density_index_multiplier: -0.2 +# joint_sr2_ASC_no_auto: 0 +# joint_sr2_ASC_auto_deficient: 0 +# joint_sr2_ASC_auto_sufficient: 0 +# joint_drive_transit_ASC_no_auto: 0 +cost_share_s2: 0.57 +cost_share_s3: 0.37 +vot_threshold_low: 8.81 +vot_threshold_med: 18.00 +max_walk_time: 60 +max_bike_time: 60 +max_walk_distance: 3 +max_bike_distance: 12 +# Location of attractions for associated special size terms +cabrillo_maz: 23831 +seaworld_maz: 8127 +legoland_maz: 24245 +safari_maz: 14345 +midway_maz: 10646 +zoo_maz: 10561 +torrypines_maz: 4093 + +# Micromobility constants +microSpeed: 12 +microVarCost: 20 #cents +microFixedCost: 100 #cents +microRentTime: 1 +microConstant: 60 +microAccessThreshold: 100 +ebikeownership: 0.008 +ebikeMaxDist: 10.50 +escooterMaxDist: 2.00 + +# Microtransit and NEV constants +microtransitSpeed: 30 +microtransitCost: 125 #cents +microtransitWaitTime: 12 +microtransitMaxDist: 4.5 +microtransitDiversionConstant: 5 +microtransitDiversionFactor: 1.25 +nevSpeed: 17 +nevCost: 125 #cents +nevWaitTime: 12 +nevMaxDist: 3 +nevDwellMinutesPerMile: 0.5 +nevDiversionConstant: 5 +nevDiversionFactor: 1.25 +maxWalkIfMTAccessAvailable: 0.5 # Maximum distance to walk to premium transit if microtransit access is available + +# cost of "average" monthly transit pass cost. Used in transit pass ownership model. +# cost of pass divided by 2 for age < 18 and 65+. +monthly_transit_pass_cost: 100 + +# year after which a vehicle (therefore its household) must have transponder +hhTR_Vehyear: 2035 diff --git a/configs/common/network_los.yaml b/configs/common/network_los.yaml new file mode 100644 index 0000000..a7d195a --- /dev/null +++ b/configs/common/network_los.yaml @@ -0,0 +1,75 @@ +inherit_settings: True + +zone_system: 2 + +skim_dict_factory: NumpyArraySkimFactory +#skim_dict_factory: MemMapSkimFactory + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False + +# taz_skims: +# - traffic_skims_processed_EA.omx +# - traffic_skims_processed_AM.omx +# - traffic_skims_processed_MD.omx +# - traffic_skims_processed_PM.omx +# - traffic_skims_processed_EV.omx +# #- transit_skims_processed.omx +# - transit_skims_EA.omx +# - transit_skims_AM.omx +# - transit_skims_MD.omx +# - transit_skims_PM.omx +# - transit_skims_EV.omx + +# series15 +taz_skims: + - traffic_skims*.omx + - transit_skims*.omx + +maz: land_use.csv # mgra.csv + +maz_to_maz: + tables: + - maz_maz_walk.csv + - maz_maz_bike.csv + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: 2 + + +skim_time_periods: + time_window: 1440 + period_minutes: 30 + # periods: [0, 11, 17, 30, 37, 48] # asim xborder + # periods: [0, 3, 9, 22, 29, 48] # ctramp xborder + periods: [0, 6, 12, 25, 32, 48] # time periods to match documentation + labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] + +demographic_segments: &demographic_segments + - &low_income_segment_id 0 + - &high_income_segment_id 1 + + + +# CONSTANTS: +# c_ivt: -0.028 +# c_cost: -0.0011 +# # transit "factors" from SANDAG +# eb_fac: 1.0 +# brt_fac: 0.9000 +# lrt_fac: 0.6500 +# cr_fac: 0.6500 +# hyp_fac: 0.6500 +# # coeffs used in maz_tap and tap_tap utility expressions +# c_walk: 1.7 +# c_fwt: 1.5 +# c_waux: 2.5 +# c_xwt: 2 +# C_UNAVAILABLE: -9999 +# # alternative-specific constants +# exp_asc: 10 # express bus asc +# brt_asc: -10 # BRT asc +# lrt_asc: -20 # LRT alternative-specific constant +# cr_asc: 0.00 # Commuter Rail alternative-specific constant + diff --git a/configs/common/outputs.yaml b/configs/common/outputs.yaml new file mode 100644 index 0000000..d4762d4 --- /dev/null +++ b/configs/common/outputs.yaml @@ -0,0 +1,788 @@ +trips: + remove_columns: + - DRIVEALONE_NOTR_EA_LOW + - SHARED2_NOTR_EA_LOW + - SHARED3_NOTR_EA_LOW + - DRIVEALONE_TR_EA_LOW + - SHARED2_TR_EA_LOW + - SHARED3_TR_EA_LOW + - DRIVEALONE_NOTR_EA_MED + - SHARED2_NOTR_EA_MED + - SHARED3_NOTR_EA_MED + - DRIVEALONE_TR_EA_MED + - SHARED2_TR_EA_MED + - SHARED3_TR_EA_MED + - DRIVEALONE_NOTR_EA_HIGH + - SHARED2_NOTR_EA_HIGH + - SHARED3_NOTR_EA_HIGH + - DRIVEALONE_TR_EA_HIGH + - SHARED2_TR_EA_HIGH + - SHARED3_TR_EA_HIGH + - WALK_LOC_EA + - WALK_PRM_EA + - WALK_MIX_EA + - PNR_LOCOUT_EA + - PNR_PRMOUT_EA + - PNR_MIXOUT_EA + - KNR_LOCOUT_EA + - KNR_PRMOUT_EA + - KNR_MIXOUT_EA + - TNC_LOCOUT_EA + - TNC_PRMOUT_EA + - TNC_MIXOUT_EA + - PNR_LOCIN_EA + - PNR_PRMIN_EA + - PNR_MIXIN_EA + - KNR_LOCIN_EA + - KNR_PRMIN_EA + - KNR_MIXIN_EA + - TNC_LOCIN_EA + - TNC_PRMIN_EA + - TNC_MIXIN_EA + - BIKE_EA + - WALK_EA + - DRIVEALONE_NOTR_AM_LOW + - SHARED2_NOTR_AM_LOW + - SHARED3_NOTR_AM_LOW + - DRIVEALONE_TR_AM_LOW + - SHARED2_TR_AM_LOW + - SHARED3_TR_AM_LOW + - DRIVEALONE_NOTR_AM_MED + - SHARED2_NOTR_AM_MED + - SHARED3_NOTR_AM_MED + - DRIVEALONE_TR_AM_MED + - SHARED2_TR_AM_MED + - SHARED3_TR_AM_MED + - DRIVEALONE_NOTR_AM_HIGH + - SHARED2_NOTR_AM_HIGH + - SHARED3_NOTR_AM_HIGH + - DRIVEALONE_TR_AM_HIGH + - SHARED2_TR_AM_HIGH + - SHARED3_TR_AM_HIGH + - WALK_LOC_AM + - WALK_PRM_AM + - WALK_MIX_AM + - PNR_LOCOUT_AM + - PNR_PRMOUT_AM + - PNR_MIXOUT_AM + - KNR_LOCOUT_AM + - KNR_PRMOUT_AM + - KNR_MIXOUT_AM + - TNC_LOCOUT_AM + - TNC_PRMOUT_AM + - TNC_MIXOUT_AM + - PNR_LOCIN_AM + - PNR_PRMIN_AM + - PNR_MIXIN_AM + - KNR_LOCIN_AM + - KNR_PRMIN_AM + - KNR_MIXIN_AM + - TNC_LOCIN_AM + - TNC_PRMIN_AM + - TNC_MIXIN_AM + - BIKE_AM + - WALK_AM + - DRIVEALONE_NOTR_MD_LOW + - SHARED2_NOTR_MD_LOW + - SHARED3_NOTR_MD_LOW + - DRIVEALONE_TR_MD_LOW + - SHARED2_TR_MD_LOW + - SHARED3_TR_MD_LOW + - DRIVEALONE_NOTR_MD_MED + - SHARED2_NOTR_MD_MED + - SHARED3_NOTR_MD_MED + - DRIVEALONE_TR_MD_MED + - SHARED2_TR_MD_MED + - SHARED3_TR_MD_MED + - DRIVEALONE_NOTR_MD_HIGH + - SHARED2_NOTR_MD_HIGH + - SHARED3_NOTR_MD_HIGH + - DRIVEALONE_TR_MD_HIGH + - SHARED2_TR_MD_HIGH + - SHARED3_TR_MD_HIGH + - WALK_LOC_MD + - WALK_PRM_MD + - WALK_MIX_MD + - PNR_LOCOUT_MD + - PNR_PRMOUT_MD + - PNR_MIXOUT_MD + - KNR_LOCOUT_MD + - KNR_PRMOUT_MD + - KNR_MIXOUT_MD + - TNC_LOCOUT_MD + - TNC_PRMOUT_MD + - TNC_MIXOUT_MD + - PNR_LOCIN_MD + - PNR_PRMIN_MD + - PNR_MIXIN_MD + - KNR_LOCIN_MD + - KNR_PRMIN_MD + - KNR_MIXIN_MD + - TNC_LOCIN_MD + - TNC_PRMIN_MD + - TNC_MIXIN_MD + - BIKE_MD + - WALK_MD + - DRIVEALONE_NOTR_PM_LOW + - SHARED2_NOTR_PM_LOW + - SHARED3_NOTR_PM_LOW + - DRIVEALONE_TR_PM_LOW + - SHARED2_TR_PM_LOW + - SHARED3_TR_PM_LOW + - DRIVEALONE_NOTR_PM_MED + - SHARED2_NOTR_PM_MED + - SHARED3_NOTR_PM_MED + - DRIVEALONE_TR_PM_MED + - SHARED2_TR_PM_MED + - SHARED3_TR_PM_MED + - DRIVEALONE_NOTR_PM_HIGH + - SHARED2_NOTR_PM_HIGH + - SHARED3_NOTR_PM_HIGH + - DRIVEALONE_TR_PM_HIGH + - SHARED2_TR_PM_HIGH + - SHARED3_TR_PM_HIGH + - WALK_LOC_PM + - WALK_PRM_PM + - WALK_MIX_PM + - PNR_LOCOUT_PM + - PNR_PRMOUT_PM + - PNR_MIXOUT_PM + - KNR_LOCOUT_PM + - KNR_PRMOUT_PM + - KNR_MIXOUT_PM + - TNC_LOCOUT_PM + - TNC_PRMOUT_PM + - TNC_MIXOUT_PM + - PNR_LOCIN_PM + - PNR_PRMIN_PM + - PNR_MIXIN_PM + - KNR_LOCIN_PM + - KNR_PRMIN_PM + - KNR_MIXIN_PM + - TNC_LOCIN_PM + - TNC_PRMIN_PM + - TNC_MIXIN_PM + - BIKE_PM + - WALK_PM + - DRIVEALONE_NOTR_EV_LOW + - SHARED2_NOTR_EV_LOW + - SHARED3_NOTR_EV_LOW + - DRIVEALONE_TR_EV_LOW + - SHARED2_TR_EV_LOW + - SHARED3_TR_EV_LOW + - DRIVEALONE_NOTR_EV_MED + - SHARED2_NOTR_EV_MED + - SHARED3_NOTR_EV_MED + - DRIVEALONE_TR_EV_MED + - SHARED2_TR_EV_MED + - SHARED3_TR_EV_MED + - DRIVEALONE_NOTR_EV_HIGH + - SHARED2_NOTR_EV_HIGH + - SHARED3_NOTR_EV_HIGH + - DRIVEALONE_TR_EV_HIGH + - SHARED2_TR_EV_HIGH + - SHARED3_TR_EV_HIGH + - WALK_LOC_EV + - WALK_PRM_EV + - WALK_MIX_EV + - PNR_LOCOUT_EV + - PNR_PRMOUT_EV + - PNR_MIXOUT_EV + - KNR_LOCOUT_EV + - KNR_PRMOUT_EV + - KNR_MIXOUT_EV + - TNC_LOCOUT_EV + - TNC_PRMOUT_EV + - TNC_MIXOUT_EV + - PNR_LOCIN_EV + - PNR_PRMIN_EV + - PNR_MIXIN_EV + - KNR_LOCIN_EV + - KNR_PRMIN_EV + - KNR_MIXIN_EV + - TNC_LOCIN_EV + - TNC_PRMIN_EV + - TNC_MIXIN_EV + - BIKE_EV + - WALK_EV + - DRIVEALONE_EA_LOW + - SHARED2_EA_LOW + - SHARED3_EA_LOW + - DRIVEALONE_EA_MED + - SHARED2_EA_MED + - SHARED3_EA_MED + - DRIVEALONE_EA_HIGH + - SHARED2_EA_HIGH + - SHARED3_EA_HIGH + - WALK_SET_set1_EA + - WALK_SET_set2_EA + - WALK_SET_set3_EA + - PNROUT_SET_set1_EA + - PNROUT_SET_set2_EA + - PNROUT_SET_set3_EA + - KNROUT_SET_set1_EA + - KNROUT_SET_set2_EA + - KNROUT_SET_set3_EA + - TNCOUT_SET_set1_EA + - TNCOUT_SET_set2_EA + - TNCOUT_SET_set3_EA + - PNRIN_SET_set1_EA + - PNRIN_SET_set2_EA + - PNRIN_SET_set3_EA + - KNRIN_SET_set1_EA + - KNRIN_SET_set2_EA + - KNRIN_SET_set3_EA + - TNCIN_SET_set1_EA + - TNCIN_SET_set2_EA + - TNCIN_SET_set3_EA + - DRIVEALONE_AM_LOW + - SHARED2_AM_LOW + - SHARED3_AM_LOW + - DRIVEALONE_AM_MED + - SHARED2_AM_MED + - SHARED3_AM_MED + - DRIVEALONE_AM_HIGH + - SHARED2_AM_HIGH + - SHARED3_AM_HIGH + - WALK_SET_set1_AM + - WALK_SET_set2_AM + - WALK_SET_set3_AM + - PNROUT_SET_set1_AM + - PNROUT_SET_set2_AM + - PNROUT_SET_set3_AM + - KNROUT_SET_set1_AM + - KNROUT_SET_set2_AM + - KNROUT_SET_set3_AM + - TNCOUT_SET_set1_AM + - TNCOUT_SET_set2_AM + - TNCOUT_SET_set3_AM + - PNRIN_SET_set1_AM + - PNRIN_SET_set2_AM + - PNRIN_SET_set3_AM + - KNRIN_SET_set1_AM + - KNRIN_SET_set2_AM + - KNRIN_SET_set3_AM + - TNCIN_SET_set1_AM + - TNCIN_SET_set2_AM + - TNCIN_SET_set3_AM + - DRIVEALONE_MD_LOW + - SHARED2_MD_LOW + - SHARED3_MD_LOW + - DRIVEALONE_MD_MED + - SHARED2_MD_MED + - SHARED3_MD_MED + - DRIVEALONE_MD_HIGH + - SHARED2_MD_HIGH + - SHARED3_MD_HIGH + - WALK_SET_set1_MD + - WALK_SET_set2_MD + - WALK_SET_set3_MD + - PNROUT_SET_set1_MD + - PNROUT_SET_set2_MD + - PNROUT_SET_set3_MD + - KNROUT_SET_set1_MD + - KNROUT_SET_set2_MD + - KNROUT_SET_set3_MD + - TNCOUT_SET_set1_MD + - TNCOUT_SET_set2_MD + - TNCOUT_SET_set3_MD + - PNRIN_SET_set1_MD + - PNRIN_SET_set2_MD + - PNRIN_SET_set3_MD + - KNRIN_SET_set1_MD + - KNRIN_SET_set2_MD + - KNRIN_SET_set3_MD + - TNCIN_SET_set1_MD + - TNCIN_SET_set2_MD + - TNCIN_SET_set3_MD + - DRIVEALONE_PM_LOW + - SHARED2_PM_LOW + - SHARED3_PM_LOW + - DRIVEALONE_PM_MED + - SHARED2_PM_MED + - SHARED3_PM_MED + - DRIVEALONE_PM_HIGH + - SHARED2_PM_HIGH + - SHARED3_PM_HIGH + - WALK_SET_set1_PM + - WALK_SET_set2_PM + - WALK_SET_set3_PM + - PNROUT_SET_set1_PM + - PNROUT_SET_set2_PM + - PNROUT_SET_set3_PM + - KNROUT_SET_set1_PM + - KNROUT_SET_set2_PM + - KNROUT_SET_set3_PM + - TNCOUT_SET_set1_PM + - TNCOUT_SET_set2_PM + - TNCOUT_SET_set3_PM + - PNRIN_SET_set1_PM + - PNRIN_SET_set2_PM + - PNRIN_SET_set3_PM + - KNRIN_SET_set1_PM + - KNRIN_SET_set2_PM + - KNRIN_SET_set3_PM + - TNCIN_SET_set1_PM + - TNCIN_SET_set2_PM + - TNCIN_SET_set3_PM + - DRIVEALONE_EV_LOW + - SHARED2_EV_LOW + - SHARED3_EV_LOW + - DRIVEALONE_EV_MED + - SHARED2_EV_MED + - SHARED3_EV_MED + - DRIVEALONE_EV_HIGH + - SHARED2_EV_HIGH + - SHARED3_EV_HIGH + - WALK_SET_set1_EV + - WALK_SET_set2_EV + - WALK_SET_set3_EV + - PNROUT_SET_set1_EV + - PNROUT_SET_set2_EV + - PNROUT_SET_set3_EV + - KNROUT_SET_set1_EV + - KNROUT_SET_set2_EV + - KNROUT_SET_set3_EV + - TNCOUT_SET_set1_EV + - TNCOUT_SET_set2_EV + - TNCOUT_SET_set3_EV + - PNRIN_SET_set1_EV + - PNRIN_SET_set2_EV + - PNRIN_SET_set3_EV + - KNRIN_SET_set1_EV + - KNRIN_SET_set2_EV + - KNRIN_SET_set3_EV + - TNCIN_SET_set1_EV + - TNCIN_SET_set2_EV + - TNCIN_SET_set3_EV + - DRIVEALONE_EA_low + - SHARED2_EA_low + - SHARED3_EA_low + - DRIVEALONE_EA_med + - SHARED2_EA_med + - SHARED3_EA_med + - DRIVEALONE_EA_high + - SHARED2_EA_high + - SHARED3_EA_high + - TAXI_EA + - TNC_SINGLE_EA + - TNC_SHARED_EA + - DRIVEALONE_AM_low + - SHARED2_AM_low + - SHARED3_AM_low + - DRIVEALONE_AM_med + - SHARED2_AM_med + - SHARED3_AM_med + - DRIVEALONE_AM_high + - SHARED2_AM_high + - SHARED3_AM_high + - TAXI_AM + - TNC_SINGLE_AM + - TNC_SHARED_AM + - DRIVEALONE_MD_low + - SHARED2_MD_low + - SHARED3_MD_low + - DRIVEALONE_MD_med + - SHARED2_MD_med + - SHARED3_MD_med + - DRIVEALONE_MD_high + - SHARED2_MD_high + - SHARED3_MD_high + - TAXI_MD + - TNC_SINGLE_MD + - TNC_SHARED_MD + - DRIVEALONE_PM_low + - SHARED2_PM_low + - SHARED3_PM_low + - DRIVEALONE_PM_med + - SHARED2_PM_med + - SHARED3_PM_med + - DRIVEALONE_PM_high + - SHARED2_PM_high + - SHARED3_PM_high + - TAXI_PM + - TNC_SINGLE_PM + - TNC_SHARED_PM + - DRIVEALONE_EV_low + - SHARED2_EV_low + - SHARED3_EV_low + - DRIVEALONE_EV_med + - SHARED2_EV_med + - SHARED3_EV_med + - DRIVEALONE_EV_high + - SHARED2_EV_high + - SHARED3_EV_high + - TAXI_EV + - TNC_SINGLE_EV + - TNC_SHARED_EV + - is_walk_LOC_transit + - is_walk_PRM_transit + - is_walk_MIX_transit + - is_knr_loc + - is_knr_prm + - is_knr_mix + - is_tnc_loc + - is_tnc_prm + - is_tnc_mix + - is_pnr_loc + - is_pnr_prm + - is_pnr_mix + - is_drivealone + - is_shared2 + - is_shared3 + - is_walk + - is_drive_loc + - is_drive_prm + - is_drive_mix + + rename_columns: + distance: distance_total + weightTrip: weight_trip + weightPersonTrip: weight_person_trip + weightPerson_trip: weight_person_trip + timeDrive: time_drive + ownsTransponder: owns_transponder + distanceDrive: distance_drive + distanceDriveTransit: distance_drive_transit + timeTransitInVehicle: time_transit_in_vehicle + timeRapidTransitInVehicle: time_rapid_transit_in_vehicle + timeExpressBusTransitInVehicle: time_express_bus_transit_in_vehicle + timeLightRailTransitInVehicle: time_light_rail_transit_in_vehicle + timeCommuterRailTransitInVehicle: time_commuter_rail_transit_in_vehicle + timeTransitInitialWait: time_transit_initial_wait + timeBike: time_bike + timeLocalBusTransitInVehicle: time_local_bus_transit_in_vehicle + timeWalk: time_walk + timeTotal: time_total + costTollDrive: cost_toll_drive + costOperatingDrive: cost_operating_drive + costFareTransit: cost_fare_transit + costTotal: cost_total + transfersTransit: transfers_transit + +households: + remove_columns: + - taz + - dudenbin + - othercollege_All_Graduate + - PopEmpDenPerMi + - remoteAVParking + - parking_type + - emp_gov + - mstallsoth + - walk_dist_premium_transit + - mgra.1 + - othercollege_ft_Undergrad + - gq_mil + - emp_ag_min + - hotelroomtotal + - enrollgrade9to12 + - emp_non_ws_wfh + - dstallsoth + - refueling_stations + - emp_hlth + - totintbin + - emp_mil + - exp_daily + - parkactive + - AVGTTS + - hch_dist + - emp_whl + - truckregiontype + - othercollegeenroll + - enrollgradekto8 + - empdenbin + - othercollege_pt_Undergrad + - dparkcost + - emp_food + - emp_ret + - terminal_time + - major_All_Graduate + - duden + - luxuryroom + - midpriceroom + - hh + - walk_dist_local_bus + - majorcollege_pt_Undergrad + - emp_bus_svcs + - hstallsoth + - hparkcost + - emp_retail + - pseudomsa + - exp_hourly + - ech_dist + - othercollege_All_Undergrad + - mparkcost + - effective_acres + - MicroAccessTime + - TAZ.1 + - land_acres + - empden + - acres + - emp_fin_res_mgm + - PCTDETOUR + - pop + - majorcollege_All_Undergrad + - hhp + - milestocoast + - popden + - majorcollege_ft_Undergrad + - emp_restaurant_bar + - external_work + - is_parking_zone + - emp_amusement + - external_TAZ + - totint + - hhs + - emp_total + - emp_mnf + - dstallssam + - external_nonwork + - poe_id + - is_cbd + - exp_monthly + - density_index + - tot_collegeenroll + - collegeenroll + - preschool_target + - retempden + - gq_civ + - household_density + - numfreehrs + - i10 + - emp_educ + - emp_utl + - emp_con + - parkarea + - mstallssam + - parking_spaces + - population_density + - emp_accm + - end_iteration + - district27 + - emp_hotel.1 + - employment_density + - openspaceparkpreserve + - emp_trn_wrh + - emp_oth + - emp_ent + - beachactive + - upscaleroom + - hstallssam + - external_MAZ + - emp_hotel + - ML_DIST + - emp_non_ws_oth + - budgetroom + - economyroom + rename_columns: + bldgsz: building_category + hhid: household_id + +land_use: + remove_columns: + - taz + - std_wait_1 + - std_wait_2 + - std_wait_3 + - std_wait_4 + - std_wait_5 + - std_wait_6 + - std_wait_7 + - std_wait_8 + - std_wait_9 + - std_wait_10 + - std_wait_11 + - std_wait_12 + - std_wait_13 + - std_wait_14 + - std_wait_15 + - std_wait_16 + - std_wait_17 + - std_wait_18 + - std_wait_19 + - std_wait_20 + - std_wait_21 + - std_wait_22 + - std_wait_23 + - std_wait_24 + - std_wait_25 + - std_wait_26 + - std_wait_27 + - std_wait_28 + - std_wait_29 + - std_wait_30 + - std_wait_31 + - std_wait_32 + - std_wait_33 + - std_wait_34 + - std_wait_35 + - std_wait_36 + - std_wait_37 + - std_wait_38 + - std_wait_39 + - std_wait_40 + - std_wait_41 + - std_wait_42 + - std_wait_43 + - std_wait_44 + - std_wait_45 + - std_wait_46 + - std_wait_47 + - std_wait_48 + - sentri_wait_1 + - sentri_wait_2 + - sentri_wait_3 + - sentri_wait_4 + - sentri_wait_5 + - sentri_wait_6 + - sentri_wait_7 + - sentri_wait_8 + - sentri_wait_9 + - sentri_wait_10 + - sentri_wait_11 + - sentri_wait_12 + - sentri_wait_13 + - sentri_wait_14 + - sentri_wait_15 + - sentri_wait_16 + - sentri_wait_17 + - sentri_wait_18 + - sentri_wait_19 + - sentri_wait_20 + - sentri_wait_21 + - sentri_wait_22 + - sentri_wait_23 + - sentri_wait_24 + - sentri_wait_25 + - sentri_wait_26 + - sentri_wait_27 + - sentri_wait_28 + - sentri_wait_29 + - sentri_wait_30 + - sentri_wait_31 + - sentri_wait_32 + - sentri_wait_33 + - sentri_wait_34 + - sentri_wait_35 + - sentri_wait_36 + - sentri_wait_37 + - sentri_wait_38 + - sentri_wait_39 + - sentri_wait_40 + - sentri_wait_41 + - sentri_wait_42 + - sentri_wait_43 + - sentri_wait_44 + - sentri_wait_45 + - sentri_wait_46 + - sentri_wait_47 + - sentri_wait_48 + - ped_wait_1 + - ped_wait_2 + - ped_wait_3 + - ped_wait_4 + - ped_wait_5 + - ped_wait_6 + - ped_wait_7 + - ped_wait_8 + - ped_wait_9 + - ped_wait_10 + - ped_wait_11 + - ped_wait_12 + - ped_wait_13 + - ped_wait_14 + - ped_wait_15 + - ped_wait_16 + - ped_wait_17 + - ped_wait_18 + - ped_wait_19 + - ped_wait_20 + - ped_wait_21 + - ped_wait_22 + - ped_wait_23 + - ped_wait_24 + - ped_wait_25 + - ped_wait_26 + - ped_wait_27 + - ped_wait_28 + - ped_wait_29 + - ped_wait_30 + - ped_wait_31 + - ped_wait_32 + - ped_wait_33 + - ped_wait_34 + - ped_wait_35 + - ped_wait_36 + - ped_wait_37 + - ped_wait_38 + - ped_wait_39 + - ped_wait_40 + - ped_wait_41 + - ped_wait_42 + - ped_wait_43 + - ped_wait_44 + - ped_wait_45 + - ped_wait_46 + - ped_wait_47 + - ped_wait_48 + - ready_wait_1 + - ready_wait_2 + - ready_wait_3 + - ready_wait_4 + - ready_wait_5 + - ready_wait_6 + - ready_wait_7 + - ready_wait_8 + - ready_wait_9 + - ready_wait_10 + - ready_wait_11 + - ready_wait_12 + - ready_wait_13 + - ready_wait_14 + - ready_wait_15 + - ready_wait_16 + - ready_wait_17 + - ready_wait_18 + - ready_wait_19 + - ready_wait_20 + - ready_wait_21 + - ready_wait_22 + - ready_wait_23 + - ready_wait_24 + - ready_wait_25 + - ready_wait_26 + - ready_wait_27 + - ready_wait_28 + - ready_wait_29 + - ready_wait_30 + - ready_wait_31 + - ready_wait_32 + - ready_wait_33 + - ready_wait_34 + - ready_wait_35 + - ready_wait_36 + - ready_wait_37 + - ready_wait_38 + - ready_wait_39 + - ready_wait_40 + - ready_wait_41 + - ready_wait_42 + - ready_wait_43 + - ready_wait_44 + - ready_wait_45 + - ready_wait_46 + - ready_wait_47 + - ready_wait_48 + +persons: + remove_columns: + - naics2_original_code + rename_columns: + rac1p: race + hisp: hispanic + perid: person_id + +tours: + rename_columns: + vehicle_occup_3.5: vehicle_occup_3_5 \ No newline at end of file diff --git a/configs/resident/accessibility.csv b/configs/resident/accessibility.csv new file mode 100644 index 0000000..df03420 --- /dev/null +++ b/configs/resident/accessibility.csv @@ -0,0 +1,55 @@ +Description,Target,Expression +#,, +#,, auto peak +#,, +#,, assume peak occurs in AM for outbound and PM for inbound +peak round trip distance,_auPkTime,"skim_od[('SOV_TR_M_TIME', 'AM')] + skim_do[('SOV_TR_M_TIME', 'PM')]" +decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile) +auto peak retail,auPkRetail,df.emp_ret * _decay +auto peak total,auPkTotal,df.emp_total * _decay +#,, +#,, auto off-peak +#,, +#,, assume midday occurs entirely in the midday period +off-peak round trip distance,_auOpTime,"skim_od[('SOV_TR_M_TIME', 'MD')] + skim_do[('SOV_TR_M_TIME', 'MD')]" +decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile) +auto off-peak retail,auOpRetail,df.emp_ret * _decay +auto off-peak total,auOpTotal,df.emp_total * _decay +#,, +#,, transit peak +#,, assume peak outbound transit occurs in AM +o-d peak transit time,_inVehicletime_od,"skim_od[('WALK_MIX_TOTALIVTT', 'AM')]" +o-d out of vehicle transit time,_outOfVehicleTime_od,"skim_od[('WALK_MIX_FIRSTWAIT', 'AM')] + skim_od[('WALK_MIX_XFERWAIT', 'AM')] + reindex(df.walk_dist_local_bus, df.orig) + skim_od[('WALK_MIX_XFERWALK', 'AM')] + reindex(df.walk_dist_local_bus, df.dest)" +total o-d peak transit time,_trPkTime_od,(_inVehicletime_od + out_of_vehicle_time_weight * _outOfVehicleTime_od) +#,, assume peak inbound transit occurs in PM +o-d peak transit time,_inVehicletime_do,"skim_do[('WALK_MIX_TOTALIVTT', 'PM')]" +o-d out of vehicle transit time,_outOfVehicleTime_do,"skim_do[('WALK_MIX_FIRSTWAIT', 'PM')] + skim_do[('WALK_MIX_XFERWAIT', 'PM')] + reindex(df.walk_dist_local_bus, df.orig) + skim_do[('WALK_MIX_XFERWALK', 'PM')] + reindex(df.walk_dist_local_bus, df.dest)" +total o-d peak transit time,_trPkTime_do,(_inVehicletime_do + out_of_vehicle_time_weight * _outOfVehicleTime_do) +#,, +#o-d peak transit time,_trPkTime_do,"skim_do[('WALK_MIX_TOTALIVTT', 'PM')]" +#,_outOfVehicleTime_do,"skim_do[('WLK_TRN_IWAIT', 'PM')] + skim_do[('WLK_TRN_XWAIT', 'PM')] + skim_do[('WLK_TRN_WACC', 'PM')] + skim_do[('WLK_TRN_WAUX', 'PM')] + skim_do[('WLK_TRN_WEGR', 'PM')]" +peak transit time,_trPkTime,(_trPkTime_od + _trPkTime_do).clip(0) +round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0) +decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit) +transit peak retail,trPkRetail,df.emp_ret * _decay +transit peak total,trPkTotal,df.emp_total * _decay +transit peak total,trPkHH,df.hh * _decay +####,, +####,, transit off-peak +####,, +####,, assume off-peak inbound and outbound transit occurs in the MD time period +o-d off-peak transit time,_trOpTime_od,"skim_od[('WALK_MIX_TOTALIVTT', 'MD')]" +d-o off-peak transit time,_trOpTime_do,"skim_do[('WALK_MIX_TOTALIVTT', 'MD')]" +off-peak transit time,_trOpTime,(_trOpTime_od + _trPkTime_do).clip(0) +round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0) +decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit) +transit off-peak retail,trOpRetail,df.emp_ret * _decay +transit off-peak total,trOpTotal,df.emp_total * _decay +#,, +#,, non motorized +#,, +non-motorized round trip distance,_nmDist,skim_od['DIST'] + skim_do['DIST'] +round trip path is available,_rt_available,_nmDist <= maximum_walk_distance +decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk) +retail accessibility,nmRetail,df.emp_ret * _decay +total accessibility,nmTotal,df.emp_total * _decay diff --git a/configs/resident/accessibility.yaml b/configs/resident/accessibility.yaml new file mode 100644 index 0000000..927af56 --- /dev/null +++ b/configs/resident/accessibility.yaml @@ -0,0 +1,13 @@ + +# columns from land_use table to add to df +land_use_columns: ['emp_ret', 'emp_total', 'hh', 'walk_dist_local_bus'] + +CONSTANTS: + # dispersion parameters + dispersion_parameter_automobile: -0.05 + dispersion_parameter_transit: -0.05 + dispersion_parameter_walk: -1.00 + # maximum walk distance in miles + maximum_walk_distance: 3.0 + # perceived minute of in-vehicle time for every minute of out-of-vehicle time + out_of_vehicle_time_weight: 2.0 diff --git a/configs/resident/annotate_disaggregate_accessibility.csv b/configs/resident/annotate_disaggregate_accessibility.csv new file mode 100644 index 0000000..821dda4 --- /dev/null +++ b/configs/resident/annotate_disaggregate_accessibility.csv @@ -0,0 +1,11 @@ +# annotating the proto_disaggregate_accessibilty table +Description,Target,Expression +workplace location for zero auto only,workplace_location_accessibility_0,"np.where(df.auto_ownership == 0, df.workplace_location_accessibility, np.nan)" +workplace location for auto deficient,workplace_location_accessibility_1,"np.where(df.auto_ownership == 1, df.workplace_location_accessibility, np.nan)" +workplace location for auto sufficient,workplace_location_accessibility_2,"np.where(df.auto_ownership == 2, df.workplace_location_accessibility, np.nan)" +other discretionary for zero auto only,othdiscr_accessibility_0,"np.where(df.auto_ownership == 0, df.othdiscr_accessibility, np.nan)" +other discretionary for auto deficient,othdiscr_accessibility_1,"np.where(df.auto_ownership ==1, df.othdiscr_accessibility, np.nan)" +other discretionary for auto sufficient,othdiscr_accessibility_2,"np.where(df.auto_ownership == 2, df.othdiscr_accessibility, np.nan)" +shopping for zero auto only,shopping_accessibility_0,"np.where(df.auto_ownership == 0, df.shopping_accessibility, np.nan)" +shopping for auto deficient,shopping_accessibility_1,"np.where(df.auto_ownership ==1, df.shopping_accessibility, np.nan)" +shopping for auto sufficient,shopping_accessibility_2,"np.where(df.auto_ownership == 2, df.shopping_accessibility, np.nan)" \ No newline at end of file diff --git a/configs/resident/annotate_households.csv b/configs/resident/annotate_households.csv new file mode 100644 index 0000000..f52913d --- /dev/null +++ b/configs/resident/annotate_households.csv @@ -0,0 +1,43 @@ +Description,Target,Expression +#,, annotate households table after import +,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +#,,FIXME households.income can be negative - so we clip? +income_in_thousands,income_in_thousands,(households.income / 1000).clip(lower=0) +income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[INCOME_SEGMENT_LOW, INCOME_SEGMENT_MED, INCOME_SEGMENT_HIGH, INCOME_SEGMENT_VERYHIGH]).astype(int)" +#,, +number of non_workers,num_non_workers,households.hhsize - households.num_workers +#,, +#,,we assume that everyone 16 and older is a potential driver +number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)" +num_adults,num_adults,"_PERSON_COUNT('age >= 18', persons, households)" +num_children,num_children,"_PERSON_COUNT('age < 18', persons, households)" +num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)" +num_children_6_to_12,num_children_6_to_12,"_PERSON_COUNT('6 <= age <= 12', persons, households)" +num_children_5_to_15,num_children_5_to_15,"_PERSON_COUNT('5 <= age <= 15', persons, households)" +num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)" +num_gradeschool,num_gradeschool,"_PERSON_COUNT('6 <= age <= 17', persons, households)" +num_highschool,num_highschool,"_PERSON_COUNT('14 <= age <= 17', persons, households)" +num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)" +num_young_adults,num_young_adults,"_PERSON_COUNT('25 <= age <= 34', persons, households)" +number of pre-driving age children in the household,num_predrive_child,"_PERSON_COUNT('ptype == 7', persons, households)" +number of non-working adult in the household,num_nonworker_adults,"_PERSON_COUNT('ptype == 4', persons, households)" +number of university students in the household,num_university_students,"_PERSON_COUNT('ptype == 3', persons, households)" +number of full time workers,num_fullTime_workers,"_PERSON_COUNT('is_fulltime_worker', persons, households)" +number of part time workers,num_partTime_workers,"_PERSON_COUNT('is_parttime_worker', persons, households)" +number of retired adults in the household,num_retired_adults,"_PERSON_COUNT('ptype == 5', persons, households)" +num_highschool_graduates,num_highschool_graduates,"_PERSON_COUNT('9 <= educ', persons, households)" +# Used in auto ownership,, +,num_children_6_to_15,"_PERSON_COUNT('6 <= age <= 15', persons, households)" +,num_young_retirees,"_PERSON_COUNT('65 <= age <= 79', persons, households)" +,num_old_retirees,"_PERSON_COUNT('80 <= age', persons, households)" +#,, +non_family,non_family,households.HHT.isin(HHT_NONFAMILY) +family,family,households.HHT.isin(HHT_FAMILY) +#,,FIXME is this the best replacement for area_type?? +home_is_urban,home_is_urban,"reindex(land_use.pseudomsa, households.home_zone_id) <= urban_threshold" +home_is_rural,home_is_rural,"reindex(land_use.pseudomsa, households.home_zone_id) > rural_threshold" +#,, +,num_hh_in_zone,"reindex(households.groupby('home_zone_id').size(), households.home_zone_id)" +#,multiple_auto_hh_in_zone,"reindex_i(df.groupby('home_zone_id').multiple_auto_hh.sum(), df.index)" +#,multiple_auto_hh_in_zone_share,multiple_auto_hh_in_zone/num_hh_in_zone +hh owns an ebike,ebike_owner,"rng.random_for_df(households)[:,0] < ebikeownership" \ No newline at end of file diff --git a/configs/resident/annotate_households_auto_ownership.csv b/configs/resident/annotate_households_auto_ownership.csv new file mode 100644 index 0000000..d0c6e63 --- /dev/null +++ b/configs/resident/annotate_households_auto_ownership.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +#,, annotate households table after auto_ownership model has run +#,, need to select the appropriate accessibility value from the persons table for the household +,_wla0,persons_merged.groupby('household_id').workplace_location_accessibility_0.mean().reindex(df.index) +,_wla1,persons_merged.groupby('household_id').workplace_location_accessibility_1.mean().reindex(df.index) +,_wla2,persons_merged.groupby('household_id').workplace_location_accessibility_2.mean().reindex(df.index) +,workplace_location_accessibility,"np.where(df.auto_ownership == 0, _wla0, np.where(df.auto_ownership == 1, _wla1, _wla2))" +,_sha0,persons_merged.groupby('household_id').shopping_accessibility_0.mean().reindex(df.index) +,_sha1,persons_merged.groupby('household_id').shopping_accessibility_1.mean().reindex(df.index) +,_sha2,persons_merged.groupby('household_id').shopping_accessibility_2.mean().reindex(df.index) +,shopping_accessibility,"np.where(df.auto_ownership == 0, _sha0, np.where(df.auto_ownership == 1, _sha1, _sha2))" +,_otha0,persons_merged.groupby('household_id').othdiscr_accessibility_0.mean().reindex(df.index) +,_otha1,persons_merged.groupby('household_id').othdiscr_accessibility_1.mean().reindex(df.index) +,_otha2,persons_merged.groupby('household_id').othdiscr_accessibility_2.mean().reindex(df.index) +,othdiscr_accessibility,"np.where(df.auto_ownership == 0, _otha0, np.where(df.auto_ownership == 1, _otha1, _otha2))" diff --git a/configs/resident/annotate_households_cdap.csv b/configs/resident/annotate_households_cdap.csv new file mode 100644 index 0000000..eb4e679 --- /dev/null +++ b/configs/resident/annotate_households_cdap.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +#,, annotate households table after cdap model has run +num_under16_not_at_school,num_under16_not_at_school,persons.under16_not_at_school.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults +num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers +joint tours determined in cdap model,participates_in_jtf_model,(df.has_joint_tour == 1) diff --git a/configs/resident/annotate_households_vehicle_type.csv b/configs/resident/annotate_households_vehicle_type.csv new file mode 100644 index 0000000..a3e0a26 --- /dev/null +++ b/configs/resident/annotate_households_vehicle_type.csv @@ -0,0 +1,2 @@ +Description,Target,Expression +number of household AVs,numAVowned,"vehicles[vehicles['vehicle_type'].str.contains('-AV', na=False)].groupby('household_id').size().reindex(households.index).fillna(0)" \ No newline at end of file diff --git a/configs/resident/annotate_households_workplace.csv b/configs/resident/annotate_households_workplace.csv new file mode 100644 index 0000000..92439c9 --- /dev/null +++ b/configs/resident/annotate_households_workplace.csv @@ -0,0 +1,7 @@ +Description,Target,Expression +#,, annotate households table after workplace_location model has run +#,, hh_work_auto_savings_ratio is sum of persons work_auto_savings_ratio +#,, +#,, FIXME: all we have is a logsum, not an actual transit choice... +##### ,hh_work_auto_savings_ratio,persons.work_auto_savings_ratio.groupby(persons.household_id).sum().reindex(households.index).fillna(0.0) +,hh_work_auto_savings_ratio,float(0.0) diff --git a/configs/resident/annotate_landuse.csv b/configs/resident/annotate_landuse.csv new file mode 100644 index 0000000..b312125 --- /dev/null +++ b/configs/resident/annotate_landuse.csv @@ -0,0 +1,11 @@ +Description,Target,Expression +#,, annotate landuse table after import +household_density,household_density,land_use.hh / land_use.acres.clip(lower=1) +population_density,population_density,land_use['pop'] / land_use.acres.clip(lower=1) +,is_cbd,land_use.pseudomsa == 1 +total college enrollment for mandatory constraint target,tot_collegeenroll,land_use.collegeenroll + land_use.othercollegeenroll +preschool target for mandatory constraint - sum of all size terms,preschool_target,land_use['pop'] + land_use.emp_bus_svcs + land_use.emp_educ + land_use.emp_fin_res_mgm + land_use.emp_gov + land_use.emp_hlth + land_use.emp_mil + land_use.emp_oth +Is a zone with parking,is_parking_zone,(land_use.parking_type == 1) +Total Employment excluding non-salaried workers,total_emp_revised,land_use['emp_total'] - land_use['emp_non_ws_wfh'] - land_use['emp_non_ws_oth'] +employment_density,employment_density,(land_use['emp_total'] / land_use.acres.clip(lower=1)).clip(upper=500) +density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) \ No newline at end of file diff --git a/configs/resident/annotate_persons.csv b/configs/resident/annotate_persons.csv new file mode 100644 index 0000000..d74f934 --- /dev/null +++ b/configs/resident/annotate_persons.csv @@ -0,0 +1,61 @@ +Description,Target,Expression +#,, annotate persons table after import +age_16_to_19,age_16_to_19,"persons.age.between(16, 19)" +age_16_p,age_16_p,persons.age >= 16 +adult,adult,persons.age >= 18 +male,male,persons.sex == 1 +female,female,persons.sex == 2 +presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, persons.ptype == PTYPE_NONWORK)" +presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, persons.ptype == PTYPE_RETIRED)" +presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, persons.ptype == PTYPE_PRESCHOOL)" +presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, persons.ptype == PTYPE_DRIVING)" +presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, persons.ptype == PTYPE_SCHOOL)" +presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_FULL)" +presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_PART)" +presence of university student other than self in household,has_university,"other_than(persons.household_id, persons.ptype == PTYPE_UNIVERSITY)" +student_is_employed,student_is_employed,"(persons.ptype.isin([PTYPE_UNIVERSITY, PTYPE_DRIVING]) & persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART]))" +nonstudent_to_school,nonstudent_to_school,"(persons.ptype.isin([PTYPE_FULL, PTYPE_PART, PTYPE_NONWORK, PTYPE_RETIRED]) & persons.pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY]))" +#,, +#,, FIXME - if person is a university student but has school age student category value then reset student category value +,pstudent,"persons.pstudent.where(persons.ptype!=PTYPE_UNIVERSITY, PSTUDENT_UNIVERSITY)" +#,, FIXME if person is a student of any kind but has full-time employment status then reset student category value to non-student +,pstudent,"pstudent.where(persons.ptype!=PTYPE_FULL, PSTUDENT_NOT)" +#,, FIXME if student category is non-student and employment is student then reset student category value to student +,pstudent,"pstudent.where((persons.ptype!=PTYPE_DRIVING) & (persons.ptype!=PTYPE_SCHOOL), PSTUDENT_GRADE_OR_HIGH)" +,pstudent,"pstudent.where(persons.ptype!=PTYPE_PRESCHOOL, PSTUDENT_GRADE_OR_HIGH)" +#,, +is_student,is_student,"pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY])" +preschool age can go to preschool,is_student,"is_student.where(persons.age > GRADE_SCHOOL_MIN_AGE, True)" +preschool age can go to preschool,pstudent,"pstudent.where(persons.age > GRADE_SCHOOL_MIN_AGE, PSTUDENT_GRADE_OR_HIGH)" +is_preschool,is_preschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MIN_AGE) +is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE) & (persons.age > GRADE_SCHOOL_MIN_AGE) +is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE) +is_university,is_university,pstudent == PSTUDENT_UNIVERSITY +school_segment preschool,school_segment,"np.where(is_preschool, SCHOOL_SEGMENT_PREK, SCHOOL_SEGMENT_NONE)" +school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, school_segment)" +school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)" +school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)" +#,, +is_worker,is_worker,"persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART])" +is_fulltime_worker,is_fulltime_worker,persons.pemploy == 1 +is_parttime_worker,is_parttime_worker,persons.pemploy == 2 +#extrnal model variable initialization treating everyone as internal initially,, +,is_internal_worker,is_worker +,is_external_worker,0 +#,, +home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" +#,, +,_mean_work,distributed_time_factor_work_mean +,_stddev_work,distributed_time_factor_work_stddev +,_mean_nonwork,distributed_time_factor_nonwork_mean +,_stddev_nonwork,distributed_time_factor_nonwork_stddev +,_min_time_factor,distributed_time_factor_min +,_max_time_factor,distributed_time_factor_max +#,,"Below expression was previously done using np.clip, but was producing a NotImplemented error for mixed DataFrame and Series Inputs. Reimplemented using min & max" +travel time sensitivity factor for work travel,time_factor_work,"np.minimum(_max_time_factor, np.maximum(rng.lognormal_for_df(persons, mu=_mean_work, sigma=_stddev_work, scale=True), _min_time_factor))" +travel time sensitivity factor for non-work travel,time_factor_nonwork,"np.minimum(_max_time_factor, np.maximum(rng.lognormal_for_df(persons, mu=_mean_nonwork, sigma=_stddev_nonwork, scale=True), _min_time_factor)) " +#,, +,_naics_code,"np.where(persons.naics2_original_code=='3M', 3000, np.where(persons.naics2_original_code=='4M', 4000, np.where(persons.naics2_original_code=='MIL',9000, persons.naics2_original_code))) if 'naics2_original_code' in persons.columns else 0" +,naics_code,_naics_code.astype('int') if all(_naics_code!=0) else 0 +#,, +occupation category based on SOC codes,occupation,persons.soc2.map(soc_occupation_xwalk) diff --git a/configs/resident/annotate_persons_after_hh.csv b/configs/resident/annotate_persons_after_hh.csv new file mode 100644 index 0000000..5123de4 --- /dev/null +++ b/configs/resident/annotate_persons_after_hh.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +#,, annotate persons table after annotate_households +,_hh_income,"reindex(households.income, persons.household_id)" +,_num_adults,"reindex(households.num_adults, persons.household_id)" +,_num_predrive_child,"reindex(households.num_predrive_child, persons.household_id)" +,_num_nonworker_adults,"reindex(households.num_nonworker_adults, persons.household_id)" +,_num_full_time_workers,"reindex(households.num_fullTime_workers, persons.household_id)" +Income less than 25K,is_income_less25K,(_hh_income)<25000 +Income 25K to 60K,is_income_25K_to_60K,((_hh_income)>=25000) & ((_hh_income)<60000) +Income 60K to 120K,is_income_60K_to_120K, ((_hh_income)>=60000) & ((_hh_income)<120000) +Income greater than 60K,is_income_greater60K,((_hh_income)>=60000) +Income greater than 120K,is_income_greater120K,((_hh_income)>=120000) +Presence of nonworker in HHs,is_non_worker_in_HH,_num_nonworker_adults>0 +all the adults in the HH are full time workers,is_all_adults_full_time_workers,(_num_adults) == (_num_full_time_workers) +Presence of predrive child in HHs,is_pre_drive_child_in_HH,_num_predrive_child>0 diff --git a/configs/resident/annotate_persons_cdap.csv b/configs/resident/annotate_persons_cdap.csv new file mode 100644 index 0000000..ca35550 --- /dev/null +++ b/configs/resident/annotate_persons_cdap.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +#,, annotate persons table after cdap model has run +travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME +under16_not_at_school,under16_not_at_school,"persons.ptype.isin([PTYPE_SCHOOL, PTYPE_PRESCHOOL]) & persons.cdap_activity.isin(['N', 'H'])" +has_preschool_kid_at_home,has_preschool_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_PRESCHOOL) & (persons.cdap_activity == 'H'))" +has_school_kid_at_home,has_school_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_SCHOOL) & (persons.cdap_activity == 'H'))" \ No newline at end of file diff --git a/configs/resident/annotate_persons_jtp.csv b/configs/resident/annotate_persons_jtp.csv new file mode 100644 index 0000000..a72c866 --- /dev/null +++ b/configs/resident/annotate_persons_jtp.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +#,, annotate persons table after joint_tour_participation model has run +num_joint_tours,num_joint_tours,"joint_tour_participants.groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" diff --git a/configs/resident/annotate_persons_mtf.csv b/configs/resident/annotate_persons_mtf.csv new file mode 100644 index 0000000..47e4160 --- /dev/null +++ b/configs/resident/annotate_persons_mtf.csv @@ -0,0 +1,10 @@ +Description,Target,Expression +#,, annotate persons table after mandatory_tour_frequency model has run +,_PERSON_TOUR_COUNT,"lambda exp, persons, tours: tours.query(exp).groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" +,_Q,lambda s: "'{}'".format(s) +work_and_school_and_worker,work_and_school_and_worker,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_worker +work_and_school_and_student,work_and_school_and_student,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_student +number of mandatory tours for each person,num_mand,"_PERSON_TOUR_COUNT('tour_category==%s' % _Q('mandatory'), persons, tours).fillna(0)" +number of work tours for each person,num_work_tours,"_PERSON_TOUR_COUNT('tour_type==%s' % _Q('work'), persons, tours).fillna(0)" +presence of pre school kid with mandatory tours,has_pre_school_child_with_mandatory,"other_than(persons.household_id, (persons.ptype == 8) & (num_mand > 0))" +presense of driving age school children with mandatory tours,has_driving_age_child_with_mandatory,"other_than(persons.household_id, (persons.ptype == 6) & (num_mand > 0))" diff --git a/configs/resident/annotate_persons_nmtf.csv b/configs/resident/annotate_persons_nmtf.csv new file mode 100644 index 0000000..11f8b11 --- /dev/null +++ b/configs/resident/annotate_persons_nmtf.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +#,, annotate persons table after non_mandatory_tour_frequency model has run +num_non_mand,num_non_mand,tours[tours.tour_category=='non_mandatory'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_escort_tours,num_escort_tours,tours[tours.tour_type == 'escort'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_eatout_tours,num_eatout_tours,tours[tours.tour_type == 'eatout'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_shop_tours,num_shop_tours,tours[tours.tour_type == 'shopping'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_maint_tours,num_maint_tours,tours[tours.tour_type == 'othmaint'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_discr_tours,num_discr_tours,tours[tours.tour_type == 'othdiscr'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_social_tours,num_social_tours,tours[tours.tour_type == 'social'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_non_escort_tours,num_non_escort_tours,num_non_mand-num_escort_tours +total shopping and maintenance tours,num_shop_maint_tours,num_shop_tours + num_maint_tours +"total shopping, maintenance and escort tours",num_shop_maint_escort_tours,num_shop_tours + num_maint_tours + num_escort_tours +number of additional shopping and maintenance tours,num_add_shop_maint_tours,"np.where (num_shop_maint_tours>0, 1, 0) * (num_shop_maint_tours - 1)" +total social and discretionary tours,num_soc_discr_tours,num_social_tours + num_discr_tours +number of additional social and discretionary,num_add_soc_discr_tours,"np.where (num_soc_discr_tours>0, 1, 0) * (num_soc_discr_tours - 1)" diff --git a/configs/resident/annotate_persons_school.csv b/configs/resident/annotate_persons_school.csv new file mode 100644 index 0000000..95747ae --- /dev/null +++ b/configs/resident/annotate_persons_school.csv @@ -0,0 +1,7 @@ +Description,Target,Expression +#,, annotate persons table after school_location model has run +,distance_to_school,"np.where(persons.school_zone_id >= 0,skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, 'DIST'), np.nan)" +#,, this uses the free flow travel time in both directions. MTC TM1 was MD and MD +temp auto_time_to_school,_auto_time_to_school,"skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('SOV_TR_M_TIME', 'MD'))" +temp auto_time_return,_auto_time_return,"skim_dict.lookup(persons.school_zone_id, persons.home_zone_id, ('SOV_TR_M_TIME', 'MD'))" +free flow roundtrip_auto_time_to_school,roundtrip_auto_time_to_school,"np.where(persons.school_zone_id >= 0,_auto_time_to_school + _auto_time_return,0)" diff --git a/configs/resident/annotate_persons_workplace.csv b/configs/resident/annotate_persons_workplace.csv new file mode 100644 index 0000000..11713ac --- /dev/null +++ b/configs/resident/annotate_persons_workplace.csv @@ -0,0 +1,34 @@ +Description,Target,Expression +#,, annotate persons table after workplace_location model has run +,workplace_zone_id,"np.where(persons.get('external_workplace_zone_id',-1) > 0, persons.get('external_workplace_zone_id',-1), persons.workplace_zone_id)" +,workplace_location_logsum,"np.where(persons.get('external_workplace_zone_id',-1) > 0, persons.get('external_workplace_location_logsum',-1), persons.workplace_location_logsum)" +,workplace_modechoice_logsum,"np.where(persons.get('external_workplace_zone_id',-1) > 0, persons.get('external_workplace_modechoice_logsum',-1), persons.workplace_modechoice_logsum)" +,distance_to_work,"np.where(workplace_zone_id>=0,skim_dict.lookup(persons.home_zone_id, workplace_zone_id, 'DIST'),np.nan)" +workplace_in_cbd,workplace_in_cbd,"reindex(land_use.pseudomsa, persons.workplace_zone_id) < cbd_threshold" +work_zone_area_type,work_zone_area_type,"reindex(land_use.pseudomsa, persons.workplace_zone_id)" +#,, auto time to work - free flow travel time in both directions. MTC TM1 was MD and MD +#,,roundtrip_auto_time_to_work +,auto_time_home_to_work,"skim_dict.lookup(persons.home_zone_id, workplace_zone_id, ('SOV_TR_M_TIME', 'AM'))" +,_auto_time_work_to_home,"skim_dict.lookup(workplace_zone_id, persons.home_zone_id, ('SOV_TR_M_TIME', 'PM'))" +,roundtrip_auto_time_to_work,"np.where(workplace_zone_id>=0, auto_time_home_to_work + _auto_time_work_to_home,0)" +# calculating work_auto_savings for transit pass ownership,, +,_transit_ivt_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WALK_LOC_TOTALIVTT', 'MD'))" +,_transit_ivt_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WALK_LOC_TOTALIVTT', 'MD'))" +,_work_transit_available,(_transit_ivt_home_to_work > 0) & (_transit_ivt_work_to_home > 0) +,_transit_iwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WALK_LOC_FIRSTWAIT', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WALK_LOC_FIRSTWAIT', 'MD'))" +,_transit_xwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WALK_LOC_XFERWAIT', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WALK_LOC_XFERWAIT', 'MD'))" +,_transit_waux,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WALK_LOC_XFERWALK', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WALK_LOC_XFERWALK', 'MD'))" +,_transit_wacc_home,"reindex(land_use.walk_dist_local_bus, persons.home_zone_id) if 'walk_dist_local_bus' in land_use else shortWalk" +,_transit_wacc_work,"reindex(land_use.walk_dist_local_bus, persons.workplace_zone_id) if 'walk_dist_local_bus' in land_use else shortWalk" +round trip walk access & egress,_transit_wacc_tot, 2 * (_transit_wacc_home + _transit_wacc_work) * 60 / walkSpeed +,_roundtrip_transit_time_to_work,_transit_ivt_home_to_work + _transit_ivt_work_to_home + _transit_iwait + _transit_xwait + _transit_waux + _transit_wacc_tot +#,,work_auto_savings_ratio +,_min_work_walk_transit,"np.where(_work_transit_available, np.minimum(_roundtrip_transit_time_to_work, roundtrip_auto_time_to_work), roundtrip_auto_time_to_work)" +,work_auto_savings,"np.where(persons.is_worker, _min_work_walk_transit - roundtrip_auto_time_to_work, 0)" +Parking cost at work,exp_daily_work,"reindex(land_use.exp_daily, df.workplace_zone_id).fillna(0)" +# calculating toll and non-toll costs for transponder ownership model,, +,non_toll_time_work,"np.where(df.workplace_zone_id >= 0, skim_dict.lookup(df.home_zone_id, df.workplace_zone_id, ('SOV_NT_L_TIME', 'AM')), 0)" +,toll_time_work,"np.where(df.workplace_zone_id >= 0, skim_dict.lookup(df.home_zone_id, df.workplace_zone_id, ('SOV_TR_H_TIME', 'AM')), 0)" +,toll_dist_work,"np.where(df.workplace_zone_id >= 0, skim_dict.lookup(df.home_zone_id, df.workplace_zone_id, ('SOV_TR_H_TOLLDIST', 'AM')), 0)" +,toll_cost_work,"np.where(df.workplace_zone_id >= 0, skim_dict.lookup(df.home_zone_id, df.workplace_zone_id, ('SOV_TR_H_TOLLCOST', 'AM')), 0)" +,toll_travel_time_savings_work,non_toll_time_work - toll_time_work \ No newline at end of file diff --git a/configs/resident/annotate_proto_persons.csv b/configs/resident/annotate_proto_persons.csv new file mode 100644 index 0000000..12ec1f4 --- /dev/null +++ b/configs/resident/annotate_proto_persons.csv @@ -0,0 +1,4 @@ +Description,Target,Expression +flag denoting proto person,is_proto_person,True +setting work time factor to 1 for disaggregate accessibilities,time_factor_work,1 +setting non-work time factor to 1 for disaggregate accessibilities,time_factor_nonwork,1 diff --git a/configs/resident/annotate_tours_external_joint_identification.csv b/configs/resident/annotate_tours_external_joint_identification.csv new file mode 100644 index 0000000..84de424 --- /dev/null +++ b/configs/resident/annotate_tours_external_joint_identification.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +#,, annotate tours table after first external tour identification model has run +finding external workers,_is_external_worker,"reindex(persons.get('is_external_worker', False), df.person_id)" +#,, want to set external tour indicators for mandatory tours too +setting external work trips,is_external_tour,"np.where(_is_external_worker & (df.tour_category == 'mandatory') & (df.tour_type == 'work'), True, is_external_tour)" +setting internal work trips,is_external_tour,"np.where((~_is_external_worker) & (df.tour_category == 'mandatory') & (df.tour_type == 'work'), False, is_external_tour)" +#atwork subtours not created yet...,, +#atwork tours are internal,is_external_tour,"np.where((df.tour_category == 'atwork'), False, is_external_tour)" +setting corresponding internal tour indicator,is_internal_tour,"np.where(is_external_tour == True, False, True)" diff --git a/configs/resident/annotate_tours_tour_mode_choice.csv b/configs/resident/annotate_tours_tour_mode_choice.csv new file mode 100644 index 0000000..1ebe015 --- /dev/null +++ b/configs/resident/annotate_tours_tour_mode_choice.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +non-motorized and walk tran and tnc set to null,selected_vehicle,np.nan +tours using sov option,selected_vehicle,"np.where(df.tour_mode.isin(['DRIVEALONE','PNR_LOC','PNR_PRM','PNR_MIX']), df.vehicle_occup_1, selected_vehicle)" +tours using sr2 option,selected_vehicle,"np.where(df.tour_mode.isin(['SHARED2','KNR_LOC', 'KNR_PRM', 'KNR_MIX']), df.vehicle_occup_2, selected_vehicle)" +tours using sr3 option,selected_vehicle,"np.where(df.tour_mode.isin(['SHARED3']), df['vehicle_occup_3.5'], selected_vehicle)" +parent tour vehicle,_parent_selected_vehicle,"reindex(df.selected_vehicle, df.parent_tour_id) if 'selected_vehicle' in df.columns else np.nan" +assign parent tour vehicle to atwork subtours,selected_vehicle,"np.where(df.tour_category == 'atwork', _parent_selected_vehicle, selected_vehicle)" +is non-driving tour,_is_non_driving_tour,"df.tour_mode.isin(['WALK','BIKE','WALK_LOC','WALK_PRM','WALK_MIX','TAXI','TNC_SINGLE','TNC_SHARED','SCH_BUS'])" +atwork subtours whose parent tour did not use a vehicle use non_hh_veh,selected_vehicle,"np.where(~_is_non_driving_tour & (df.tour_category == 'atwork') & pd.isna(selected_vehicle),'non_hh_veh', selected_vehicle)" diff --git a/configs/resident/atwork_subtour_destination.csv b/configs/resident/atwork_subtour_destination.csv new file mode 100644 index 0000000..fbf3895 --- /dev/null +++ b/configs/resident/atwork_subtour_destination.csv @@ -0,0 +1,26 @@ +Label,Description,Expression,atwork +,,"_DIST@np.minimum(skims['DIST'], 20)",1 +,,_DIST_SQUARED@_DIST**2,1 +,,_DIST_CUBED@_DIST**3,1 +,,_DIST_LOGGED@np.log(_DIST + 0.001),1 +util_Distance,Distance,@_DIST,coef_dist_atwork +util_Distance_squared,Distance_squared,@_DIST_SQUARED,coef_distsqrd_atwork +util_Distance_fulltime_worker,Distance_fulltime_worker,@_DIST * (df.ptype==1),coef_dist_ftworker_atwork +util_Size_variable,Size_variable,@df['size_term'].apply(np.log1p),1 +util_No_attractions,No_attractions,@df['size_term']==0,-999 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +#,,, +util_Calibration_Distance,Calibration_Distance,@_DIST,coef_Calibration_Distance_atwork +util_Calibration_Distance_squared,Calibration_Distance_squared,@_DIST_SQUARED,coef_Calibration_Distance_squared_atwork +util_Calibration_Distance_cubed,Calibration_Distance_cubed,@_DIST_CUBED,coef_Calibration_Distance_cubed_atwork +util_Calibration_Distance_logged,Calibration_Distance_logged,@_DIST_LOGGED,coef_Calibration_Distance_logged_atwork +util_ABM2_calibration_distance,ABM2_calibration_distance,@_DIST,coef_ABM2_calibration_distance_atwork +util_ABM2_calibration_distance_squared,ABM2_calibration_distance_squared,@_DIST_SQUARED,coef_ABM2_calibration_distance_squared_atwork +util_ABM2_calibration_distance_cubed,ABM2_calibration_distance_cubed,@_DIST_CUBED,coef_ABM2_calibration_distance_cubed_atwork +util_ABM2_calibration_distance_logged,ABM2_calibration_distance_logged,@_DIST_LOGGED,coef_ABM2_calibration_distance_logged_atwork +util_ABM2_calibration_0_1_miles,ABM2_calibration_0_1_miles,@(_DIST < 1),coef_ABM2_calibration_0_1_miles_atwork +util_ABM2_calibration_1_2_miles,ABM2_calibration_1_2_miles,@(_DIST < 2) * (_DIST >= 1),coef_ABM2_calibration_1_2_miles_atwork +util_ABM2_calibration_2_3_miles,ABM2_calibration_2_3_miles,@(_DIST < 3) * (_DIST >= 2),coef_ABM2_calibration_2_3_miles_atwork +util_ABM2_calibration_3_4_miles,ABM2_calibration_3_4_miles,@(_DIST < 4) * (_DIST >= 3),coef_ABM2_calibration_3_4_miles_atwork +util_ABM2_calibration_4_5_miles,ABM2_calibration_4_5_miles,@(_DIST < 5) * (_DIST >= 4),coef_ABM2_calibration_4_5_miles_atwork +util_ABM2_calibration_0_8_miles,ABM2_calibration_0_8_miles,@(_DIST < 8) * _DIST,coef_ABM2_calibration_0_8_miles_atwork diff --git a/configs/resident/atwork_subtour_destination.yaml b/configs/resident/atwork_subtour_destination.yaml new file mode 100644 index 0000000..232e1dc --- /dev/null +++ b/configs/resident/atwork_subtour_destination.yaml @@ -0,0 +1,33 @@ + +SPEC: atwork_subtour_destination.csv +SAMPLE_SPEC: atwork_subtour_destination_sample.csv +COEFFICIENTS: atwork_subtour_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: atwork + +SEGMENTS: + - atwork + +ORIG_ZONE_ID: workplace_zone_id + +SIMULATE_CHOOSER_COLUMNS: + - person_id + - income_segment + - workplace_zone_id + - ptype + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: workplace_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 14 + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample diff --git a/configs/resident/atwork_subtour_destination_coefficients.csv b/configs/resident/atwork_subtour_destination_coefficients.csv new file mode 100644 index 0000000..7b13c01 --- /dev/null +++ b/configs/resident/atwork_subtour_destination_coefficients.csv @@ -0,0 +1,20 @@ +coefficient_name,value,constrain +coef_dist_atwork,-0.705802227,F +coef_distsqrd_atwork,0.014952721,F +coef_dist_ftworker_atwork,0.119028062,F +coef_mode_choice_logsum,0.5,T +#,, +coef_Calibration_Distance_atwork,-0.5666,T +coef_Calibration_Distance_squared_atwork,0.13,T +coef_Calibration_Distance_cubed_atwork,-0.0047,T +coef_Calibration_Distance_logged_atwork,-0.3494,T +coef_ABM2_calibration_distance_atwork,0.3065,T +coef_ABM2_calibration_distance_squared_atwork,-0.0037,T +coef_ABM2_calibration_distance_cubed_atwork,0,T +coef_ABM2_calibration_distance_logged_atwork,-1.5153,T +coef_ABM2_calibration_0_1_miles_atwork,-0.2192,T +coef_ABM2_calibration_1_2_miles_atwork,0.0836,T +coef_ABM2_calibration_2_3_miles_atwork,0.5432,T +coef_ABM2_calibration_3_4_miles_atwork,0.6058,T +coef_ABM2_calibration_4_5_miles_atwork,0.7145,T +coef_ABM2_calibration_0_8_miles_atwork,0.1737,T diff --git a/configs/resident/atwork_subtour_destination_sample.csv b/configs/resident/atwork_subtour_destination_sample.csv new file mode 100644 index 0000000..e47677f --- /dev/null +++ b/configs/resident/atwork_subtour_destination_sample.csv @@ -0,0 +1,7 @@ +Label,Description,Expression,atwork +,,_DIST@skims['DIST'],1 +util_Distance,Distance,"@_DIST.clip(0,20)",coef_dist_atwork +util_Distance_squared,Distance_squared,"@_DIST.clip(0,20)**2",coef_distsqrd_atwork +util_Distance_fulltime_worker,Distance_fulltime_worker,"@_DIST.clip(0,20) * (df.ptype==1)",coef_dist_ftworker_atwork +util_Size_variable,Size_variable,@df['size_term'].apply(np.log1p),1 +util_No_attractions,No_attractions,@df['size_term']==0,-999 diff --git a/configs/resident/atwork_subtour_frequency.csv b/configs/resident/atwork_subtour_frequency.csv new file mode 100644 index 0000000..36b1bf2 --- /dev/null +++ b/configs/resident/atwork_subtour_frequency.csv @@ -0,0 +1,23 @@ +Label,Expression,no_subtours,eat,business1,maint,business2,eat_business +util_dummy_for_full_time_worker,pemploy==1,coefficient_dummy_for_full_time_worker_no_subtours,coefficient_dummy_for_full_time_worker_eat,coefficient_dummy_for_full_time_worker_business1,coefficient_dummy_for_full_time_worker_maint,coefficient_dummy_for_full_time_worker_business2,coefficient_dummy_for_full_time_worker_eat_business +util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_time_worker_no_subtours,coefficient_dummy_for_non_full_time_worker_eat,coefficient_dummy_for_non_full_time_worker_business1,coefficient_dummy_for_non_full_time_worker_maint,coefficient_dummy_for_non_full_time_worker_business2,coefficient_dummy_for_non_full_time_worker_eat_business +util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business +util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business +util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business +util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business +util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business +util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business +util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business +util_main_shop_escort_tours_allocated_to_full_time_worker,@(df.pemploy==1)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business +util_main_shop_escort_tours_allocated_to_part_time_worker,@(df.pemploy==2)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business +util_participation_in_joint_shop_main_eat_tours,num_joint_maint_shop_eat,coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,coefficient_participation_in_joint_shop_main_eat_tours_eat,coefficient_participation_in_joint_shop_main_eat_tours_business1,coefficient_participation_in_joint_shop_main_eat_tours_maint,coefficient_participation_in_joint_shop_main_eat_tours_business2,coefficient_participation_in_joint_shop_main_eat_tours_eat_business +util_participation_in_joint_discretionary_tours,num_joint_discr,coefficient_participation_in_joint_discretionary_tours_no_subtours,coefficient_participation_in_joint_discretionary_tours_eat,coefficient_participation_in_joint_discretionary_tours_business1,coefficient_participation_in_joint_discretionary_tours_maint,coefficient_participation_in_joint_discretionary_tours_business2,coefficient_participation_in_joint_discretionary_tours_eat_business +util_log_of_the_work_tour_duration,@np.log(df.duration+0.5),coefficient_log_of_the_work_tour_duration_no_subtours,coefficient_log_of_the_work_tour_duration_eat,coefficient_log_of_the_work_tour_duration_business1,coefficient_log_of_the_work_tour_duration_maint,coefficient_log_of_the_work_tour_duration_business2,coefficient_log_of_the_work_tour_duration_eat_business +util_dummy_for_drive_alone_mode_for_work_tour,work_tour_is_SOV,coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business +util_two_work_tours_by_person,num_work_tours==2,coefficient_two_work_tours_by_person_no_subtours,coefficient_two_work_tours_by_person_eat,coefficient_two_work_tours_by_person_business1,coefficient_two_work_tours_by_person_maint,coefficient_two_work_tours_by_person_business2,coefficient_two_work_tours_by_person_eat_business +util_workplace_urban_area_dummy,work_zone_area_type<4,coefficient_workplace_urban_area_dummy_no_subtours,coefficient_workplace_urban_area_dummy_eat,coefficient_workplace_urban_area_dummy_business1,coefficient_workplace_urban_area_dummy_maint,coefficient_workplace_urban_area_dummy_business2,coefficient_workplace_urban_area_dummy_eat_business +util_workplace_suburban_area_dummy,(work_zone_area_type>3) & (work_zone_area_type<6),coefficient_workplace_suburban_area_dummy_no_subtours,coefficient_workplace_suburban_area_dummy_eat,coefficient_workplace_suburban_area_dummy_business1,coefficient_workplace_suburban_area_dummy_maint,coefficient_workplace_suburban_area_dummy_business2,coefficient_workplace_suburban_area_dummy_eat_business +util_auto_accessibility_to_retail_for_work_taz,auOpRetail,coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,coefficient_auto_accessibility_to_retail_for_work_taz_eat,coefficient_auto_accessibility_to_retail_for_work_taz_business1,coefficient_auto_accessibility_to_retail_for_work_taz_maint,coefficient_auto_accessibility_to_retail_for_work_taz_business2,coefficient_auto_accessibility_to_retail_for_work_taz_eat_business +util_walk_accessibility_to_retail_for_work_taz,nmRetail,coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,coefficient_walk_accessibility_to_retail_for_work_taz_eat,coefficient_walk_accessibility_to_retail_for_work_taz_business1,coefficient_walk_accessibility_to_retail_for_work_taz_maint,coefficient_walk_accessibility_to_retail_for_work_taz_business2,coefficient_walk_accessibility_to_retail_for_work_taz_eat_business +util_dummy_for_worker_or_student_with_non_mandatory_tour,(is_worker | is_student) * num_non_mand,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business +util_at_work_sub_tour_alternative_specific_constant,1,coefficient_at_work_sub_tour_asc_no_subtours,coefficient_at_work_sub_tour_asc_eat,coefficient_at_work_sub_tour_asc_business1,coefficient_at_work_sub_tour_asc_maint,coefficient_at_work_sub_tour_asc_business2,coefficient_at_work_sub_tour_asc_eat_business diff --git a/configs/resident/atwork_subtour_frequency.yaml b/configs/resident/atwork_subtour_frequency.yaml new file mode 100644 index 0000000..ed3ab3e --- /dev/null +++ b/configs/resident/atwork_subtour_frequency.yaml @@ -0,0 +1,11 @@ + +SPEC: atwork_subtour_frequency.csv +COEFFICIENTS: atwork_subtour_frequency_coefficients.csv + +preprocessor: + SPEC: atwork_subtour_frequency_annotate_tours_preprocessor + DF: df + TABLES: + - land_use + - tours + - joint_tour_participants diff --git a/configs/resident/atwork_subtour_frequency_alternatives.csv b/configs/resident/atwork_subtour_frequency_alternatives.csv new file mode 100644 index 0000000..ba99419 --- /dev/null +++ b/configs/resident/atwork_subtour_frequency_alternatives.csv @@ -0,0 +1,8 @@ +#,,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,eat,business,maint +no_subtours,0,0,0 +eat,1,0,0 +business1,0,1,0 +maint,0,0,1 +business2,0,2,0 +eat_business,1,1,0 diff --git a/configs/resident/atwork_subtour_frequency_annotate_tours_preprocessor.csv b/configs/resident/atwork_subtour_frequency_annotate_tours_preprocessor.csv new file mode 100644 index 0000000..3e4f151 --- /dev/null +++ b/configs/resident/atwork_subtour_frequency_annotate_tours_preprocessor.csv @@ -0,0 +1,9 @@ +Description,Target,Expression +,num_maint_shop_escort,df.num_maint_tours+df.num_shop_tours+df.num_escort_tours +#,num_joint_maint_shop_eat,"reindex_i(tours[(tours.tour_category=='joint') & tours.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" +#,num_eatout_tours,"reindex_i(tours[~tours.is_joint & (tours.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" +joint tour participants annotated with tour type,_PARTICIPANTS,"pd.merge(joint_tour_participants[['tour_id', 'person_id']], tours[['tour_type']], left_on='tour_id', right_index=True, how='left')" +,num_joint_discr,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type=='othdiscr'].groupby('person_id').size(), df.person_id)" +,num_joint_maint_shop_eat,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" +#,, +,work_tour_is_SOV,"df.tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])" diff --git a/configs/resident/atwork_subtour_frequency_coefficients.csv b/configs/resident/atwork_subtour_frequency_coefficients.csv new file mode 100644 index 0000000..f719a69 --- /dev/null +++ b/configs/resident/atwork_subtour_frequency_coefficients.csv @@ -0,0 +1,133 @@ +coefficient_name,value,constrain +coefficient_dummy_for_full_time_worker_business1,-7.375,F +coefficient_dummy_for_full_time_worker_business2,-14.28,F +coefficient_dummy_for_full_time_worker_eat,-7.28,F +coefficient_dummy_for_full_time_worker_eat_business,-14.79,F +coefficient_dummy_for_full_time_worker_maint,-8.093,F +coefficient_dummy_for_full_time_worker_no_subtours,-0.6,F +coefficient_dummy_for_non_full_time_worker_business1,-8.319,F +coefficient_dummy_for_non_full_time_worker_business2,-14.28,F +coefficient_dummy_for_non_full_time_worker_eat,-8.604,F +coefficient_dummy_for_non_full_time_worker_eat_business,-14.79,F +coefficient_dummy_for_non_full_time_worker_maint,-8.214,F +coefficient_dummy_for_non_full_time_worker_no_subtours,-0.6,F +coefficient_dummy_for_non_workers_business1,-5,T +coefficient_dummy_for_non_workers_business2,-5,T +coefficient_dummy_for_non_workers_eat,0,T +coefficient_dummy_for_non_workers_eat_business,-5,T +coefficient_dummy_for_non_workers_maint,-5,T +coefficient_dummy_for_non_workers_no_subtours,0,T +coefficient_medium_hh_income_dummy_business1,0.5555,F +coefficient_medium_hh_income_dummy_business2,1.111,F +coefficient_medium_hh_income_dummy_eat,0.61,F +coefficient_medium_hh_income_dummy_eat_business,1.1655,F +coefficient_medium_hh_income_dummy_maint,0.1527,F +coefficient_medium_hh_income_dummy_no_subtours,0,T +coefficient_high_hh_income_dummy_business1,1.066,F +coefficient_high_hh_income_dummy_business2,2.132,F +coefficient_high_hh_income_dummy_eat,0.8693,F +coefficient_high_hh_income_dummy_eat_business,1.9353,F +coefficient_high_hh_income_dummy_maint,0.1651,F +coefficient_high_hh_income_dummy_no_subtours,0,T +coefficient_zero_cars_owned_by_hh_dummy_business1,-0.3391,F +coefficient_zero_cars_owned_by_hh_dummy_business2,0,T +coefficient_zero_cars_owned_by_hh_dummy_eat,0,T +coefficient_zero_cars_owned_by_hh_dummy_eat_business,-0.3391,F +coefficient_zero_cars_owned_by_hh_dummy_maint,0.1762,F +coefficient_zero_cars_owned_by_hh_dummy_no_subtours,0,T +coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,0.7045,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,1.409,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,0.2334,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business,0.9379,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,0.5061,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,0,T +coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,0.7045,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,1.409,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,0.6776,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business,1.3821,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,0.5061,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,0,T +coefficient_individual_eating_out_tours_made_by_person_business1,0.5434,F +coefficient_individual_eating_out_tours_made_by_person_business2,1.0868,F +coefficient_individual_eating_out_tours_made_by_person_eat,0.5491,F +coefficient_individual_eating_out_tours_made_by_person_eat_business,1.0925,F +coefficient_individual_eating_out_tours_made_by_person_maint,0.9166,F +coefficient_individual_eating_out_tours_made_by_person_no_subtours,0,T +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,-0.1903,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,-0.3806,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,0.052,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business,-0.2423,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,0.1446,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,0,T +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,-0.1903,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,-0.3806,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,-0.3099,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business,-0.5002,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,-0.2723,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,0,T +coefficient_participation_in_joint_shop_main_eat_tours_business1,0.083,F +coefficient_participation_in_joint_shop_main_eat_tours_business2,0.166,F +coefficient_participation_in_joint_shop_main_eat_tours_eat,0.2458,F +coefficient_participation_in_joint_shop_main_eat_tours_eat_business,0.3288,F +coefficient_participation_in_joint_shop_main_eat_tours_maint,0.0803,F +coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,0,T +coefficient_participation_in_joint_discretionary_tours_business1,-0.2637,F +coefficient_participation_in_joint_discretionary_tours_business2,-0.5274,F +coefficient_participation_in_joint_discretionary_tours_eat,0.3588,F +coefficient_participation_in_joint_discretionary_tours_eat_business,0.0951,F +coefficient_participation_in_joint_discretionary_tours_maint,0.5822,F +coefficient_participation_in_joint_discretionary_tours_no_subtours,0,T +coefficient_log_of_the_work_tour_duration_business1,1.142,F +coefficient_log_of_the_work_tour_duration_business2,2.284,F +coefficient_log_of_the_work_tour_duration_eat,1.55,F +coefficient_log_of_the_work_tour_duration_eat_business,2.692,F +coefficient_log_of_the_work_tour_duration_maint,1.659,F +coefficient_log_of_the_work_tour_duration_no_subtours,0,T +coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,0.9901,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,1.9802,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,0.4804,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business,1.4705,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,1.153,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,0,T +coefficient_two_work_tours_by_person_business1,0.3753,F +coefficient_two_work_tours_by_person_business2,0.7506,F +coefficient_two_work_tours_by_person_eat,-0.9862,F +coefficient_two_work_tours_by_person_eat_business,-0.6109,F +coefficient_two_work_tours_by_person_maint,-0.2312,F +coefficient_two_work_tours_by_person_no_subtours,0,T +coefficient_workplace_urban_area_dummy_business1,-0.2235,F +coefficient_workplace_urban_area_dummy_business2,-0.447,F +coefficient_workplace_urban_area_dummy_eat,-0.4182,F +coefficient_workplace_urban_area_dummy_eat_business,-0.6417,F +coefficient_workplace_urban_area_dummy_maint,-0.1479,F +coefficient_workplace_urban_area_dummy_no_subtours,0,T +coefficient_workplace_suburban_area_dummy_business1,-0.1102,F +coefficient_workplace_suburban_area_dummy_business2,-0.2204,F +coefficient_workplace_suburban_area_dummy_eat,-0.2916,F +coefficient_workplace_suburban_area_dummy_eat_business,-0.4018,F +coefficient_workplace_suburban_area_dummy_maint,0,T +coefficient_workplace_suburban_area_dummy_no_subtours,0,T +coefficient_auto_accessibility_to_retail_for_work_taz_business1,0.0534,F +coefficient_auto_accessibility_to_retail_for_work_taz_business2,0.1067,F +coefficient_auto_accessibility_to_retail_for_work_taz_eat,0.015,F +coefficient_auto_accessibility_to_retail_for_work_taz_eat_business,0.0683,F +coefficient_auto_accessibility_to_retail_for_work_taz_maint,0.0265,F +coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_business1,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_business2,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_eat,0.06,F +coefficient_walk_accessibility_to_retail_for_work_taz_eat_business,0.06,F +coefficient_walk_accessibility_to_retail_for_work_taz_maint,0.04,F +coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,-0.3573,F +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,0,T +coefficient_at_work_sub_tour_asc_business1,-0.5372,F +coefficient_at_work_sub_tour_asc_business2,-2.1337,F +coefficient_at_work_sub_tour_asc_eat,0.8576,F +coefficient_at_work_sub_tour_asc_eat_business,-0.9721,F +coefficient_at_work_sub_tour_asc_maint,-0.6198,F +coefficient_at_work_sub_tour_asc_no_subtours,0,T diff --git a/configs/resident/auto_ownership.csv b/configs/resident/auto_ownership.csv new file mode 100644 index 0000000..c75f941 --- /dev/null +++ b/configs/resident/auto_ownership.csv @@ -0,0 +1,30 @@ +Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 +util_drivers_1,alternative specific constant for 1 driver household,@df.num_drivers==1,coef_alternative_specific_constant_for_1_driver_household_0_CARS,,coef_alternative_specific_constant_for_1_driver_household_2_CARS,coef_alternative_specific_constant_for_1_driver_household_3_CARS,coef_alternative_specific_constant_for_1_driver_household_4_CARS +util_drivers_2,alternative specific constant for 2 driver household,@df.num_drivers==2,coef_alternative_specific_constant_for_2_driver_household_0_CARS,coef_alternative_specific_constant_for_2_driver_household_1_CAR,,coef_alternative_specific_constant_for_2_driver_household_3_CARS,coef_alternative_specific_constant_for_2_driver_household_4_CARS +util_drivers_3,alternative specific constant for 3 driver household,@df.num_drivers==3,coef_alternative_specific_constant_for_3_driver_household_0_CARS,coef_alternative_specific_constant_for_3_driver_household_1_CAR,coef_alternative_specific_constant_for_3_driver_household_2_CARS,,coef_alternative_specific_constant_for_3_driver_household_4_CARS +util_drivers_4p,alternative specific constant for 4+ driver household,@df.num_drivers>=4,coef_alternative_specific_constant_for_4_driver_household_0_CARS,coef_alternative_specific_constant_for_4_driver_household_1_CAR,coef_alternative_specific_constant_for_4_driver_household_2_CARS,coef_alternative_specific_constant_for_4_driver_household_3_CARS, +util_has_0_4,Presence of children age 0-4,(num_young_children>0),coef_cars0_has_0_4,,coef_cars2_has_0_4,coef_cars34_has_0_4,coef_cars34_has_0_4 +util_has_18_24,Presence of persons age 18-24,(num_college_age > 0),coef_cars0_has_18_24,,coef_cars2_has_18_24,coef_cars34_has_18_24,coef_cars34_has_18_24 +util_has_25_34,Presence of persons age 35-34,(num_young_adults > 0),coef_cars0_has_25_34,,coef_cars2_has_25_34,coef_cars34_has_25_34,coef_cars34_has_25_34 +util_has_80plus,Presence of persons age 80+,(num_old_retirees > 0),coef_cars0_has_80plus,,coef_cars2_has_80plus,coef_cars34_has_80plus,coef_cars34_has_80plus +util_has_1_worker,Has one worker,@df.num_workers==1,coef_cars0_workers_1,,coef_cars2_workers_1,coef_cars34_workers_1,coef_cars34_workers_1 +util_has_2_workers,Has two workers,@df.num_workers==2,coef_cars0_workers_2,,coef_cars2_workers_2,coef_cars34_workers_2,coef_cars34_workers_1 +util_has_3plus_workers,Has three or more workers,@df.num_workers>2,coef_cars0_workers_3plus,,coef_cars2_workers_3plus,coef_cars34_workers_3plus,coef_cars34_workers_3plus +util_hh_income_verylow,household income <15k,@(df.income<15000),coef_household_income_15k_0_CARS,,coef_household_income_15k_2_CARS,coef_household_income_15k_3_CARS,coef_household_income_15k_4_CARS +util_hh_income_low,household income 15k-30k,@((df.income>=15000) & (df.income<30000)),coef_household_income_1530k_0_CARS,,coef_household_income_1530k_2_CARS,coef_household_income_1530k_3_CARS,coef_household_income_1530k_4_CARS +util_hh_income_mid,household income 30-60k,@((df.income>=30000) & (df.income<60000)),coef_household_income_3060k_0_CARS,,coef_household_income_3060k_2_CARS,coef_household_income_3060k_3_CARS,coef_household_income_3060k_4_CARS +util_hh_income_veryhigh,household income 100k+,@(df.income>=100000),coef_household_income_100k_0_CARS,,coef_household_income_100k_2_CARS,coef_household_income_100k_3_CARS,coef_household_income_100k_4_CARS +util_attached,attached dwelling,"@np.where(df.bldgsz == 2, 0, 1)",coef_attached_0_CARS,,coef_attached_2_CARS,coef_attached_3_CARS,coef_attached_4_CARS +# 0.785 sq miles in 1/2 mile radius * 640 acres per square mile = 502,,,,,,, +util_intersection_density,Intersection count in 1/2 mile radius of household,totint/502,coef_intersection_density_0_CARS,,coef_intersection_density_2_CARS,coef_intersection_density_3_CARS,coef_intersection_density_4_CARS +util_pop_density,Population Density in 1/2 mile radius of household,popden,coef_population_density_0_CARS,,coef_population_density_2_CARS,coef_population_density_34_CARS,coef_population_density_34_CARS +util_emp_density,Employment Density in 1/2 mile radius of household,empden,coef_emp_density_0_CARS,,coef_emp_density_2_CARS,coef_emp_density_3_CARS,coef_emp_density_4_CARS +util_nm_accessibility,Non-motorized zonal accessbility,nmRetail,coef_nonmotorized_zonal_accessbility_0_CARS,,coef_nonmotorized_zonal_accessbility_2_CARS,coef_nonmotorized_zonal_accessbility_3_CARS,coef_nonmotorized_zonal_accessbility_4_CARS +# Disaggregate accessibilities,,,,,,, +util_nonmand_logsum_0auto,Average non-mandatory disaggrage accessibility -- 0 autos,(shopping_accessibility_0 + othdiscr_accessibility_0) / 2,coef_nonmand_logsum_0,,,, +util_nonmand_logsum_1auto,Average non-mandatory disaggrage accessibility -- 1 autos,(shopping_accessibility_1 + othdiscr_accessibility_1) / 2,,coef_nonmand_logsum_1,,, +util_nonmand_logsum_2pauto,Average non-mandatory disaggrage accessibility -- 2+ autos,(shopping_accessibility_2 + othdiscr_accessibility_2) / 2,,,coef_nonmand_logsum_2p,coef_nonmand_logsum_2p,coef_nonmand_logsum_2p +util_asc,alternative specific constant adjustment for all households,1,asc_allhhs_0_autos,,asc_allhhs_2_autos,asc_allhs_3_autos,asc_allhhs_4plus_autos +util_asc_gq,alternative specific constant adjustment for GQ households,@(df.HHT==0),asc_GQ_0_autos,,coef_unavailable,coef_unavailable,coef_unavailable +# coefficients for av ownership,,,,,,, +#util_household_owns_av,Decreasing the number of household autos if hh owns an AV,av_ownership,coef_unavailable,,coef_cars2_av_ownership,coef_cars3_av_ownership,coef_unavailable diff --git a/configs/resident/auto_ownership.yaml b/configs/resident/auto_ownership.yaml new file mode 100644 index 0000000..cf513a9 --- /dev/null +++ b/configs/resident/auto_ownership.yaml @@ -0,0 +1,17 @@ + +SPEC: auto_ownership.csv +COEFFICIENTS: auto_ownership_coefficients.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: auto_ownership_preprocessor + DF: choosers + TABLES: + - persons_merged + +annotate_households: + SPEC: annotate_households_auto_ownership + DF: households + TABLES: + - persons_merged \ No newline at end of file diff --git a/configs/resident/auto_ownership_coefficients.csv b/configs/resident/auto_ownership_coefficients.csv new file mode 100644 index 0000000..fe526af --- /dev/null +++ b/configs/resident/auto_ownership_coefficients.csv @@ -0,0 +1,99 @@ +coefficient_name,value,constrain +coef_alternative_specific_constant_for_1_driver_household_0_CARS,-3.400614855,F +coef_alternative_specific_constant_for_1_driver_household_2_CARS,-0.352797459,F +coef_alternative_specific_constant_for_1_driver_household_3_CARS,-1.65228754,F +coef_alternative_specific_constant_for_1_driver_household_4_CARS,-2.217521436,F +coef_alternative_specific_constant_for_2_driver_household_0_CARS,-5.644614423,F +coef_alternative_specific_constant_for_2_driver_household_1_CAR,-2.078124802,F +coef_alternative_specific_constant_for_2_driver_household_3_CARS,-1.110305396,F +coef_alternative_specific_constant_for_2_driver_household_4_CARS,-1.672613333,F +coef_alternative_specific_constant_for_3_driver_household_0_CARS,-5.663986002,F +coef_alternative_specific_constant_for_3_driver_household_1_CAR,-2.219196761,F +coef_alternative_specific_constant_for_3_driver_household_2_CARS,-0.428191009,F +coef_alternative_specific_constant_for_3_driver_household_4_CARS,-0.158614724,F +coef_alternative_specific_constant_for_4_driver_household_0_CARS,-6.09109227,F +coef_alternative_specific_constant_for_4_driver_household_1_CAR,-3.245812862,F +coef_alternative_specific_constant_for_4_driver_household_2_CARS,-1.256377783,F +coef_alternative_specific_constant_for_4_driver_household_3_CARS,-0.736107251,F +coef_cars0_has_16_17,0,F +coef_cars0_has_18_24,0.303347961,F +coef_cars0_has_25_34,0,T +coef_cars0_has_65_79,0,T +coef_cars0_has_80plus,0,T +coef_cars0_has_0_4,0,T +coef_cars0_has_5_17,0,T +coef_cars0_workers_1,-0.626860919,F +coef_cars0_workers_2,-0.687643006,F +coef_cars0_workers_3plus,1.247591794,F +coef_household_income_15k_0_CARS,2.026792987,F +coef_household_income_1530k_0_CARS,1.115116386,F +coef_household_income_3060k_0_CARS,0,T +coef_household_income_100k_0_CARS,-0.901938407,F +coef_attached_0_CARS,0.583266391,F +coef_intersection_density_0_CARS,0.788935628,F +coef_population_density_0_CARS,0.016016885,F +coef_emp_density_0_CARS,0.014187024,F +coef_nonmotorized_zonal_accessbility_0_CARS,0.304730094,F +coef_autotransit_zonal_accessibility_0_CARS,0,T +coef_cars2_has_16_17,0,F +coef_cars2_has_18_24,0,T +coef_cars2_has_25_34,0,T +coef_cars2_has_0_4,0.588834468,F +coef_cars2_has_5_17,0,T +coef_cars2_has_65_79,0,T +coef_cars2_has_80plus,-0.718935811,F +coef_cars2_workers_1,0,T +coef_cars2_workers_2,0.775530112,F +coef_cars2_workers_3plus,0,T +coef_household_income_15k_2_CARS,-1.264502448,F +coef_household_income_1530k_2_CARS,-0.823659861,F +coef_household_income_3060k_2_CARS,-0.284592043,F +coef_household_income_100k_2_CARS,0,T +coef_attached_2_CARS,-0.65382238,F +coef_intersection_density_2_CARS,0.00678398,F +coef_population_density_2_CARS,-0.012314708,F +coef_emp_density_2_CARS,0,T +coef_nonmotorized_zonal_accessbility_2_CARS,-0.051503169,F +coef_autotransit_zonal_accessibility_2_CARS,0,T +coef_cars34_has_16_17,0,T +coef_cars34_has_18_24,0,T +coef_cars34_has_25_34,-0.205410684,F +coef_cars34_has_0_4,0,T +coef_cars34_has_5_17,0,T +coef_cars34_has_65_79,0,T +coef_cars34_has_80plus,-0.973202303,F +coef_cars34_workers_1,0.415094751,F +coef_cars34_workers_2,0.964206165,F +coef_cars34_workers_3plus,1.190507925,F +coef_household_income_15k_3_CARS,-1.827402022,F +coef_household_income_1530k_3_CARS,-1.243285944,F +coef_household_income_3060k_3_CARS,-0.510748344,F +coef_household_income_100k_3_CARS,0,T +coef_attached_3_CARS,-1.426386743,F +coef_intersection_density_3_CARS,-0.875111252,F +coef_population_density_34_CARS,-0.015701116,F +coef_emp_density_3_CARS,-0.01090242,F +coef_nonmotorized_zonal_accessbility_3_CARS,-0.068584652,F +coef_autotransit_zonal_accessibility_3_CARS,-0.000872604,F +coef_cars4_num_workers_clip_3,0,F +coef_household_income_15k_4_CARS,-2.743515835,F +coef_household_income_1530k_4_CARS,-2.050237487,F +coef_household_income_3060k_4_CARS,-0.903012312,F +coef_household_income_100k_4_CARS,0,T +coef_attached_4_CARS,-1.85939905,F +coef_intersection_density_4_CARS,-0.593060324,F +coef_emp_density_4_CARS,-0.016815877,F +coef_nonmotorized_zonal_accessbility_4_CARS,-0.133879458,F +coef_autotransit_zonal_accessibility_4_CARS,-0.002525807,F +coef_nonmand_logsum_0,0.422778026,F +coef_nonmand_logsum_1,0.517335299,F +coef_nonmand_logsum_2p,0.495923942,F +coef_cars2_av_ownership,0,T +coef_cars3_av_ownership,0,T +coef_unavailable,-999,T +asc_GQ_0_autos,2.468181736,F +# Calibration constants (2021 ACS Data),, +asc_allhhs_0_autos,-0.205031923,F +asc_allhhs_2_autos,0.103098218,F +asc_allhs_3_autos,0.217732491,F +asc_allhhs_4plus_autos,0.178545411,F diff --git a/configs/resident/auto_ownership_preprocessor.csv b/configs/resident/auto_ownership_preprocessor.csv new file mode 100644 index 0000000..9fa6933 --- /dev/null +++ b/configs/resident/auto_ownership_preprocessor.csv @@ -0,0 +1,8 @@ +Description,Target,Expression +# averaging disaggregate accessibilities across all household members +,shopping_accessibility_0,persons_merged.groupby('household_id').shopping_accessibility_0.mean().reindex(df.index) +,shopping_accessibility_1,persons_merged.groupby('household_id').shopping_accessibility_1.mean().reindex(df.index) +,shopping_accessibility_2,persons_merged.groupby('household_id').shopping_accessibility_2.mean().reindex(df.index) +,othdiscr_accessibility_0,persons_merged.groupby('household_id').othdiscr_accessibility_0.mean().reindex(df.index) +,othdiscr_accessibility_1,persons_merged.groupby('household_id').othdiscr_accessibility_1.mean().reindex(df.index) +,othdiscr_accessibility_2,persons_merged.groupby('household_id').othdiscr_accessibility_2.mean().reindex(df.index) \ No newline at end of file diff --git a/configs/resident/av_ownership.csv b/configs/resident/av_ownership.csv new file mode 100644 index 0000000..677db13 --- /dev/null +++ b/configs/resident/av_ownership.csv @@ -0,0 +1,8 @@ +Label,Description,Expression,owns_av,does_not_own_av +util_low_income,Household income less than 50K,income < 50000,coef_low_income,0 +util_high_income,Household income greater than 100K,income > 100000,coef_high_income,0 +# util_hh_head_age_lt_35,Head of household under age 35,hhhead_age < 35,coef_hh_head_age_lt_35,0 +# util_hh_head_age_gt_65,Head of household over age 65,hhhead_age > 65,coef_hh_head_age_gt_65,0 +# util_per_hour_commute_time,Coefficient per hour of total household commute time,hh_commute_time,coef_per_hour_commute_time,0 +util_static_constant,Static constant that does not change via iterations,1,coef_static,0 +util_av_target_share,Constant to adjust to match set AV target,1,coef_av_target_share,0 diff --git a/configs/resident/av_ownership.yaml b/configs/resident/av_ownership.yaml new file mode 100644 index 0000000..b72482d --- /dev/null +++ b/configs/resident/av_ownership.yaml @@ -0,0 +1,18 @@ + +SPEC: av_ownership.csv +COEFFICIENTS: av_ownership_coeffs.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: av_ownership_preprocessor + DF: choosers + TABLES: + - persons + +# iterative what-if analysis example +# omit these settings to not iterate +AV_OWNERSHIP_ITERATIONS: 5 +AV_OWNERSHIP_TARGET_PERCENT: 0.0 +AV_OWNERSHIP_TARGET_PERCENT_TOLERANCE: 0.01 +AV_OWNERSHIP_COEFFICIENT_CONSTANT: coef_av_target_share \ No newline at end of file diff --git a/configs/resident/av_ownership_coeffs.csv b/configs/resident/av_ownership_coeffs.csv new file mode 100644 index 0000000..18a6a7a --- /dev/null +++ b/configs/resident/av_ownership_coeffs.csv @@ -0,0 +1,8 @@ +coefficient_name,value,constrain +coef_low_income,-1.0,F +coef_high_income,1.0,F +coef_hh_head_age_lt_35,0.5,F +coef_hh_head_age_gt_65,-1.0,F +coef_per_hour_commute_time,0.25,F +coef_static,-3.5,F +coef_av_target_share,0,F diff --git a/configs/resident/av_ownership_preprocessor.csv b/configs/resident/av_ownership_preprocessor.csv new file mode 100644 index 0000000..f6db3c2 --- /dev/null +++ b/configs/resident/av_ownership_preprocessor.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +age of the head of household,hhhead_age,persons.groupby('household_id').age.max().reindex(df.index) +#total household commute time -- cant compute if before workplace location choice,hh_commute_time,persons.groupby('household_id').roundtrip_auto_time_to_work.sum().reindex(df.index) \ No newline at end of file diff --git a/configs/resident/cdap.yaml b/configs/resident/cdap.yaml new file mode 100644 index 0000000..ea6ae20 --- /dev/null +++ b/configs/resident/cdap.yaml @@ -0,0 +1,47 @@ + +INDIV_AND_HHSIZE1_SPEC: cdap_indiv_and_hhsize1.csv +COEFFICIENTS: cdap_coefficients.csv +INTERACTION_COEFFICIENTS: cdap_interaction_coefficients.csv + +FIXED_RELATIVE_PROPORTIONS_SPEC: cdap_fixed_relative_proportions.csv + +# set to True if want to introduce joint tour utility in cdap +# otherwise set to False or comment out, defaulted to False +ADD_JOINT_TOUR_UTILITY: True +JOINT_TOUR_COEFFICIENTS: cdap_joint_tour_coefficients.csv +JOINT_TOUR_USEFUL_COLUMNS: + - auto_ownership + - income + - num_workers + - shopping_accessibility + - workplace_modechoice_logsum + +CONSTANTS: + FULL: 1 + PART: 2 + UNIVERSITY: 3 + NONWORK: 4 + RETIRED: 5 + DRIVING: 6 + SCHOOL: 7 + PRESCHOOL: 8 + +PERSON_TYPE_MAP: + WORKER: + - 1 + - 2 + CHILD: + - 6 + - 7 + - 8 + +annotate_persons: + SPEC: annotate_persons_cdap + DF: persons + + +annotate_households: + SPEC: annotate_households_cdap + DF: households + TABLES: + - persons diff --git a/configs/resident/cdap_coefficients.csv b/configs/resident/cdap_coefficients.csv new file mode 100644 index 0000000..af7e246 --- /dev/null +++ b/configs/resident/cdap_coefficients.csv @@ -0,0 +1,198 @@ +coefficient_name,value,constrain +coef_UNAVAILABLE,-999.0,T +coef_age_80_plus_N,-0.40552014545569237,F +coef_base_work_from_home_2016_N,-0.412755915492793,F +coef_base_work_from_home_N,-0.36137098196813544,F +coef_base_zero_auto_M,-0.3750683512059161,F +coef_base_zero_auto_N,-0.5866902617568075,F +coef_driving_age_child_2016_asc_M,0.9762795235480467,F +coef_driving_age_child_2016_asc_N,-0.24488773870065403,F +coef_driving_age_child_asc_M,-0.028791867283176956,F +coef_driving_age_child_asc_N,-0.35221765455627785,F +coef_driving_age_child_auto_deficient_N,-0.9468282864008857,F +coef_full_time_worker_2016_asc_M,1.223481320989442,F +coef_full_time_worker_2016_asc_N,0.6920533294338312,F +coef_full_time_worker_age_less_than_35_M,-0.12285898207075491,F +coef_full_time_worker_age_less_than_35_N,-0.15920673068983657,F +coef_full_time_worker_asc_M,1.5644720845560531,F +coef_full_time_worker_asc_N,1.4614984268627733,F +coef_full_time_worker_income_less_than_30k_H,0.15736137368023284,F +coef_non_working_adult_2016_asc_N,0.232621494267051,F +coef_non_working_adult_asc_N,1.1808900957814006,F +coef_non_working_adult_auto_deficient_N,-0.8370587331043843,F +coef_non_working_adult_zero_auto_N,0.0,F +coef_part_time_worker_2016_asc_M,0.2983857953450747,F +coef_part_time_worker_2016_asc_N,0.35204682792828224,F +coef_part_time_worker_age_less_than_35_M,0.23178722435239257,F +coef_part_time_worker_asc_M,1.6387513010169674,F +coef_part_time_worker_asc_N,1.606230763394354,F +coef_part_time_worker_auto_deficient_M,0.37748443629482326,F +coef_part_time_worker_income_less_than_30k_H,0.23400742652772027,F +coef_part_time_worker_work_from_home_2016_N,1.4299461243823042,F +coef_part_time_worker_work_from_home_N,-1.2281808814800665,F +coef_pre_driving_age_child_2016_asc_M,0.11101687817213937,F +coef_pre_driving_age_child_2016_asc_N,0.10384140613549096,F +coef_pre_driving_age_child_asc_M,0.8049646260769578,F +coef_pre_driving_age_child_asc_N,-0.06982784895274624,F +coef_pre_driving_age_child_auto_deficient_M,0.0,F +coef_pre_driving_age_child_auto_deficient_N,0.0,F +coef_preschool_child_2016_asc_M,0.06913803289094414,F +coef_preschool_child_2016_asc_N,0.8594372063809559,F +coef_preschool_child_asc_M,-0.4237607056861521,F +coef_preschool_child_asc_N,-0.7825949113888206,F +coef_preschool_child_auto_deficient_M,-0.6176520350813967,F +coef_preschool_child_income_between_60k_and_100k_M,0.27169762977541834,F +coef_retired_2016_asc_N,0.3104586071157866,F +coef_retired_asc_N,1.1538566746686725,F +coef_retired_auto_deficient_N,-0.5763380569296244,F +coef_telecommute_1_day_week_2016_H,0.6205919905910637,F +coef_telecommute_1_day_week_2016_N,0.5297551190509434,F +coef_telecommute_1_day_week_H,-0.7795490402549963,F +coef_telecommute_1_day_week_N,-0.7150941297946883,F +coef_telecommute_2_3_days_week_2016_H,-0.4085117546195023,F +coef_telecommute_2_3_days_week_2016_N,-0.5948535892511913,F +coef_telecommute_2_3_days_week_H,1.487776947249398,F +coef_telecommute_2_3_days_week_N,0.995563845622819,F +coef_telecommute_4_days_week_2016_H,0.8369274593715018,F +coef_telecommute_4_days_week_2016_N,1.022480425972746,F +coef_telecommute_4_days_week_H,0.7932098123998844,F +coef_telecommute_4_days_week_N,0.2758341263673416,F +coef_university_student_2016_asc_M,1.3474703710372755,F +coef_university_student_2016_asc_N,0.6441076940029465,F +coef_university_student_asc_M,0.2934044679640938,F +coef_university_student_asc_N,0.7161370279501159,F +coef_university_student_auto_deficient_M,0.0,F +coef_university_student_auto_deficient_N,-0.4793921704199631,F +coef_university_student_income_between_60k_and_100k_H,0.0,F +coef_university_student_zero_auto_N,0.0,F +coef_non_mand_accessibility_med_low_N,0.2098418874105526,F +coef_non_mand_accessibility_med_high_N,0.23778715336968032,F +coef_non_mand_accessibility_high_N,0.4489226773950751,F +coef_non_mand_accessibility_med_low_2016_N,-0.14676390165726438,F +coef_non_mand_accessibility_med_high_2016_N,-0.1657971018672975,F +coef_non_mand_accessibility_high_2016_N,-0.3075771697132568,F +coef_H_11,0.9081676720163869,F +coef_H_12,0.5879605906687824,F +coef_H_13,0.5293653823036347,F +coef_H_14,0.6121582837956262,F +coef_H_15,0.785784651471339,F +coef_H_16,0.0,F +coef_H_17,0.1687232310036398,F +coef_H_18,0.370497961792488,F +coef_H_22,1.0023866203429688,F +coef_H_23,0.0,F +coef_H_24,0.0,F +coef_H_25,0.5841783984603512,F +coef_H_26,0.0,F +coef_H_27,0.0,F +coef_H_28,0.0,F +coef_H_33,1.3535972783148953,F +coef_H_34,0.4815185221939276,F +coef_H_35,0.0,F +coef_H_36,0.0,T +coef_H_37,0.0,T +coef_H_38,0.0,T +coef_H_44,0.5566224376246068,F +coef_H_45,0.5008668115587485,F +coef_H_46,0.0,T +coef_H_47,0.0,T +coef_H_48,0.0,T +coef_H_55,0.27176137157094504,F +coef_H_56_57,0.0,T +coef_H_58,0.0,T +coef_H_66,3.14482613013081,F +coef_H_67,0.0,F +coef_H_68,0.0,F +coef_H_77,1.9693172113603012,F +coef_H_78,1.622709457576422,F +coef_H_88,1.9456615126911383,F +coef_M_11,0.20582236458467118,F +coef_M_12,0.0,F +coef_M_13,0.13759875304842933,F +coef_M_16,0.0,F +coef_M_17,0.1008692647165656,F +coef_M_18,0.30906951422329104,F +coef_M_22,0.0,F +coef_M_23,0.0,F +coef_M_26,0.0,F +coef_M_27,0.24621546849867038,F +coef_M_28,0.0,F +coef_M_33,0.2552795327531223,F +coef_M_36,0.0,T +coef_M_37,0.0,T +coef_M_38,0.0,T +coef_M_66,0.0,F +coef_M_67,0.0,F +coef_M_68,0.0,F +coef_M_77,1.180347121732678,F +coef_M_78,0.5390396802361401,F +coef_M_88,1.3066654391714445,F +coef_N_11,0.0,F +coef_N_12,0.0,F +coef_N_13,0.0,F +coef_N_14,0.0,F +coef_N_15,-0.3059631767785066,F +coef_N_16,0.0,F +coef_N_17,0.14867426687105245,F +coef_N_18,0.22534260567363343,F +coef_N_22,0.0,F +coef_N_23,0.0,F +coef_N_24,0.0,F +coef_N_25,0.0,F +coef_N_26,0.9791798564652399,F +coef_N_27,0.0,F +coef_N_28,0.3010315611792873,F +coef_N_33,0.0,F +coef_N_34,0.0,F +coef_N_35,-0.8021689612128808,F +coef_N_36,0.0,T +coef_N_37,0.0,T +coef_N_38,0.0,T +coef_N_44,0.0,F +coef_N_45,0.0,F +coef_N_46,0.0,T +coef_N_47,0.0,T +coef_N_48,0.0,T +coef_N_55,-0.15036765019338055,F +coef_N_56_57_58,0.0,T +coef_N_66,2.241353046656697,F +coef_N_67,1.9957786284419086,F +coef_N_68,0.0,F +coef_N_77,1.6220342829913563,F +coef_N_78,1.1156499097895638,F +coef_N_88,1.2180412202476305,F +coef_H_114,0.0,F +coef_H_117_118,0.0,F +coef_H_127_128,0.0,F +coef_H_147_148,0.0,T +coef_H_177_178_187_188,0.0,F +coef_H_277_278_287_288,0.0,F +coef_H_447_448,0.0,T +coef_H_477_478_487_488,0.0,T +coef_H_777_778_788_888,-1.1692289537913036,F +coef_M_111,-0.32284228918318064,F +coef_M_112,0.0,F +coef_M_122,0.5302446008838457,F +coef_M_127_128,0.2847930736739325,F +coef_M_177_178_187_188,0.0,F +coef_M_227_228,0.0,F +coef_M_277_278_287_288,0.0,F +coef_M_777_778_788_888,-0.5669303177188842,F +coef_N_117_118,0.0,F +coef_N_144,0.0,F +coef_N_127_128,0.0,F +coef_N_147_148,0.0,T +coef_N_177_178_187_188,0.0,F +coef_N_222,0.0,T +coef_N_277_278_287_288,0.0,F +coef_N_477_478_487_488,0.0,T +coef_N_777_778_788_888,-0.7322489904595986,F +coef_M_xxx,0.0,T +coef_N_xxx,0.0,T +coef_H_xxx,0.0,T +coef_M_xxxx,0.0,T +coef_N_xxxx,0.0,T +coef_H_xxxx,0.0,T +coef_M_xxxxx,0.3270563409817982,F +coef_N_xxxxx,0.3500149649035247,F +coef_H_xxxxx,0.0,F diff --git a/configs/resident/cdap_fixed_relative_proportions.csv b/configs/resident/cdap_fixed_relative_proportions.csv new file mode 100644 index 0000000..49ab7c4 --- /dev/null +++ b/configs/resident/cdap_fixed_relative_proportions.csv @@ -0,0 +1,13 @@ +Description,Expression,M,N,H +Full-time worker,(ptype == 1) & ~work_from_home,0.79647,0.09368,0.10985 +Full-time worker,(ptype == 1) & work_from_home,0,0.460276126,0.539723874 +Part-time worker,(ptype == 2) & ~work_from_home,0.61678,0.25757,0.12565 +Part-time worker,(ptype == 2) & work_from_home,0,0.672120453,0.327879547 +University student,ptype == 3,0.69229,0.15641,0.1513 +Non-working adult,ptype == 4,0,0.67169,0.32831 +Retired,ptype == 5,0,0.54295,0.45705 +Driving-age child who is in school,ptype == 6,0.77609,0.06004,0.16387 +Pre-driving-age child who is in school,ptype == 7,0.68514,0.09144,0.22342 +#Child who is too young for school,ptype == 8,0.14056,0.06512,0.79432 +Child who is too young for school,(ptype == 8) & (school_zone_id >= 0),0.14056,0.06512,0.79432 +Child who is too young for school,(ptype == 8) & (school_zone_id < 0),0,0.1354,0.8646 \ No newline at end of file diff --git a/configs/resident/cdap_indiv_and_hhsize1.csv b/configs/resident/cdap_indiv_and_hhsize1.csv new file mode 100644 index 0000000..1f07f76 --- /dev/null +++ b/configs/resident/cdap_indiv_and_hhsize1.csv @@ -0,0 +1,66 @@ +Description,Expression,M,N,H +Full-time worker ASC,ptype == 1,coef_full_time_worker_asc_M,coef_full_time_worker_asc_N, +Part-time worker ASC,ptype == 2,coef_part_time_worker_asc_M,coef_part_time_worker_asc_N, +University student ASC,ptype == 3,coef_university_student_asc_M,coef_university_student_asc_N, +Non-working adult ASC,ptype == 4,,coef_non_working_adult_asc_N, +Retired ASC,ptype == 5,,coef_retired_asc_N, +Driving-age child who is in school ASC,ptype == 6,coef_driving_age_child_asc_M,coef_driving_age_child_asc_N, +Pre-driving-age child who is in school ASC,ptype == 7,coef_pre_driving_age_child_asc_M,coef_pre_driving_age_child_asc_N, +Preschool child ASC,ptype == 8,coef_preschool_child_asc_M,coef_preschool_child_asc_N, +Full-time worker interaction with age less than 35,(ptype == 1) & (age < 35),coef_full_time_worker_age_less_than_35_M,coef_full_time_worker_age_less_than_35_N, +Part-time worker interaction with age less than 35,(ptype == 2) & (age < 35),coef_part_time_worker_age_less_than_35_M,coef_full_time_worker_age_less_than_35_N, +Age over 80,(age >= 80),,coef_age_80_plus_N, +Zero auto ownership base,(auto_ownership == 0),coef_base_zero_auto_M,coef_base_zero_auto_N, +Auto Deficient part-time worker,(ptype == 2) & (auto_ownership > 0) & (auto_ownership < num_adults),coef_part_time_worker_auto_deficient_M,, +Auto Deficient university_student,(ptype == 3) & (auto_ownership > 0) & (auto_ownership < num_adults),,coef_university_student_auto_deficient_N, +Auto Deficient non_working_adult,(ptype == 4) & (auto_ownership > 0) & (auto_ownership < num_adults),,coef_non_working_adult_auto_deficient_N, +Auto Deficient retired,(ptype == 5) & (auto_ownership > 0) & (auto_ownership < num_adults),,coef_retired_auto_deficient_N, +Auto Deficient driving_age_child,(ptype == 6) & (auto_ownership > 0) & (auto_ownership < num_adults),,coef_driving_age_child_auto_deficient_N, +Auto Deficient preschool_child,(ptype == 8) & (auto_ownership > 0) & (auto_ownership < num_adults),coef_preschool_child_auto_deficient_M,, +Full-time worker interaction with income less than $30k,(ptype == 1) & (income < 30000),,,coef_full_time_worker_income_less_than_30k_H +Part-time worker interaction with income less than $30k,(ptype == 2) & (income < 30000),,,coef_part_time_worker_income_less_than_30k_H +Preschool child interaction with income between $60k and $100k,(ptype == 8) & (income >= 60000) & (income <= 100000),coef_preschool_child_income_between_60k_and_100k_M,, +Base works from home,(work_from_home),,coef_base_work_from_home_N, +Base works from home 2016,(work_from_home),,coef_base_work_from_home_2016_N, +Part time worker who works from home,(ptype == 2) & (work_from_home),,coef_part_time_worker_work_from_home_N, +Part time worker who works from home,@(df.ptype == 2) & (df.work_from_home) & (PRE_COVID),,coef_part_time_worker_work_from_home_2016_N, +Telecommutes 1 day per week,telecommute_frequency=='1_day_week',,coef_telecommute_1_day_week_N,coef_telecommute_1_day_week_H +Telecommutes 2-3 days per week,telecommute_frequency=='2_3_days_week',,coef_telecommute_2_3_days_week_N,coef_telecommute_2_3_days_week_H +Telecommutes 4 days per week,telecommute_frequency=='4_days_week',,coef_telecommute_4_days_week_N,coef_telecommute_4_days_week_H +Full-time worker 2016 ASC,@(df.ptype == 1) & (PRE_COVID),coef_full_time_worker_2016_asc_M,coef_full_time_worker_2016_asc_N, +Part-time worker 2016 ASC,@(df.ptype == 2) & (PRE_COVID),coef_part_time_worker_2016_asc_M,coef_part_time_worker_2016_asc_N, +University student 2016 ASC,@(df.ptype == 3) & (PRE_COVID),coef_university_student_2016_asc_M,coef_university_student_2016_asc_N, +Non-working adult 2016 ASC,@(df.ptype == 4) & (PRE_COVID),,coef_non_working_adult_2016_asc_N, +Retired 2016 ASC,@(df.ptype == 5) & (PRE_COVID),,coef_retired_2016_asc_N, +Driving-age child who is in school 2016 ASC,@(df.ptype == 6) & (PRE_COVID),coef_driving_age_child_2016_asc_M,coef_driving_age_child_2016_asc_N, +Pre-driving-age child who is in school 2016 ASC,@(df.ptype == 7) & (PRE_COVID),coef_pre_driving_age_child_2016_asc_M,coef_pre_driving_age_child_2016_asc_N, +Preschool child 2016 ASC,@(df.ptype == 8) & (PRE_COVID),coef_preschool_child_2016_asc_M,coef_preschool_child_2016_asc_N, +Telecommutes 1 day per week 2016,@(df.telecommute_frequency=='1_day_week') & (PRE_COVID),,coef_telecommute_1_day_week_2016_N,coef_telecommute_1_day_week_2016_H +Telecommutes 2-3 days per week 2016,@(df.telecommute_frequency=='2_3_days_week') & (PRE_COVID),,coef_telecommute_2_3_days_week_2016_N,coef_telecommute_2_3_days_week_2016_H +Telecommutes 4 days per week 2016,@(df.telecommute_frequency=='4_days_week') & (PRE_COVID),,coef_telecommute_4_days_week_2016_N,coef_telecommute_4_days_week_2016_H +Mandatory pattern unavailable if not worker or student,(is_student == False) & (is_worker == False),coef_UNAVAILABLE,, +Mandatory pattern unavailable if not works from home,(work_from_home == True) & (is_student == False),coef_UNAVAILABLE,, +# cutting non-mandatory accessibility into roughly quartiles with low as base,,,, +Non-mandatory accessibility med low,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) >= 12) & (((df.shopping_accessibility + df.othdiscr_accessibility) / 2) < 13.5),,coef_non_mand_accessibility_med_low_N, +Non-mandatory accessibility med high,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) >= 13.5) & (((df.shopping_accessibility + df.othdiscr_accessibility) / 2) < 15),,coef_non_mand_accessibility_med_high_N, +Non-mandatory accessibility high,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) > 15),,coef_non_mand_accessibility_high_N, +Non-mandatory accessibility med low,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) >= 12) & (((df.shopping_accessibility + df.othdiscr_accessibility) / 2) < 13.5) & (PRE_COVID),,coef_non_mand_accessibility_med_low_2016_N, +Non-mandatory accessibility med high,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) >= 13.5) & (((df.shopping_accessibility + df.othdiscr_accessibility) / 2) < 15) & (PRE_COVID),,coef_non_mand_accessibility_med_high_2016_N, +Non-mandatory accessibility high,@(((df.shopping_accessibility + df.othdiscr_accessibility) / 2) > 15) & (PRE_COVID),,coef_non_mand_accessibility_high_2016_N, +#,,,, +Full-time worker weighted target ASC,ptype == 1,-0.076,0.033,0.288 +Part-time worker weighted target ASC,ptype == 2,0.118,-0.124,0.029 +University student weighted target ASC,ptype == 3,-0.081,0.038,0.164 +Non-working adult weighted target ASC,ptype == 4,,-0.110,0.284 +Retired weighted target ASC,ptype == 5,,-0.131,0.313 +Driving-age child who is in school weighted target ASC,ptype == 6,-0.134,0.314,0.026 +Pre-driving-age child who is in school weighted target ASC,ptype == 7,0.028,-0.084,0.010 +Preschool child weighted target ASC,ptype == 8,0.104,-0.223,0.116 +ABM3 calibration ASC ptype=1,ptype==1,0.5540,-0.218,-0.427 +ABM3 calibration ASC ptype=2,ptype==2,-0.107,0.0276,0.223 +ABM3 calibration ASC ptype=3,ptype==3,0.427,-0.198,-0.164 +ABM3 calibration ASC ptype=4,ptype==4,0.0,-0.029,0.0889 +ABM3 calibration ASC ptype=5,ptype==5,0.0,0.114,-0.227 +ABM3 calibration ASC ptype=6,ptype==6,3.650,-0.241,0.298 +ABM3 calibration ASC ptype=7,ptype==7,2.000,-0.171,0.096 +ABM3 calibration ASC ptype=8,ptype==8,0.436,0.454,-0.731 \ No newline at end of file diff --git a/configs/resident/cdap_interaction_coefficients.csv b/configs/resident/cdap_interaction_coefficients.csv new file mode 100644 index 0000000..f94fa17 --- /dev/null +++ b/configs/resident/cdap_interaction_coefficients.csv @@ -0,0 +1,177 @@ +activity,interaction_ptypes,coefficient +# 2-way interactions,, +H,11,coef_H_11 +H,12,coef_H_12 +H,13,coef_H_13 +H,14,coef_H_14 +H,15,coef_H_15 +H,16,coef_H_16 +H,17,coef_H_17 +H,18,coef_H_18 +H,22,coef_H_22 +H,23,coef_H_23 +H,24,coef_H_24 +H,25,coef_H_25 +H,26,coef_H_26 +H,27,coef_H_27 +H,28,coef_H_28 +H,33,coef_H_33 +H,34,coef_H_34 +H,35,coef_H_35 +H,36,coef_H_36 +H,37,coef_H_37 +H,38,coef_H_38 +H,44,coef_H_44 +H,45,coef_H_45 +H,46,coef_H_46 +H,47,coef_H_47 +H,48,coef_H_48 +H,55,coef_H_55 +H,56,coef_H_56_57 +H,57,coef_H_56_57 +H,58,coef_H_58 +H,66,coef_H_66 +H,67,coef_H_67 +H,68,coef_H_68 +H,77,coef_H_77 +H,78,coef_H_78 +H,88,coef_H_88 +M,11,coef_M_11 +M,12,coef_M_12 +M,13,coef_M_13 +M,16,coef_M_16 +M,17,coef_M_17 +M,18,coef_M_18 +M,22,coef_M_22 +M,23,coef_M_23 +M,26,coef_M_26 +M,27,coef_M_27 +M,28,coef_M_28 +M,33,coef_M_33 +M,36,coef_M_36 +M,37,coef_M_37 +M,38,coef_M_38 +M,66,coef_M_66 +M,67,coef_M_67 +M,68,coef_M_68 +M,77,coef_M_77 +M,78,coef_M_78 +M,88,coef_M_88 +N,11,coef_N_11 +N,12,coef_N_12 +N,13,coef_N_13 +N,14,coef_N_14 +N,15,coef_N_15 +N,16,coef_N_16 +N,17,coef_N_17 +N,18,coef_N_18 +N,22,coef_N_22 +N,23,coef_N_23 +N,24,coef_N_24 +N,25,coef_N_25 +N,26,coef_N_26 +N,27,coef_N_27 +N,28,coef_N_28 +N,33,coef_N_33 +N,34,coef_N_34 +N,35,coef_N_35 +N,36,coef_N_36 +N,37,coef_N_37 +N,38,coef_N_38 +N,44,coef_N_44 +N,45,coef_N_45 +N,46,coef_N_46 +N,47,coef_N_47 +N,48,coef_N_48 +N,55,coef_N_55 +N,56,coef_N_56_57_58 +N,57,coef_N_56_57_58 +N,58,coef_N_56_57_58 +N,66,coef_N_66 +N,67,coef_N_67 +N,68,coef_N_68 +N,77,coef_N_77 +N,78,coef_N_78 +N,88,coef_N_88 +# 3-way interactions,, +H,114,coef_H_114 +H,117,coef_H_117_118 +H,118,coef_H_117_118 +H,127,coef_H_127_128 +H,128,coef_H_127_128 +H,147,coef_H_147_148 +H,148,coef_H_147_148 +H,177,coef_H_177_178_187_188 +H,178,coef_H_177_178_187_188 +H,187,coef_H_177_178_187_188 +H,188,coef_H_177_178_187_188 +H,277,coef_H_277_278_287_288 +H,278,coef_H_277_278_287_288 +H,287,coef_H_277_278_287_288 +H,288,coef_H_277_278_287_288 +H,447,coef_H_447_448 +H,448,coef_H_447_448 +H,477,coef_H_477_478_487_488 +H,478,coef_H_477_478_487_488 +H,487,coef_H_477_478_487_488 +H,488,coef_H_477_478_487_488 +H,777,coef_H_777_778_788_888 +H,778,coef_H_777_778_788_888 +H,788,coef_H_777_778_788_888 +H,888,coef_H_777_778_788_888 +M,111,coef_M_111 +M,112,coef_M_112 +M,122,coef_M_122 +M,127,coef_M_127_128 +M,128,coef_M_127_128 +M,177,coef_M_177_178_187_188 +M,178,coef_M_177_178_187_188 +M,187,coef_M_177_178_187_188 +M,188,coef_M_177_178_187_188 +M,227,coef_M_227_228 +M,228,coef_M_227_228 +M,277,coef_M_277_278_287_288 +M,278,coef_M_277_278_287_288 +M,287,coef_M_277_278_287_288 +M,288,coef_M_277_278_287_288 +M,777,coef_M_777_778_788_888 +M,778,coef_M_777_778_788_888 +M,788,coef_M_777_778_788_888 +M,888,coef_M_777_778_788_888 +N,117,coef_N_117_118 +N,118,coef_N_117_118 +N,144,coef_N_144 +N,127,coef_N_127_128 +N,128,coef_N_127_128 +N,147,coef_N_147_148 +N,148,coef_N_147_148 +N,177,coef_N_177_178_187_188 +N,178,coef_N_177_178_187_188 +N,187,coef_N_177_178_187_188 +N,188,coef_N_177_178_187_188 +N,222,coef_N_222 +N,277,coef_N_277_278_287_288 +N,278,coef_N_277_278_287_288 +N,287,coef_N_277_278_287_288 +N,288,coef_N_277_278_287_288 +N,477,coef_N_477_478_487_488 +N,478,coef_N_477_478_487_488 +N,487,coef_N_477_478_487_488 +N,488,coef_N_477_478_487_488 +N,777,coef_N_777_778_788_888 +N,778,coef_N_777_778_788_888 +N,788,coef_N_777_778_788_888 +N,888,coef_N_777_778_788_888 +# cdap_final_rules,, +M,5,coef_UNAVAILABLE +M,4,coef_UNAVAILABLE +# cdap_all_people,, +M,***,coef_M_xxx +N,***,coef_N_xxx +H,***,coef_H_xxx +M,****,coef_M_xxxx +N,****,coef_N_xxxx +H,****,coef_H_xxxx +M,*****,coef_M_xxxxx +N,*****,coef_N_xxxxx +H,*****,coef_H_xxxxx diff --git a/configs/resident/cdap_joint_tour_coefficients.csv b/configs/resident/cdap_joint_tour_coefficients.csv new file mode 100644 index 0000000..1eb8d6a --- /dev/null +++ b/configs/resident/cdap_joint_tour_coefficients.csv @@ -0,0 +1,19 @@ +Label,description,Expression,dependency,coefficient +,constant - joint tour,1,,-3.1506 +,joint tour for hhsize 2,hhsize == 2,,-0.550050686 +,joint tour for hhsize 3,hhsize == 3,,-1.066203116 +,joint tour for hhsize 4,hhsize == 4,,-1.041167491 +,joint tour for hhsize 5+,hhsize >= 5,,-1.281586989 +,person x is adult and DAP is M,ptype_px < 7,M_px,0.008 +,person x is adult and DAP is N,ptype_px < 7,N_px,1.2557 +,person x is kid and DAP is M,ptype_px > 6,M_px,0.1088 +,person x is kid and DAP is N,ptype_px > 6,N_px,1.6898 +,Accessibility to retail employment/Non-Mandatory Attractions,shopping_accessibility_p1,,0.055031985 +,Income less than $30k,income_p1 < 30000,,-0.192506367 +,Income between $60k and $100k,(income_p1 >= 60000) & (income_p1 < =100000),,0.104325349 +,Income more than $100k,income_p1 > 100000,,0.104325349 +,No Car Households,auto_ownership_p1 == 0,,0 +,Cars Less than Workers,auto_ownership_p1 < num_workers_p1,,0.088402389 +,Cars More than Workers,auto_ownership_p1 > num_workers_p1,,-0.005896499 +,WorkAccessForMandatoryDap,"@df.workplace_modechoice_logsum_px.fillna(0)",M_px,0.17217579 +,If All Adults stay at Home/ None of the Adults have Dap 1 or 2,(ptype_pxprod < 7) | (ptype_pxprod > 6),H_px,-0.988838929 diff --git a/configs/resident/destination_choice_size_terms.csv b/configs/resident/destination_choice_size_terms.csv new file mode 100644 index 0000000..a0cf39c --- /dev/null +++ b/configs/resident/destination_choice_size_terms.csv @@ -0,0 +1,38 @@ +model_selector,segment,hh,pop,emp_accm,emp_ag_min,emp_bus_svcs,emp_con,emp_educ,emp_ent,emp_fin_res_mgm,emp_food,emp_gov,emp_hlth,emp_mil,emp_mnf,emp_oth,emp_ret,emp_trn_wrh,emp_utl,emp_whl,emp_total,enrollgradekto8,enrollgrade9to12,othercollegeenroll,collegeenroll,parkactive,beachactive,external_work,external_nonwork +workplace,constr_maint,0,0,0.007723607,0.114467809,0.149701138,1,0.014673052,0.021406453,0.053921216,0.001836382,0.137710644,0.007849633,0,0.078208267,0.160935475,0.057720006,0.042038744,0.021622497,0.019444044,0,0,0,0,0,0,0,0,0 +workplace,health,0,0,0.001161773,0,0.064585993,0.000229486,0.019735804,0.006167439,0.028269818,8.61E-05,0.181566529,1,0,0.003012005,0.078627673,0.056869523,0,0.001247831,0.000272515,0,0,0,0,0,0,0,0,0 +workplace,military,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +workplace,mngt_busi_scic_arts,0,0,0.014582422,0.023150473,1,0.105478261,0.046020203,0.044384337,0.520588696,0.071675764,0.357056089,0.12997306,0,0.345664415,0.101893393,0.063194096,0.019155288,0.03001787,0.033921273,0,0,0,0,0,0,0,0,0 +workplace,prod_trans_move,0,0,0.039515775,0.044941354,0.289719626,0.059838173,0.010851157,0.045537226,0.055761149,0.116759706,0.169102427,0.051119614,0,1,0.300100358,0.577996613,0.692090573,0.057611491,0.160289782,0,0,0,0,0,0,0,0,0 +workplace,sales_office,0,0,0.031177613,0.011768937,0.494698223,0.051276887,0.048183584,0.052183296,1,0.216603122,0.337702324,0.170534494,0,0.201999856,0.142953744,0.914135674,0.206618229,0.01090569,0.174045033,0,0,0,0,0,0,0,0,0 +workplace,services,0,0,0.079950222,0.03214445,0.477412008,0.013782902,0.264904083,0.154746531,0.064277161,1,0.565075489,0.145096152,0,0.02819977,0.403566649,0.04669046,0.007689779,0.001232713,0.002031041,0,0,0,0,0,0,0,0,0 +school,preschool,0,0.1888,0,0,0,0,1,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0 +school,gradeschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 +school,highschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 +school,university,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.859,1,0,0,0,0 +non_mandatory,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +non_mandatory,shopping,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,othmaint,0,0,0,0,0.42255,0,0,0,0,0,0.24001,0.42255,0,0,0,1.60296,0,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,eatout,0.5512,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,social,0.3006,0,0,0,0,0,0,0,0,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +non_mandatory,othdiscr,0.04333,0,0.03453,0,0.004465,0,0,0.20337,0.004465,0.15911,0.005953,0,0,0,1,0.042025,0,0,0,0,0.03167,0.05136,0,0.02258,3.71685,6,0,0 +atwork,atwork,0,0,0,0,0.0145,0,0,0,0,0.367,0,0,0,0,0,0.104,0,0,0,0,0,0,0,0,0,0,0,0 +trip,work,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +trip,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0 +trip,shopping,0.000001,0,0,0,0,0,0,0,0,0.214689,0,0,0,0,0,0.375,0,0,0,0.0001,0,0,0,0,0,0,0,0 +trip,othmaint,0.000001,0,0,0,0.025,0,0,0,0,0,0.661904,1.464014,0,0,0,1.2379255,0,0,0,0.0001,0,0,0,0,0,0,0,0 +trip,eatout,0.010162,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0689145,0,0,0,0,0,0,0,0,0,0,0,0 +trip,social,0.495249,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +trip,othdiscr,0.027342,0,0.092343,0,0,0,0,0.473744,0,0.122872,0,0,0,0,1,0.0194215,0,0,0,0,0,0,0,0,3.71685,6,0.05,0 +trip,univ,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0 +# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +#trip,gradeschool,0,,,,,,,,,,,,,,,,,,,,,,,,,,, +#trip,highschool,0,,,,,,,,,,,,,,,,,,,,,,,,,,, +external_workplace,external_workplace,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +external_nonwork,external_nonwork,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,escort,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,shopping,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,othmaint,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,eatout,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,social,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +external_non_mandatory,othdiscr,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 diff --git a/configs/resident/disaggregate_accessibility.yaml b/configs/resident/disaggregate_accessibility.yaml new file mode 100644 index 0000000..f12c209 --- /dev/null +++ b/configs/resident/disaggregate_accessibility.yaml @@ -0,0 +1,187 @@ +# Sampling size. 0 = no limit +# can be whole integer value or a sample rate (percent of zones) +# zero or missing defaults to full sample! +DESTINATION_SAMPLE_SIZE: 100 +ORIGIN_SAMPLE_SIZE: 5000 + +# select origin zones weighted by population (or another landuse variable) +ORIGIN_WEIGHTING_COLUMN: pop + +# Specify the tables to be created and their variables. +# Single values are constants +# Lists are varying and will generate the cartesian product (i.e., all possible non-repeating combinations) + +# Example result for households below: +#hhid veh hinccat1 hinc hworkers persons hht bldgsz +# 1 0 1 14000 1 2 1 2 +# 2 1 1 14000 1 2 1 2 +# 3 2 1 14000 1 2 1 2 +# 4 0 2 67000 1 2 1 2 +# 5 1 2 67000 1 2 1 2 +# 6 2 2 67000 1 2 1 2 +# .... + +#Note: parameters in ALL CAPS below are required by the program for synthesis +CREATE_TABLES: + PROTO_HOUSEHOLDS: + index_col: proto_household_id + zone_col: home_zone_id + rename_columns: + zone_id: home_zone_id + VARIABLES: + hinccat1: [1, 2, 3, 4] # Income categories + hworkers: 1 # Household workers + auto_ownership: [0, 1, 2] # Household vehicles + persons: 2 # Two persons household + HHT: 1 # Married-couple family household + bldgsz: 2 # Building size - Single family detached + # Additional columns that are mapped to another (e.g., hhinccat1: 1 = hinc: 14000) + mapped_fields: + hinccat1: # List new fields mapped to this field + income: # Median income within each quartile + 1: 15000 # 14000 (for 3 level) + 2: 45000 # 67000 (for 3 level) + 3: 80000 # 120000 (for 3 level) + 4: 150000 + persons: + hhsize: + 1: 1 + 2: 2 + 3: 3 + hworkers: + num_workers: + 1: 1 + 2: 2 + 3: 3 + + PROTO_PERSONS: + index_col: proto_person_id + VARIABLES: + pnum: [1, 2] # Person number + military: 4 # Not military + pstudent: 3 # Not attending school + educ: 13 # Bachelor's + grade: 0 # Not attending + timeFactorWork: 1 # mean + timeFactorNonWork: 1 # mean + mapped_fields: + pnum: + age: + 1: 35 + 2: 55 + sex: # Female male + 1: 2 + 2: 1 + ptype: # Person type, full-time and non-working adult + 1: 1 + 2: 4 + pemploy: # Full-time and unemployed + 1: 1 + 2: 3 + weeks: # 50-52 weeks, none + 1: 1 + 2: 0 + hours: # Hours per week + 1: 35 + 2: 0 + DAP: # Mandatory, Non-mandatory + 1: "M" + 2: "N" + soc2: #occupation codes + 1: 11 # Management Occupations + 2: 0 # Non-worker + + PROTO_TOURS: + index_col: proto_tour_id + VARIABLES: + tour_num: 1 # Tour number, 1 tour per person + purpose: [1, 2, 3] + mapped_fields: + purpose: + person_num: # In this case it was easier to map the person number directly to the purposez + 1: 1 + 2: 2 + 3: 2 + tour_type: + 1: "work" + 2: "shopping" + 3: "othdiscr" + tour_category: # tour purpose category, mandatory/non-mandatory + 1: "mandatory" + 2: "non_mandatory" + 3: "non_mandatory" + filter_rows: # list any matching conditions as pandas expression + - ~((df.tour_type == "work") & (df.person_num == 1)) + JOIN_ON: + person_num: pnum # Specifies which person variable to join the tours on + +# Merge on variables +# will merge onto the persons table +MERGE_ON: + by: # These should be categorical variables at the household level + - home_zone_id + - income_segment +# Note that auto ownership is not included in the merge above. +# Below columns are created in the post-processor and separate out accessibility by auto ownership. +# Since auto ownership is not yet decided in the synthetic population, we want them all. +# Disaggreate accessibility table is grouped by the "by" cols above and the KEEP_COLS are averaged +# across the group. Initializing the below as NA if not in the auto ownership level, they are skipped +# in the groupby mean and the values are correct. +# (It's a way to avoid having to update code to reshape the table and introduce new functionality there.) +KEEP_COLS: + - workplace_location_accessibility_0 + - workplace_location_accessibility_1 + - workplace_location_accessibility_2 + - othdiscr_accessibility_0 + - othdiscr_accessibility_1 + - othdiscr_accessibility_2 + - shopping_accessibility_0 + - shopping_accessibility_1 + - shopping_accessibility_2 + +# Include any annotations for persons, households, land_use, or tours. +# The purpose of a separate annotation setup is that annotation expressions for the main model +# may require data that aren't in the proto population. +# This step enables users to annotate the proto-population by referencing custom annotation scripts. +# Of course, users can also just reference existing configs if they work, but they must be referenced here too. + +annotate_proto_tables: + - tablename: proto_persons + annotate: + SPEC: annotate_persons + DF: proto_persons + TABLES: + - proto_households + +# specific annotation for proto_persons + - tablename: proto_persons + annotate: + SPEC: annotate_proto_persons + DF: proto_persons + TABLES: + - proto_households + +# Annotate the proto_households table using the main model annotations files + - tablename: proto_households + annotate: + SPEC: annotate_households + DF: proto_households + TABLES: + - proto_persons + - land_use + +# Annotate the proto_persons table using the main model annotations files + - tablename: proto_persons + annotate: + SPEC: annotate_persons_after_hh + DF: proto_persons + TABLES: + - proto_households + +postprocess_proto_tables: + - tablename: proto_disaggregate_accessibility + annotate: + SPEC: annotate_disaggregate_accessibility + DF: proto_disaggregate_accessibility + TABLES: + - land_use \ No newline at end of file diff --git a/configs/resident/external_joint_tour_destination.yaml b/configs/resident/external_joint_tour_destination.yaml new file mode 100644 index 0000000..ca25967 --- /dev/null +++ b/configs/resident/external_joint_tour_destination.yaml @@ -0,0 +1 @@ +include_settings: external_non_mandatory_destination.yaml diff --git a/configs/resident/external_joint_tour_identification.yaml b/configs/resident/external_joint_tour_identification.yaml new file mode 100644 index 0000000..1b9e997 --- /dev/null +++ b/configs/resident/external_joint_tour_identification.yaml @@ -0,0 +1 @@ +include_settings: external_non_mandatory_identification.yaml diff --git a/configs/resident/external_non_mandatory_destination.csv b/configs/resident/external_non_mandatory_destination.csv new file mode 100644 index 0000000..be0471b --- /dev/null +++ b/configs/resident/external_non_mandatory_destination.csv @@ -0,0 +1,5 @@ +Label,Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +local_dist,Distance capped at 20 miles,"_DIST@np.minimum(skims['DIST'], 20)",1,1,1,1,1,1 +util_dist,dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_maint,coef_dist_social,coef_dist_discr +util_size_term,Size variable,@df['size_term'].apply(np.log1p),coef_size,coef_size,coef_size,coef_size,coef_size,coef_size +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum diff --git a/configs/resident/external_non_mandatory_destination.yaml b/configs/resident/external_non_mandatory_destination.yaml new file mode 100644 index 0000000..82af7ed --- /dev/null +++ b/configs/resident/external_non_mandatory_destination.yaml @@ -0,0 +1,43 @@ +SAMPLE_SPEC: external_non_mandatory_destination_sample.csv +SPEC: external_non_mandatory_destination.csv +COEFFICIENTS: external_non_mandatory_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: external_non_mandatory + +CHOOSER_SEGMENT_COLUMN_NAME: tour_type + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: external_tour_destination_sample + +# these segments need to match in the destination size term table +SEGMENTS: + - shopping + - othmaint + - othdiscr + - eatout + - social + - escort + +SIMULATE_CHOOSER_COLUMNS: + - tour_type + - home_zone_id + - person_id + - female + - income + - income_segment + - age + - tour_type_count + - tour_type_num + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 14 diff --git a/configs/resident/external_non_mandatory_destination_coefficients.csv b/configs/resident/external_non_mandatory_destination_coefficients.csv new file mode 100644 index 0000000..62c9a42 --- /dev/null +++ b/configs/resident/external_non_mandatory_destination_coefficients.csv @@ -0,0 +1,9 @@ +coefficient_name,value,constrain +coef_mode_logsum,0.27,F +coef_dist_escort,-0.356,F +coef_dist_shopping,-0.42,F +coef_dist_maint,-0.18,F +coef_dist_eatout,-0.312,F +coef_dist_social,-0.273,F +coef_dist_discr,-0.345,F +coef_size,1,T diff --git a/configs/resident/external_non_mandatory_destination_sample.csv b/configs/resident/external_non_mandatory_destination_sample.csv new file mode 100644 index 0000000..44bbaac --- /dev/null +++ b/configs/resident/external_non_mandatory_destination_sample.csv @@ -0,0 +1,5 @@ +Label,Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +local_dist,Distance capped at 20 miles,"_DIST@np.minimum(skims['DIST'], 20)",1,1,1,1,1,1 +util_dist,dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_maint,coef_dist_social,coef_dist_discr +util_size_term,Size variable,@df['size_term'].apply(np.log1p),coef_size,coef_size,coef_size,coef_size,coef_size,coef_size +utl_zero_size_term,,@df['size_term']==0,-999,-999,-999,-999,-999,-999 \ No newline at end of file diff --git a/configs/resident/external_non_mandatory_identification.csv b/configs/resident/external_non_mandatory_identification.csv new file mode 100644 index 0000000..6ef90b9 --- /dev/null +++ b/configs/resident/external_non_mandatory_identification.csv @@ -0,0 +1,17 @@ +Label,Description,Expression,external_tour,internal_tour +util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,coef_dist_to_nearest_ext_station, +util_log_size,Log size of nearest station,"@np.log1p(reindex(land_use.external_nonwork, df.closest_external_zone))",coef_log_size_of_nearest_ext_station, +util_escort,escort tour ASC,"@np.where(df.tour_type == 'escort', 1, 0)",coef_escort, +util_shopping,shopping tour ASC,"@np.where(df.tour_type == 'shopping', 1, 0)",coef_shopping, +util_othmaint,othmaint tour ASC,"@np.where(df.tour_type == 'othmaint', 1, 0)",coef_othmaint, +util_eatout,eatout tour ASC,"@np.where(df.tour_type == 'eatout', 1, 0)",coef_eatout, +util_social,social tour ASC,"@np.where(df.tour_type == 'social', 1, 0)",coef_social, +util_othdiscr,othdiscr tour ASC,"@np.where(df.tour_type == 'othdiscr', 1, 0)",coef_othdiscr, +util_inc_lt15,Income less than $15k,"@np.where(df['income']<15000,1,0)",coef_income_lt_15, +util_inc_15_50,Income 15 to 50k,"@np.where((df['income']>=15000) * (df['income']<=49999),1,0)",coef_income_15_50, +util_inc_100_250,Income 100k to 250k,"@np.where((df['income']>=100000) * (df['income']<=249999),1,0)",coef_income_100_250, +util_inc_250plus,Income 250k plus,"@np.where(df['income']>=250000,1,0)",coef_income_250_plus, +util_autos_0,0 auto household,"@np.where(df.auto_ownership==0,1,0)",coef_autos_0, +util_global_switch,Global switch to have everything internal,@NO_EXTERNAL,-999, +util_dist_lt_2p5,Distance less than 2.5 miles,"@np.where(df.dist_to_external_zone<2.5,1,0)",coef_dist_lt_2p5, +util_calibration,,1,0.43, diff --git a/configs/resident/external_non_mandatory_identification.yaml b/configs/resident/external_non_mandatory_identification.yaml new file mode 100644 index 0000000..c2a882b --- /dev/null +++ b/configs/resident/external_non_mandatory_identification.yaml @@ -0,0 +1,11 @@ +# using the same spec for both non-mandatory and joint tours +SPEC: external_non_mandatory_identification.csv +COEFFICIENTS: external_non_mandatory_identification_coefficients.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +# Adds this column to the persons table and is set to True if workplace is external +EXTERNAL_COL_NAME: is_external_tour +# set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True +INTERNAL_COL_NAME: is_internal_tour diff --git a/configs/resident/external_non_mandatory_identification_coefficients.csv b/configs/resident/external_non_mandatory_identification_coefficients.csv new file mode 100644 index 0000000..be5f021 --- /dev/null +++ b/configs/resident/external_non_mandatory_identification_coefficients.csv @@ -0,0 +1,15 @@ +coefficient_name,value,constrain +coef_dist_to_nearest_ext_station,-0.06913447619232407,F +coef_log_size_of_nearest_ext_station,0.40435031197856747,F +coef_escort,-8.056823449462565,F +coef_shopping,-7.413630670471193,F +coef_othmaint,-6.453644115005783,F +coef_eatout,-7.27668098767437,F +coef_social,-6.566754062811491,F +coef_othdiscr,-7.415691288097906,F +coef_income_lt_15,-0.706806612224066,F +coef_income_15_50,-0.2533987244055551,F +coef_income_100_250,0.11811329788815791,F +coef_income_250_plus,0.6221129017397624,F +coef_autos_0,-0.5505407390480237,F +coef_dist_lt_2p5,2.6495984313112557,F diff --git a/configs/resident/external_school_location.csv b/configs/resident/external_school_location.csv new file mode 100644 index 0000000..f2eead3 --- /dev/null +++ b/configs/resident/external_school_location.csv @@ -0,0 +1,12 @@ +Label,Description,Expression,external_nonwork +local_dist,,_DIST@skims['DIST'],1 +util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_grade_dist_0_1 +util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_high_grade_dist_1_2 +util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_high_grade_dist_2_5 +util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_grade_dist_5_15 +util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_grade_dist_15_up +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 +util_no_attractions,No attractions,@df['size_term']==0,-999 +#util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum +#util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1 diff --git a/configs/resident/external_school_location.yaml b/configs/resident/external_school_location.yaml new file mode 100644 index 0000000..1cdc206 --- /dev/null +++ b/configs/resident/external_school_location.yaml @@ -0,0 +1,52 @@ +SAMPLE_SIZE: 30 + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - school_segment + - household_id + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: external_school_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: external_school_location_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: external_school_location_sample + +SAMPLE_SPEC: external_school_location.csv +SPEC: external_school_location.csv +COEFFICIENTS: external_school_location_coefficients.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor + +LOGSUM_TOUR_PURPOSE: univ + +# required by initialize_households when creating school_destination_size table +CHOOSER_TABLE_NAME: persons + +# size_terms model_selector +MODEL_SELECTOR: external_nonwork + +# chooser column with segment_id for this segment type +CHOOSER_SEGMENT_COLUMN_NAME: is_external_student + +# boolean column to filter choosers (True means keep) +# CHOOSER_FILTER_COLUMN_NAME: is_student +CHOOSER_FILTER_COLUMN_NAME: is_external_student + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +SEGMENT_IDS: + external_nonwork: True # using only one segment + +# model adds these tables (informational - not added if commented out) +# SHADOW_PRICE_TABLE: school_shadow_prices +# MODELED_SIZE_TABLE: school_modeled_size + +# not loaded if commented out +# SAVED_SHADOW_PRICE_TABLE_NAME: school_shadow_prices.csv diff --git a/configs/resident/external_school_location_coefficients.csv b/configs/resident/external_school_location_coefficients.csv new file mode 100644 index 0000000..b9ef59c --- /dev/null +++ b/configs/resident/external_school_location_coefficients.csv @@ -0,0 +1,17 @@ +coefficient_name,value,constrain +coef_univ_dist_0_1,-3.2451,F +coef_univ_dist_1_2,-2.7011,F +coef_univ_dist_2_5,-0.5707,F +coef_univ_dist_5_15,-0.5002,F +coef_univ_dist_15_up,-0.073,F +coef_high_dist_0_1,-0.9523,F +coef_high_grade_dist_1_2,-0.57,F +coef_high_grade_dist_2_5,-0.57,F +coef_high_dist_5_15,-0.193,F +coef_high_dist_15_up,-0.1882,F +coef_grade_dist_0_1,-1.6419,F +#coef_high_grade_dist_1_2,-0.57,F +#coef_high_grade_dist_2_5,-0.57,F +coef_grade_dist_5_15,-0.2031,F +coef_grade_dist_15_up,-0.046,F +coef_mode_logsum,0.5358,F diff --git a/configs/resident/external_student_identification.csv b/configs/resident/external_student_identification.csv new file mode 100644 index 0000000..8e12841 --- /dev/null +++ b/configs/resident/external_student_identification.csv @@ -0,0 +1,5 @@ +Label,Description,Expression,school_external,school_internal +util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,coef_dist_to_nearest_ext_station,0 +util_size_of_nearest_ext_station,Size of nearest external station,"@reindex(land_use.external_nonwork,df.closest_external_zone)",coef_size_of_nearest_ext_station,0 +util_child_age,Age of school child,"age",coef_child_age,0 +util_asc_placeholder,ASC to play with,1,-5,0 diff --git a/configs/resident/external_student_identification.yaml b/configs/resident/external_student_identification.yaml new file mode 100644 index 0000000..1999c0f --- /dev/null +++ b/configs/resident/external_student_identification.yaml @@ -0,0 +1,15 @@ + +SPEC: external_student_identification.csv +COEFFICIENTS: external_student_identification_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +# boolean column to filter choosers (True means keep) +# will only expose these people to the model +CHOOSER_FILTER_COLUMN_NAME: is_student + +# Adds this column to the persons table and is set to True if workplace is external +EXTERNAL_COL_NAME: is_external_student +# set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True +INTERNAL_COL_NAME: is_internal_student diff --git a/configs/resident/external_student_identification_coeffs.csv b/configs/resident/external_student_identification_coeffs.csv new file mode 100644 index 0000000..39a368f --- /dev/null +++ b/configs/resident/external_student_identification_coeffs.csv @@ -0,0 +1,4 @@ +coefficient_name,value,constrain +coef_dist_to_nearest_ext_station,-0.05,F +coef_size_of_nearest_ext_station,0.001,F +coef_child_age,0.1,F diff --git a/configs/resident/external_worker_identification.csv b/configs/resident/external_worker_identification.csv new file mode 100644 index 0000000..b584488 --- /dev/null +++ b/configs/resident/external_worker_identification.csv @@ -0,0 +1,14 @@ +Label,Description,Expression,work_external,work_internal +util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,coef_dist_to_nearest_ext_station, +util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(reindex(land_use.external_work,df.closest_external_zone))",coef_size_of_nearest_ext_station, +util_dist_lt_2p5,Distance less than 2.5 miles,"@np.where(df.dist_to_external_zone<2.5,1,0)",coef_dist_lt_2p5, +util_part_time,Part time worker,"@np.where(df.is_parttime_worker==""TRUE"",1,0)",coef_part_time, +util_inc_lt15,Household Income less than $15k,@(df.income<15000),coef_inc_lt15, +util_inc_15_25,Household income $15k-$25k,@(df.income>=15000) & (df.income<25000) ,coef_inc_15_25, +util_inc_25_50,Household Income $25k-$50k,@(df.income>=25000) & (df.income<50000) ,coef_inc_25_50, +util_inc_150_250,Household Income $150k-$50k,@(df.income>=150000) * (df.income<250000) ,coef_inc_150_250, +util_inc_250plus,Household Income $250k+,@(df.income>=250000),coef_inc_250plus, +util_asc,Alternative-specific constant for external worker,1,asc_external_worker, +util_global_switch,Global switch to have everything internal,@NO_EXTERNAL,-999, +util_2016,Constant for pre-COVID conditions,@PRE_COVID,asc_external_2016, +util_calib,Constant for calibration,1,0.35, diff --git a/configs/resident/external_worker_identification.yaml b/configs/resident/external_worker_identification.yaml new file mode 100644 index 0000000..e9660b6 --- /dev/null +++ b/configs/resident/external_worker_identification.yaml @@ -0,0 +1,14 @@ + +SPEC: external_worker_identification.csv +COEFFICIENTS: external_worker_identification_coeffs.csv + +LOGIT_TYPE: MNL + +# boolean column to filter choosers (True means keep) +# will only expose these people to the model +CHOOSER_FILTER_COLUMN_NAME: is_out_of_home_worker + +# Adds this column to the persons table and is set to True if workplace is external +EXTERNAL_COL_NAME: is_external_worker +# set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True +INTERNAL_COL_NAME: is_internal_worker diff --git a/configs/resident/external_worker_identification_coeffs.csv b/configs/resident/external_worker_identification_coeffs.csv new file mode 100644 index 0000000..d316d86 --- /dev/null +++ b/configs/resident/external_worker_identification_coeffs.csv @@ -0,0 +1,12 @@ +coefficient_name,value,constrain +coef_dist_to_nearest_ext_station,-0.03637843085617849,F +coef_size_of_nearest_ext_station,0.7890441071064493,F +coef_part_time,-0.22281386510906526,F +asc_external_worker,-10.723198329563822,F +asc_external_2016,0.7108412488374917,F +coef_inc_lt15,-1.3314204997581536,F +coef_inc_25_50,-0.5733965244215453,F +coef_inc_250plus,1.2296738201382527,F +coef_dist_lt_2p5,2.302815581601645,F +coef_inc_15_25,-0.9334826063624445,F +coef_inc_150_250,0.8316476615543997,F diff --git a/configs/resident/external_workplace_location.csv b/configs/resident/external_workplace_location.csv new file mode 100644 index 0000000..6eeb3fc --- /dev/null +++ b/configs/resident/external_workplace_location.csv @@ -0,0 +1,6 @@ +Label,Description,Expression,external_workplace +local_dist,,_DIST@skims['DIST'],1 +util_dist,,"@np.minimum(_DIST,10)",coef_dist_capped +util_size_variable,Size variable,@(df['size_term']).apply(np.log1p),coef_size +util_no_attractions,No attractions,@df['size_term']==0,-999 +mode_choice_logsum,Mode choice logsum,@df.mode_choice_logsum if 'mode_choice_logsum' in df.columns else 0,coef_mode_logsum diff --git a/configs/resident/external_workplace_location.yaml b/configs/resident/external_workplace_location.yaml new file mode 100644 index 0000000..f106547 --- /dev/null +++ b/configs/resident/external_workplace_location.yaml @@ -0,0 +1,53 @@ +SAMPLE_SIZE: 30 + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - household_id + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: external_workplace_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: external_workplace_location_logsum +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +MODE_CHOICE_LOGSUM_COLUMN_NAME: external_workplace_modechoice_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: external_workplace_location_sample + +SAMPLE_SPEC: external_workplace_location.csv +SPEC: external_workplace_location.csv +COEFFICIENTS: external_workplace_location_coefficients.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor + +LOGSUM_TOUR_PURPOSE: work + +# required by initialize_households when creating workplace_destination_size table +CHOOSER_TABLE_NAME: persons + +# size_terms model_selector +MODEL_SELECTOR: external_workplace + +# chooser column with segment_id for this segment type +CHOOSER_SEGMENT_COLUMN_NAME: is_external_worker + +# boolean column to filter choosers (True means keep) +# CHOOSER_FILTER_COLUMN_NAME: is_worker +CHOOSER_FILTER_COLUMN_NAME: is_external_worker + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +SEGMENT_IDS: + external_workplace: True # using only one segment + +# model adds these tables (informational - not added if commented out) +# SHADOW_PRICE_TABLE: workplace_shadow_prices +# MODELED_SIZE_TABLE: workplace_modeled_size + +# not loaded if commented out +# SAVED_SHADOW_PRICE_TABLE_NAME: workplace_shadow_prices.csv diff --git a/configs/resident/external_workplace_location_archive.csv b/configs/resident/external_workplace_location_archive.csv new file mode 100644 index 0000000..48c3a8a --- /dev/null +++ b/configs/resident/external_workplace_location_archive.csv @@ -0,0 +1,6 @@ +Label,Description,Expression,external_workplace +local_dist,,_DIST@skims['DIST'],1 +util_dist,,"@np.minimum(_DIST,10)",coef_dist_capped +util_size_variable,Size variable,@(df['size_term']).apply(np.log1p),coef_size +util_no_attractions,No attractions,@df['size_term']==0,-999 +mode_choice_logsum not available for sample,Mode choice logsum,@df.mode_choice_logsum if 'mode_choice_logsum' in df.columns else 0,coef_mode_logsum \ No newline at end of file diff --git a/configs/resident/external_workplace_location_coefficients.csv b/configs/resident/external_workplace_location_coefficients.csv new file mode 100644 index 0000000..7ba9aa4 --- /dev/null +++ b/configs/resident/external_workplace_location_coefficients.csv @@ -0,0 +1,4 @@ +coefficient_name,value,constrain +coef_mode_logsum,0.064105,F +coef_dist_capped,-0.519016,F +coef_size,1,T diff --git a/configs/resident/free_parking.csv b/configs/resident/free_parking.csv new file mode 100644 index 0000000..8fa0f37 --- /dev/null +++ b/configs/resident/free_parking.csv @@ -0,0 +1,6 @@ +Label,Description,Expression,free,pay +util_income_very_high,Very high income household dummy,@df.income>=100000,coef_income_very_high,0.0 +util_income_high,High income housheold dummy,@(df.income>=60000) & (df.income<100000),coef_income_high,0.0 +util_hh_size_4_up,Household size is greater than 3 dummy,@df.hhsize>3,coef_hh_size_4_up,0.0 +util_more_autos_than_workers,More automobiles than workers dummy,@df.auto_ownership>df.num_workers,coef_more_autos_than_workers,0.0 +util_fewer_autos_than_workers,Fewer automobiles than workers dummy,@df.auto_ownership num_workers) * othmaint,coef_hh_has_more_autos_than_workers_maintenance +util_income_gtr_100k_maintenance,Income > $100k /Maintenance,(income>100000) * othmaint,coef_income_gtr_100k_maintenance +util_income_less_than_29999__eating_out,"Income Less than $29,999 / Eating Out",(income<30000) * eatout,coef_income_less_than_29999__eating_out +util_income_between_30000_to_59999__eating_out,"Income Between $30,000 to $59,999 / Eating Out",((income>=30000) & (income<60000)) * othmaint,coef_income_between_30000_to_59999__eating_out +util_income_less_than_29999__discretionary,"Income Less than $29,999 / Discretionary",(income<30000) * othdiscr,coef_income_less_than_29999__discretionary +util_income_between_30000_to_59999__discretionary,"Income Between $30,000 to $59,999 / Discretionary",((income>=30000) & (income<60000)) * othdiscr,coef_income_between_30000_to_59999__discretionary +# util_shopping_hov_accessibility_for_2_tours,Shopping HOV accessibility for 2 Tours,((auto_ownershipnum_workers)*shop_hov_oversufficient_accessibility)*(num_joint_tours==2)*shopping,coef_shopping_hov_accessibility_for_2_tours +# util_maintenance_hov_accessibility,Maintenance HOV Accessibility,((auto_ownershipnum_workers)*maint_hov_oversufficient_accessibility)*othmaint,coef_maintenance_hov_accessibility +# util_discretionary_hov_accessibility,Discretionary HOV Accessibility,((auto_ownershipnum_workers)*discr_hov_oversufficient_accessibility)*othdiscr,coef_discretionary_hov_accessibility +util_shopping_hov_accessibility_for_2_tours,Shopping HOV accessibility for 2 Tours,(shopping_accessibility) * (num_joint_tours == 2) * shopping,coef_shopping_hov_accessibility_for_2_tours +util_maintenance_hov_accessibility,Maintenance HOV Accessibility,shopping_accessibility * othmaint,coef_maintenance_hov_accessibility +util_discretionary_hov_accessibility,Discretionary HOV Accessibility,othdiscr_accessibility * othdiscr,coef_discretionary_hov_accessibility +util_constant_for_children_party_shopping_tour,Constant for Children Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_constant_for_children_party_shopping_tour +util_constant_for_children_party_maintenance_tour,Constant for Children Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==2)+(df.purpose2==6)*(df.party2==2),coef_constant_for_children_party_maintenance_tour +util_constant_for_children_party_eating_out_tour,Constant for Children Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==2)+(df.purpose2==7)*(df.party2==2),coef_constant_for_children_party_eating_out_tour +util_constant_for_children_party_visiting_tour,Constant for Children Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==2)+(df.purpose2==8)*(df.party2==2),coef_constant_for_children_party_visiting_tour +util_constant_for_children_party_discretionary_tour,Constant for Children Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==2)+(df.purpose2==9)*(df.party2==2),coef_constant_for_children_party_discretionary_tour +util_constant_for_mixed_party_shopping_tour,Constant for Mixed Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_constant_for_mixed_party_shopping_tour +util_constant_for_mixed_party_maintenance_tour,Constant for Mixed Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==3)+(df.purpose2==6)*(df.party2==3),coef_constant_for_mixed_party_maintenance_tour +util_constant_for_mixed_party_eating_out_tour,Constant for Mixed Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==3)+(df.purpose2==7)*(df.party2==3),coef_constant_for_mixed_party_eating_out_tour +util_constant_for_mixed_party_visiting_tour,Constant for Mixed Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==3)+(df.purpose2==8)*(df.party2==3),coef_constant_for_mixed_party_visiting_tour +util_constant_for_mixed_party_discretionary_tour,Constant for Mixed Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==3)+(df.purpose2==9)*(df.party2==3),coef_constant_for_mixed_party_discretionary_tour +util_number_of_active_full_time_workers_adult_party,Number of Active Full time workers /Adult Party,num_travel_active_full_time_workers * (party1==1) + num_travel_active_full_time_workers * (party2==1),coef_number_of_active_full_time_workers_adult_party +util_number_of_active_part_time_workers_adult_party,Number of Active Part time workers /Adult Party,num_travel_active_part_time_workers * (party1==1) + num_travel_active_part_time_workers * (party2==1),coef_number_of_active_part_time_workers_adult_party +util_number_of_active_university_students_adult_party,Number of Active University Students /Adult Party,num_travel_active_university_students * (party1==1) + num_travel_active_university_students * (party2==1),coef_number_of_active_university_students_adult_party +util_number_of_active_nonworkers_adult_party,Number of Active Non-workers /Adult Party,num_travel_active_non_workers * (party1==1) + num_travel_active_non_workers * (party2==1),coef_number_of_active_nonworkers_adult_party +util_number_of_active_retirees_adult_party,Number of Active Retirees /Adult Party,num_travel_active_retirees * (party1==1) + num_travel_active_retirees * (party2==1),coef_number_of_active_retirees_adult_party +util_number_of_active_driving_age_school_children_children_party,Number of Active Driving Age School Children /Children Party,num_travel_active_driving_age_students * (party1==1) + num_travel_active_driving_age_students * (party2==1),coef_number_of_active_driving_age_school_children_children_party +util_number_of_active_pre_driving_age_school_children_children_party,Number of Active Pre- Driving Age School Children /Children Party,num_travel_active_pre_driving_age_school_kids * (party1==2) + num_travel_active_pre_driving_age_school_kids * (party2==2),coef_number_of_active_pre_driving_age_school_children_children_party +util_number_of_active_part_time_workers_mixed_party,Number of Active Part time workers /Mixed Party,num_travel_active_part_time_workers * (party1==2) + num_travel_active_part_time_workers * (party2==2),coef_number_of_active_part_time_workers_mixed_party +util_number_of_active_driving_age_school_children_mixed_party,Number of Active Driving Age School Children /Mixed Party,num_travel_active_driving_age_students * (party1==3) + num_travel_active_driving_age_students * (party2==3),coef_number_of_active_driving_age_school_children_mixed_party +util_number_of_active_pre_driving_age_school_children_mixed_party,Number of Active Pre- Driving Age School Children /Mixed Party,num_travel_active_pre_driving_age_school_kids * (party1==3) + num_travel_active_pre_driving_age_school_kids * (party2==3),coef_number_of_active_pre_driving_age_school_children_mixed_party +util_number_of_active_preschool_children_mixed_party,Number of Active Preschool Children /Mixed Party,num_travel_active_pre_school_kids * (party1==3) + num_travel_active_pre_school_kids * (party2==3),coef_number_of_active_preschool_children_mixed_party +util_hh_has_no_autos__mixed_party,HH has no autos / Mixed Party,@(df.auto_ownership==0)*(df.party1==3)+(df.auto_ownership==0)*(df.party2==3),coef_hh_has_no_autos__mixed_party +util_hh_has_less_autos_than_workers_mixed_party,HH has less autos than workers/ Mixed Party,@(df.auto_ownership100000)*(df.party1==2)+(df.income>100000)*(df.party2==2),coef_income_more_than_100k_child_party +util_income_more_than_100k_mixed_party,Income more than $100k /Mixed Party,@(df.income>100000)*(df.party1==3)+(df.income>100000)*(df.party2==3),coef_income_more_than_100k_mixed_party +util_log_of_max_window_overlaps_between_adults,Log of max window overlaps between adults,@df.log_time_window_overlap_adult*((df.party1==1)+(df.party2==1)),coef_log_of_max_window_overlaps_between_adults +util_log_of_max_window_overlaps_between_children,Log of max window overlaps between children,@df.log_time_window_overlap_child*((df.party1==2)+(df.party2==2)),coef_log_of_max_window_overlaps_between_children +util_log_of_max_window_overlaps_between_adult_child,Log of max window overlaps between adult & child,@df.log_time_window_overlap_adult_child*((df.party1==3)+(df.party2==3)),coef_log_of_max_window_overlaps_between_adult_child +util_not_more_than_one_active_adult,Not more than 1 travel active adult in HH,@(df.num_travel_active_adults < 2)*(((df.party1==1)+(df.party2==1))>0),coef_unavailable +util_not_more_than_one_active_child,Not more than 1 travel active child in HH,@(df.num_travel_active_children < 2)*(((df.party1==2)+(df.party2==2))>0),coef_unavailable +util_not_more_than_one_active_mixed,No travel-active pair adult-child in HH ,@((df.num_travel_active_adults*df.num_travel_active_children) ==0)*(((df.party1==3)+(df.party2==3))>0),coef_unavailable +util_adjustment_for_children_party_shopping_tour,Adjustment for Children Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_adjustment_for_children_party_shopping_tour +util_adjustment_for_children_party_maintenance_tour,Adjustment for Children Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==2)+(df.purpose2==6)*(df.party2==2),coef_adjustment_for_children_party_maintenance_tour +util_adjustment_for_children_party_eating_out_tour,Adjustment for Children Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==2)+(df.purpose2==7)*(df.party2==2),coef_adjustment_for_children_party_eating_out_tour +util_adjustment_for_children_party_visiting_tour,Adjustment for Children Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==2)+(df.purpose2==8)*(df.party2==2),coef_adjustment_for_children_party_visiting_tour +util_adjustment_for_children_party_discretionary_tour,Adjustment for Children Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==2)+(df.purpose2==9)*(df.party2==2),coef_adjustment_for_children_party_discretionary_tour +util_adjustment_for_mixed_party_shopping_tour,Adjustment for Mixed Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_adjustment_for_mixed_party_shopping_tour +util_adjustment_for_mixed_party_maintenance_tour,Adjustment for Mixed Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==3)+(df.purpose2==6)*(df.party2==3),coef_adjustment_for_mixed_party_maintenance_tour +util_adjustment_for_mixed_party_eating_out_tour,Adjustment for Mixed Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==3)+(df.purpose2==7)*(df.party2==3),coef_adjustment_for_mixed_party_eating_out_tour +util_adjustment_for_mixed_party_visiting_tour,Adjustment for Mixed Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==3)+(df.purpose2==8)*(df.party2==3),coef_adjustment_for_mixed_party_visiting_tour +util_adjustment_for_mixed_party_discretionary_tour,Adjustment for Mixed Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==3)+(df.purpose2==9)*(df.party2==3),coef_adjustment_for_mixed_party_discretionary_tour +util_adjustment_for_shopping_tour,Adjustment for shopping tour,shopping,coef_adjustment_for_shopping_tour +util_adjustment_for_maintenance_tour,Adjustment for Maintenance tour,othmaint,coef_adjustment_for_maintenance_tour +util_adjustment_for_eating_out_tour,Adjustment for eating out tour,eatout,coef_adjustment_for_eating_out_tour +util_adjustment_for_visiting_tour,Adjustment for visiting tour,social,coef_adjustment_for_visiting_tour +util_adjustment_for_discretionary_tour,Adjustment for discretionary tour,othdiscr,coef_adjustment_for_discretionary_tour +util_adjustment_for_share_of_2_joint_tours,Adjustment for share of 2 Joint Tours,num_joint_tours==2,coef_adjustment_for_share_of_2_joint_tours +util_tm2_adjustment_for_children_party,TM2 Adjustment for Children Party,@(df.party1==2)+(df.party2==2),coef_tm2_adjustment_for_children_party +util_tm2_adjustment_for_mixed_party,TM2 Adjustment for Mixed Party,@(df.party1==3)+(df.party2==3),coef_tm2_adjustment_for_mixed_party +util_tm2_adjustment_for_share_of_2_joint_tours,TM2 adjustment for share of 2 Joint Tours,num_joint_tours==2,coef_tm2_adjustment_for_share_of_2_joint_tours +util_tm2_adjustment_for_share_of_1_joint_tours,TM2 adjustment for share of 1 Joint Tours,num_joint_tours==1,coef_tm2_adjustment_for_share_of_1_joint_tours +util_tm2_adjustment_for_share_of_0_joint_tours,TM2 adjustment for share of 0 Joint Tours,num_joint_tours==0,coef_tm2_adjustment_for_share_of_0_joint_tours diff --git a/configs/resident/joint_tour_frequency_composition.yaml b/configs/resident/joint_tour_frequency_composition.yaml new file mode 100644 index 0000000..452ec92 --- /dev/null +++ b/configs/resident/joint_tour_frequency_composition.yaml @@ -0,0 +1,35 @@ +LOGIT_TYPE: MNL + +SPEC: joint_tour_frequency_composition.csv +COEFFICIENTS: joint_tour_frequency_composition_coeffs.csv + +preprocessor: + SPEC: joint_tour_frequency_composition_annotate_households_preprocessor.csv + DF: households + TABLES: + - persons_merged + +ALTS_PREPROCESSOR: + SPEC: joint_tour_frequency_composition_annotate_alt_preprocessor.csv + DF: alt_tdd + +# define the structure of alternative table +ALTS_TABLE_STRUCTURE: + PURPOSE: + COLUMNS: + - purpose1 + - purpose2 + VALUE_MAP: + 5: shopping + 6: othmaint + 7: eatout + 8: social + 9: othdiscr + COMPOSITION: + COLUMNS: + - party1 + - party2 + VALUE_MAP: + 1: adults + 2: children + 3: mixed \ No newline at end of file diff --git a/configs/resident/joint_tour_frequency_composition_alternatives.csv b/configs/resident/joint_tour_frequency_composition_alternatives.csv new file mode 100644 index 0000000..657f987 --- /dev/null +++ b/configs/resident/joint_tour_frequency_composition_alternatives.csv @@ -0,0 +1,151 @@ +alt,purpose1,purpose2,party1,party2 +1,5,0,1,0 +2,5,0,2,0 +3,5,0,3,0 +4,6,0,1,0 +5,6,0,2,0 +6,6,0,3,0 +7,7,0,1,0 +8,7,0,2,0 +9,7,0,3,0 +10,8,0,1,0 +11,8,0,2,0 +12,8,0,3,0 +13,9,0,1,0 +14,9,0,2,0 +15,9,0,3,0 +16,5,5,1,1 +17,5,5,1,2 +18,5,5,1,3 +19,5,5,2,1 +20,5,5,2,2 +21,5,5,2,3 +22,5,5,3,1 +23,5,5,3,2 +24,5,5,3,3 +25,5,6,1,1 +26,5,6,1,2 +27,5,6,1,3 +28,5,6,2,1 +29,5,6,2,2 +30,5,6,2,3 +31,5,6,3,1 +32,5,6,3,2 +33,5,6,3,3 +34,5,7,1,1 +35,5,7,1,2 +36,5,7,1,3 +37,5,7,2,1 +38,5,7,2,2 +39,5,7,2,3 +40,5,7,3,1 +41,5,7,3,2 +42,5,7,3,3 +43,5,8,1,1 +44,5,8,1,2 +45,5,8,1,3 +46,5,8,2,1 +47,5,8,2,2 +48,5,8,2,3 +49,5,8,3,1 +50,5,8,3,2 +51,5,8,3,3 +52,5,9,1,1 +53,5,9,1,2 +54,5,9,1,3 +55,5,9,2,1 +56,5,9,2,2 +57,5,9,2,3 +58,5,9,3,1 +59,5,9,3,2 +60,5,9,3,3 +61,6,6,1,1 +62,6,6,1,2 +63,6,6,1,3 +64,6,6,2,1 +65,6,6,2,2 +66,6,6,2,3 +67,6,6,3,1 +68,6,6,3,2 +69,6,6,3,3 +70,6,7,1,1 +71,6,7,1,2 +72,6,7,1,3 +73,6,7,2,1 +74,6,7,2,2 +75,6,7,2,3 +76,6,7,3,1 +77,6,7,3,2 +78,6,7,3,3 +79,6,8,1,1 +80,6,8,1,2 +81,6,8,1,3 +82,6,8,2,1 +83,6,8,2,2 +84,6,8,2,3 +85,6,8,3,1 +86,6,8,3,2 +87,6,8,3,3 +88,6,9,1,1 +89,6,9,1,2 +90,6,9,1,3 +91,6,9,2,1 +92,6,9,2,2 +93,6,9,2,3 +94,6,9,3,1 +95,6,9,3,2 +96,6,9,3,3 +97,7,7,1,1 +98,7,7,1,2 +99,7,7,1,3 +100,7,7,2,1 +101,7,7,2,2 +102,7,7,2,3 +103,7,7,3,1 +104,7,7,3,2 +105,7,7,3,3 +106,7,8,1,1 +107,7,8,1,2 +108,7,8,1,3 +109,7,8,2,1 +110,7,8,2,2 +111,7,8,2,3 +112,7,8,3,1 +113,7,8,3,2 +114,7,8,3,3 +115,7,9,1,1 +116,7,9,1,2 +117,7,9,1,3 +118,7,9,2,1 +119,7,9,2,2 +120,7,9,2,3 +121,7,9,3,1 +122,7,9,3,2 +123,7,9,3,3 +124,8,8,1,1 +125,8,8,1,2 +126,8,8,1,3 +127,8,8,2,1 +128,8,8,2,2 +129,8,8,2,3 +130,8,8,3,1 +131,8,8,3,2 +132,8,8,3,3 +133,8,9,1,1 +134,8,9,1,2 +135,8,9,1,3 +136,8,9,2,1 +137,8,9,2,2 +138,8,9,2,3 +139,8,9,3,1 +140,8,9,3,2 +141,8,9,3,3 +142,9,9,1,1 +143,9,9,1,2 +144,9,9,1,3 +145,9,9,2,1 +146,9,9,2,2 +147,9,9,2,3 +148,9,9,3,1 +149,9,9,3,2 +150,9,9,3,3 diff --git a/configs/resident/joint_tour_frequency_composition_annotate_alt_preprocessor.csv b/configs/resident/joint_tour_frequency_composition_annotate_alt_preprocessor.csv new file mode 100644 index 0000000..389d28a --- /dev/null +++ b/configs/resident/joint_tour_frequency_composition_annotate_alt_preprocessor.csv @@ -0,0 +1,7 @@ +Description,Target,Expression +,shopping,"np.where(alt_tdd.purpose1 == 5, 1, 0) + np.where(alt_tdd.purpose2 == 5, 1, 0)" +,othmaint,"np.where(alt_tdd.purpose1 == 6, 1, 0) + np.where(alt_tdd.purpose2 == 6, 1, 0)" +,eatout,"np.where(alt_tdd.purpose1 == 7, 1, 0) + np.where(alt_tdd.purpose2 == 7, 1, 0)" +,social,"np.where(alt_tdd.purpose1 == 8, 1, 0) + np.where(alt_tdd.purpose2 == 8, 1, 0)" +,othdiscr,"np.where(alt_tdd.purpose1 == 9, 1, 0) + np.where(alt_tdd.purpose2 == 9, 1, 0)" +,num_joint_tours,shopping+othmaint+eatout+social+othdiscr \ No newline at end of file diff --git a/configs/resident/joint_tour_frequency_composition_annotate_households_preprocessor.csv b/configs/resident/joint_tour_frequency_composition_annotate_households_preprocessor.csv new file mode 100644 index 0000000..2185a09 --- /dev/null +++ b/configs/resident/joint_tour_frequency_composition_annotate_households_preprocessor.csv @@ -0,0 +1,21 @@ +Description,Target,Expression +,_HH_OVERLAPS,"hh_time_window_overlap(households, persons_merged)" +,time_window_overlap_adult,_HH_OVERLAPS['aa'] +,time_window_overlap_child,_HH_OVERLAPS['cc'] +,time_window_overlap_adult_child,_HH_OVERLAPS['ac'] +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +,_PERSON_COUNT,"lambda query, persons_merged, households: persons_merged.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +number of travel active fulltime workers,num_travel_active_full_time_workers,"_PERSON_COUNT('(ptype == 1) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active parttime workers,num_travel_active_part_time_workers,"_PERSON_COUNT('(ptype == 2) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active university student,num_travel_active_university_students,"_PERSON_COUNT('(ptype == 3) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active non-workers,num_travel_active_non_workers,"_PERSON_COUNT('(ptype == 4) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active retirees,num_travel_active_retirees,"_PERSON_COUNT('(ptype == 5) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active driving age students,num_travel_active_driving_age_students,"_PERSON_COUNT('(ptype == 6) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active pre-driving age school kids,num_travel_active_pre_driving_age_school_kids,"_PERSON_COUNT('(ptype == 7) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active pre-school kids,num_travel_active_pre_school_kids,"_PERSON_COUNT('(ptype == 8) & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active adults,num_travel_active_adults,"_PERSON_COUNT('adult & (cdap_activity != \'H\')', persons_merged, households)" +number of travel active chilren,num_travel_active_children,"_PERSON_COUNT('~adult & (cdap_activity != \'H\')', persons_merged, households)" +shopping_accessibility,shopping_accessibility,"persons_merged.groupby('household_id')['shopping_accessibility'].mean().reindex(households.index)" +othdiscr_accessibility,othdiscr_accessibility,"persons_merged.groupby('household_id')['othdiscr_accessibility'].mean().reindex(households.index)" \ No newline at end of file diff --git a/configs/resident/joint_tour_frequency_composition_coeffs.csv b/configs/resident/joint_tour_frequency_composition_coeffs.csv new file mode 100644 index 0000000..e0d15c2 --- /dev/null +++ b/configs/resident/joint_tour_frequency_composition_coeffs.csv @@ -0,0 +1,102 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_constant_for_shopping_tour,0,T +coef_constant_for_maintenance_tour,-1.477,F +coef_constant_for_eating_out_tour,0.5796,F +coef_constant_for_visiting_tour,-1.0037,F +coef_constant_for_discretionary_tour,-1.1195,F +coef_constant_for_2_shopping_tour,-12.009,F +coef_1_shopping_and_1_maintenance_tour,-12.13684801,F +coef_1_shopping_and_1_eating_out_tour,-12.79892713,F +coef_1_shopping_and_1_visiting_tour,-12.22077221,F +coef_1_shopping_and_1_discretionary_tour,-12.57867869,F +coef_constant_for_2_maintenance_tour,-13.43572169,F +coef_1_maintenance_and_1_eating_out_tour,-12.18915033,F +coef_1_maintenance_and_1_visiting_tour,-11.81267905,F +coef_1_maintenance_and_1_discretionary_tour,-12.28867197,F +coef_constant_for_2_eating_out_tour,-13.15388273,F +coef_1_eating_out_and_1_visiting_tour,-13.15388273,F +coef_1_eating_out_and_1_discretionary_tour,-12.56102569,F +coef_constant_for_2_visiting_tour,-13.15388273,F +coef_1_visiting_and_1_discretionary_tour,-12.37222202,F +coef_constant_for_2_discretionary_tour,-13.23532342,F +coef_constant_for_1_discretionary_tour,-1.200,F +coef_constant_for_1_eating_out_tour,0.460,F +coef_constant_for_1_visiting_tour,1.720,F +coef_number_of_active_full_time_workers_shopping,0.09864381,F +coef_number_of_active_nonworkers_shopping,0.393630718,F +coef_number_of_active_pre_driving_age_school_children_shopping,-0.313021042,F +coef_number_of_active_preschool_children_shopping,-1.213950718,F +coef_number_of_active_nonworkers_maintenance,0.322738327,F +coef_number_of_active_retirees_maintenance,0.298632515,F +coef_number_of_active_driving_age_school_children_maintenance,0.503901856,F +coef_number_of_active_preschool_children_maintenance,-1.160523204,F +coef_number_of_active_full_time_workers_eating_out,-0.305934663,F +coef_number_of_active_university_students_eating_out,-0.65706029,F +coef_number_of_active_retirees_eating_out,-0.391710731,F +coef_number_of_active_pre_driving_age_school_children_eating_out,-0.25140082,F +coef_number_of_active_preschool_children_eating_out,-1.700731169,F +coef_number_of_active_pre_driving_age_school_children_visiting,0.162335324,F +coef_number_of_active_preschool_children_visiting,-0.96955678,F +coef_number_of_active_part_time_workers_discretionary,0.217898126,F +coef_number_of_active_university_students_discretionary,-0.610578234,F +coef_number_of_active_driving_age_school_children_discretionary,0.359485499,F +coef_number_of_active_preschool_children_discretionary,-1.244458661,F +coef_hh_has_more_autos_than_workers_maintenance,-0.336188392,F +coef_income_gtr_100k_maintenance,-0.475730683,F +coef_income_less_than_29999__eating_out,-1.282190776,F +coef_income_between_30000_to_59999__eating_out,-0.275046208,F +coef_income_less_than_29999__discretionary,-0.352579013,F +coef_income_between_30000_to_59999__discretionary,-0.191735343,F +coef_shopping_hov_accessibility_for_2_tours,0.039513822,F +coef_maintenance_hov_accessibility,0.128132691,F +coef_discretionary_hov_accessibility,0.089590553,F +coef_constant_for_children_party_shopping_tour,-5.374907972,F +coef_constant_for_children_party_maintenance_tour,-5.144798184,F +coef_constant_for_children_party_eating_out_tour,-4.09806907,F +coef_constant_for_children_party_visiting_tour,-4.09806907,F +coef_constant_for_children_party_discretionary_tour,-4.09806907,F +coef_constant_for_mixed_party_shopping_tour,0.575879495,F +coef_constant_for_mixed_party_maintenance_tour,0.515873866,F +coef_constant_for_mixed_party_eating_out_tour,0.168592084,F +coef_constant_for_mixed_party_visiting_tour,0.078060666,F +coef_constant_for_mixed_party_discretionary_tour,0.856042068,F +coef_number_of_active_full_time_workers_adult_party,0.599335502,F +coef_number_of_active_part_time_workers_adult_party,1.113944272,F +coef_number_of_active_university_students_adult_party,0.231138167,F +coef_number_of_active_nonworkers_adult_party,0.341446999,F +coef_number_of_active_retirees_adult_party,0.657220801,F +coef_number_of_active_driving_age_school_children_children_party,0.580109231,F +coef_number_of_active_pre_driving_age_school_children_children_party,0.580109231,F +coef_number_of_active_part_time_workers_mixed_party,0.522327137,F +coef_number_of_active_driving_age_school_children_mixed_party,0.216908669,F +coef_number_of_active_pre_driving_age_school_children_mixed_party,0.31440104,F +coef_number_of_active_preschool_children_mixed_party,0.897670235,F +coef_hh_has_no_autos__mixed_party,-2.920728233,F +coef_hh_has_less_autos_than_workers_mixed_party,-0.546339245,F +coef_income_more_than_100k_child_party,-1.189112151,F +coef_income_more_than_100k_mixed_party,-0.303217156,F +coef_log_of_max_window_overlaps_between_adults,2.96902119,F +coef_log_of_max_window_overlaps_between_children,4.673601828,F +coef_log_of_max_window_overlaps_between_adult_child,3.523795377,F +coef_adjustment_for_children_party_shopping_tour,0.407745781,F +coef_adjustment_for_children_party_maintenance_tour,0.284925252,F +coef_adjustment_for_children_party_eating_out_tour,0,T +coef_adjustment_for_children_party_visiting_tour,0.966264444,F +coef_adjustment_for_children_party_discretionary_tour,0.144740532,F +coef_adjustment_for_mixed_party_shopping_tour,0.681178558,F +coef_adjustment_for_mixed_party_maintenance_tour,0.476324741,F +coef_adjustment_for_mixed_party_eating_out_tour,0.827297043,F +coef_adjustment_for_mixed_party_visiting_tour,0.691826384,F +coef_adjustment_for_mixed_party_discretionary_tour,0.697808415,F +coef_adjustment_for_shopping_tour,0,T +coef_adjustment_for_maintenance_tour,0.06575155,F +coef_adjustment_for_eating_out_tour,0.0643679,F +coef_adjustment_for_visiting_tour,0.00785518,F +coef_adjustment_for_discretionary_tour,0.076075677,F +coef_adjustment_for_share_of_2_joint_tours,-1.000,F +coef_tm2_adjustment_for_children_party,-4.0,F +coef_tm2_adjustment_for_mixed_party,-1.800,F +coef_tm2_adjustment_for_share_of_2_joint_tours,0.00,F +coef_tm2_adjustment_for_share_of_1_joint_tours,1.50,F +coef_tm2_adjustment_for_share_of_0_joint_tours,-1.00,F \ No newline at end of file diff --git a/configs/resident/joint_tour_participation.csv b/configs/resident/joint_tour_participation.csv new file mode 100644 index 0000000..29f5548 --- /dev/null +++ b/configs/resident/joint_tour_participation.csv @@ -0,0 +1,67 @@ +Label,Description,Expression,participate,not_participate +util_full_time_worker_adults_only_party,Dummy for full-time worker in adult party,(ptype==1) & (composition=='adults'),coef_full_time_worker_adults_only_party, +util_part_time_worker_adults_only_party,Dummy for part-time worker in adult party,(ptype==2) & (composition=='adults'),coef_part_time_worker_adults_only_party, +util_university_students_adults_only_party,Dummy for university student in adult party,(ptype==3) & (composition=='adults'),coef_university_students_adults_only_party, +util_non_worker_adults_only_party,Dummy for nonworker in adult party,(ptype==4) & (composition=='adults'),coef_non_worker_adults_only_party, +util_retiree_adults_only_party,Dummy for retiree in adult party,(ptype==5) & (composition=='adults'),coef_retiree_adults_only_party, +util_driving_age_student_children_only_party,Dummy for driving school child in children party,(ptype==6) & (composition=='children'),coef_driving_age_student_children_only_party, +util_pre_driving_age_student_children_only_party,Dummy for pre-driving school child in children party,(ptype==7) & (composition=='children'),coef_pre_driving_age_student_children_only_party, +util_child_too_young_for_school_children_only_party,Dummy for preschool child in children party,(ptype==8) & (composition=='children'),coef_child_too_young_for_school_children_only_party, +util_full_time_worker_mixed_party,Dummy for full-time worker in mixed party,(ptype==1) & (composition=='mixed'),coef_full_time_worker_mixed_party, +util_part_time_worker_mixed_party,Dummy for part-time worker in mixed party,(ptype==2) & (composition=='mixed'),coef_part_time_worker_mixed_party, +util_university_student_mixed_party,Dummy for university student in mixed party,(ptype==3) & (composition=='mixed'),coef_university_student_mixed_party, +util_non_worker_mixed_party,Dummy for nonworker in mixed party,(ptype==4) & (composition=='mixed'),coef_non_worker_mixed_party, +util_retiree_mixed_party,Dummy for retiree in mixed party,(ptype==5) & (composition=='mixed'),coef_retiree_mixed_party, +util_child_too_young_for_school_mixed_party,Dummy for driving school child in mixed party,(ptype==6) & (composition=='mixed'),coef_child_too_young_for_school_mixed_party, +util_pre_driving_age_student_mixed_party,Dummy for pre-driving school child in mixed party,(ptype==7) & (composition=='mixed'),coef_pre_driving_age_student_mixed_party, +util_driving_age_student_mixed_party,Dummy for preschool child in mixed party,(ptype==8) & (composition=='mixed'),coef_driving_age_student_mixed_party, +#,,,, +util_part_time_worker_specific_to_maintenance_joint_tours,Dummy for part-time worker on maintenance joint tour ,(ptype==2) & (tour_type=='othmaint'),coef_part_time_worker_specific_to_maintenance_joint_tours, +util_non_worker_specific_to_maintenance_joint_tours,Dummy for non-worker on maintenance joint tour ,(ptype==4) & (tour_type=='othmaint'),coef_non_worker_specific_to_maintenance_joint_tours, +util_pre_school_kid_specific_to_maintenance_joint_tours,Dummy for preschool child on maintenance joint tour ,(ptype==8) & (tour_type=='othmaint'),coef_pre_school_kid_specific_to_maintenance_joint_tours, +util_full_time_worker_specific_to_eating_out_joint_tours,Dummy for full-time worker on eating out joint tour ,(ptype==1) & (tour_type=='eatout'),coef_full_time_worker_specific_to_eating_out_joint_tours, +util_part_time_worker_specific_to_eating_out_joint_tours,Dummy for part-time worker on eating out joint tour ,(ptype==2) & (tour_type=='eatout'),coef_part_time_worker_specific_to_eating_out_joint_tours, +util_pre_driving_age_student_specific_to_eating_out_joint_tours,Dummy for pre-driving age school child on eating out joint tour ,(ptype==7) & (tour_type=='eatout'),coef_pre_driving_age_student_specific_to_eating_out_joint_tours, +util_part_time_worker_specific_to_discretionary_joint_tours,Dummy for part-time worker on discretionary joint tour ,(ptype==2) & (tour_type=='othdiscr'),coef_part_time_worker_specific_to_discretionary_joint_tours, +util_retiree_specific_to_discretionary_joint_tours,Dummy for retiree on discretionary joint tour ,(ptype==5) & (tour_type=='othdiscr'),coef_retiree_specific_to_discretionary_joint_tours, +util_driving_age_student_specific_to_discretionary_joint_tours,Dummy for driving school child on discretionary joint tour ,(ptype==6) & (tour_type=='othdiscr'),coef_driving_age_student_specific_to_discretionary_joint_tours, +util_pre_driving_age_student_specific_to_discretionary_joint_tours,Dummy for pre-driving school child on discretionary joint tour ,(ptype==7) & (tour_type=='othdiscr'),coef_pre_driving_age_student_specific_to_discretionary_joint_tours, +util_part_time_worker_specific_to_visiting_joint_tours,Dummy for part-time worker on visiting joint tour ,(ptype==2) & (tour_type=='social'),coef_part_time_worker_specific_to_visiting_joint_tours, +util_non_worker_specific_to_visiting_joint_tours,Dummy for non-worker on visiting joint tour ,(ptype==4) & (tour_type=='social'),coef_non_worker_specific_to_visiting_joint_tours, +util_retiree_specific_to_visiting_joint_tours,Dummy for retiree on visiting joint tour ,(ptype==5) & (tour_type=='social'),coef_retiree_specific_to_visiting_joint_tours, +util_driving_age_student_specific_to_visiting_joint_tours,Dummy for driving school child on visiting joint tour ,(ptype==6) & (tour_type=='social'),coef_driving_age_student_specific_to_visiting_joint_tours, +util_pre_school_kid_specific_to_visiting_joint_tours,Dummy for preschool child on visiting joint tour ,(ptype==8) & (tour_type=='social'),coef_pre_school_kid_specific_to_visiting_joint_tours, +#,,,, +util_adult_fewer_automobiles_than_workers_adult_only_party,Dummy for low car ownership for adult in adult party,(auto_ownershipnum_workers)&(composition=='mixed')&adult,coef_adult_more_automobiles_than_workers_mixed_party, +util_child_zero_automobiles_mixed_party,Dummy for no car for child in mixed party,(auto_ownership==0)&(composition=='mixed')&child,coef_child_zero_automobiles_mixed_party, +#,,,, +util_dummy_for_low_income_for_adult_in_adult_party,Dummy for low income for adult in adult party,(income<30000)&(composition=='adults')&adult,coef_dummy_for_low_income_for_adult_in_adult_party, +util_dummy_for_high_income_for_adult_in_mixed_party,Dummy for high income for adult in mixed party,(income>100000)&(composition=='mixed')&adult,coef_dummy_for_high_income_for_adult_in_mixed_party, +util_dummy_for_high_income_for_child_in_mixed_party,Dummy for high income for child in mixed party,(income>100000)&(composition=='mixed')&child,coef_dummy_for_high_income_for_child_in_mixed_party, +#,,,, +util_adult_number_of_joint_tours_adult_only,No of HH joint tours for adult in adult party,(adult&(composition=='adults'))*num_hh_joint_tours,coef_adult_number_of_joint_tours_adult_only, +util_adult_number_of_joint_tours_mixed,No of HH joint tours for adult in mixed party,(adult&(composition=='mixed'))*num_hh_joint_tours,coef_adult_number_of_joint_tours_mixed, +util_child_number_of_joint_tours_child_only,No of HH joint tours for child in children party,(child&(composition=='children'))*num_hh_joint_tours,coef_child_number_of_joint_tours_child_only, +util_child_number_of_joint_tours_mixed,No of HH joint tours for child in mixed party,(child&(composition=='mixed'))*num_hh_joint_tours,coef_child_number_of_joint_tours_mixed, +#,,,, +util_adult_number_of_other_adults_in_the_household_adults_only_party,No of other HH adults for adult in adult party,(adult & (composition=='adults')) * (num_adults.clip(upper=4) - 1),coef_adult_number_of_other_adults_in_the_household_adults_only_party, +util_adult_number_of_other_adults_in_the_household_mixed_party,No of other HH adults for adult in mixed party,(adult & (composition=='mixed')) * (num_adults.clip(upper=4) - 1),coef_adult_number_of_other_adults_in_the_household_mixed_party, +util_child_number_of_other_children_in_the_household_child_only_party,No of other HH children for child in children party,(~adult & (composition=='children')) * (num_children.clip(upper=4) - 1),coef_child_number_of_other_children_in_the_household_child_only_party, +util_child_number_of_other_children_in_the_household_mixed,No of other HH children for child in mixed party,(~adult & (composition=='mixed')) * (num_children.clip(upper=4) - 1),coef_child_number_of_other_children_in_the_household_mixed, +#,,,, +util_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,"Adult, log of max window overlap with an adult, adult-only party",(adult & (composition=='adults')) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party, +util_adult_log_of_max_window_overlap_with_a_child_mixed,"Adult, log of max window overlap with a child, mixed",(adult & (composition=='mixed')) * log_time_window_overlap_adult_child,coef_adult_log_of_max_window_overlap_with_a_child_mixed, +util_child_log_of_max_window_overlap_with_an_adult_mixed,"Child, log of max window overlap with an adult, mixed",(~adult &(composition=='mixed')) * log_time_window_overlap_adult_child,coef_child_log_of_max_window_overlap_with_an_adult_mixed, +util_child_log_of_max_window_overlap_with_a_child_child,"Child, log of max window overlap with a child, child",(~adult & (composition=='children')) * log_time_window_overlap_child,coef_child_log_of_max_window_overlap_with_a_child_child, +#,,,, +util_adults_are_prohibited_in_participating_in_child_only_tours,Adults are prohibited in participating in child-only tours,adult & (composition=='children'),coef_unavailable, +util_children_are_prohibited_in_participating_in_adult_only_tours,Children are prohibited in participating in adult-only tours,~adult & (composition=='adults'),coef_unavailable, +util_persons_with_home_activity_patterns_are_prohibilted_from_participating,Persons with Home activity patterns are prohibilted from participating,~travel_active,coef_unavailable, +#,,,, +util_if_only_two_available_adults_both_must_participate_in_adult_only_tour,"If only two available adults, both must participate in adult-only tour",adult & travel_active & (composition=='adults') & (num_travel_active_adults<3),,coef_unavailable +util_if_only_one_available_adult_traveler_must_participate_in_mixed_tour,"If only one available adult, traveler must participate in mixed tour",adult & travel_active & (composition=='mixed') & (num_travel_active_adults<2),,coef_unavailable +util_if_only_two_available_children_both_must_participate_in_child_only_tour,"If only two available children, both must participate in child-only tour",~adult & travel_active & (composition=='children') & (num_travel_active_children<3),,coef_unavailable +util_if_only_one_available_child_traveler_must_participate_in_mixed_tour,"If only one available child, traveler must participate in mixed tour",~adult & travel_active & (composition == 'mixed') & (num_travel_active_children<2),,coef_unavailable +#,,,, +util_globabl_adjustment_constant,Global adjustment constant for whether person participated or not,1,coef_global_adj_for_paticipation, diff --git a/configs/resident/joint_tour_participation.yaml b/configs/resident/joint_tour_participation.yaml new file mode 100644 index 0000000..1d17a22 --- /dev/null +++ b/configs/resident/joint_tour_participation.yaml @@ -0,0 +1,24 @@ + +SPEC: joint_tour_participation.csv +COEFFICIENTS: joint_tour_participation_coefficients.csv + +LOGIT_TYPE: MNL + +max_participation_choice_iterations: 1000 + +# will force anyone with a probability > 0 onto the tour after the max iterations +FORCE_PARTICIPATION: True + +preprocessor: + SPEC: joint_tour_participation_annotate_participants_preprocessor + DF: participants + TABLES: + - tours +# - persons +# - accessibility + +annotate_persons: + SPEC: annotate_persons_jtp + DF: persons + TABLES: + - joint_tour_participants diff --git a/configs/resident/joint_tour_participation_annotate_participants_preprocessor.csv b/configs/resident/joint_tour_participation_annotate_participants_preprocessor.csv new file mode 100644 index 0000000..38d26c7 --- /dev/null +++ b/configs/resident/joint_tour_participation_annotate_participants_preprocessor.csv @@ -0,0 +1,14 @@ +Description,Target,Expression +,_P_OVERLAPS,person_time_window_overlap(persons) +,time_window_overlap_adult,"reindex(_P_OVERLAPS.aa, participants.person_id)" +,time_window_overlap_child,"reindex(_P_OVERLAPS.cc, participants.person_id)" +,time_window_overlap_adult_child,"reindex(_P_OVERLAPS.ac, participants.person_id)" +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +#,, +,_JOINT_TOURS,tours[tours.tour_category=='joint'] +,num_hh_joint_tours,"reindex_i(_JOINT_TOURS.groupby('household_id').size(), participants.household_id)" +#,, +,person_is_preschool,participants.ptype == 8 +,child,~participants.adult \ No newline at end of file diff --git a/configs/resident/joint_tour_participation_coefficients.csv b/configs/resident/joint_tour_participation_coefficients.csv new file mode 100644 index 0000000..8126d30 --- /dev/null +++ b/configs/resident/joint_tour_participation_coefficients.csv @@ -0,0 +1,59 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_full_time_worker_adults_only_party,-0.844885174,F +coef_part_time_worker_adults_only_party,-1.837563723,F +coef_university_students_adults_only_party,-0.970251833,F +coef_non_worker_adults_only_party,-0.757939204,F +coef_retiree_adults_only_party,1.196798335,F +coef_driving_age_student_children_only_party,-12.08924396,F +coef_pre_driving_age_student_children_only_party,-16.16999328,F +coef_child_too_young_for_school_children_only_party,-16.16999328,F +coef_full_time_worker_mixed_party,0.453048547,F +coef_part_time_worker_mixed_party,1.263225489,F +coef_university_student_mixed_party,1.561741231,F +coef_non_worker_mixed_party,2.90040237,F +coef_retiree_mixed_party,1.043276837,F +coef_child_too_young_for_school_mixed_party,-1.916267403,F +coef_pre_driving_age_student_mixed_party,-1.91645719,F +coef_driving_age_student_mixed_party,-0.933863842,F +#,, +coef_part_time_worker_specific_to_maintenance_joint_tours,0.766072366,F +coef_non_worker_specific_to_maintenance_joint_tours,0.971450303,F +coef_pre_school_kid_specific_to_maintenance_joint_tours,-0.52817951,F +coef_full_time_worker_specific_to_eating_out_joint_tours,0.535579735,F +coef_part_time_worker_specific_to_eating_out_joint_tours,1.23257663,F +coef_pre_driving_age_student_specific_to_eating_out_joint_tours,1.535608884,F +coef_part_time_worker_specific_to_discretionary_joint_tours,0.539337302,F +coef_retiree_specific_to_discretionary_joint_tours,1.105118702,F +coef_driving_age_student_specific_to_discretionary_joint_tours,-1.151066894,F +coef_pre_driving_age_student_specific_to_discretionary_joint_tours,0.798684189,F +coef_part_time_worker_specific_to_visiting_joint_tours,1.075535443,F +coef_non_worker_specific_to_visiting_joint_tours,1.075535443,F +coef_retiree_specific_to_visiting_joint_tours,-1.930328716,F +coef_driving_age_student_specific_to_visiting_joint_tours,-1.334515344,F +coef_pre_school_kid_specific_to_visiting_joint_tours,1.552698221,F +#,, +coef_adult_fewer_automobiles_than_workers_adult_only_party,1.292641814,F +coef_adult_more_automobiles_than_workers_mixed_party,-0.390671553,F +coef_child_zero_automobiles_mixed_party,-1.546687521,F +#,, +coef_dummy_for_low_income_for_adult_in_adult_party,-0.680688378,F +coef_dummy_for_high_income_for_adult_in_mixed_party,-0.20257569,F +coef_dummy_for_high_income_for_child_in_mixed_party,-0.74160668,F +#,, +coef_adult_number_of_joint_tours_adult_only,-0.599443484,F +coef_adult_number_of_joint_tours_mixed,-0.218541079,F +coef_child_number_of_joint_tours_child_only,-0.313832442,F +coef_child_number_of_joint_tours_mixed,-0.241748337,F +#,, +coef_adult_number_of_other_adults_in_the_household_adults_only_party,-0.747528682,F +coef_adult_number_of_other_adults_in_the_household_mixed_party,-0.285988025,F +coef_child_number_of_other_children_in_the_household_child_only_party,-2.305908575,F +coef_child_number_of_other_children_in_the_household_mixed,-0.471545203,F +#,, +coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,1.634378325,F +coef_adult_log_of_max_window_overlap_with_a_child_mixed,0.057012356,F +coef_child_log_of_max_window_overlap_with_an_adult_mixed,1.616796615,F +coef_child_log_of_max_window_overlap_with_a_child_child,10.70256432,F +#,, +coef_global_adj_for_paticipation,-1,T diff --git a/configs/resident/joint_tour_scheduling.yaml b/configs/resident/joint_tour_scheduling.yaml new file mode 100644 index 0000000..1a8cdcd --- /dev/null +++ b/configs/resident/joint_tour_scheduling.yaml @@ -0,0 +1,13 @@ + +SPEC: tour_scheduling_joint.csv +COEFFICIENTS: tour_scheduling_joint_coefficients.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: joint_tour_scheduling_annotate_tours_preprocessor + DF: joint_tours + TABLES: + - land_use + - households + - joint_tour_participants diff --git a/configs/resident/joint_tour_scheduling_annotate_tours_preprocessor.csv b/configs/resident/joint_tour_scheduling_annotate_tours_preprocessor.csv new file mode 100644 index 0000000..42337d2 --- /dev/null +++ b/configs/resident/joint_tour_scheduling_annotate_tours_preprocessor.csv @@ -0,0 +1,2 @@ +Description,Target,Expression +,origin_to_destination_distance,"skim_dict.lookup(joint_tours.origin, joint_tours.destination, 'DIST')" diff --git a/configs/resident/logging.yaml b/configs/resident/logging.yaml new file mode 100644 index 0000000..7742c3e --- /dev/null +++ b/configs/resident/logging.yaml @@ -0,0 +1,54 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: INFO + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + diff --git a/configs/resident/mandatory_tour_frequency.csv b/configs/resident/mandatory_tour_frequency.csv new file mode 100644 index 0000000..509d583 --- /dev/null +++ b/configs/resident/mandatory_tour_frequency.csv @@ -0,0 +1,78 @@ +Label,Description,Expression,work1,work2,school1,school2,work_and_school +# availiability conditions just based on worker or student status,,,,,,, +util_work_unavailable_for_non_workers,work not available for non-workers,is_worker == False,coef_unavailable,coef_unavailable,,,coef_unavailable +util_work_unavailable_for_wfh,work not available if work from home,work_from_home == True,coef_unavailable,coef_unavailable,,,coef_unavailable +util_school_unavailable_for_non_students,school unavailable for non-students,is_student == False,,,coef_unavailable,coef_unavailable,coef_unavailable +#,,,,,,, +util_alternative_specific_constant_for_full_time_work,alternative specific constant for full_time work,@df.ptype==1,coef_zero,coef_ft_worker_work2_asc,coef_ft_worker_school1_asc,,coef_ft_worker_work_and_school_asc +util_alternative_specific_constant_for_part_time_work,alternative specific constant for part_time work,@df.ptype==2,coef_zero,coef_pt_worker_work2_asc,coef_pt_worker_school1_asc,,coef_pt_worker_work_and_school_asc +util_alternative_specific_constant_for_university_student,alternative specific constant for university student,@df.ptype==3,coef_univ_work1_asc,,coef_zero,coef_univ_school2_asc,coef_univ_work_and_school_asc +util_alternative_specific_constant_for_non_worker_U65,alternative specific constant for non_worker U65,@df.ptype==4,coef_zero,,coef_non_worker_school1_asc,coef_zero, +util_alternative_specific_constant_for_retiree_65,alternative specific constant for retiree 65+,@df.ptype==5,coef_zero,,coef_ret_worker_school1_asc,coef_zero, +util_alternative_specific_constant_for_school_child_16_17,alternative specific constant for school child 16_17,@df.ptype==6,coef_driving_age_child_work1_asc,,coef_zero,coef_driving_age_child_school2_asc,coef_driving_age_child_work_and_school_asc +util_alternative_specific_constant_for_school_child_6_15,alternative specific constant for school child 6_15,@df.ptype==7,coef_zero,,coef_zero,coef_pre_driving_age_child_school2_asc, +#,,,,,,, +util_person_is_female_full_time_worker,person is female_ full time worker,@(df.female==1)*(df.ptype==1),coef_zero,coef_female_ftworker_work2,coef_female_ftworker_school1,,coef_female_ftworker_work_and_school +util_person_is_female_part_time_worker,person is female_ part time worker,@(df.female==1)*(df.ptype==2),coef_zero,coef_female_ptworker_work2,coef_female_ptworker_school1,,coef_zero +util_person_is_female_university_student,person is female_ university student,@(df.female==1)*(df.ptype==3),coef_female_univ_work1,,coef_zero,coef_female_univ_school2,coef_female_univ_work_and_school +util_person_is_female_non_worker_Under_65,person is female_ non_worker Under 65,@(df.female==1)*(df.ptype==4),,,,, +util_person_is_female_retiree_65_and_plus,person is female_ retiree 65 and plus,@(df.female==1)*(df.ptype==5),,,,, +util_person_is_female_school_child_16_17,person is female_ school child 16_17,@(df.female==1)*(df.ptype==6),coef_female_driving_age_child_work1,,coef_zero,coef_female_driving_age_child_school2,coef_female_driving_age_child_work_and_school +util_person_is_female_school_child_6_15,person is female_ school child 6_15,@(df.female==1)*(df.ptype==7),,,coef_zero,coef_female_child_school2, +util_young_adult_full_time_worker,young adult_ full time worker,@(df.age>17)*(df.age<=35)*(df.ptype==1),coef_zero,coef_zero,coef_youngadult_ftworker_school1,,coef_zero +util_older_adult_university_student,older adult_ university student,@(df.age>35)*(df.ptype==3),coef_olderadult_univ_work1,,coef_zero,coef_zero, +#,,,,,,, +util_workplace_within_distance_band_1full_time_worker,workplace within distance band 1 (0 to 0.5 mile)_ full time worker,@(df.distance_to_work<=0.5)*(df.ptype==1),,coef_distband1_ftworker_work2,,, +util_workplace_within_distance_band_1part_time_worker,workplace within distance band 1 (0 to 0.5 mile)_ part time worker,@(df.distance_to_work<=0.5)*(df.ptype==2),,coef_distband1_ptworker_work2,,, +util_workplace_within_distance_band_2_full_time_worker,workplace within distance band 2 (0.5 to 3 mile)_ full time worker,@(df.distance_to_work>0.5)*(df.distance_to_work<=3)*(df.ptype==1),,coef_distband2_ftworker_work2,,, +util_workplace_within_distance_band_2_part_time_worker,workplace within distance band 2 (0.5 to 3 mile)_ part time worker,@(df.distance_to_work>0.5)*(df.distance_to_work<=3)*(df.ptype==2),,coef_distband2_ptworker_work2,,, +util_school_wtihin_distance_band_2_university_student,school wtihin distance band 2 (0.5 to 2 mile)_ university student,@(df.distance_to_school>0.5)*(df.distance_to_school<=2)*(df.ptype==3),,,,coef_distband2_univ_school2, +util_school_wtihin_distance_band_2_school_child_16_17,school wtihin distance band 2 (0.5 to 2 mile)_ school child 16_17,@(df.distance_to_school>0.5)*(df.distance_to_school<=2)*(df.ptype==6),,,,coef_distband2_driving_age_child_school2, +util_school_wtihin_distance_band_2_school_child_6_15,school wtihin distance band 2 (0.5 to 2 mile)_ school child 6_15,@(df.distance_to_school>0.5)*(df.distance_to_school<=2)*(df.ptype==7),,,,coef_distband2_child_school2, +util_work_or_school_distance_within_distance_band_1_full_time_worker,work or school distance within distance band 1_ full time worker,@((df.distance_to_work<=0.5)+(df.distance_to_school<=0.5))*(df.ptype==1),,,,,coef_distband1_ftworker_work_and_school +util_work_or_school_distance_within_distance_band_1_part_time_worker,work or school distance within distance band 1_ part time worker,@((df.distance_to_work<=0.5)+(df.distance_to_school<=0.5))*(df.ptype==2),,,,,coef_distband1_ptworker_work_and_school +util_work_or_school_distance_within_distance_band_1_university_student,work or school distance within distance band 1_ university student,@((df.distance_to_work<=0.5)+(df.distance_to_school<=0.5))*(df.ptype==3),,,,,coef_distband1_univ_work_and_school +util_work_or_school_distance_within_distance_band_1_school_child_16_17,work or school distance within distance band 1_ school child 16_17,@((df.distance_to_work<=0.5)+(df.distance_to_school<=0.5))*(df.ptype==6),,,,,coef_distband1_driving_age_child_work_and_school +util_work_or_school_distance_within_distance_band_2_full_time_worker,work or school distance within distance band 2_ full time worker,@((df.distance_to_work>0.5)*(df.distance_to_work<=3)+(df.distance_to_school>0.5)*(df.distance_to_school<=2))*(df.ptype==1),,,,,coef_distband2_ftworker_work_and_school +util_work_or_school_distance_within_distance_band_2_part_time_worker,work or school distance within distance band 2_ part time worker,@((df.distance_to_work>0.5)*(df.distance_to_work<=3)+(df.distance_to_school>0.5)*(df.distance_to_school<=2))*(df.ptype==2),,,,,coef_distband2_ptworker_work_and_school +util_work_or_school_distance_within_distance_band_2_university_student,work or school distance within distance band 2_ university student,@((df.distance_to_work>0.5)*(df.distance_to_work<=3)+(df.distance_to_school>0.5)*(df.distance_to_school<=2))*(df.ptype==3),,,,,coef_distband2_univ_work_and_school +util_work_or_school_distance_within_distance_band_2_school_child_16_17,work or school distance within distance band 2_ school child 16_17,@((df.distance_to_work>0.5)*(df.distance_to_work<=3)+(df.distance_to_school>0.5)*(df.distance_to_school<=2))*(df.ptype==6),,,,,coef_distband2_driving_age_child_work_and_school +#,,,,,,, +util_minimum_travel_time_to_workplace_full_time_worker,minimum travel time to workplace (exclues non_motorized)_ full time worker,@df.ptype==1 * df.roundtrip_auto_time_to_work,,coef_ftworker_timetowork_work2,,,coef_ftworker_timetowork_work_and_school +util_minimum_travel_time_to_workplace_part_time_worker,minimum travel time to workplace (excludes non_motorized)_ part time worker,@df.ptype==2 * df.roundtrip_auto_time_to_work,,coef_ptworker_timetowork_work2,,,coef_ptworker_timetowork_work_and_school +util_minimum_travel_time_to_workplace_university_student,minimum travel time to workplace (excludes non_motorized)_ university student,@df.ptype==3 * df.roundtrip_auto_time_to_work,,,,,coef_univ_timetowork_work_and_school +util_minimum_travel_time_to_workplace_non_worker_under_65,minimum travel time to workplace (excludes non_motorized)_ non_worker under 65,@df.ptype==4 * df.roundtrip_auto_time_to_work,,,,, +util_minimum_travel_time_to_workplace_retiree_65_and_plus,minimum travel time to workplace (excludes non_motorized)_ retiree 65 and plus,@df.ptype==5 * df.roundtrip_auto_time_to_work,,,,, +#,,,,,,, +util_zero_car_full_time_worker,zero car full time worker,@(df.auto_ownership==0)*(df.ptype==1),,coef_ftworker_zerocar_work2,,,coef_ftworker_zerocar_work_and_school +util_zero_car_part_time_worker,zero car part time worker,@(df.auto_ownership==0)*(df.ptype==2),,coef_ptworker_zerocar_work2,,,coef_ptworker_zerocar_work_and_school +util_zero_car_university_student,zero car university student,@(df.auto_ownership==0)*(df.ptype==3),,,,coef_univ_zerocar_school2,coef_univ_zerocar_work_and_school +util_zero_car_non_worker_under_65,zero car non_worker under 65,@(df.auto_ownership==0)*(df.ptype==4),,,,, +util_zero_car_retiree_65_and_plus,zero car retiree 65 and plus,@(df.auto_ownership==0)*(df.ptype==5),,,,, +util_zero_car_school_child_16_17,zero car school child 16_17,@(df.auto_ownership==0)*(df.ptype==6),,,,coef_driving_age_child_zerocar_school2,coef_driving_age_child_zerocar_work_and_school +util_zero_car_school_child_6_15,zero car school child 6_15,@(df.auto_ownership==0)*(df.ptype==7),,,,coef_child_zerocar_school2, +util_car_fewer_than_drivers_university_student,car fewer than drivers_ university student,@(df.auto_ownership=60000)*(df.ptype==1),,,,, +util_mid_and_high_household_income_part_time_worker,mid and high household income_ part time worker,@(df.income_in_thousands>=60000)*(df.ptype==2),,,,, +util_mid_and_high_household_income_university_student,mid and high household income_ university student,@(df.income_in_thousands>=60000)*(df.ptype==3),coef_univ_midhighincome_work1,,,, +#,,,,,,, +util_alternative_specific_constant_adjustment_for_full_time_work,alternative specific constant adjustment for full_time work,@df.ptype==1,,0.1423726,,, +util_alternative_specific_constant_adjustment_for_part_time_work,alternative specific constant adjustment for part_time work,@df.ptype==2,,0.2684171,,, +util_alternative_specific_constant_adjustment_for_university_student,alternative specific constant adjustment for university student,@df.ptype==3,0.362762501,,,0.52545937,1.0066602 +util_alternative_specific_constant_adjustment_for_school_child_16_17,alternative specific constant adjustment for school child 16_17,@df.ptype==6,,,,0.1478192,0.4715931 +util_alternative_specific_constant_adjustment_for_school_child_6_15,alternative specific constant adjustment for school child 6_15,@df.ptype==7,,,,0.0969167, +util_alternative_specific_constant_adjustment_for_school_child_0_5,alternative specific constant adjustment for school child 0_5,@df.ptype==8,,,0.72,-0.67, +#,,,,,,, diff --git a/configs/resident/mandatory_tour_frequency.yaml b/configs/resident/mandatory_tour_frequency.yaml new file mode 100644 index 0000000..de8e115 --- /dev/null +++ b/configs/resident/mandatory_tour_frequency.yaml @@ -0,0 +1,10 @@ + +SPEC: mandatory_tour_frequency.csv +COEFFICIENTS: mandatory_tour_frequency_coeffs.csv + +annotate_persons: + SPEC: annotate_persons_mtf + DF: persons + TABLES: + - tours + diff --git a/configs/resident/mandatory_tour_frequency_alternatives.csv b/configs/resident/mandatory_tour_frequency_alternatives.csv new file mode 100644 index 0000000..025decb --- /dev/null +++ b/configs/resident/mandatory_tour_frequency_alternatives.csv @@ -0,0 +1,7 @@ +#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,work,school +work1,1,0 +work2,2,0 +school1,0,1 +school2,0,2 +work_and_school,1,1 diff --git a/configs/resident/mandatory_tour_frequency_coeffs.csv b/configs/resident/mandatory_tour_frequency_coeffs.csv new file mode 100644 index 0000000..d01a9e3 --- /dev/null +++ b/configs/resident/mandatory_tour_frequency_coeffs.csv @@ -0,0 +1,83 @@ +coefficient_name,value,constrain,,,, +coef_zero,0,T,,,, +coef_unavailable,-999,T,,,, +#,,,,,, +coef_ft_worker_work2_asc,-2.548,F,,,, +coef_ft_worker_school1_asc,-2.29548389,F,,,, +coef_ft_worker_work_and_school_asc,-2.124547196,F,,,, +coef_pt_worker_work2_asc,-3.255,F,,,, +coef_pt_worker_school1_asc,-1.523338502,F,,,, +coef_pt_worker_work_and_school_asc,-1.916270321,F,,,, +coef_univ_work1_asc,-0.935,F,,,, +coef_driving_age_child_work1_asc,-2.81,F,,,, +coef_univ_school2_asc,-2.329,F,,,, +coef_univ_work_and_school_asc,-0.898,F,,,, +coef_driving_age_child_school2_asc,-2.230,F,,,, +coef_pre_driving_age_child_school2_asc,-3.838,F,,,, +coef_driving_age_child_work_and_school_asc,-3.449,F,,,, +coef_non_worker_school1_asc,14.600,F,,,, +coef_ret_worker_school1_asc,14.600,F,,,, +#,,,,,, +coef_female_ftworker_work2,-0.171976,F,,,, +coef_female_ftworker_school1,-0.372102879,F,,,, +coef_female_ftworker_work_and_school,0.656500245,F,,,, +coef_female_ptworker_work2,0.72635447,F,,,, +coef_female_ptworker_school1,0.634251329,F,,,, +coef_female_univ_work1,-0.186104106,F,,,, +coef_female_univ_school2,-1.207464296,F,,,, +coef_female_univ_work_and_school,-0.627268064,F,,,, +coef_female_driving_age_child_work1,-0.84458299,F,,,, +coef_female_driving_age_child_school2,-1.565680968,F,,,, +coef_female_driving_age_child_work_and_school,2.22484406,F,,,, +coef_female_child_school2,-1.124163745,F,,,, +coef_youngadult_ftworker_school1,0.336552714,F,,,, +coef_olderadult_univ_work1,1.373765885,F,,,, +#,,,,,, +coef_distband1_ftworker_work2,0.64195998,F,,,, +coef_distband1_ptworker_work2,0.64195998,F,,,, +coef_distband2_ftworker_work2,1.217340344,F,,,, +coef_distband2_ptworker_work2,1.217340344,F,,,, +coef_distband2_univ_school2,0.491551442,F,,,, +coef_distband2_driving_age_child_school2,0.491551442,F,,,, +coef_distband2_child_school2,0.491551442,F,,,, +coef_distband1_ftworker_work_and_school,0.194304616,F,,,, +coef_distband1_ptworker_work_and_school,0.194304616,F,,,, +coef_distband1_univ_work_and_school,0.194304616,F,,,, +coef_distband1_driving_age_child_work_and_school,0.194304616,F,,,, +coef_distband2_ftworker_work_and_school,0.183705706,F,,,, +coef_distband2_ptworker_work_and_school,0.183705706,F,,,, +coef_distband2_univ_work_and_school,0.183705706,F,,,, +coef_distband2_driving_age_child_work_and_school,0.183705706,F,,,, +#,,,,,, +coef_ftworker_timetowork_work2,-0.022406282,F,,,, +coef_ptworker_timetowork_work2,-0.022406282,F,,,, +coef_ftworker_timetowork_work_and_school,-0.007707408,F,,,, +coef_ptworker_timetowork_work_and_school,-0.007707408,F,,,, +coef_univ_timetowork_work_and_school,-0.007707408,F,,,, +#,,,,,, +coef_ftworker_zerocar_work2,-0.661753779,F,,,, +coef_ptworker_zerocar_work2,-0.661753779,F,,,, +coef_univ_zerocar_school2,-0.661753779,F,,,, +coef_driving_age_child_zerocar_school2,-0.661753779,F,,,, +coef_child_zerocar_school2,-0.661753779,F,,,, +coef_univ_autodef_school2,-0.955356561,F,,,, +coef_driving_age_child_autodef_school2,-0.955356561,F,,,, +coef_child_autodef_school2,-0.955356561,F,,,, +coef_ftworker_zerocar_work_and_school,-0.661753779,F,,,, +coef_ptworker_zerocar_work_and_school,-0.661753779,F,,,, +coef_univ_zerocar_work_and_school,-0.661753779,F,,,, +coef_driving_age_child_zerocar_work_and_school,-0.661753779,F,,,, +#,,,,,, +coef_univ_nonfamily_work1,-1.094011735,F,,,, +coef_driving_age_child_nonfamily_work2,-1.094011735,F,,,, +coef_univ_midhighincome_work1,0.688596236,F,,,, +coef_ftworker_numprekchildren_work2,-0.039040872,F,,,, +coef_ptworker_numprekchildren_work2,-0.039040872,F,,,, +coef_ftworker_scng2school_work2,-0.436718886,F,,,, +coef_univ_numprekchildren_school2,-1.533952899,F,,,, +coef_ftworker_numprekchildren_work_and_school,-0.142979462,F,,,, +coef_ptworker_numprekchildren_work_and_school,-0.142979462,F,,,, +coef_univ_numprekchildren_work_and_school,-0.142979462,F,,,, +coef_univ_nonfamily_work_and_school,-1.094011735,F,,,, +coef_driving_age_child_nonfamily_work_and_school,-1.094011735,F,,,, +#,,,,,, diff --git a/configs/resident/mandatory_tour_scheduling.yaml b/configs/resident/mandatory_tour_scheduling.yaml new file mode 100644 index 0000000..ea9ca0a --- /dev/null +++ b/configs/resident/mandatory_tour_scheduling.yaml @@ -0,0 +1,52 @@ + +SIMULATE_CHOOSER_COLUMNS: + - age + - female + - ptype + - is_university + - is_income_less25K + - is_income_25K_to_60K + - is_income_60K_to_120K + - is_income_greater120K + - is_pre_drive_child_in_HH + - is_non_worker_in_HH + - auto_ownership + - is_all_adults_full_time_workers + - distance_to_school + - roundtrip_auto_time_to_work + - roundtrip_auto_time_to_school + - free_parking_at_work + - workplace_zone_id + - school_zone_id + - home_zone_id + - TAZ + - transit_pass_ownership + - transit_pass_subsidy + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +TOUR_SPEC_SEGMENTS: + work: work + school: school + univ: univ + +ALTS_PREPROCESSOR: + work: + SPEC: mandatory_tour_scheduling_annotate_alts_preprocessor.csv + DF: alt_tdd + +SPEC_SEGMENTS: + work: + 'SPEC': tour_scheduling_work.csv + 'COEFFICIENTS': tour_scheduling_work_coeffs.csv + school: + 'SPEC': tour_scheduling_school.csv + 'COEFFICIENTS': tour_scheduling_school_coeffs.csv + univ: + 'SPEC': tour_scheduling_university.csv + 'COEFFICIENTS': tour_scheduling_university_coeffs.csv + +DESTINATION_FOR_TOUR_PURPOSE: + work: workplace_zone_id + school: school_zone_id + univ: school_zone_id diff --git a/configs/resident/mandatory_tour_scheduling_annotate_alts_preprocessor.csv b/configs/resident/mandatory_tour_scheduling_annotate_alts_preprocessor.csv new file mode 100644 index 0000000..d961142 --- /dev/null +++ b/configs/resident/mandatory_tour_scheduling_annotate_alts_preprocessor.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +# make expressions are consistent with reference bins,, +departure_reference_bin,departureRefBin,9 +arrival_reference_bin,arrivalRefBin,29 +duration_reference_bin,durationRefBin,20 +departure_shift,departureLinearShift1,"(9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)" +arrival_shift,arrivalLinearShift1,"(29-df.end)*(df.end<=29) + (df.end-29)*(df.end>29)" +duration_shift,durationShift,"(20-df.duration)*(df.duration<=20) + (df.duration-20)*(df.duration>20)" +# school specific bins +departure_reference_bin_school,departureRefBin_school,9 +arrival_reference_bin_school,arrivalRefBin_school,29 +duration_reference_bin_school,durationRefBin_school,16 +departure_shift_school,departureLinearShift1_school,"(9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)" +arrival_shift_school,arrivalLinearShift1_school,"(29-df.end)*(df.end<=29) + (df.end-29)*(df.end>29)" +duration_shift_school,durationShift_school,"(16-df.duration)*(df.duration<=16) + (df.duration-16)*(df.duration>16)" diff --git a/configs/resident/non_mandatory_tour_destination.csv b/configs/resident/non_mandatory_tour_destination.csv new file mode 100644 index 0000000..0adf836 --- /dev/null +++ b/configs/resident/non_mandatory_tour_destination.csv @@ -0,0 +1,72 @@ +Label,Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +,,"_DIST@np.minimum(skims['DIST'], 20)",1,1,1,1,1,1 +,,_DIST_SQUARED@_DIST**2,1,1,1,1,1,1 +,,_DIST_CUBED@_DIST**3,1,1,1,1,1,1 +,,_DIST_LOGGED@np.log(_DIST + 0.001),1,1,1,1,1,1 +,,"_CALIB_DIST@np.where(df.tour_type == 'escort', 20, np.where(df.tour_type == 'othmaint', 8, 10))",1,1,1,1,1,1 +util_dist,dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_maint,coef_dist_social,coef_dist_discr +util_dist_squared,dist_squared,@_DIST_SQUARED,coef_distsqrd_escort,coef_distsqrd_shopping,coef_distsqrd_eatout,coef_distsqrd_maint,coef_distsqrd_social,coef_distsqrd_discr +util_dist_cubed,dist_cubed,@_DIST_CUBED,coef_zero,coef_distcubed_shopping,coef_distcubed_eatout,coef_zero,coef_distcubed_social,coef_distcubed_discr +util_dist_logged,dist_logged,@_DIST_LOGGED,coef_zero,coef_distlogged_shopping,coef_distlogged_eatout,coef_zero,coef_distlogged_social,coef_distlogged_discr +util_dist_female,dist_female,"@_DIST* df.female * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_zero,coef_distfemale_shopping,coef_zero,coef_zero,coef_zero,coef_distfemale_disc +util_dist_income_segment_escort,Distance - low income_segment_escort,@_DIST * (df.income < 30000),coef_distlowinc_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +util_dist_lowincome_segment_maint,Distance - low income_segment_maint,@_DIST * (df.income_segment <= 2),coef_zero,coef_zero,coef_zero,coef_distlowinc_maint,coef_distlowinc_social,coef_zero +util_dist_lowmed_income_segment_maint,Distance - lowmed income_segment_maint,@_DIST * (df.income_segment <= 3),coef_zero,coef_zero,coef_zero,coef_zero,coef_distmidlowinc_social,coef_zero +#Distance - Time Pressure calculated as the log of the maxtime over tours left,#Distance - Time Pressure calculated as the log of the maxtime over tours left,@_DIST* np.log((person_max_window(persons)/(df.tour_type_count - df.tour_type_num + 1))+0.0001),coef_zero,coef_disttimepressure_shopping,coef_disttimepressure_eatout,coef_disttimepressure_maint,coef_zero,coef_disttimepressure_disc +Accessibility,#Accessibility,shopping_accessibility,coef_zero,coef_accessibility_shopping,coef_zero,coef_zero,coef_zero,coef_zero +util_dist_age_16to24,Distance - age 16 to 24,"@_DIST * df.age.between(16, 24) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_zero,coef_zero,coef_zero,coef_distage1624_maint,coef_zero,coef_zero +util_dist_age_56to64,Distance - age 56 to 64,"@_DIST * (df.age.between(56, 64))",coef_distage5664_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +util_dist_age_65+,Distance - age 65+,@_DIST * (df.age > 64),coef_distage64plus_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +#escort size is zero for escort tour,#escort size is zero for escort tour,@(@@householdsDestAlt * 1 + df.num_young_children * @@householdsDestAlt * 1 + df.num_gradeschool * @@gradeSchoolEnrollmentDestAlt * 100 + df.numHighSchoolStudents * @@highSchoolEnrollmentDestAlt * 100) == 0,-999,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +#escort size is non-zero for escort tour,#escort size is non-zero for escort tour,"@np.where(@@householdsDestAlt * 1 + df.num_young_children * @@householdsDestAlt * 1 + df.num_gradeschool * @@gradeSchoolEnrollmentDestAlt * 100 + df.numHighSchoolStudents * @@highSchoolEnrollmentDestAlt * 100 > 0, np.log(@@householdsDestAlt * 1 + df.num_y",1,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +util_size_term,Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 +util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum_socdisc,coef_mode_logsum_socdisc +util_sample_alts_correction,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1 +#Calibration constants,#Calibration constants,,,,,,, +util_calib_dist,Calibration - Distance,"@np.minimum(_DIST, _CALIB_DIST)",coef_Calibration_Distance_escort,coef_Calibration_Distance_shopping,coef_Calibration_Distance_eatout,coef_Calibration_Distance_maint,coef_Calibration_Distance_social,coef_Calibration_Distance_disc +util_calib_dist_squared,Calibration - Distance_squared,"@np.minimum(_DIST_SQUARED, _CALIB_DIST**2)",coef_Calibration_Distance_squared_escort,coef_Calibration_Distance_squared_shopping,coef_Calibration_Distance_squared_eatout,coef_Calibration_Distance_squared_maint,coef_Calibration_Distance_squared_social,coef_Calibration_Distance_squared_disc +util_calib_dist_cubed,Calibration - Distance_cubed,"@np.minimum(_DIST_CUBED, _CALIB_DIST**3)",coef_Calibration_Distance_cubed_escort,coef_Calibration_Distance_cubed_shopping,coef_Calibration_Distance_cubed_eatout,coef_Calibration_Distance_cubed_maint,coef_Calibration_Distance_cubed_social,coef_Calibration_Distance_cubed_disc +util_calib_dist_logged,Calibration - Distance_logged,"@np.minimum(_DIST_LOGGED, np.log(_CALIB_DIST))",coef_Calibration_Distance_logged_escort,coef_Calibration_Distance_logged_shopping,coef_Calibration_Distance_logged_eatout,coef_Calibration_Distance_logged_maint,coef_Calibration_Distance_logged_social,coef_Calibration_Distance_logged_disc +util_calib_0_1_miles,Calibration - 0-1 miles,@(_DIST<1),,coef_Calibration_0_1_miles_shopping,coef_Calibration_0_1_miles_eatout,coef_Calibration_0_1_miles_maint,coef_Calibration_0_1_miles_social, +util_calib_1_2_miles,Calibration - 1-2 miles,@(_DIST>=1) * (_DIST<2),,coef_Calibration_1_2_miles_shopping,,coef_Calibration_1_2_miles_maint,coef_Calibration_1_2_miles_social, +util_calib_2_5_miles,Calibration - 2-5 miles,@(_DIST>=2) * (_DIST<5),,coef_Calibration_2_5_miles_shopping,,coef_Calibration_2_5_miles_maint,, +util_abm2_calib_0_5_miles,ABM2 calibration - 0-5 miles,@(_DIST < 5) * _DIST,,,coef_ABM2_calibration_0_5_miles_eatout,,coef_ABM2_calibration_0_5_miles_social,coef_ABM2_calibration_0_5_miles_disc +util_abm2_calib_0to1_miles_all,"ABM2 calibration - 0-1 miles, all tours",@(_DIST < 1),,,coef_ABM2_calibration_0_1_miles_eatout,,coef_ABM2_calibration_0_1_miles_social,coef_ABM2_calibration_0_1_miles_disc +util_abm2_calib_1to2_miles_all,"ABM2 calibration - 1-2 miles, all tours",@(_DIST < 2) * (_DIST >= 1),,,coef_ABM2_calibration_1_2_miles_eatout,,coef_ABM2_calibration_1_2_miles_social,coef_ABM2_calibration_1_2_miles_disc +util_abm2_calib_2to3_miles_all,"ABM2 calibration - 2-3 miles, all tours",@(_DIST < 3) * (_DIST >= 2),,,,,coef_ABM2_calibration_2_3_miles_social,coef_ABM2_calibration_2_3_miles_disc +util_abm2_calib_3to4_miles_all,"ABM2 calibration - 3-4 miles, all tours",@(_DIST < 4) * (_DIST >= 3),,,,,coef_ABM2_calibration_3_4_miles_social,coef_ABM2_calibration_3_4_miles_disc +util_abm2_calib_0to1_miles_joint,"ABM2 calibration - 0-1 miles, joint tour","@(_DIST < 1) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_ABM2_calibration_0_1_miles_escort,,,,, +util_abm2_calib_1to2_miles_joint,"ABM2 calibration - 1-2 miles, joint tour","@(_DIST < 2) * (_DIST >= 1) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_ABM2_calibration_1_2_miles_escort,,,coef_ABM2_calibration_1_2_miles_maint,, +util_abm2_calib_2to3_miles_joint,"ABM2 calibration - 2-3 miles, joint tour","@(_DIST < 3) * (_DIST >= 2) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_ABM2_calibration_2_3_miles_escort,,,coef_ABM2_calibration_2_3_miles_maint,, +util_abm2_calib_distance_logged,"ABM2 calibration - distance logged, indiv tour","@np.minimum(_DIST_LOGGED, np.log(_CALIB_DIST)) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",,,,coef_ABM2_calibration_distance_logged_maint,, +util_abm2_calib_4to5_miles,ABM2 calibration - 4-5 miles,@(_DIST < 5) * (_DIST >= 4),,coef_ABM2_calibration_4_5_miles_shopping,coef_ABM2_calibration_4_5_miles_eatout,coef_ABM2_calibration_4_5_miles_maint,coef_ABM2_calibration_4_5_miles_social,coef_ABM2_calibration_4_5_miles_disc +util_abm2_calib_5to6_miles,ABM2 calibration - 5-6 miles,@(_DIST < 6) * (_DIST >= 5),,coef_ABM2_calibration_5_6_miles_shopping,,coef_ABM2_calibration_5_6_miles_maint,, +util_abm2_calib_6to7_miles,ABM2 calibration - 6-7 miles,@(_DIST < 7) * (_DIST >= 6),,coef_ABM2_calibration_6_7_miles_shopping,,coef_ABM2_calibration_6_7_miles_maint,, +util_abm2_calib_0to20_miles,ABM2 calibration - 0-20 miles,@(_DIST < 20) * _DIST,coef_ABM2_calibration_0_20_miles_escort,coef_ABM2_calibration_0_20_miles_shopping,,coef_ABM2_calibration_0_20_miles_maint,, +util_abm2_calib_distance,ABM2 calibration - distance,@_DIST,coef_ABM2_calibration_distance_escort,coef_ABM2_calibration_distance_shopping,coef_ABM2_calibration_distance_eatout,coef_ABM2_calibration_distance_maint,coef_ABM2_calibration_distance_social,coef_ABM2_calibration_distance_disc +util_abm2_calib_distance_squared,ABM2 calibration - distance squared,@_DIST_SQUARED,coef_ABM2_calibration_distance_squared_escort,coef_ABM2_calibration_distance_squared_shopping,coef_ABM2_calibration_distance_squared_eatout,coef_ABM2_calibration_distance_squared_maint,coef_ABM2_calibration_distance_squared_social,coef_ABM2_calibration_distance_squared_disc +util_abm2_calib_distance_cubed,ABM2 calibration - distance cubed,@_DIST_CUBED,coef_ABM2_calibration_distance_cubed_escort,coef_ABM2_calibration_distance_cubed_shopping,coef_ABM2_calibration_distance_cubed_eatout,coef_ABM2_calibration_distance_cubed_maint,coef_ABM2_calibration_distance_cubed_social,coef_ABM2_calibration_distance_cubed_disc +util_abm2_calib_distance_logged2,ABM2 calibration - distance logged,"@np.minimum(_DIST_LOGGED, np.log(_CALIB_DIST)) ",coef_ABM2_calibration_distance_logged_escort,coef_ABM2_calibration_distance_logged_shopping,coef_ABM2_calibration_distance_logged_eatout,,coef_ABM2_calibration_distance_logged_social,coef_ABM2_calibration_distance_logged_disc +"util_abm2_calib_0to1_miles_joint_tour""","ABM2 calibration - 0-1 miles, joint tour","@np.where(df.get('tour_category', default=False) == 'joint', 1, 0) * (_DIST<1)",,coef_ABM2_calibration_0_1_miles_joint_shopping,coef_ABM2_calibration_0_1_miles_joint_eatout,coef_ABM2_calibration_0_1_miles_joint_maint,coef_ABM2_calibration_0_1_miles_joint_social,coef_ABM2_calibration_0_1_miles_joint_disc +"util_abm2_calib_1to2_miles_joint_tour""","ABM2 calibration - 1-2 miles, joint tour","@np.where(df.get('tour_category', default=False) == 'joint', 1, 0) * (_DIST>=1) * (_DIST<2)",,coef_ABM2_calibration_1_2_miles_joint_shopping,coef_ABM2_calibration_1_2_miles_joint_eatout,coef_ABM2_calibration_1_2_miles_joint_maint,coef_ABM2_calibration_1_2_miles_joint_social,coef_ABM2_calibration_1_2_miles_joint_disc +"util_abm2_calib_2to3_miles_joint_tour""","ABM2 calibration - 2-3 miles, joint tour","@np.where(df.get('tour_category', default=False) == 'joint', 1, 0) * (_DIST>=2) * (_DIST<3)",,coef_ABM2_calibration_2_3_miles_joint_shopping,,coef_ABM2_calibration_2_3_miles_joint_maint,, +"util_abm2_calib_1to2_miles_indiv_tour""","ABM2 calibration - 1-2 miles, indiv tour","@(_DIST < 2) * (_DIST >= 1) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",,coef_ABM2_calibration_1_2_miles_shopping,,,, +"util_abm2_calib_2to3_miles_indiv_tour""","ABM2 calibration - 2-3 miles, indiv tour","@(_DIST < 3) * (_DIST >= 2) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",,coef_ABM2_calibration_2_3_miles_shopping,,,, +util_abm2_calib_distance_logged3,ABM2+ calibration - distance logged,"@(_DIST < 20) * np.minimum(_DIST_LOGGED, np.log(_CALIB_DIST))",coef_ABM2p_calibration_distance_logged_escort,,,,, +util_abm2_calib_0to1_miles,ABM2+ calibration - 0-1 miles,@(_DIST < 1),coef_ABM2p_calibration_0_1_miles_escort,coef_ABM2p_calibration_0_1_miles_shopping,coef_ABM2p_calibration_0_1_miles_eatout,coef_ABM2p_calibration_0_1_miles_maint,coef_ABM2p_calibration_0_1_miles_social,coef_ABM2p_calibration_0_1_miles_disc +util_abm2_calib_1to2_miles,ABM2+ calibration - 1-2 miles,@(_DIST < 2) * (_DIST >= 1),coef_ABM2p_calibration_1_2_miles_escort,coef_ABM2p_calibration_1_2_miles_shopping,coef_ABM2p_calibration_1_2_miles_eatout,coef_ABM2p_calibration_1_2_miles_maint,coef_ABM2p_calibration_1_2_miles_social,coef_ABM2p_calibration_1_2_miles_disc +util_abm2_calib_2to3_miles,ABM2+ calibration - 2-3 miles,@(_DIST < 3) * (_DIST >= 2),coef_ABM2p_calibration_2_3_miles_escort,coef_ABM2p_calibration_2_3_miles_shopping,coef_ABM2p_calibration_2_3_miles_eatout,coef_ABM2p_calibration_2_3_miles_maint,coef_ABM2p_calibration_2_3_miles_social,coef_ABM2p_calibration_2_3_miles_disc +util_abm2_calib_3to4_miles,ABM2+ calibration - 3-4 miles,@(_DIST < 4) * (_DIST >= 3),coef_ABM2p_calibration_3_4_miles_escort,coef_ABM2p_calibration_3_4_miles_shopping,coef_ABM2p_calibration_3_4_miles_eatout,coef_ABM2p_calibration_3_4_miles_maint,coef_ABM2p_calibration_3_4_miles_social,coef_ABM2p_calibration_3_4_miles_disc +#,#,,,,,,, +#util_mission_valley_mall_constant,#Mission Valley Mall Constant,@df.destination==6187,,coef_Mission_Valley_Mall_Constant_shopping_6187,,,, +#util_mission_valley_mall_constant,#Mission Valley Mall Constant,@df.destination==6196,,coef_Mission_Valley_Mall_Constant_shopping_6196,,,, +#,#,,,,,,, +util_indiv_0_2_ASC,indiv tours in 0_2bin calibration constant,"@(_DIST.between(0,2)) * (df.get('tour_category', default=False) != 'joint')",coef_abm3_dist_0_2indivescort_asc,coef_abm3_dist_0_2indivmaint_asc,coef_abm3_dist_0_2indivdisc_asc,coef_abm3_dist_0_2indivmaint_asc,coef_abm3_dist_0_2indivdisc_asc,coef_abm3_dist_0_2indivdisc_asc +util_indiv_2_5_ASC,indiv tours in 2_5bin calibration constant,"@(_DIST.between(2,5)) * (df.get('tour_category', default=False) != 'joint')",coef_abm3_dist_2_5indivescort_asc,coef_abm3_dist_2_5indivmaint_asc,coef_abm3_dist_2_5indivdisc_asc,coef_abm3_dist_2_5indivmaint_asc,coef_abm3_dist_2_5indivdisc_asc,coef_abm3_dist_2_5indivdisc_asc +util_indiv_5_10_ASC,indiv tours in 5_10bin calibration constant,"@(_DIST.between(5,10)) * (df.get('tour_category', default=False) != 'joint')",coef_abm3_dist_5_10indivescort_asc,coef_abm3_dist_5_10indivmaint_asc,coef_abm3_dist_5_10indivdisc_asc,coef_abm3_dist_5_10indivmaint_asc,coef_abm3_dist_5_10indivdisc_asc,coef_abm3_dist_5_10indivdisc_asc +util_indiv_10_30_ASC,indiv tours in 10_30bin calibration constant,"@(_DIST.between(10,30)) * (df.get('tour_category', default=False) != 'joint')",coef_abm3_dist_10_30indivescort_asc,coef_abm3_dist_10_30indivmaint_asc,coef_abm3_dist_10_30indivdisc_asc,coef_abm3_dist_10_30indivmaint_asc,coef_abm3_dist_10_30indivdisc_asc,coef_abm3_dist_10_30indivdisc_asc +util_joint_0_2_ASC,joint tours in 0_2bin calibration constant,"@(_DIST.between(0,2)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_0_2jointmaint_asc,coef_abm3_dist_0_2jointdisc_asc,coef_abm3_dist_0_2jointmaint_asc,coef_abm3_dist_0_2jointdisc_asc,coef_abm3_dist_0_2jointdisc_asc +util_joint_2_5_ASC,joint tours in 2_5bin calibration constant,"@(_DIST.between(2,5)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_2_5jointmaint_asc,coef_abm3_dist_2_5jointdisc_asc,coef_abm3_dist_2_5jointmaint_asc,coef_abm3_dist_2_5jointdisc_asc,coef_abm3_dist_2_5jointdisc_asc +util_joint_5_10_ASC,joint tours in 5_10bin calibration constant,"@(_DIST.between(5,10)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_5_10jointmaint_asc,coef_abm3_dist_5_10jointdisc_asc,coef_abm3_dist_5_10jointmaint_asc,coef_abm3_dist_5_10jointdisc_asc,coef_abm3_dist_5_10jointdisc_asc +util_joint_10_30_ASC,joint tours in 10_30bin calibration constant,"@(_DIST.between(10,30)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_10_30jointmaint_asc,coef_abm3_dist_10_30jointdisc_asc,coef_abm3_dist_10_30jointmaint_asc,coef_abm3_dist_10_30jointdisc_asc,coef_abm3_dist_10_30jointdisc_asc diff --git a/configs/resident/non_mandatory_tour_destination.yaml b/configs/resident/non_mandatory_tour_destination.yaml new file mode 100644 index 0000000..f8daa2c --- /dev/null +++ b/configs/resident/non_mandatory_tour_destination.yaml @@ -0,0 +1,58 @@ +SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv +SPEC: non_mandatory_tour_destination.csv +COEFFICIENTS: non_mandatory_tour_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: non_mandatory + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: tour_type + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample + + +SEGMENTS: + - shopping + - othmaint + - othdiscr + - eatout + - social + - escort + +SIMULATE_CHOOSER_COLUMNS: + - tour_type + - home_zone_id + - person_id + - female + - income + - income_segment + - age + - tour_type_count + - tour_type_num + - tour_category + + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: + shopping: 14 + othmaint: 14 + othdiscr: 14 + eatout: 14 + social: 14 + escort: 14 +OUT_PERIOD: + shopping: 14 + othmaint: 14 + othdiscr: 14 + eatout: 14 + social: 14 + escort: 14 \ No newline at end of file diff --git a/configs/resident/non_mandatory_tour_destination_coefficients.csv b/configs/resident/non_mandatory_tour_destination_coefficients.csv new file mode 100644 index 0000000..3b50ac7 --- /dev/null +++ b/configs/resident/non_mandatory_tour_destination_coefficients.csv @@ -0,0 +1,206 @@ +coefficient_name,value,constrain +coef_zero,0.0,T +coef_mode_logsum,0.5,T +coef_mode_logsum_socdisc,0.4,T +#,, +coef_dist_escort,-1.033376651,F +coef_distsqrd_escort,0.022130838,F +coef_distlowinc_escort,0.157496938,F +coef_distage5664_escort,0.189508203,F +coef_distage64plus_escort,0.309309986,F +#,, +coef_dist_shopping,-0.255810802,F +coef_distsqrd_shopping,-0.003098511,F +coef_distcubed_shopping,0.0,F +coef_distlogged_shopping,-0.229414306,F +coef_distfemale_shopping,0.0,F +coef_disttimepressure_shopping,0.029450792,F +coef_accessibility_shopping,0.377323356,F +#,, +coef_dist_eatout,0.094724831,F +coef_distsqrd_eatout,-0.029120711,F +coef_distcubed_eatout,0.000647926,F +coef_distlogged_eatout,-0.664600521,F +coef_disttimepressure_eatout,0.027648186,F +#,, +coef_dist_maint,-0.035327,F +coef_distsqrd_maint,-0.007959,F +coef_distlowinc_maint,0.0,F +coef_disttimepressure_maint,0.025736,F +coef_distage1624_maint,-0.503857,F +#,, +coef_dist_social,-0.082372,F +coef_distsqrd_social,-0.003052,F +coef_distcubed_social,0.0,F +coef_distlogged_social,-0.430261,F +coef_distlowinc_social,0.038684,F +coef_distmidlowinc_social,0.0,F +#,, +coef_dist_discr,0.543421359,F +coef_distsqrd_discr,-0.065310929,F +coef_distcubed_discr,0.001719762,F +coef_distlogged_discr,-1.524851692,F +coef_distfemale_disc,0.0,F +coef_disttimepressure_disc,0.054073655,F +#,, +coef_ABM2_calibration_0_1_miles_escort,-0.1714,T +coef_ABM2_calibration_1_2_miles_escort,-0.0966,T +coef_ABM2_calibration_2_3_miles_escort,-0.0739,T +coef_ABM2_calibration_0_20_miles_escort,0.13377,T +coef_ABM2_calibration_distance_escort,0.0,T +coef_ABM2_calibration_distance_squared_escort,-0.007376946,T +coef_ABM2_calibration_distance_cubed_escort,0.0,T +coef_ABM2_calibration_distance_logged_escort,-0.1373,T +coef_Calibration_Distance_squared_escort,0.0732726,T +coef_Calibration_Distance_cubed_escort,-0.0026709,T +coef_Calibration_Distance_escort,-0.2321275,T +coef_Calibration_Distance_logged_escort,-0.0801,T +coef_ABM2p_calibration_distance_logged_escort,0.1648,T +coef_ABM2p_calibration_0_1_miles_escort,0.222,T +coef_ABM2p_calibration_1_2_miles_escort,0.1599,T +coef_ABM2p_calibration_2_3_miles_escort,-0.0749,T +coef_ABM2p_calibration_3_4_miles_escort,-0.0095,T +#,, +coef_ABM2_calibration_0_1_miles_joint_shopping,-0.137065295,T +coef_ABM2_calibration_2_3_miles_joint_shopping,0.0,T +coef_ABM2_calibration_1_2_miles_joint_shopping,-0.389228729,T +coef_ABM2_calibration_0_1_miles_shopping,-0.338567459,T +coef_ABM2_calibration_1_2_miles_shopping,0.380266207,T +coef_ABM2_calibration_0_20_miles_shopping,0.0,T +coef_ABM2_calibration_2_3_miles_shopping,0.233495387,T +coef_ABM2_calibration_3_4_miles_shopping,-0.236188729,T +coef_ABM2_calibration_4_5_miles_shopping,-0.178169632,T +coef_ABM2_calibration_5_6_miles_shopping,0.0,T +coef_ABM2_calibration_6_7_miles_shopping,0.0,T +coef_Calibration_0_1_miles_shopping,-0.3477,T +coef_Calibration_1_2_miles_shopping,-0.2341,T +coef_Calibration_2_5_miles_shopping,-0.0208,T +coef_ABM2_calibration_distance_shopping,0.0,T +coef_ABM2_calibration_distance_squared_shopping,-0.0005121,T +coef_ABM2_calibration_distance_cubed_shopping,-7.584e-05,T +#coef_Mission_Valley_Mall_Constant_shopping_6187,-0.5,T +#coef_Mission_Valley_Mall_Constant_shopping_6196,-0.5,T +coef_Calibration_Distance_squared_shopping,-0.1357403,T +coef_Calibration_Distance_cubed_shopping,0.0090424,T +coef_Calibration_Distance_shopping,0.5816206,T +coef_Calibration_Distance_logged_shopping,-1.332207,T +coef_ABM2_calibration_distance_logged_shopping,-0.23912,T +coef_ABM2p_calibration_0_1_miles_shopping,-0.3386,T +coef_ABM2p_calibration_1_2_miles_shopping,-0.2771,T +coef_ABM2p_calibration_2_3_miles_shopping,-0.3181,T +coef_ABM2p_calibration_3_4_miles_shopping,-0.2362,T +#,, +coef_ABM2_calibration_1_2_miles_joint_eatout,-0.3004,T +coef_ABM2_calibration_0_1_miles_joint_eatout,-0.4739,T +coef_ABM2_calibration_0_1_miles_eatout,-0.7869,T +coef_ABM2_calibration_1_2_miles_eatout,0.5209,T +coef_ABM2_calibration_2_3_miles_eatout,0.4549,T +coef_ABM2_calibration_3_4_miles_eatout,0.1109,T +coef_ABM2_calibration_4_5_miles_eatout,-0.027,T +coef_ABM2_calibration_0_5_miles_eatout,0.107,T +coef_Calibration_0_1_miles_eatout,0.1074,T +coef_ABM2_calibration_distance_eatout,0.8713,T +coef_ABM2_calibration_distance_squared_eatout,-0.0353,T +coef_ABM2_calibration_distance_cubed_eatout,0.0004,T +coef_Calibration_Distance_squared_eatout,0.0333,T +coef_Calibration_Distance_cubed_eatout,-0.001,T +coef_Calibration_Distance_eatout,-0.19,T +coef_ABM2_calibration_distance_logged_eatout,-2.349,T +coef_Calibration_Distance_logged_eatout,-0.2569,T +coef_ABM2p_calibration_0_1_miles_eatout,-0.0563,T +coef_ABM2p_calibration_1_2_miles_eatout,-0.0582,T +coef_ABM2p_calibration_2_3_miles_eatout,-0.1056,T +coef_ABM2p_calibration_3_4_miles_eatout,-0.0262,T +#,, +coef_ABM2_calibration_0_1_miles_joint_maint,-0.1371,T +coef_ABM2_calibration_2_3_miles_joint_maint,0.0,T +coef_ABM2_calibration_1_2_miles_joint_maint,-0.3892,T +coef_ABM2_calibration_0_1_miles_maint,-0.3386,T +coef_ABM2_calibration_1_2_miles_maint,0.3803,T +coef_ABM2_calibration_0_20_miles_maint,0.0,T +coef_ABM2_calibration_2_3_miles_maint,0.2335,T +coef_ABM2_calibration_3_4_miles_maint,-0.2362,T +coef_ABM2_calibration_4_5_miles_maint,0.0,T +coef_ABM2_calibration_5_6_miles_maint,0.0,T +coef_ABM2_calibration_6_7_miles_maint,0.0,T +coef_Calibration_0_1_miles_maint,-0.0214,T +coef_Calibration_1_2_miles_maint,-0.1938,T +coef_Calibration_2_5_miles_maint,0.0,T +coef_ABM2_calibration_distance_maint,0.0,T +coef_ABM2_calibration_distance_squared_maint,-0.0005,T +coef_ABM2_calibration_distance_cubed_maint,-0.0001,T +coef_Calibration_Distance_squared_maint,-0.0396,T +coef_Calibration_Distance_cubed_maint,0.0023,T +coef_Calibration_Distance_maint,0.2641,T +coef_Calibration_Distance_logged_maint,-1.4297,T +coef_ABM2_calibration_distance_logged_maint,-0.2391,T +coef_ABM2p_calibration_0_1_miles_maint,-0.3386,T +coef_ABM2p_calibration_1_2_miles_maint,-0.2771,T +coef_ABM2p_calibration_2_3_miles_maint,-0.3181,T +coef_ABM2p_calibration_3_4_miles_maint,-0.2362,T +#,, +coef_ABM2_calibration_0_1_miles_joint_social,-0.4739,T +coef_ABM2_calibration_1_2_miles_joint_social,-0.3004,T +coef_ABM2_calibration_0_1_miles_social,-0.7869,T +coef_ABM2_calibration_1_2_miles_social,0.5209,T +coef_ABM2_calibration_2_3_miles_social,0.5605,T +coef_ABM2_calibration_3_4_miles_social,0.1371,T +coef_ABM2_calibration_4_5_miles_social,-0.027,T +coef_ABM2_calibration_0_5_miles_social,0.107,T +coef_Calibration_0_1_miles_social,1.0697,T +coef_Calibration_1_2_miles_social,0.5699,T +coef_ABM2_calibration_distance_social,0.8713,T +coef_ABM2_calibration_distance_squared_social,-0.0353,T +coef_ABM2_calibration_distance_cubed_social,0.0004,T +coef_Calibration_Distance_squared_social,0.0,T +coef_Calibration_Distance_cubed_social,0.0,T +coef_Calibration_Distance_social,0.0,T +coef_ABM2_calibration_distance_logged_social,-2.349,T +coef_Calibration_Distance_logged_social,0.0,T +coef_ABM2p_calibration_0_1_miles_social,-0.0563,T +coef_ABM2p_calibration_1_2_miles_social,-0.0582,T +coef_ABM2p_calibration_2_3_miles_social,-0.1056,T +coef_ABM2p_calibration_3_4_miles_social,-0.0262,T +#,, +coef_Distance_Calibration_adjustment_disc,0.0066,T +coef_ABM2_calibration_0_1_miles_joint_disc,-0.4739,T +coef_ABM2_calibration_1_2_miles_joint_disc,-0.3004,T +coef_ABM2_calibration_0_1_miles_disc,-0.7869,T +coef_ABM2_calibration_1_2_miles_disc,0.5209,T +coef_ABM2_calibration_2_3_miles_disc,0.5605,T +coef_ABM2_calibration_3_4_miles_disc,0.1371,T +coef_ABM2_calibration_4_5_miles_disc,-0.027,T +coef_ABM2_calibration_0_5_miles_disc,0.107,T +coef_ABM2_calibration_distance_disc,0.8713,T +coef_ABM2_calibration_distance_squared_disc,-0.0353,T +coef_ABM2_calibration_distance_cubed_disc,0.0004,T +coef_Calibration_Distance_squared_disc,-0.0523,T +coef_Calibration_Distance_cubed_disc,0.0027,T +coef_Calibration_Distance_disc,0.2181,T +coef_ABM2_calibration_distance_logged_disc,-2.349,T +coef_Calibration_Distance_logged_disc,-0.1883,T +coef_ABM2p_calibration_0_1_miles_disc,-0.0563,T +coef_ABM2p_calibration_1_2_miles_disc,-0.0582,T +coef_ABM2p_calibration_2_3_miles_disc,-0.1056,T +coef_ABM2p_calibration_3_4_miles_disc,-0.0262,T +#,, +coef_abm3_dist_0_2indivescort_asc,0.0743894542403208,F +coef_abm3_dist_0_2indivmaint_asc,-1.1013926972835268,F +coef_abm3_dist_0_2indivdisc_asc,-0.5188544554826798,F +coef_abm3_dist_2_5indivescort_asc,0.3267263770696683,F +coef_abm3_dist_2_5indivmaint_asc,-0.1229585362100356,F +coef_abm3_dist_2_5indivdisc_asc,0.3658142479785504,F +coef_abm3_dist_5_10indivescort_asc,0.3268135840383142,F +coef_abm3_dist_5_10indivmaint_asc,1.2615942662937147,F +coef_abm3_dist_5_10indivdisc_asc,0.970850923095182,F +coef_abm3_dist_10_30indivescort_asc,-1.210160116890714,F +coef_abm3_dist_10_30indivmaint_asc,2.244671931775837,F +coef_abm3_dist_10_30indivdisc_asc,1.7449730188184471,F +coef_abm3_dist_0_2jointmaint_asc,0.10814282191363894,F +coef_abm3_dist_0_2jointdisc_asc,-0.3257531020899993,F +coef_abm3_dist_2_5jointmaint_asc,-0.2256620842333652,F +coef_abm3_dist_2_5jointdisc_asc,0.7878311118085077,F +coef_abm3_dist_5_10jointmaint_asc,0.26451614721496275,F +coef_abm3_dist_5_10jointdisc_asc,0.7566707355720486,F +coef_abm3_dist_10_30jointmaint_asc,1.1623489449822964,F +coef_abm3_dist_10_30jointdisc_asc,0.17580063767755247,F diff --git a/configs/resident/non_mandatory_tour_destination_sample.csv b/configs/resident/non_mandatory_tour_destination_sample.csv new file mode 100644 index 0000000..85e81ba --- /dev/null +++ b/configs/resident/non_mandatory_tour_destination_sample.csv @@ -0,0 +1,19 @@ +Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +,_DIST@skims['DIST'],1,1,1,1,1,1 +Distance,"@_DIST.clip(0,20)",coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_maint,coef_dist_social,coef_dist_discr +Distance_squared,"@_DIST.clip(0,20)**2",coef_distsqrd_escort,coef_distsqrd_shopping,coef_distsqrd_eatout,coef_distsqrd_maint,coef_distsqrd_social,coef_distsqrd_discr +Distance_cubed,"@_DIST.clip(0,20)**3",coef_zero,coef_distcubed_shopping,coef_distcubed_eatout,coef_zero,coef_distcubed_social,coef_distcubed_discr +Distance_logged,"@np.log(_DIST.clip(0,20) + 0.001)",coef_zero,coef_distlogged_shopping,coef_distlogged_eatout,coef_zero,coef_distlogged_social,coef_distlogged_discr +Distance_female,"@_DIST.clip(0,20) * df.female * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_zero,coef_distfemale_shopping,coef_zero,coef_zero,coef_zero,coef_distfemale_disc +Distance - low income_segment_escort,"@_DIST.clip(0,20) * (df.income < 30000)",coef_distlowinc_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +Distance - low income_segment_maint,"@_DIST.clip(0,20) * (df.income_segment <= 2)",coef_zero,coef_zero,coef_zero,coef_distlowinc_maint,coef_distlowinc_social,coef_zero +Distance - lowmed income_segment_maint,"@_DIST.clip(0,20) * (df.income_segment <= 3)",coef_zero,coef_zero,coef_zero,coef_zero,coef_distmidlowinc_social,coef_zero +#Distance - Time Pressure calculated as the log of the maxtime over tours left,"@_DIST.clip(0,20) * np.log((df.maxWindow/df.toursLeft)+0.0001)",coef_zero,coef_disttimepressure_shopping,coef_disttimepressure_eatout,coef_disttimepressure_maint,coef_zero,coef_disttimepressure_disc +#Accessibility,@@nonMandatoryAccessibilityAlt,coef_zero,coef_accessibility_shopping,coef_zero,coef_zero,coef_zero,coef_zero +Distance - age 16 to 24,"@_DIST.clip(0,20) * df.age.between(16, 24) * np.where(df.get('tour_category', default=False) == 'joint', 0, 1)",coef_zero,coef_zero,coef_zero,coef_distage1624_maint,coef_zero,coef_zero +Distance - age 56 to 64,"@_DIST.clip(0,20) * (df.age.between(56, 64))",coef_distage5664_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +Distance - age 65+,"@_DIST.clip(0,20) * (df.age > 64)",coef_distage64plus_escort,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +#escort size is zero for escort tour,@(@@householdsDestAlt * 1 + df.num_young_children * @@householdsDestAlt * 1 + df.num_gradeschool * @@gradeSchoolEnrollmentDestAlt * 100 + df.numHighSchoolStudents * @@highSchoolEnrollmentDestAlt * 100) == 0,-999,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +#escort size is non-zero for escort tour,"@np.where(@@householdsDestAlt * 1 + df.num_young_children * @@householdsDestAlt * 1 + df.num_gradeschool * @@gradeSchoolEnrollmentDestAlt * 100 + df.numHighSchoolStudents * @@highSchoolEnrollmentDestAlt * 100 > 0, np.log(@@householdsDestAlt * 1 + df.num_y",1,coef_zero,coef_zero,coef_zero,coef_zero,coef_zero +Size variable,@df['size_term'].apply(np.log1p),coef_zero,1,1,1,1,1 +No attractions,@df['size_term']==0,coef_zero,-999,-999,-999,-999,-999 diff --git a/configs/resident/non_mandatory_tour_frequency.csv b/configs/resident/non_mandatory_tour_frequency.csv new file mode 100644 index 0000000..34cafdf --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency.csv @@ -0,0 +1,112 @@ +Label,Description,Expression,PTYPE_FULL,PTYPE_PART,PTYPE_UNIVERSITY,PTYPE_NONWORK,PTYPE_RETIRED,PTYPE_DRIVING,PTYPE_SCHOOL,PTYPE_PRESCHOOL +util_naming_alts_explicitly,Explicitly naming what tot_tours is defined as,_total_indNM_tours@df.tot_tours,1,1,1,1,1,1,1,1 +util_escorting_tour,Escorting Tour,escort,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour +util_discretionary_tour,Discretionary Tour,othdiscr,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour +util_shopping_tour,Shopping Tour,shopping,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour +util_maintenance_tour,Maintenance Tour,othmaint,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour +util_visiting_or_social_tour,Visiting/Social Tour,social,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour +util_eating_out_tour,Eating Out Tour,eatout,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour +util_total_indNM_tours_is_0_prior_tours,Total individual NM Tours = 0 (1 or more Prior Tours),(_total_indNM_tours == 0) & (has_previous_tour == 1),,coef_total_indNM_tours_is_0_prior_tours,coef_total_indNM_tours_is_0_prior_tours,,,,coef_total_indNM_tours_is_0_prior_tours,coef_total_indNM_tours_is_0_prior_tours +util_total_indNM_tours_is_1,Total individual NM Tours = 1,(_total_indNM_tours == 1),coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1,coef_total_indNM_tours_is_1 +util_total_indNM_tours_is_2,Total individual NM Tours = 2,(_total_indNM_tours == 2),coef_total_indNM_tours_is_2,coef_total_indNM_tours_is_2,coef_total_indNM_tours_is_2,coef_total_indNM_tours_is_2,coef_total_indNM_tours_is_2,,, +util_total_indNM_tours_is_2p,Total individual NM Tours >= 2,(_total_indNM_tours >= 2),,,,,,,coef_total_indNM_tours_is_2p,coef_total_indNM_tours_is_2p +util_total_indNM_tours_is_3,Total individual NM Tours = 3,(_total_indNM_tours == 3),coef_total_indNM_tours_is_3,,,,coef_total_indNM_tours_is_3,,, +util_total_indNM_tours_is_3p,Total individual NM Tours >= 3,(_total_indNM_tours >= 3),,coef_total_indNM_tours_is_3p,coef_total_indNM_tours_is_3p,coef_total_indNM_tours_is_3p,,,, +util_total_indNM_tours_is_4,Total individual NM Tours = 4,(_total_indNM_tours >= 4),coef_total_indNM_tours_is_4p,,,,coef_total_indNM_tours_is_4p,,, +util_total_indNM_tours_2016_is_1,Total individual NM Tours = 1,(pre_covid == 1) & (_total_indNM_tours == 1),coef_total_indNM_tours_2016_is_1,coef_total_indNM_tours_2016_is_1,,coef_total_indNM_tours_2016_is_1,coef_total_indNM_tours_2016_is_1,coef_total_indNM_tours_2016_is_1,,coef_total_indNM_tours_2016_is_1 +util_total_indNM_tours_2016_is_2,Total individual NM Tours = 2,(pre_covid == 1) & (_total_indNM_tours == 2),coef_total_indNM_tours_2016_is_2,coef_total_indNM_tours_2016_is_2,,,coef_total_indNM_tours_2016_is_2,,, +util_total_indNM_tours_2016_is_2p,Total individual NM Tours >= 2,(pre_covid == 1) & (_total_indNM_tours >= 2),,,coef_total_indNM_tours_2016_is_2p,coef_total_indNM_tours_2016_is_2p,,coef_total_indNM_tours_2016_is_2p,coef_total_indNM_tours_2016_is_2p,coef_total_indNM_tours_2016_is_2p +util_total_indNM_tours_2016_is_3,Total individual NM Tours = 3,(pre_covid == 1) & (_total_indNM_tours == 3),,coef_total_indNM_tours_2016_is_3,,,,,, +util_total_indNM_tours_2016_is_3p,Total individual NM Tours >= 3,(pre_covid == 1) & (_total_indNM_tours >= 3),coef_total_indNM_tours_2016_is_3p,,,,coef_total_indNM_tours_2016_is_3p,,, +util_total_indNM_tours_2016_is_4,Total individual NM Tours = 4,(pre_covid == 1) & (_total_indNM_tours >= 4),,coef_total_indNM_tours_2016_is_4p,,,,,, +util_has_mandatory_tours_and_iNM_tour_freq_1,One or more Mandatory tour & tour frequency =1,(num_mandatory_tours > 0) & (_total_indNM_tours == 1),coef_has_mandatory_tours_and_iNM_tour_freq_1,,,,,,, +util_has_mandatory_tours_and_iNM_tour_freq_2p,One or more Mandatory tour & tour frequency =2p,(num_mandatory_tours > 0) & (_total_indNM_tours >= 2),coef_has_mandatory_tours_and_iNM_tour_freq_2p,,,,,,, +util_has_joint_tours_and_iNM_tour_freq_1,One or more Joint tour & tour frequency =1,(num_person_joint_tours > 0) & (_total_indNM_tours == 1),coef_has_joint_tours_and_iNM_tour_freq_1,coef_has_joint_tours_and_iNM_tour_freq_1,,coef_has_joint_tours_and_iNM_tour_freq_1,coef_has_joint_tours_and_iNM_tour_freq_1,,coef_has_joint_tours_and_iNM_tour_freq_1, +util_has_joint_tours_and_iNM_tour_freq_2p,One or more Joint tour & tour frequency =2p,(num_person_joint_tours > 0) & (_total_indNM_tours >= 2),coef_has_joint_tours_and_iNM_tour_freq_2p,,,coef_has_joint_tours_and_iNM_tour_freq_2p,coef_has_joint_tours_and_iNM_tour_freq_2p,,, +util_number_of_school_escort_tours,Number of School Escort Tours,escort * num_school_escort_tours,coef_prev_school_escort_tours,coef_prev_school_escort_tours,,coef_prev_school_escort_tours,,,, +# availability conditions,,,,,,,,,, +util_availability_of_zero_tours,Requires NM tour if no previous tours,(has_previous_tour == 0) & (_total_indNM_tours == 0),coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable, +# income coefficients with medium as base,,,,,,,,,, +util_low_income_escort,Low income household * number of escort tours,low_income * escort,,coef_low_income_escort,,coef_low_income_escort,coef_low_income_escort,,, +util_low_income_shopping,Low income household * number of shopping tours,low_income * shopping,,,,,coef_low_income_shopping,,, +util_low_income_othmaint,Low income household * number of othmaint tours,low_income * othmaint,coef_low_income_othmaint,,,coef_low_income_othmaint,coef_low_income_othmaint,,, +util_low_income_eatout,Low income household * number of eatout tours,low_income * eatout,,,,,coef_low_income_eatout,,, +util_low_income_othdiscr,Low income household * number of othdiscr tours,low_income * othdiscr,coef_low_income_othdiscr,coef_low_income_othdiscr,,,coef_low_income_othdiscr,,, +util_medium_low_income_escort,Medium-low income household * number of escort tours,medium_low_income * escort,coef_medium_low_income_escort,coef_medium_low_income_escort,,coef_medium_low_income_escort,,,, +util_medium_low_income_othmaint,Medium-low income household * number of othmaint tours,medium_low_income * othmaint,,,,coef_medium_low_income_othmaint,coef_medium_low_income_othmaint,,, +util_medium_low_income_othdiscr,Medium-low income household * number of othdiscr tours,medium_low_income * othdiscr,coef_medium_low_income_othdiscr,,,,,,, +util_medium_high_income_othdiscr,Medium-high income household * number of othdiscr tours,medium_high_income * othdiscr,coef_medium_high_income_othdiscr,,,coef_medium_high_income_othdiscr,,,, +util_high_income_escort,High income household * number of escort tours,high_income * escort,coef_high_income_escort,,,,,,, +util_high_income_shopping,High income household * number of shopping tours,high_income * shopping,,,,,coef_high_income_shopping,,, +util_high_income_othdiscr,High income household * number of othdiscr tours,high_income * othdiscr,coef_high_income_othdiscr,,,,,,, +# car ownership coefficients,,,,,,,,,, +util_zero_auto_iNM_tour_freq_1,zero auto ownership & tour freq is 1,(auto_ownership == 0) & (_total_indNM_tours == 1),coef_zero_auto_iNM_tour_freq_1,coef_zero_auto_iNM_tour_freq_1,coef_zero_auto_iNM_tour_freq_1,,,,, +util_zero_auto_iNM_tour_freq_2,zero auto ownership & tour freq is 2,(auto_ownership == 0) & (_total_indNM_tours == 2),coef_zero_auto_iNM_tour_freq_2,,,,,,, +util_zero_auto_iNM_tour_freq_2p,zero auto ownership & tour freq is 2p,(auto_ownership == 0) & (_total_indNM_tours >= 2),,coef_zero_auto_iNM_tour_freq_2p,coef_zero_auto_iNM_tour_freq_2p,,,,, +util_zero_auto_iNM_tour_freq_3p,zero auto ownership & tour freq is 3p,(auto_ownership == 0) & (_total_indNM_tours >= 3),coef_zero_auto_iNM_tour_freq_3p,,,coef_zero_auto_iNM_tour_freq_3p,,,, +util_auto_deficient_iNM_tour_freq_1,auto deficient & tour freq is 1,(auto_ownership > 0) & (auto_ownership < num_adults) & (_total_indNM_tours == 1),coef_auto_deficient_iNM_tour_freq_1,coef_auto_deficient_iNM_tour_freq_1,coef_auto_deficient_iNM_tour_freq_1,coef_auto_deficient_iNM_tour_freq_1,coef_auto_deficient_iNM_tour_freq_1,,, +util_auto_deficient_iNM_tour_freq_2,auto deficient & tour freq is 2,(auto_ownership > 0) & (auto_ownership < num_adults) & (_total_indNM_tours == 2),,,coef_auto_deficient_iNM_tour_freq_2,,coef_auto_deficient_iNM_tour_freq_2,,, +util_auto_deficient_iNM_tour_freq_2p,auto deficient & tour freq is 2p,(auto_ownership > 0) & (auto_ownership < num_adults) & (_total_indNM_tours >= 2),coef_auto_deficient_iNM_tour_freq_2p,coef_auto_deficient_iNM_tour_freq_2p,,coef_auto_deficient_iNM_tour_freq_2p,,,, +util_auto_deficient_iNM_tour_freq_3p,auto deficient & tour freq is 3p,(auto_ownership > 0) & (auto_ownership < num_adults) & (_total_indNM_tours >= 3),,,coef_auto_deficient_iNM_tour_freq_3p,,coef_auto_deficient_iNM_tour_freq_3p,,, +# household member interactions,,,,,,,,,, +util_num_children_escort,Number of household children * number of escort tours,num_children * escort,coef_num_children_escort,coef_num_children_escort,coef_num_children_escort,coef_num_children_escort,coef_num_children_escort,coef_num_children_escort,,coef_num_children_escort +util_num_children_shopping,Number of household children * number of shopping tours,num_children * shopping,coef_num_children_shopping,coef_num_children_shopping,,,,,, +util_num_children_othmaint,Number of household children * number of othmaint tours,num_children * othmaint,coef_num_children_othmaint,,,,,,, +util_num_children_social,Number of household children * number of social tours,num_children * social,coef_num_children_social,,,coef_num_children_social,,,, +util_num_children_othdiscr,Number of household children * number of othdiscr tours,num_children * othdiscr,coef_num_children_othdiscr,coef_num_children_othdiscr,,,,,, +util_num_full_time_workers_escort,Number of household full time workers * number of escort tours,num_fullTime_workers * escort,coef_num_full_time_workers_escort,,,coef_num_full_time_workers_escort,coef_num_full_time_workers_escort,,coef_num_full_time_workers_escort, +util_num_full_time_workers_othmaint,Number of household full time workers * number of othmaint tours,num_fullTime_workers * othmaint,,,,coef_num_full_time_workers_othmaint,,,, +util_num_full_time_workers_eatout,Number of household full time workers * number of eatout tours,num_fullTime_workers * eatout,,coef_num_full_time_workers_eatout,coef_num_full_time_workers_eatout,,,coef_num_full_time_workers_eatout,, +oef_num_full_time_workers_social +util_num_full_time_workers_othdiscr,Number of household full time workers * number of othdiscr tours,num_fullTime_workers * othdiscr,coef_num_full_time_workers_othdiscr,,,,,,, +util_hhsize_escort,household size * number of escort tours,hhsize * escort,,,,coef_hhsize_escort,,,coef_hhsize_escort, +util_hhsize_shopping,household size * number of shopping tours,hhsize * shopping,coef_hhsize_shopping,,,,coef_hhsize_shopping,coef_hhsize_shopping,, +util_hhsize_othmaint,household size * number of othmaint tours,hhsize * othmaint,coef_hhsize_othmaint,,,coef_hhsize_othmaint,coef_hhsize_othmaint,,, +util_hhsize_eatout,household size * number of eatout tours,hhsize * eatout,coef_hhsize_eatout,,coef_hhsize_eatout,coef_hhsize_eatout,coef_hhsize_eatout,,, +util_hhsize_social,household size * number of social tours,hhsize * social,coef_hhsize_social,,,,,,, +util_hhsize_othdiscr,household size * number of othdiscr tours,hhsize * othdiscr,coef_hhsize_othdiscr,,,,coef_hhsize_othdiscr,,,coef_hhsize_othdiscr +util_retired_hh_escort,Retirees and Non-workers only household * Escorting,retiredHh * escort,,,,,coef_retired_hh_escort,,, +# age effects by purpose,,,,,,,,,, +util_retired_80p_escort,Retiree over age 80 escort,(age >= 80) * escort,,,,,coef_age_80p_escort,,, +util_retired_80p_eatout,Retiree over age 80 eatout,(age >= 80) * eatout,,,,,coef_age_80p_eatout,,, +util_adult_18to24_escort,Age 18 to 24 escort,((age > 18) & (age <= 24)) * escort,coef_age_18to24_escort,coef_age_18to24_escort,,,,,, +util_adult_18to24_shopping,Age 18 to 24 shopping,((age > 18) & (age <= 24)) * shopping,,,coef_age_18to24_shopping,,,,, +util_adult_18to24_eatout,Age 18 to 24 eatout,((age > 18) & (age <= 24)) * eatout,,,coef_age_18to24_eatout,,,,, +util_adult_18to24_social,Age 18 to 24 social,((age > 18) & (age <= 24)) * social,,,,coef_age_18to24_social,,,, +util_child_24to35_escort,Age 24 to 35 escort,((age > 24) & (age <= 35)) * escort,coef_age_24to35_escort,coef_age_24to35_escort,,coef_age_24to35_escort,,,, +util_child_24to35_shopping,Age 24 to 35 shopping,((age > 24) & (age <= 35)) * shopping,,coef_age_24to35_shopping,coef_age_24to35_shopping,coef_age_24to35_shopping,,,, +util_child_24to35_eatout,Age 24 to 35 eatout,((age > 24) & (age <= 35)) * eatout,coef_age_24to35_eatout,,,,,,, +util_child_24to35_othdiscr,Age 24 to 35 othdiscr,((age > 24) & (age <= 35)) * othdiscr,,coef_age_24to35_othdiscr,,coef_age_24to35_othdiscr,,,, +util_child_50to79_escort,Age 50 to 79 escort,((age >= 50) & (age <= 79)) * escort,coef_age_50to79_escort,coef_age_50to79_escort,,coef_age_50to79_escort,,,, +util_child_50to79_eatout,Age 50 to 79 eatout,((age >= 50) & (age <= 79)) * eatout,coef_age_50to79_eatout,,,,,,, +util_child_50to79_othdiscr,Age 50 to 79 othdiscr,((age >= 50) & (age <= 79)) * othdiscr,coef_age_50to79_othdiscr,,coef_age_50to79_othdiscr,,,,, +# age effects by frequency,,,,,,,,,, +util_retired_80p_and_iNM_tour_freq_2,Age 80+ & Tour Frequency =2,(age >= 80) & (_total_indNM_tours == 2),,,,,coef_age_80p_and_iNM_tour_freq_2,,, +util_adult_18to24_and_iNM_tour_freq_2,Age 18 to 24 & Tour Frequency =2,((age > 18) & (age <= 24)) & (_total_indNM_tours == 2),coef_age_18to24_and_iNM_tour_freq_2,,,coef_age_18to24_and_iNM_tour_freq_2,,,, +# residual time window,,,,,,,,,, +util_log_total_hours_available_7am_to_10pm_window_escort,log_Total hours available 1am to 10pm tours escort,log_total_hours_available_7am_to_10pm * escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort,coef_log_total_hours_available_7am_to_10pm_escort +util_log_total_hours_available_7am_to_10pm_window_shopping,log_Total hours available 1am to 10pm tours shopping,log_total_hours_available_7am_to_10pm * shopping,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping,,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping,coef_log_total_hours_available_7am_to_10pm_shopping +util_log_total_hours_available_7am_to_10pm_window_othmaint,log_Total hours available 1am to 10pm tours othmaint,log_total_hours_available_7am_to_10pm * othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint,coef_log_total_hours_available_7am_to_10pm_othmaint +util_log_total_hours_available_7am_to_10pm_window_eatout,log_Total hours available 1am to 10pm tours eatout,log_total_hours_available_7am_to_10pm * eatout,coef_log_total_hours_available_7am_to_10pm_eatout,coef_log_total_hours_available_7am_to_10pm_eatout,coef_log_total_hours_available_7am_to_10pm_eatout,,,coef_log_total_hours_available_7am_to_10pm_eatout,coef_log_total_hours_available_7am_to_10pm_eatout,coef_log_total_hours_available_7am_to_10pm_eatout +util_log_total_hours_available_7am_to_10pm_window_social,log_Total hours available 1am to 10pm tours social,log_total_hours_available_7am_to_10pm * social,coef_log_total_hours_available_7am_to_10pm_social,coef_log_total_hours_available_7am_to_10pm_social,,,,coef_log_total_hours_available_7am_to_10pm_social,coef_log_total_hours_available_7am_to_10pm_social, +util_log_total_hours_available_7am_to_10pm_window_othdiscr,log_Total hours available 1am to 10pm tours othdiscr,log_total_hours_available_7am_to_10pm * othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr,,,coef_log_total_hours_available_7am_to_10pm_othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr,coef_log_total_hours_available_7am_to_10pm_othdiscr +util_consecutive_hours_available_7am_to_10pm_window_escort,Consecutive hours available 1am to 10pmtours escort,consecutive_hours_available_7am_to_10pm * escort,,,,,coef_consecutive_hours_available_7am_to_10pm_escort,,, +util_consecutive_hours_available_7am_to_10pm_window_shopping,Consecutive hours available 1am to 10pmtours shopping,consecutive_hours_available_7am_to_10pm * shopping,coef_consecutive_hours_available_7am_to_10pm_shopping,coef_consecutive_hours_available_7am_to_10pm_shopping,,coef_consecutive_hours_available_7am_to_10pm_shopping,,,, +util_consecutive_hours_available_7am_to_10pm_window_othmaint,Consecutive hours available 1am to 10pmtours othmaint,consecutive_hours_available_7am_to_10pm * othmaint,coef_consecutive_hours_available_7am_to_10pm_othmaint,,,coef_consecutive_hours_available_7am_to_10pm_othmaint,,,coef_consecutive_hours_available_7am_to_10pm_othmaint, +util_consecutive_hours_available_7am_to_10pm_window_eatout,Consecutive hours available 1am to 10pmtours eatout,consecutive_hours_available_7am_to_10pm * eatout,,,,,coef_consecutive_hours_available_7am_to_10pm_eatout,,, +util_consecutive_hours_available_7am_to_10pm_window_othdiscr,Consecutive hours available 1am to 10pmtours othdiscr,consecutive_hours_available_7am_to_10pm * othdiscr,,,coef_consecutive_hours_available_7am_to_10pm_othdiscr,coef_consecutive_hours_available_7am_to_10pm_othdiscr,,,, +# accessibilities,,,,,,,,,, +util_othdiscr_accessibility_for_escorting,Retail Accessibility for Escorting,shopping_accessibility * escort,,coef_shopping_accessibility_escorting,,,,,coef_shopping_accessibility_escorting, +util_shopping_accessibility_shopping,Retail Accessibility for Shopping,shopping_accessibility * shopping,coef_shopping_accessibility_shopping,coef_shopping_accessibility_shopping,coef_shopping_accessibility_shopping,coef_shopping_accessibility_shopping,,coef_shopping_accessibility_shopping,,coef_shopping_accessibility_shopping +util_shopping_accessibility_maintenance,Retail Accessibility for Maintenance,shopping_accessibility * othmaint,,,coef_shopping_accessibility_maintenance,,,,coef_shopping_accessibility_maintenance,coef_shopping_accessibility_maintenance +util_othdiscr_accessibility_eating_out,Retail Accessibility for Eating Out,othdiscr_accessibility * eatout,coef_othdiscr_accessibility_eating_out,coef_othdiscr_accessibility_eating_out,coef_othdiscr_accessibility_eating_out,,coef_othdiscr_accessibility_eating_out,,, +util_othdiscr_accessibility_discretionary,Retail Accessibility for Discretionary,othdiscr_accessibility * othdiscr,coef_othdiscr_accessibility_othdiscr,coef_othdiscr_accessibility_othdiscr,coef_othdiscr_accessibility_othdiscr,coef_othdiscr_accessibility_othdiscr,,,,coef_othdiscr_accessibility_othdiscr +util_othdiscr_accessibility_visiting,Retail Accessibility for Visiting,othdiscr_accessibility * social,coef_othdiscr_accessibility_social,coef_othdiscr_accessibility_social,,coef_othdiscr_accessibility_social,,,coef_othdiscr_accessibility_social,coef_othdiscr_accessibility_social +# work from home and telecommute_frequency,,,,,,,,,, +util_work_from_home_and_iNM_tour_freq_2,Work From Home & Tour Frequency =2,(work_from_home) & (_total_indNM_tours == 2),coef_work_from_home_and_iNM_tour_freq_2,,,,,,, +util_work_from_home_and_iNM_tour_freq_3p,Work From Home & Tour Frequency =3p,(work_from_home) & (_total_indNM_tours >= 3),coef_work_from_home_and_iNM_tour_freq_3p,,,,,,, +util_telecommute_2_3_days_week_and_iNM_tour_freq_2,Telecommute 1 day per week & Tour Frequency =2,(telecommute_frequency == '2_3_days_week') & (_total_indNM_tours == 2),coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_2,,,,,,, +util_telecommute_2_3_days_week_and_iNM_tour_freq_3p,Telecommute 1 day per week & Tour Frequency =3p,(telecommute_frequency == '2_3_days_week') & (_total_indNM_tours >= 3),coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_3p,,,,,,, +util_telecommute_4_days_week_and_iNM_tour_freq_1,Telecommute 1 day per week & Tour Frequency =1,(telecommute_frequency == '4_days_week') & (_total_indNM_tours == 1),,coef_telecommute_4_days_week_and_iNM_tour_freq_1,,,,,, +util_telecommute_4_days_week_and_iNM_tour_freq_2,Telecommute 1 day per week & Tour Frequency =2,(telecommute_frequency == '4_days_week') & (_total_indNM_tours == 2),coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_2,,,,,,, +util_telecommute_4_days_week_and_iNM_tour_freq_3p,Telecommute 1 day per week & Tour Frequency =3p,(telecommute_frequency == '4_days_week') & (_total_indNM_tours >= 3),coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_3p,,,,,,, diff --git a/configs/resident/non_mandatory_tour_frequency.yaml b/configs/resident/non_mandatory_tour_frequency.yaml new file mode 100644 index 0000000..cc0bec4 --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency.yaml @@ -0,0 +1,54 @@ + +SEGMENT_COL: ptype +SPEC: non_mandatory_tour_frequency.csv + +SPEC_SEGMENTS: + - NAME: PTYPE_FULL + PTYPE: 1 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv + - NAME: PTYPE_PART + PTYPE: 2 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv + - NAME: PTYPE_UNIVERSITY + PTYPE: 3 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv + - NAME: PTYPE_NONWORK + PTYPE: 4 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv + - NAME: PTYPE_RETIRED + PTYPE: 5 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv + - NAME: PTYPE_DRIVING + PTYPE: 6 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv + - NAME: PTYPE_SCHOOL + PTYPE: 7 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv + - NAME: PTYPE_PRESCHOOL + PTYPE: 8 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv + +annotate_persons: + SPEC: annotate_persons_nmtf + DF: persons + TABLES: + - tours + +preprocessor: + SPEC: non_mandatory_tour_frequency_annotate_persons_preprocessor + DF: persons + TABLES: + - tours + - households + - joint_tour_participants +# - accessibility + +CONSTANTS: + PTYPE_FULL: 1 + PTYPE_PART: 2 + PTYPE_UNIVERSITY: 3 + PTYPE_NONWORK: 4 + PTYPE_RETIRED: 5 + PTYPE_DRIVING: 6 + PTYPE_SCHOOL: 7 + PTYPE_PRESCHOOL: 8 diff --git a/configs/resident/non_mandatory_tour_frequency_alternatives.csv b/configs/resident/non_mandatory_tour_frequency_alternatives.csv new file mode 100644 index 0000000..e4d4f2e --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency_alternatives.csv @@ -0,0 +1,198 @@ +escort,shopping,othmaint,eatout,social,othdiscr +0,0,0,0,0,0 +0,0,0,0,0,1 +0,0,0,0,0,2 +0,0,0,0,1,0 +0,0,0,0,1,1 +0,0,0,0,1,2 +0,0,0,1,0,0 +0,0,0,1,0,1 +0,0,0,1,0,2 +0,0,0,1,1,0 +0,0,0,1,1,1 +0,0,0,1,1,2 +0,0,1,0,0,0 +0,0,1,0,0,1 +0,0,1,0,0,2 +0,0,1,0,1,0 +0,0,1,0,1,1 +0,0,1,0,1,2 +0,0,1,1,0,0 +0,0,1,1,0,1 +0,0,1,1,0,2 +0,0,1,1,1,0 +0,0,1,1,1,1 +0,0,1,1,1,2 +0,0,2,0,0,0 +0,0,2,0,0,1 +0,0,2,0,0,2 +0,0,2,0,1,0 +0,0,2,0,1,1 +0,0,2,0,1,2 +0,0,2,1,0,0 +0,0,2,1,0,1 +0,0,2,1,0,2 +0,0,2,1,1,0 +0,0,2,1,1,1 +0,1,0,0,0,0 +0,1,0,0,0,1 +0,1,0,0,0,2 +0,1,0,0,1,0 +0,1,0,0,1,1 +0,1,0,0,1,2 +0,1,0,1,0,0 +0,1,0,1,0,1 +0,1,0,1,0,2 +0,1,0,1,1,0 +0,1,0,1,1,1 +0,1,0,1,1,2 +0,1,1,0,0,0 +0,1,1,0,0,1 +0,1,1,0,0,2 +0,1,1,0,1,0 +0,1,1,0,1,1 +0,1,1,0,1,2 +0,1,1,1,0,0 +0,1,1,1,0,1 +0,1,1,1,0,2 +0,1,1,1,1,0 +0,1,1,1,1,1 +0,1,2,0,0,0 +0,1,2,0,0,1 +0,1,2,0,0,2 +0,1,2,0,1,0 +0,1,2,0,1,1 +0,1,2,1,0,0 +0,1,2,1,0,1 +0,1,2,1,1,0 +0,2,0,0,0,0 +0,2,0,0,0,1 +0,2,0,0,0,2 +0,2,0,0,1,0 +0,2,0,0,1,1 +0,2,0,0,1,2 +0,2,0,1,0,0 +0,2,0,1,0,1 +0,2,0,1,0,2 +0,2,0,1,1,0 +0,2,0,1,1,1 +0,2,1,0,0,0 +0,2,1,0,0,1 +0,2,1,0,0,2 +0,2,1,0,1,0 +0,2,1,0,1,1 +0,2,1,1,0,0 +0,2,1,1,0,1 +0,2,1,1,1,0 +0,2,2,0,0,0 +0,2,2,0,0,1 +0,2,2,0,1,0 +0,2,2,1,0,0 +1,0,0,0,0,0 +1,0,0,0,0,1 +1,0,0,0,0,2 +1,0,0,0,1,0 +1,0,0,0,1,1 +1,0,0,0,1,2 +1,0,0,1,0,0 +1,0,0,1,0,1 +1,0,0,1,0,2 +1,0,0,1,1,0 +1,0,0,1,1,1 +1,0,0,1,1,2 +1,0,1,0,0,0 +1,0,1,0,0,1 +1,0,1,0,0,2 +1,0,1,0,1,0 +1,0,1,0,1,1 +1,0,1,0,1,2 +1,0,1,1,0,0 +1,0,1,1,0,1 +1,0,1,1,0,2 +1,0,1,1,1,0 +1,0,1,1,1,1 +1,0,2,0,0,0 +1,0,2,0,0,1 +1,0,2,0,0,2 +1,0,2,0,1,0 +1,0,2,0,1,1 +1,0,2,1,0,0 +1,0,2,1,0,1 +1,0,2,1,1,0 +1,1,0,0,0,0 +1,1,0,0,0,1 +1,1,0,0,0,2 +1,1,0,0,1,0 +1,1,0,0,1,1 +1,1,0,0,1,2 +1,1,0,1,0,0 +1,1,0,1,0,1 +1,1,0,1,0,2 +1,1,0,1,1,0 +1,1,0,1,1,1 +1,1,1,0,0,0 +1,1,1,0,0,1 +1,1,1,0,0,2 +1,1,1,0,1,0 +1,1,1,0,1,1 +1,1,1,1,0,0 +1,1,1,1,0,1 +1,1,1,1,1,0 +1,1,2,0,0,0 +1,1,2,0,0,1 +1,1,2,0,1,0 +1,1,2,1,0,0 +1,2,0,0,0,0 +1,2,0,0,0,1 +1,2,0,0,0,2 +1,2,0,0,1,0 +1,2,0,0,1,1 +1,2,0,1,0,0 +1,2,0,1,0,1 +1,2,0,1,1,0 +1,2,1,0,0,0 +1,2,1,0,0,1 +1,2,1,0,1,0 +1,2,1,1,0,0 +1,2,2,0,0,0 +2,0,0,0,0,0 +2,0,0,0,0,1 +2,0,0,0,0,2 +2,0,0,0,1,0 +2,0,0,0,1,1 +2,0,0,0,1,2 +2,0,0,1,0,0 +2,0,0,1,0,1 +2,0,0,1,0,2 +2,0,0,1,1,0 +2,0,0,1,1,1 +2,0,1,0,0,0 +2,0,1,0,0,1 +2,0,1,0,0,2 +2,0,1,0,1,0 +2,0,1,0,1,1 +2,0,1,1,0,0 +2,0,1,1,0,1 +2,0,1,1,1,0 +2,0,2,0,0,0 +2,0,2,0,0,1 +2,0,2,0,1,0 +2,0,2,1,0,0 +2,1,0,0,0,0 +2,1,0,0,0,1 +2,1,0,0,0,2 +2,1,0,0,1,0 +2,1,0,0,1,1 +2,1,0,1,0,0 +2,1,0,1,0,1 +2,1,0,1,1,0 +2,1,1,0,0,0 +2,1,1,0,0,1 +2,1,1,0,1,0 +2,1,1,1,0,0 +2,1,2,0,0,0 +2,2,0,0,0,0 +2,2,0,0,0,1 +2,2,0,0,1,0 +2,2,0,1,0,0 +2,2,1,0,0,0 diff --git a/configs/resident/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv b/configs/resident/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv new file mode 100644 index 0000000..ac791b3 --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv @@ -0,0 +1,41 @@ +Description,Target,Expression +#,, +,low_income,persons.income<=30000 +,medium_low_income,(persons.income > 30000) & (persons.income <= 60000) +,medium_income,(persons.income > 60000) & (persons.income <= 100000) +,medium_high_income,(persons.income > 100000) & (persons.income <= 150000) +,high_income,(persons.income > 150000) +,total_hours_available_7am_to_10pm,"person_available_periods(persons, start_bin=8, end_bin=37, continuous=False) / 2" +,consecutive_hours_available_7am_to_10pm,"person_available_periods(persons, start_bin=8, end_bin=37, continuous=True) / 2" +,log_total_hours_available_7am_to_10pm,"np.log1p(total_hours_available_7am_to_10pm)" +#,, +# joint tour counts per persons,, +join joint tour participants with tour purpose,_JOINT_TOURS_PERSONS,"pd.merge(joint_tour_participants[['person_id', 'tour_id']], tours.reset_index()[['tour_id', 'tour_type']], on='tour_id', how='left')" +,num_person_joint_tours,"reindex_i(_JOINT_TOURS_PERSONS.groupby(['person_id']).size(), persons.index)" +,num_person_joint_shop_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_type=='Shop'].groupby(['person_id']).size(), persons.index)" +,num_person_joint_maint_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_type=='Maintenance'].groupby(['person_id']).size(), persons.index)" +,num_person_joint_eatout_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_type=='Eating Out'].groupby(['person_id']).size(), persons.index)" +,num_person_joint_visit_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_type=='Visiting'].groupby(['person_id']).size(), persons.index)" +,num_person_joint_othdiscr_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_type=='Discretionary'].groupby(['person_id']).size(), persons.index)" +,_TOUR_COUNT,"lambda query, tours, persons: tours.query(query).groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" +# non_mandatory tour frequency extension,, +,_INDIV_TOURS,tours[tours.tour_category!='joint'] +,num_mandatory_tours,"_TOUR_COUNT('(tour_category == \'mandatory\')', tours, persons)" +,num_school_escort_tours,"_TOUR_COUNT('(tour_category == \'non_mandatory\') & (tour_type == \'escort\')', tours, persons)" +,has_mandatory_tour,(num_mandatory_tours > 0) * 1 +,has_joint_tour,(num_person_joint_tours > 0) * 1 +,has_school_escort_tour,(num_school_escort_tours > 0) * 1 +,has_previous_tour,((has_mandatory_tour + has_joint_tour + has_school_escort_tour) > 0) * 1 +# number of person types in household in addition to self,, +,num_full_time_workers_not_self,"np.where(df.ptype == PTYPE_FULL, df.num_fullTime_workers-1, df.num_fullTime_workers)" +,num_part_time_workers_not_self,"np.where(df.ptype == PTYPE_PART, df.num_partTime_workers-1, df.num_partTime_workers)" +,num_university_students_not_self,"np.where(df.ptype == PTYPE_UNIVERSITY, df.num_university_students-1, df.num_university_students)" +,num_non_workers_not_self,"np.where(df.ptype == PTYPE_NONWORK, df.num_nonworker_adults - 1, df.num_nonworker_adults)" +,num_retirees_not_self,"np.where(df.ptype == PTYPE_RETIRED, df.num_retired_adults-1, df.num_retired_adults)" +,num_driving_age_students_not_self,"np.where(df.ptype == PTYPE_DRIVING, df.num_children_16_to_17 - 1, df.num_children_16_to_17)" +,num_pre_driving_age_school_kids_not_self,"np.where(df.ptype == PTYPE_SCHOOL, df.num_predrive_child - 1, df.num_predrive_child)" +,num_pre_school_kids_not_self,"np.where(df.ptype == PTYPE_PRESCHOOL, df.num_young_children - 1, df.num_young_children)" +,retiredHh,"np.where(df.num_retired_adults == df.hhsize, 1, 0)" +,num_travel_active_pre_drive_students,((persons.ptype == PTYPE_SCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).transform('sum') +# need to grab globals in pre-processor for interaction simulate models +,pre_covid,PRE_COVID \ No newline at end of file diff --git a/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv new file mode 100644 index 0000000..a161a28 --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv @@ -0,0 +1,21 @@ +coefficient_name,value,constrain +coef_escorting_tour,-24.98358380306237,F +coef_discretionary_tour,-6.04202752908342,F +coef_shopping_tour,-17.060985684374533,F +coef_maintenance_tour,-12.281343755464228,F +coef_visiting_or_social_tour,-7.344997314865568,F +coef_eating_out_tour,-16.502624376762842,F +coef_total_indNM_tours_is_1,0.9818773484130143,F +coef_total_indNM_tours_2016_is_1,0.16342218551186613,F +coef_total_indNM_tours_2016_is_2p,0.5030507042378277,F +coef_unavailable,-999.0,T +coef_num_children_escort,0.45679201292699456,F +coef_num_full_time_workers_eatout,1.5938976863951606,F +coef_hhsize_shopping,0.5247883688801986,F +coef_log_total_hours_available_7am_to_10pm_escort,8.1137051684989,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.7560083984013213,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.373601477816496,F +coef_log_total_hours_available_7am_to_10pm_eatout,4.120907553017707,F +coef_log_total_hours_available_7am_to_10pm_social,1.2479600156915076,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.2824546823717637,F +coef_shopping_accessibility_shopping,0.5528279422510315,F diff --git a/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv new file mode 100644 index 0000000..093d40e --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv @@ -0,0 +1,67 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_escorting_tour,-9.980738057374221,F +coef_discretionary_tour,-7.464263172179179,F +coef_shopping_tour,-8.726268980359634,F +coef_maintenance_tour,-11.50076886788746,F +coef_visiting_or_social_tour,-9.13878408618917,F +coef_eating_out_tour,-7.996439827456814,F +coef_total_indNM_tours_is_1,3.65902242986596,F +coef_total_indNM_tours_is_2,3.783116973334075,F +coef_total_indNM_tours_is_3,3.94980139461646,F +coef_total_indNM_tours_is_4p,4.044250929541874,F +coef_total_indNM_tours_2016_is_1,0.5451562250077215,F +coef_total_indNM_tours_2016_is_2,0.4485657739373237,F +coef_total_indNM_tours_2016_is_3p,0.3569363280476178,F +coef_has_mandatory_tours_and_iNM_tour_freq_1,-2.712110237880168,F +coef_has_mandatory_tours_and_iNM_tour_freq_2p,-1.8591259334530463,F +coef_has_joint_tours_and_iNM_tour_freq_1,-2.681436185164753,F +coef_has_joint_tours_and_iNM_tour_freq_2p,-2.582456735203676,F +coef_prev_school_escort_tours,-0.5659071632851075,F +coef_low_income_othmaint,0.1684373052095534,F +coef_low_income_othdiscr,-0.4247110056225974,F +coef_medium_low_income_escort,-0.26216121389680647,F +coef_medium_low_income_othdiscr,-0.2178944567340515,F +coef_medium_high_income_othdiscr,0.28578380808780474,F +coef_high_income_escort,0.5293466734142718,F +coef_high_income_othdiscr,0.5523603590834895,F +coef_zero_auto_iNM_tour_freq_1,-0.7778478707312145,F +coef_zero_auto_iNM_tour_freq_2,-1.745830549610859,F +coef_zero_auto_iNM_tour_freq_3p,-1.493273297127197,F +coef_auto_deficient_iNM_tour_freq_1,-0.13330115546054844,F +coef_auto_deficient_iNM_tour_freq_2p,-0.16552557917619873,F +coef_num_children_escort,0.7840653709271845,F +coef_num_children_shopping,0.16433950279556886,F +coef_num_children_othmaint,0.23002702126710234,F +coef_num_children_social,0.4360126244341865,F +coef_num_children_othdiscr,0.21305244576581855,F +coef_num_full_time_workers_escort,0.26574742217239844,F +coef_num_full_time_workers_othdiscr,-0.1268307960384887,F +coef_hhsize_shopping,-0.12965078886635895,F +coef_hhsize_othmaint,-0.11388617419409013,F +coef_hhsize_eatout,-0.26843735070052444,F +coef_hhsize_social,-0.4955951679184127,F +coef_hhsize_othdiscr,-0.18715371728147756,F +coef_age_18to24_escort,-0.7850576862260017,F +coef_age_24to35_escort,-0.33234113400691667,F +coef_age_24to35_eatout,-0.14650997264922844,F +coef_age_50to79_escort,-0.16636961878236434,F +coef_age_50to79_eatout,-0.33718093997956605,F +coef_age_50to79_othdiscr,-0.11702301453418044,F +coef_age_18to24_and_iNM_tour_freq_2,0.5184071993755616,F +coef_log_total_hours_available_7am_to_10pm_escort,2.3084987446913487,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.9947536383662492,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.361513223893925,F +coef_log_total_hours_available_7am_to_10pm_eatout,1.8577971133678188,F +coef_log_total_hours_available_7am_to_10pm_social,1.6066161223464683,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.935955432868534,F +coef_consecutive_hours_available_7am_to_10pm_shopping,0.03850875066248657,F +coef_consecutive_hours_available_7am_to_10pm_othmaint,0.05407491523536202,F +coef_shopping_accessibility_shopping,0.024876328142334057,F +coef_othdiscr_accessibility_eating_out,0.046921175676984284,F +coef_othdiscr_accessibility_othdiscr,0.029252086458666476,F +coef_othdiscr_accessibility_social,0.07973729159196855,F +coef_work_from_home_and_iNM_tour_freq_2,0.2882503610075806,F +coef_work_from_home_and_iNM_tour_freq_3p,0.43258258391404014,F +coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_2,0.060220889952691474,F +coef_telecommute_2_3_4_days_week_and_iNM_tour_freq_3p,0.1399978999743526,F diff --git a/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv new file mode 100644 index 0000000..a9181f0 --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv @@ -0,0 +1,44 @@ +coefficient_name,value,constrain +coef_escorting_tour,-11.210780876486327,F +coef_discretionary_tour,-2.787013651357596,F +coef_shopping_tour,-3.3897422131085118,F +coef_maintenance_tour,-2.0561696310183835,F +coef_visiting_or_social_tour,-4.541591324213227,F +coef_eating_out_tour,-2.105295178854616,F +coef_total_indNM_tours_is_1,3.1482892220784664,F +coef_total_indNM_tours_is_2,3.200495227239918,F +coef_total_indNM_tours_is_3p,3.1567943211556018,F +coef_total_indNM_tours_2016_is_1,0.38155849385296414,F +coef_total_indNM_tours_2016_is_2p,0.186,F +coef_has_joint_tours_and_iNM_tour_freq_1,-3.603962491982708,F +coef_has_joint_tours_and_iNM_tour_freq_2p,-3.4626515562619895,F +coef_prev_school_escort_tours,-1.0061285103185091,F +coef_unavailable,-999.0,T +coef_low_income_escort,-0.35229582496605166,F +coef_low_income_othmaint,-0.4037879626637974,F +coef_medium_low_income_escort,-0.23160471596989218,F +coef_medium_low_income_othmaint,-0.2805507112503287,F +coef_medium_high_income_othdiscr,0.32498217994094,F +coef_zero_auto_iNM_tour_freq_3p,-1.062908160511384,F +coef_auto_deficient_iNM_tour_freq_1,-0.21649670970431778,F +coef_auto_deficient_iNM_tour_freq_2p,-0.158,F +coef_num_children_escort,0.5581986955579746,F +coef_num_children_social,0.21747519499167278,F +coef_num_full_time_workers_escort,0.26498806847910095,F +coef_num_full_time_workers_othmaint,-0.1802355063278875,F +coef_hhsize_escort,0.11111794157280563,F +coef_hhsize_othmaint,-0.10130464850722279,F +coef_hhsize_eatout,-0.13935663394655493,F +coef_age_18to24_social,1.1448624101673186,F +coef_age_24to35_escort,-0.5608881691528708,F +coef_age_24to35_shopping,-0.3108414980140646,F +coef_age_24to35_othdiscr,-0.34338639441727353,F +coef_age_50to79_escort,-0.3812786064549372,F +coef_age_18to24_and_iNM_tour_freq_2,-0.9472458465963635,F +coef_log_total_hours_available_7am_to_10pm_escort,3.2260518038123336,F +coef_consecutive_hours_available_7am_to_10pm_shopping,0.06806700796118008,F +coef_consecutive_hours_available_7am_to_10pm_othmaint,0.05307614489617537,F +coef_consecutive_hours_available_7am_to_10pm_othdiscr,0.062136717913049824,F +coef_shopping_accessibility_shopping,0.05776720283340465,F +coef_othdiscr_accessibility_othdiscr,0.026534922230928487,F +coef_othdiscr_accessibility_social,0.04531077718755425,F diff --git a/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv new file mode 100644 index 0000000..c041021 --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv @@ -0,0 +1,47 @@ +coefficient_name,value,constrain +coef_escorting_tour,-6.8444776692013,F +coef_discretionary_tour,-7.061093571650552,F +coef_shopping_tour,-7.265878398154104,F +coef_maintenance_tour,-10.631962973623128,F +coef_visiting_or_social_tour,-7.513076659640363,F +coef_eating_out_tour,-8.032068601942477,F +coef_total_indNM_tours_is_0_prior_tours,4.350891211480593,F +coef_total_indNM_tours_is_1,5.4208851654881824,F +coef_total_indNM_tours_is_2,5.8949683354189375,F +coef_total_indNM_tours_is_3p,5.94305016560457,F +coef_total_indNM_tours_2016_is_1,0.1743660942950515,F +coef_total_indNM_tours_2016_is_2,0.18772797867537452,F +coef_total_indNM_tours_2016_is_3,0.7023687951041149,F +coef_total_indNM_tours_2016_is_4p,1.2888888305430555,F +coef_has_joint_tours_and_iNM_tour_freq_1,-0.29050043860982105,F +coef_prev_school_escort_tours,-0.611900397388031,F +coef_unavailable,-999.0,T +coef_low_income_escort,-0.41827164724106775,F +coef_low_income_othdiscr,-0.19464500351479955,F +coef_medium_low_income_escort,-0.37906865383167276,F +coef_zero_auto_iNM_tour_freq_1,-1.1380175469769664,F +coef_zero_auto_iNM_tour_freq_2p,-1.3659866939532648,F +coef_auto_deficient_iNM_tour_freq_1,-0.42631646773848203,F +coef_auto_deficient_iNM_tour_freq_2p,-0.7609412509478872,F +coef_num_children_escort,0.6491494416510694,F +coef_num_children_shopping,0.1479897628434831,F +coef_num_children_othdiscr,0.09237804825842522,F +coef_num_full_time_workers_eatout,-0.24419160458661263,F +coef_age_18to24_escort,-1.2806214268139127,F +coef_age_24to35_escort,-0.7100598101390896,F +coef_age_24to35_shopping,-0.4586169269694417,F +coef_age_24to35_othdiscr,-0.3230013853315813,F +coef_age_50to79_escort,-0.8046532853606215,F +coef_log_total_hours_available_7am_to_10pm_escort,1.457991506538752,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.0540599454856179,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.1263153979195755,F +coef_log_total_hours_available_7am_to_10pm_eatout,1.450002894756668,F +coef_log_total_hours_available_7am_to_10pm_social,1.0022229262201703,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.592166386021326,F +coef_consecutive_hours_available_7am_to_10pm_shopping,0.08487848115827784,F +coef_shopping_accessibility_escorting,0.034914955261943735,F +coef_shopping_accessibility_shopping,0.05379740509782797,F +coef_othdiscr_accessibility_eating_out,0.06844529127625759,F +coef_othdiscr_accessibility_othdiscr,0.024135548666842303,F +coef_othdiscr_accessibility_social,0.034748385315176565,F +coef_telecommute_4_days_week_and_iNM_tour_freq_1,-1.0215421163576748,F diff --git a/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv new file mode 100644 index 0000000..8a4615d --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv @@ -0,0 +1,24 @@ +coefficient_name,value,constrain +coef_escorting_tour,-11.051836936413467,F +coef_discretionary_tour,-9.634146897194343,F +coef_shopping_tour,-9.036614322521848,F +coef_maintenance_tour,-19.37699515494972,F +coef_visiting_or_social_tour,-6.595936622418915,F +coef_eating_out_tour,-17.102175352222606,F +coef_total_indNM_tours_is_0_prior_tours,3.24758823306399,F +coef_total_indNM_tours_is_1,2.4595057378149705,F +coef_total_indNM_tours_is_2p,2.679341375562811,F +coef_total_indNM_tours_2016_is_1,0.6894544279565437,F +coef_total_indNM_tours_2016_is_2p,0.49280442041251626,F +coef_num_children_escort,0.3449608531280371,F +coef_num_full_time_workers_social,-0.7989076159896628,F +coef_hhsize_othdiscr,0.10761055792559372,F +coef_log_total_hours_available_7am_to_10pm_escort,3.138584054552107,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.6577579483896183,F +coef_log_total_hours_available_7am_to_10pm_othmaint,4.7337852273390135,F +coef_log_total_hours_available_7am_to_10pm_eatout,4.921758449660736,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,2.055556483820614,F +coef_shopping_accessibility_shopping,0.1461117250482414,F +coef_shopping_accessibility_maintenance,0.26356027630549783,F +coef_othdiscr_accessibility_othdiscr,0.0632885406604063,F +coef_othdiscr_accessibility_social,0.20216296220808352,F diff --git a/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv new file mode 100644 index 0000000..a724698 --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv @@ -0,0 +1,44 @@ +coefficient_name,value,constrain +coef_escorting_tour,-13.324718928099896,F +coef_discretionary_tour,-5.033358573683751,F +coef_shopping_tour,-9.13631330258294,F +coef_maintenance_tour,-9.945067353298386,F +coef_visiting_or_social_tour,-4.189801745365118,F +coef_eating_out_tour,-4.445717330935003,F +coef_total_indNM_tours_is_1,6.00131407260292,F +coef_total_indNM_tours_is_2,5.883565852357907,F +coef_total_indNM_tours_is_3,5.828445878907544,F +coef_total_indNM_tours_is_4p,5.934428647072386,F +coef_total_indNM_tours_2016_is_1,0.1698266383749659,F +coef_total_indNM_tours_2016_is_2,0.32372516555726744,F +coef_total_indNM_tours_2016_is_3p,0.2964915370362205,F +coef_has_joint_tours_and_iNM_tour_freq_1,-5.7952837722578465,F +coef_has_joint_tours_and_iNM_tour_freq_2p,-5.76,F +coef_unavailable,-999.0,T +coef_low_income_escort,-0.6839180517907705,F +coef_low_income_shopping,-0.36787550353215537,F +coef_low_income_othmaint,-0.7732373479965099,F +coef_low_income_eatout,-0.6616337553879514,F +coef_low_income_othdiscr,-0.5329988579150085,F +coef_medium_low_income_othmaint,-0.21589267232884296,F +coef_high_income_shopping,-0.25087649228047343,F +coef_auto_deficient_iNM_tour_freq_1,-0.7868235054750288,F +coef_auto_deficient_iNM_tour_freq_2,-1.0455559653756108,F +coef_auto_deficient_iNM_tour_freq_3p,-1.1157343188741007,F +coef_num_children_escort,0.754137688219863,F +coef_num_full_time_workers_escort,0.3339757816794557,F +coef_hhsize_shopping,-0.1425515978162942,F +coef_hhsize_othmaint,-0.20501266076301913,F +coef_hhsize_eatout,-0.29701211013117823,F +coef_hhsize_othdiscr,-0.17093648977342044,F +coef_retired_hh_escort,-0.3294828681385463,F +coef_age_80p_escort,-0.5906910988839469,F +coef_age_80p_eatout,-0.40828362020671494,F +coef_age_80p_and_iNM_tour_freq_2,-0.2537832085563407,F +coef_log_total_hours_available_7am_to_10pm_escort,4.560726446110003,F +coef_log_total_hours_available_7am_to_10pm_shopping,2.7270167242212993,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.1187871560709866,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.4177528382551887,F +coef_consecutive_hours_available_7am_to_10pm_escort,-0.14521052349224925,F +coef_consecutive_hours_available_7am_to_10pm_eatout,0.07669930567537751,F +coef_othdiscr_accessibility_eating_out,0.0878608354323988,F diff --git a/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv new file mode 100644 index 0000000..632a490 --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv @@ -0,0 +1,25 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_escorting_tour,-9.690966426250665,F +coef_discretionary_tour,-7.5690966961253565,F +coef_shopping_tour,-8.975997622510478,F +coef_maintenance_tour,-13.246253630607148,F +coef_visiting_or_social_tour,-14.6311575770962,F +coef_eating_out_tour,-6.7325400979612295,F +coef_total_indNM_tours_is_0_prior_tours,1.106305308197831,F +coef_total_indNM_tours_is_1,1.747170199655668,F +coef_total_indNM_tours_is_2p,1.3703636107226296,F +coef_total_indNM_tours_2016_is_2p,0.5830429241271179,F +coef_has_joint_tours_and_iNM_tour_freq_1,-0.9122450724032953,F +coef_num_full_time_workers_escort,-0.1578338449120939,F +coef_hhsize_escort,0.1551946995851052,F +coef_log_total_hours_available_7am_to_10pm_escort,2.0528332539104683,F +coef_log_total_hours_available_7am_to_10pm_shopping,1.960668017568013,F +coef_log_total_hours_available_7am_to_10pm_othmaint,3.377458574954797,F +coef_log_total_hours_available_7am_to_10pm_eatout,0.9922950368954243,F +coef_log_total_hours_available_7am_to_10pm_social,2.494008974014185,F +coef_log_total_hours_available_7am_to_10pm_othdiscr,1.7579258741166293,F +coef_consecutive_hours_available_7am_to_10pm_othmaint,-0.14726873682504554,F +coef_shopping_accessibility_escorting,0.10532543446648758,F +coef_shopping_accessibility_maintenance,0.1770030247224446,F +coef_othdiscr_accessibility_social,0.20759071360015494,F diff --git a/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv new file mode 100644 index 0000000..698749b --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv @@ -0,0 +1,34 @@ +coefficient_name,value,constrain +coef_escorting_tour,-8.498374870237669,F +coef_discretionary_tour,-4.812463904304644,F +coef_shopping_tour,-12.693463829895798,F +coef_maintenance_tour,-11.058840934154171,F +coef_visiting_or_social_tour,-4.459306542169845,F +coef_eating_out_tour,-6.157924697688962,F +coef_total_indNM_tours_is_0_prior_tours,4.999764921270562,F +coef_total_indNM_tours_is_1,5.705610555681865,F +coef_total_indNM_tours_is_2,6.832163735527388,F +coef_total_indNM_tours_is_3p,7.208937046301491,F +coef_total_indNM_tours_2016_is_2p,-0.6792878238607456,F +coef_unavailable,-999.0,T +coef_zero_auto_iNM_tour_freq_1,-1.4891145894268167,F +coef_zero_auto_iNM_tour_freq_2p,-2.1435322461710236,F +coef_auto_deficient_iNM_tour_freq_1,-0.4607257760453242,F +coef_auto_deficient_iNM_tour_freq_2,-0.69349273445043,F +coef_auto_deficient_iNM_tour_freq_3p,-1.2029607161678721,F +coef_num_children_escort,0.7701459374658138,F +coef_num_full_time_workers_eatout,0.42257286721446335,F +coef_hhsize_eatout,-0.38977424932597926,F +coef_age_18to24_shopping,-0.404262220153878,F +coef_age_18to24_eatout,0.6249447012564978,F +coef_age_24to35_shopping,-0.28923438967158355,F +coef_age_50to79_othdiscr,0.3076225296007352,F +coef_log_total_hours_available_7am_to_10pm_escort,1.8792910621880181,F +coef_log_total_hours_available_7am_to_10pm_shopping,3.0367849074959543,F +coef_log_total_hours_available_7am_to_10pm_othmaint,2.880344751247897,F +coef_log_total_hours_available_7am_to_10pm_eatout,0.8062448674910451,F +coef_consecutive_hours_available_7am_to_10pm_othdiscr,0.11890563179293542,F +coef_shopping_accessibility_shopping,0.1616020726995222,F +coef_shopping_accessibility_maintenance,0.07126681192848552,F +coef_othdiscr_accessibility_eating_out,0.08890904673851951,F +coef_othdiscr_accessibility_othdiscr,0.04477521803529839,F diff --git a/configs/resident/non_mandatory_tour_frequency_extension_probs.csv b/configs/resident/non_mandatory_tour_frequency_extension_probs.csv new file mode 100644 index 0000000..8308852 --- /dev/null +++ b/configs/resident/non_mandatory_tour_frequency_extension_probs.csv @@ -0,0 +1,193 @@ +ptype,has_mandatory_tour,has_joint_tour,nonmandatory_tour_type,0_tours,1_tours,2_tours +1,0,0,1,0.71087271,0.886861358,1 +2,0,0,1,0.640712953,0.863555773,1 +3,0,0,1,0.726456335,0.726456335,1 +4,0,0,1,0.665679012,0.903816044,1 +5,0,0,1,0.861430335,0.92284115,1 +6,0,0,1,1,1,1 +7,0,0,1,0,1,1 +8,0,0,1,0.754632385,0.918132897,1 +1,1,0,1,0.824975545,1,1 +2,1,0,1,0.881285721,0.964131273,1 +3,1,0,1,0.856158692,1,1 +4,1,0,1,1,1,1 +5,1,0,1,1,1,1 +6,1,0,1,1,1,1 +7,1,0,1,1,1,1 +8,1,0,1,1,1,1 +1,0,0,2,0.918802841,1,1 +2,0,0,2,1,1,1 +3,0,0,2,1,1,1 +4,0,0,2,1,1,1 +5,0,0,2,0.911351921,0.964395496,1 +6,0,0,2,1,1,1 +7,0,0,2,1,1,1 +8,0,0,2,0.91707294,1,1 +1,1,0,2,1,1,1 +2,1,0,2,1,1,1 +3,1,0,2,1,1,1 +4,1,0,2,1,1,1 +5,1,0,2,1,1,1 +6,1,0,2,1,1,1 +7,1,0,2,1,1,1 +8,1,0,2,1,1,1 +1,0,0,3,0.885080064,0.942540032,1 +2,0,0,3,0.832615585,1,1 +3,0,0,3,1,1,1 +4,0,0,3,1,1,1 +5,0,0,3,0.874364173,0.972270724,1 +6,0,0,3,1,1,1 +7,0,0,3,1,1,1 +8,0,0,3,1,1,1 +1,1,0,3,1,1,1 +2,1,0,3,1,1,1 +3,1,0,3,1,1,1 +4,1,0,3,1,1,1 +5,1,0,3,1,1,1 +6,1,0,3,1,1,1 +7,1,0,3,1,1,1 +8,1,0,3,1,1,1 +1,0,0,4,1,1,1 +2,0,0,4,0.99296599,1,1 +3,0,0,4,1,1,1 +4,0,0,4,0.976072814,1,1 +5,0,0,4,0.956476086,0.993572522,1 +6,0,0,4,1,1,1 +7,0,0,4,1,1,1 +8,0,0,4,0.991144007,1,1 +1,1,0,4,0.99740908,1,1 +2,1,0,4,0.939184617,1,1 +3,1,0,4,1,1,1 +4,1,0,4,0.749851433,1,1 +5,1,0,4,1,1,1 +6,1,0,4,1,1,1 +7,1,0,4,1,1,1 +8,1,0,4,0.949874016,1,1 +1,0,0,5,1,1,1 +2,0,0,5,1,1,1 +3,0,0,5,1,1,1 +4,0,0,5,0.954541082,1,1 +5,0,0,5,0.955216408,1,1 +6,0,0,5,1,1,1 +7,0,0,5,1,1,1 +8,0,0,5,0.931183243,1,1 +1,1,0,5,0.970742777,1,1 +2,1,0,5,0.882901912,1,1 +3,1,0,5,1,1,1 +4,1,0,5,0.334132623,1,1 +5,1,0,5,1,1,1 +6,1,0,5,0.934804611,0.934804611,1 +7,1,0,5,0.965932685,1,1 +8,1,0,5,1,1,1 +1,0,0,6,1,1,1 +2,0,0,6,1,1,1 +3,0,0,6,1,1,1 +4,0,0,6,0.86514867,1,1 +5,0,0,6,0.896321835,1,1 +6,0,0,6,1,1,1 +7,0,0,6,1,1,1 +8,0,0,6,1,1,1 +1,1,0,6,1,1,1 +2,1,0,6,1,1,1 +3,1,0,6,1,1,1 +4,1,0,6,1,1,1 +5,1,0,6,1,1,1 +6,1,0,6,1,1,1 +7,1,0,6,1,1,1 +8,1,0,6,1,1,1 +1,0,1,1,1,1,1 +2,0,1,1,1,1,1 +3,0,1,1,1,1,1 +4,0,1,1,1,1,1 +5,0,1,1,1,1,1 +6,0,1,1,1,1,1 +7,0,1,1,1,1,1 +8,0,1,1,1,1,1 +1,1,1,1,1,1,1 +2,1,1,1,1,1,1 +3,1,1,1,1,1,1 +4,1,1,1,1,1,1 +5,1,1,1,1,1,1 +6,1,1,1,1,1,1 +7,1,1,1,1,1,1 +8,1,1,1,1,1,1 +1,0,1,2,1,1,1 +2,0,1,2,1,1,1 +3,0,1,2,1,1,1 +4,0,1,2,1,1,1 +5,0,1,2,1,1,1 +6,0,1,2,1,1,1 +7,0,1,2,1,1,1 +8,0,1,2,1,1,1 +1,1,1,2,1,1,1 +2,1,1,2,1,1,1 +3,1,1,2,1,1,1 +4,1,1,2,1,1,1 +5,1,1,2,1,1,1 +6,1,1,2,1,1,1 +7,1,1,2,1,1,1 +8,1,1,2,1,1,1 +1,0,1,3,1,1,1 +2,0,1,3,1,1,1 +3,0,1,3,1,1,1 +4,0,1,3,1,1,1 +5,0,1,3,1,1,1 +6,0,1,3,1,1,1 +7,0,1,3,1,1,1 +8,0,1,3,1,1,1 +1,1,1,3,1,1,1 +2,1,1,3,1,1,1 +3,1,1,3,1,1,1 +4,1,1,3,1,1,1 +5,1,1,3,1,1,1 +6,1,1,3,1,1,1 +7,1,1,3,1,1,1 +8,1,1,3,1,1,1 +1,0,1,4,1,1,1 +2,0,1,4,1,1,1 +3,0,1,4,1,1,1 +4,0,1,4,1,1,1 +5,0,1,4,1,1,1 +6,0,1,4,1,1,1 +7,0,1,4,1,1,1 +8,0,1,4,1,1,1 +1,1,1,4,1,1,1 +2,1,1,4,1,1,1 +3,1,1,4,1,1,1 +4,1,1,4,1,1,1 +5,1,1,4,1,1,1 +6,1,1,4,1,1,1 +7,1,1,4,1,1,1 +8,1,1,4,1,1,1 +1,0,1,5,1,1,1 +2,0,1,5,1,1,1 +3,0,1,5,1,1,1 +4,0,1,5,1,1,1 +5,0,1,5,1,1,1 +6,0,1,5,1,1,1 +7,0,1,5,1,1,1 +8,0,1,5,1,1,1 +1,1,1,5,1,1,1 +2,1,1,5,1,1,1 +3,1,1,5,1,1,1 +4,1,1,5,1,1,1 +5,1,1,5,1,1,1 +6,1,1,5,1,1,1 +7,1,1,5,1,1,1 +8,1,1,5,1,1,1 +1,0,1,6,1,1,1 +2,0,1,6,1,1,1 +3,0,1,6,1,1,1 +4,0,1,6,1,1,1 +5,0,1,6,1,1,1 +6,0,1,6,1,1,1 +7,0,1,6,1,1,1 +8,0,1,6,1,1,1 +1,1,1,6,1,1,1 +2,1,1,6,1,1,1 +3,1,1,6,1,1,1 +4,1,1,6,1,1,1 +5,1,1,6,1,1,1 +6,1,1,6,1,1,1 +7,1,1,6,1,1,1 +8,1,1,6,1,1,1 diff --git a/configs/resident/non_mandatory_tour_scheduling.yaml b/configs/resident/non_mandatory_tour_scheduling.yaml new file mode 100644 index 0000000..45ce6de --- /dev/null +++ b/configs/resident/non_mandatory_tour_scheduling.yaml @@ -0,0 +1,93 @@ +LOGIT_TYPE: MNL + +preprocessor: + SPEC: non_mandatory_tour_scheduling_annotate_tours_preprocessor + DF: non_mandatory_tours + TABLES: + - land_use + - joint_tour_participants + - school_escort_tours + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - age + - female + - adult + - ptype + - is_university + - has_pre_school_child_with_mandatory + - has_driving_age_child_with_mandatory + - is_income_less25K + - is_income_25K_to_60K + - is_income_60K_to_120K + - auto_ownership + - num_children + - num_adults + - num_retired_adults + - hhsize + - num_mand + - num_joint_tours + - num_escort_tours + - num_non_escort_tours + - num_add_shop_maint_tours + - num_add_soc_discr_tours + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# map : : +# segmentation of tours for processing may differ from segmentation of spec +# vectorize_tour_scheduling iterates over +# using to choose spec for scheduling alt chice +# using for logsums (if enabled) +# tour_mode_choice segmentation for logsums is: eatout,escort,othdiscr,othmaint,shopping,social,school,univ,work,atwork +TOUR_SPEC_SEGMENTS: + escort: escort + shopping: shopping + eatout: eatout + othdiscr: othdiscr + othmaint: othmaint + social: social + +# spec keyed by +SPEC_SEGMENTS: + escort: + 'SPEC': tour_scheduling_nonmandatory_escort.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_escort_coefficients.csv + shopping: + 'SPEC': tour_scheduling_nonmandatory_shopping.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_shopping_coefficients.csv + eatout: + 'SPEC': tour_scheduling_nonmandatory_eatout.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_eatout_coefficients.csv + othdiscr: + 'SPEC': tour_scheduling_nonmandatory_othdiscr.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_othdiscr_coefficients.csv + othmaint: + 'SPEC': tour_scheduling_nonmandatory_othmaint.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_othmaint_coefficients.csv + social: + 'SPEC': tour_scheduling_nonmandatory_social.csv + 'COEFFICIENTS': tour_scheduling_nonmandatory_social_coefficients.csv + +## alts preprocessor keyed by +#ALTS_PREPROCESSOR: +# escort: +# SPEC: tour_scheduling_nonmandatory_escort_annotate_alts_preprocessor.csv +# DF: alt_tdd +# shopping: +# SPEC: non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv +# DF: alt_tdd +# eatout: +# SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +# DF: alt_tdd +# othdiscr: +# SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +# DF: alt_tdd +# othmaint: +# SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +# DF: alt_tdd +# social: +# SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +# DF: alt_tdd +# +DESTINATION_FOR_TOUR_PURPOSE: destination diff --git a/configs/resident/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/configs/resident/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv new file mode 100644 index 0000000..09b066e --- /dev/null +++ b/configs/resident/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv @@ -0,0 +1,11 @@ +Description,Target,Expression +#,, +number of person joint tours,num_person_joint_tours,"reindex_i(joint_tour_participants.groupby('person_id').size(), non_mandatory_tours.person_id)" +#,, +,origin_to_destination_distance,"skim_dict.lookup(non_mandatory_tours.origin, non_mandatory_tours.destination, 'DIST')" +# included for school escorting model,, +flag to denote outbound school escort tours,is_outbound_school_escort_tour,"non_mandatory_tours.index.isin(school_escort_tours[school_escort_tours['school_escort_direction'] == 'outbound'].index)" +flag to denote inbound school escort tours,is_inbound_school_escort_tour,"non_mandatory_tours.index.isin(school_escort_tours[school_escort_tours['school_escort_direction'] == 'inbound'].index)" +school escort tour start time,school_escort_tour_start,"reindex(school_escort_tours.start, non_mandatory_tours.index)" +school escort tour next start time,school_escort_tour_next_start,"reindex(school_escort_tours.next_pure_escort_start, non_mandatory_tours.index)" +school escort tour end time,school_escort_tour_end,"reindex(school_escort_tours.end, non_mandatory_tours.index)" \ No newline at end of file diff --git a/configs/resident/parking_location_choice.csv b/configs/resident/parking_location_choice.csv new file mode 100644 index 0000000..f0333d0 --- /dev/null +++ b/configs/resident/parking_location_choice.csv @@ -0,0 +1,12 @@ +Description,Expression,no_segmentation +Person is worker,_person_is_worker@((df.ptype == 1) | (df.ptype == 2)),1 +Trip is work purpose,_work_trip@(df.purpose == 'work'),1 +Parking to destination distance skim,_walk_distance@pd_skims['DISTWALK'],1 +#,, +# actual utility terms starts from here,, +Log of parking size,"@np.where(df.parking_spaces>0,np.log(df.parking_spaces),-999)",1 +Cost work trip,"@np.where(df.purpose == 'work', df.cost_parking, 0)",coef_cost_work_trip +Cost other trip,"@np.where(df.purpose != 'work', df.cost_parking, 0)",coef_cost_other_trip +Walk distance,@_walk_distance,coef_walk_distance +Walk Unavailability,"@np.where(_walk_distance == 0, 1, 0)",coef_unavailable +Require parking zone to be less than 0.75 mile from dest,"@np.where(_walk_distance > 0.75, 1, 0) * (df.destination != df.parking_zone)",coef_unavailable diff --git a/configs/resident/parking_location_choice.yaml b/configs/resident/parking_location_choice.yaml new file mode 100644 index 0000000..7e5c730 --- /dev/null +++ b/configs/resident/parking_location_choice.yaml @@ -0,0 +1,27 @@ +SPECIFICATION: parking_location_choice.csv + +COEFFICIENTS: parking_location_choice_coeffs.csv + +PREPROCESSOR: + SPEC: parking_location_choice_annotate_trips_preprocessor + DF: trips_merged + TABLES: + - land_use + - persons + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_park_eligible +CHOOSER_SEGMENT_COLUMN_NAME: parking_segment + +ALTERNATIVE_FILTER_COLUMN_NAME: is_parking_zone +ALT_DEST_COL_NAME: parking_zone + +TRIP_DEPARTURE_PERIOD: depart + +TRIP_ORIGIN: origin +TRIP_DESTINATION: destination + +AUTO_MODES: + - DRIVEALONE + - SHARED2 + - SHARED3 diff --git a/configs/resident/parking_location_choice_annotate_trips_preprocessor.csv b/configs/resident/parking_location_choice_annotate_trips_preprocessor.csv new file mode 100644 index 0000000..d43e4be --- /dev/null +++ b/configs/resident/parking_location_choice_annotate_trips_preprocessor.csv @@ -0,0 +1,8 @@ +Description,Target,Expression +Person Type,ptype,"reindex(persons.ptype, df.person_id)" +Trip mode is drive,drive_trip,"df.trip_mode.isin(['DRIVEALONE', 'SHARED2', 'SHARED3'])" +# putting all trips into the same parking segment,, +Parking segment,parking_segment,'no_segmentation' +Parking eligible trip,is_park_eligible,"(drive_trip & (df.purpose != 'home') & (reindex(land_use.parking_type,df.destination)==1))" +Next Trip Departure,_next_trip_depart,df.groupby('tour_id')['depart'].shift() +Activity duration,activity_duration,"np.where(_next_trip_depart.isna(), 0, _next_trip_depart - df.depart)" diff --git a/configs/resident/parking_location_choice_coeffs.csv b/configs/resident/parking_location_choice_coeffs.csv new file mode 100644 index 0000000..0edccfb --- /dev/null +++ b/configs/resident/parking_location_choice_coeffs.csv @@ -0,0 +1,5 @@ +coefficient_name,value,constrain +coef_unavailable,-9999,T +coef_walk_distance,-11.8,F +coef_cost_work_trip,-0.0072,F +coef_cost_other_trip,-0.0041,F diff --git a/configs/resident/school_escorting.yaml b/configs/resident/school_escorting.yaml new file mode 100644 index 0000000..dd04737 --- /dev/null +++ b/configs/resident/school_escorting.yaml @@ -0,0 +1,47 @@ + +OUTBOUND_SPEC: school_escorting_outbound.csv +OUTBOUND_COEFFICIENTS: school_escorting_coefficients_outbound.csv + +INBOUND_SPEC: school_escorting_inbound.csv +INBOUND_COEFFICIENTS: school_escorting_coefficients_inbound.csv + +OUTBOUND_COND_SPEC: school_escorting_outbound_cond.csv +OUTBOUND_COND_COEFFICIENTS: school_escorting_coefficients_outbound_cond.csv + +ALTS: school_escorting_alts.csv + +LOGIT_TYPE: MNL + +NUM_ESCORTEES: 3 +NUM_CHAPERONES: 2 + +preprocessor_outbound: + SPEC: school_escorting_preprocessor_outbound + DF: df + TABLES: + - persons + - tours + +preprocessor_inbound: + SPEC: school_escorting_preprocessor_inbound + DF: df + TABLES: + - persons + - tours + +preprocessor_outbound_cond: + SPEC: school_escorting_preprocessor_outbound + DF: df + TABLES: + - persons + - tours + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - income + - auto_ownership + - num_workers + +CONSTANTS: + max_bin_difference_between_departure_times: 1 + mins_per_time_bin: 30 diff --git a/configs/resident/school_escorting_alts.csv b/configs/resident/school_escorting_alts.csv new file mode 100644 index 0000000..eca6617 --- /dev/null +++ b/configs/resident/school_escorting_alts.csv @@ -0,0 +1,158 @@ +Alt,bundle1,bundle2,bundle3,chauf1,chauf2,chauf3,nbund1,nbund2,nbundles,nrs1,npe1,nrs2,npe2,Description +1,0,0,0,0,0,0,0,0,0,0,0,0,0,no one is escorted +2,1,0,0,1,0,0,1,0,1,1,0,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +3,1,0,0,2,0,0,1,0,1,0,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +4,1,0,0,3,0,0,0,1,1,0,0,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +5,1,0,0,4,0,0,0,1,1,0,0,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +6,0,1,0,0,1,0,1,0,1,1,0,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing +7,0,1,0,0,2,0,1,0,1,0,1,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort +8,0,1,0,0,3,0,0,1,1,0,0,1,0,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing +9,0,1,0,0,4,0,0,1,1,0,0,0,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort +10,0,0,1,0,0,1,1,0,1,1,0,0,0,child 3 is escorted in bundle 1 by chauffeur 1 as ride sharing +11,0,0,1,0,0,2,1,0,1,0,1,0,0,child 3 is escorted in bundle 1 by chauffeur 1 as pure escort +12,0,0,1,0,0,3,0,1,1,0,0,1,0,child 3 is escorted in bundle 1 by chauffeur 2 as ride sharing +13,0,0,1,0,0,4,0,1,1,0,0,0,1,child 3 is escorted in bundle 1 by chauffeur 2 as pure escort +14,1,1,0,1,1,0,1,0,1,1,0,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing +15,1,1,0,2,2,0,1,0,1,0,1,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort +16,1,1,0,3,3,0,0,1,1,0,0,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing +17,1,1,0,4,4,0,0,1,1,0,0,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort +18,1,0,1,1,0,1,1,0,1,1,0,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing +19,1,0,1,2,0,2,1,0,1,0,1,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort +20,1,0,1,3,0,3,0,1,1,0,0,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing +21,1,0,1,4,0,4,0,1,1,0,0,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort +22,0,1,1,0,1,1,1,0,1,1,0,0,0,child 2 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing +23,0,1,1,0,2,2,1,0,1,0,1,0,0,child 2 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort +24,0,1,1,0,3,3,0,1,1,0,0,1,0,child 2 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing +25,0,1,1,0,4,4,0,1,1,0,0,0,1,child 2 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort +26,1,2,0,1,2,0,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +27,1,2,0,1,3,0,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +28,1,2,0,1,4,0,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +29,1,2,0,2,1,0,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +30,1,2,0,2,2,0,2,0,2,0,2,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +31,1,2,0,2,3,0,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +32,1,2,0,2,4,0,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +33,1,2,0,3,1,0,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +34,1,2,0,3,2,0,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +35,1,2,0,3,4,0,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +36,1,2,0,4,1,0,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +37,1,2,0,4,2,0,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +38,1,2,0,4,3,0,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +39,1,2,0,4,4,0,0,2,2,0,0,0,2,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +40,1,0,2,1,0,2,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +41,1,0,2,1,0,3,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +42,1,0,2,1,0,4,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +43,1,0,2,2,0,1,2,0,2,1,1,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +44,1,0,2,2,0,2,2,0,2,0,2,0,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +45,1,0,2,2,0,3,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +46,1,0,2,2,0,4,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +47,1,0,2,3,0,1,1,1,2,1,0,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +48,1,0,2,3,0,2,1,1,2,0,1,1,0,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +49,1,0,2,3,0,4,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +50,1,0,2,4,0,1,1,1,2,1,0,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +51,1,0,2,4,0,2,1,1,2,0,1,0,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +52,1,0,2,4,0,3,0,2,2,0,0,1,1,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +53,1,0,2,4,0,4,0,2,2,0,0,0,2,child 1 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +54,0,1,2,0,1,2,2,0,2,1,1,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +55,0,1,2,0,1,3,1,1,2,1,0,1,0,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +56,0,1,2,0,1,4,1,1,2,1,0,0,1,child 2 is escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +57,0,1,2,0,2,1,2,0,2,1,1,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +58,0,1,2,0,2,2,2,0,2,0,2,0,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +59,0,1,2,0,2,3,1,1,2,0,1,1,0,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +60,0,1,2,0,2,4,1,1,2,0,1,0,1,child 2 is escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +61,0,1,2,0,3,1,1,1,2,1,0,1,0,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +62,0,1,2,0,3,2,1,1,2,0,1,1,0,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +63,0,1,2,0,3,4,0,2,2,0,0,1,1,child 2 is escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +64,0,1,2,0,4,1,1,1,2,1,0,0,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +65,0,1,2,0,4,2,1,1,2,0,1,0,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escorting +66,0,1,2,0,4,3,0,2,2,0,0,1,1,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +67,0,1,2,0,4,4,0,2,2,0,0,0,2,child 2 is escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escorting +68,1,1,1,1,1,1,1,0,1,1,0,0,0,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing" +69,1,1,1,2,2,2,1,0,1,0,1,0,0,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort" +70,1,1,1,3,3,3,0,1,1,0,0,1,0,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing" +71,1,1,1,4,4,4,0,1,1,0,0,0,1,"child 1, 2 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort" +72,1,2,3,1,2,2,3,0,3,1,2,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +73,1,2,3,1,2,3,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +74,1,2,3,1,2,4,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +75,1,2,3,1,3,2,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +76,1,2,3,1,3,4,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +77,1,2,3,1,4,2,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +78,1,2,3,1,4,3,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +79,1,2,3,1,4,4,1,2,3,1,0,0,2,"child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +80,1,2,3,2,1,2,3,0,3,1,2,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +81,1,2,3,2,1,3,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +82,1,2,3,2,1,4,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +83,1,2,3,2,2,1,3,0,3,1,2,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +84,1,2,3,2,2,2,3,0,3,0,3,0,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +85,1,2,3,2,2,3,2,1,3,0,2,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +86,1,2,3,2,2,4,2,1,3,0,2,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +87,1,2,3,2,3,1,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +88,1,2,3,2,3,2,2,1,3,0,2,1,0,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +89,1,2,3,2,3,4,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +90,1,2,3,2,4,1,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +91,1,2,3,2,4,2,2,1,3,0,2,0,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +92,1,2,3,2,4,3,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +93,1,2,3,2,4,4,1,2,3,0,1,0,2,"child 1 is escorted in bundle 1 by chauffeur 1 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +94,1,2,3,3,1,2,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +95,1,2,3,3,1,4,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +96,1,2,3,3,2,1,2,1,3,1,1,1,0,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +97,1,2,3,3,2,2,2,1,3,0,2,1,0,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +98,1,2,3,3,2,4,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +99,1,2,3,3,4,1,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +100,1,2,3,3,4,2,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +101,1,2,3,3,4,4,0,3,3,0,0,1,2,"child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +102,1,2,3,4,1,2,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +103,1,2,3,4,1,3,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +104,1,2,3,4,1,4,1,2,3,1,0,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +105,1,2,3,4,2,1,2,1,3,1,1,0,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +106,1,2,3,4,2,2,2,1,3,0,2,0,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +107,1,2,3,4,2,3,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +108,1,2,3,4,2,4,1,2,3,0,1,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 1 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +109,1,2,3,4,3,1,1,2,3,1,0,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +110,1,2,3,4,3,2,1,2,3,0,1,1,1,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +111,1,2,3,4,3,4,0,3,3,0,0,1,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing, and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +112,1,2,3,4,4,1,1,2,3,1,0,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as ride sharing" +113,1,2,3,4,4,2,1,2,3,0,1,0,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 1 as pure escort" +114,1,2,3,4,4,3,0,3,3,0,0,1,2,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as ride sharing" +115,1,2,3,4,4,4,0,3,3,0,0,0,3,"child 1 is escorted in bundle 1 by chauffeur 2 as pure escort, child 2 is escorted in bundle 2 by chauffeur 2 as pure escort and child 3 is escorted in bundle 3 by chauffeur 2 as pure escort" +116,1,1,2,1,1,2,2,0,2,1,1,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +117,1,1,2,1,1,3,1,1,2,1,0,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +118,1,1,2,1,1,4,1,1,2,1,0,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +119,1,1,2,2,2,1,2,0,2,1,1,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +120,1,1,2,2,2,2,2,0,2,0,2,0,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +121,1,1,2,2,2,3,1,1,2,0,1,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +122,1,1,2,2,2,4,1,1,2,0,1,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 1 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +123,1,1,2,3,3,1,1,1,2,1,0,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +124,1,1,2,3,3,2,1,1,2,0,1,1,0,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +125,1,1,2,3,3,4,0,2,2,0,0,1,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +126,1,1,2,4,4,1,1,1,2,1,0,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as ride sharing +127,1,1,2,4,4,2,1,1,2,0,1,0,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 1 as pure escort +128,1,1,2,4,4,3,0,2,2,0,0,1,1,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as ride sharing +129,1,1,2,4,4,4,0,2,2,0,0,0,2,child 1 and 2 are escorted in bundle 1 by chauffeur 2 as pure escort and child 3 is escorted in bundle 2 by chauffeur 2 as pure escort +130,1,2,1,1,2,1,2,0,2,1,1,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +131,1,2,1,1,3,1,1,1,2,1,0,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +132,1,2,1,1,4,1,1,1,2,1,0,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +133,1,2,1,2,1,2,2,0,2,1,1,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +134,1,2,1,2,2,2,2,0,2,0,2,0,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +135,1,2,1,2,3,2,1,1,2,0,1,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +136,1,2,1,2,4,2,1,1,2,0,1,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 1 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +137,1,2,1,3,1,3,1,1,2,1,0,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +138,1,2,1,3,2,3,1,1,2,0,1,1,0,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +139,1,2,1,3,4,3,0,2,2,0,0,1,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as ride sharing and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +140,1,2,1,4,1,4,1,1,2,1,0,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as ride sharing +141,1,2,1,4,2,4,1,1,2,0,1,0,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 1 as pure escort +142,1,2,1,4,3,4,0,2,2,0,0,1,1,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as ride sharing +143,1,2,1,4,4,4,0,2,2,0,0,0,2,child 1 and 3 are escorted in bundle 1 by chauffeur 2 as pure escort and child 2 is escorted in bundle 2 by chauffeur 2 as pure escort +144,1,2,2,1,2,2,2,0,2,1,1,0,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +145,1,2,2,1,3,3,1,1,2,1,0,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +146,1,2,2,1,4,4,1,1,2,1,0,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +147,1,2,2,2,1,1,2,0,2,1,1,0,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +148,1,2,2,2,2,2,2,0,2,0,2,0,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +149,1,2,2,2,3,3,1,1,2,0,1,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +150,1,2,2,2,4,4,1,1,2,0,1,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 1 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +151,1,2,2,3,1,1,1,1,2,1,0,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +152,1,2,2,3,2,2,1,1,2,0,1,1,0,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +153,1,2,2,3,4,4,0,2,2,0,0,1,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as ride sharing and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort +154,1,2,2,4,1,1,1,1,2,1,0,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as ride sharing +155,1,2,2,4,2,2,1,1,2,0,1,0,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 1 as pure escort +156,1,2,2,4,3,3,0,2,2,0,0,1,1,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as ride sharing +157,1,2,2,4,4,4,0,2,2,0,0,0,2,child 2 and 3 are escorted in bundle 2 by chauffeur 2 as pure escort and child 1 is escorted in bundle 1 by chauffeur 2 as pure escort diff --git a/configs/resident/school_escorting_coefficients_inbound.csv b/configs/resident/school_escorting_coefficients_inbound.csv new file mode 100644 index 0000000..d6bc194 --- /dev/null +++ b/configs/resident/school_escorting_coefficients_inbound.csv @@ -0,0 +1,41 @@ +coefficient_name,value,constrain +coef_unavail,-999,T +coef_child_age_16p_noes,0.17488,F +coef_child_age_6_to_15_noes,-0.41751,F +coef_child_age_u5_noes,-1.36718,F +coef_ln_dist_from_school_noes,-0.01787,F +coef_ln_dist_from_school_u6_noes,-0.23304,F +coef_ln_dist_from_school_6to9_noes,-0.07286,F +coef_child_age_16p_rs,1.97184 +coef_child_age_10to15_rs,1.73544 +coef_child_age_6to9_rs,1.73544 +coef_child_age_u6_rs,1.34996 +coef_hh_inc_u25k_noes,0.0,F +coef_hh_inc_25to50k_noes,0.0,F +coef_zero_auto_hh_noes,0.13165,F +coef_cars_lt_workers_rs,-3.35586,F +coef_cars_lt_workers_pe,-1.59062,F +coef_chauf_female_rs,-0.44827,F +coef_chauf_male_rs,-0.90832,F +coef_chauf_female_pe,-0.68399,F +coef_chauf_male_pe,-1.01783,F +coef_chauf_pt_worker_rs,0.51244,F +coef_chauf_pt_worker_pe,0.23496,F +coef_chauf_non_worker_pe,0.69245,F +coef_chauf_univ_stud_re,0.47395,F +coef_chauf_age_u35_pe,0.00000,F +coef_chauf_time_to_work_or_univ_rs,-0.01974,F +coef_chauf_walk_dist_to_work_or_univ_rs,-0.73155,F +coef_chauf_walk_dist_to_work_or_univ_pe,0.00000,F +coef_abs_dev_distance,-0.08011,F +coef_rel_dev_distance,0.0,F +coef_same_taz_escort,1.44053,F +coef_same_taz_no_escort,1.96760,F +coef_no_escort_outbound,2.04553,F +coef_outbound_rs,0.0,F +coef_same_chauf,1.14533,F +coef_calib_child_age_u6_rs,8.75011,F +coef_calib_child_age_16p_rs,-0.20,F +coef_calib_child_age_6to15_noes,-3.46362,F +coef_calib_child_age_u6_noes,-0.36565,F +coef_calib_child_age_6to15_rs,-1.45344,F diff --git a/configs/resident/school_escorting_coefficients_outbound.csv b/configs/resident/school_escorting_coefficients_outbound.csv new file mode 100644 index 0000000..14f1e7d --- /dev/null +++ b/configs/resident/school_escorting_coefficients_outbound.csv @@ -0,0 +1,40 @@ +coefficient_name,value,constrain +coef_unavail,-999,T +coef_child_age_16p_noes,1.07391,F +coef_child_age_10_to_15_noes,0.46127,F +coef_child_age_u9_noes,0.13590,F +coef_ln_dist_to_school_noes,-0.33583,F +coef_ln_dist_to_school_u6_noes,-1.00920,F +coef_ln_dist_to_school_6to9_noes,-0.11156,F +coef_child_age_16p_rs,0.34244,F +coef_child_age_10to15_rs,.27494,F +coef_child_age_6to9_rs,0.20757,F +coef_child_age_u6_rs,0.33051,F +coef_child_dist_pe,-0.04593,F +coef_hh_inc_u25k_noes,0.18901,F +coef_hh_inc_25to50k_noes,0.12172,F +coef_zero_auto_hh_noes,9.0,F +coef_cars_lt_workers_rs,-0.88274,F +coef_cars_lt_workers_pe,-0.55291,F +coef_chauf_female_rs,-0.27828,F +coef_chauf_male_rs,-0.67320,F +coef_chauf_female_pe,-0.84859,F +coef_chauf_male_pe,-0.80965,F +coef_chauf_pt_worker_rs,-0.11422,F +coef_chauf_pt_worker_pe,0.51792,F +coef_chauf_non_worker_pe,0.51577,F +coef_chauf_univ_stud_re,0.0,F +coef_chauf_age_u35_pe,-0.33715,F +coef_chauf_time_to_work_or_univ_rs,0.0,F +coef_chauf_walk_dist_to_work_or_univ_rs,0.77326,F +coef_chauf_walk_dist_to_work_or_univ_pe,0.0,F +coef_abs_dev_distance,-0.07136,F +coef_rel_dev_distance,-0.09951,F +coef_same_taz_escort,2.77591,F +coef_same_taz_no_escort,2.62969,F +coef_same_taz_no_escort_23,2.21201,F +coef_calib_child_age_u6_rs,8.55400,F +coef_calib_child_age_16p_rs,-0.2000,F +coef_calib_child_age_6to15_noes,-4.53067,F +coef_calib_child_age_6to15_rs,-2.41923,F +coef_calib_child_age_u6_noes,-0.67902,F diff --git a/configs/resident/school_escorting_coefficients_outbound_cond.csv b/configs/resident/school_escorting_coefficients_outbound_cond.csv new file mode 100644 index 0000000..079d1e8 --- /dev/null +++ b/configs/resident/school_escorting_coefficients_outbound_cond.csv @@ -0,0 +1,41 @@ +coefficient_name,value,constrain +coef_unavail,-999,T +coef_child_age_16p_noes,-0.33924,F +coef_child_age_10_to_15_noes,-0.78691,F +coef_child_age_u9_noes,-0.97142,F +coef_ln_dist_to_school_noes,-0.02112,F +coef_ln_dist_to_school_u6_noes,-0.81356,F +coef_ln_dist_to_school_6to9_noes,-0.12910,F +coef_child_age_16p_rs,0.93518,F +coef_child_age_10to15_rs,0.58009,F +coef_child_age_6to9_rs,0.36698,F +coef_child_age_u6_rs,0.29043,F +coef_child_dist_pe,-0.03624,F +coef_hh_inc_u25k_noes,0.33839,F +coef_hh_inc_25to50k_noes,0.05888,F +coef_zero_auto_hh_noes,9.00000,F +coef_cars_lt_workers_rs,-0.38588,F +coef_cars_lt_workers_pe,-0.01213,F +coef_chauf_female_rs,-0.49717,F +coef_chauf_male_rs,-0.94654,F +coef_chauf_female_pe,-0.98546,F +coef_chauf_male_pe,-1.05266,F +coef_chauf_pt_worker_rs,-0.74807,F +coef_chauf_pt_worker_pe,0.31729,F +coef_chauf_non_worker_pe,0.19211,F +coef_chauf_univ_stud_re,0.0,F +coef_chauf_age_u35_pe,-0.41194,F +coef_chauf_time_to_work_or_univ_rs,0.0,F +coef_chauf_walk_dist_to_work_or_univ_rs,0.38819,F +coef_chauf_walk_dist_to_work_or_univ_pe,0.0,F +coef_abs_dev_distance,-0.04497,F +coef_rel_dev_distance,-0.09067,F +coef_same_taz_escort,2.56855,F +coef_same_taz_no_escort,2.21201,F +coef_no_escort_inbound,1.72902,F +coef_same_chauf,0.99276,F +coef_calib_child_age_u6_rs,8.55400,F +coef_calib_child_age_16p_rs,-0.2000,F +coef_calib_child_age_6to15_noes,-4.53067,F +coef_calib_child_age_6to15_rs,-2.41923,F +coef_calib_child_age_u6_noes,-0.67902,F diff --git a/configs/resident/school_escorting_inbound.csv b/configs/resident/school_escorting_inbound.csv new file mode 100644 index 0000000..2912752 --- /dev/null +++ b/configs/resident/school_escorting_inbound.csv @@ -0,0 +1,175 @@ +Label,Description,Expression,Coefficient +# ,Availability Conditions,, +util_one_child_to_school,Availability based on number of eligible children,((bundle2 + bundle3) > 0) & (num_children_going_to_school==1),coef_unavail +util_two_children_to_school,Availability based on number of eligible children,(bundle3 > 0) & (num_children_going_to_school == 2),coef_unavail +util_one_potential_chauffeur,Availability based on number of eligible chauffeurs,((bundle1 + bundle2 + bundle3) > 0) & (num_potential_chauffeurs == 0),coef_unavail +util_two_potential_chauffeurs,Availability based on number of eligible chauffeurs,((chauf1 > 2) | (chauf2 > 2) | (chauf3 > 2)) & (num_potential_chauffeurs == 1),coef_unavail +util_avail_rs_cdap_child1_chauf1,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child1_chauf2,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf1,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf2,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf1,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf2,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_sync_child1_chauf1,Availability for RideSharing by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 1) & (np.abs(df.pref_depart_time_chauf1 + (df.time_mand1_to_school1 / mins_per_time_bin) - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child1_chauf2,Availability for RideSharing by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 3) & (np.abs(df.pref_depart_time_chauf2 + (df.time_mand2_to_school1 / mins_per_time_bin) - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf1,Availability for RideSharing by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 1) & (np.abs(df.pref_depart_time_chauf1 + (df.time_mand1_to_school2 / mins_per_time_bin) - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf2,Availability for RideSharing by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 3) & (np.abs(df.pref_depart_time_chauf2 + (df.time_mand2_to_school2 / mins_per_time_bin) - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf1,Availability for RideSharing by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 1) & (np.abs(df.pref_depart_time_chauf1 + (df.time_mand1_to_school3 / mins_per_time_bin) - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf2,Availability for RideSharing by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 3) & (np.abs(df.pref_depart_time_chauf2 + (df.time_mand2_to_school3 / mins_per_time_bin) - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_cdap_child1_chauf1,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child1_chauf2,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf1,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf2,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf1,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf2,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_sync_child1_chauf1,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'H') & (((df.pref_depart_time_chauf1 + (df.time_mand_to_home1 / mins_per_time_bin)) - (df.pref_depart_time_school1 - (df.time_home_to_school1 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child1_chauf2,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'H') & (((df.pref_depart_time_chauf2 + (df.time_mand_to_home2 / mins_per_time_bin)) - (df.pref_depart_time_school1 - (df.time_home_to_school1 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf1,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'H') & (((df.pref_depart_time_chauf1 + (df.time_mand_to_home1 / mins_per_time_bin)) - (df.pref_depart_time_school2 - (df.time_home_to_school2 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf2,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'H') & (((df.pref_depart_time_chauf2 + (df.time_mand_to_home2 / mins_per_time_bin)) - (df.pref_depart_time_school2 - (df.time_home_to_school2 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf1,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'H') & (((df.pref_depart_time_chauf1 + (df.time_mand_to_home1 / mins_per_time_bin)) - (df.pref_depart_time_school3 - (df.time_home_to_school3 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf2,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'H') & (((df.pref_depart_time_chauf2 + (df.time_mand_to_home2 / mins_per_time_bin)) - (df.pref_depart_time_school3 - (df.time_home_to_school3 / mins_per_time_bin))) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and2,Availability for bundling child 1 and child 2,@(df.bundle1 == df.bundle2) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and3,Availability for bundling child 1 and child 3,@(df.bundle1 == df.bundle3) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_2and3,Availability for bundling child 2 and child 3,@(df.bundle2 == df.bundle3) & (df.bundle2 > 0) & (np.abs(df.pref_depart_time_school2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_mult_bundles_outbound1,Availability Chauffeur 1 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs1 + npe1) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_mult_bundles_outbound2,Availability Chauffeur 2 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs2 + npe2) > 1) & ~avail_multiple_bundles,coef_unavail +util_time_span11,Chauffeur 1 and Child 1 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf1 == 1) | (chauf1 == 2)) & (return_bin_outbound_school_escorting1 > pref_depart_time_school1),coef_unavail +util_time_span12,Chauffeur 1 and Child 2 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf2 == 1) | (chauf2 == 2)) & (return_bin_outbound_school_escorting1 > pref_depart_time_school2),coef_unavail +util_time_span13,Chauffeur 1 and Child 3 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf3 == 1) | (chauf3 == 2)) & (return_bin_outbound_school_escorting1 > pref_depart_time_school3),coef_unavail +util_time_span21,Chauffeur 2 and Child 1 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf1 == 3) | (chauf1 == 4)) & (return_bin_outbound_school_escorting2 > pref_depart_time_school1),coef_unavail +util_time_span22,Chauffeur 2 and Child 2 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf2 == 3) | (chauf2 == 4)) & (return_bin_outbound_school_escorting2 > pref_depart_time_school2),coef_unavail +util_time_span23,Chauffeur 2 and Child 3 - time span for this alternative must not overlap time span reserved from outbound conditional,(bundle1 > 0) & ((chauf3 == 3) | (chauf3 == 4)) & (return_bin_outbound_school_escorting2 > pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child1_chauf1,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child1_chauf2,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child2_chauf1,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child2_chauf2,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child3_chauf1,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child3_chauf2,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 + max_bin_difference_between_departure_times) >= df.pref_depart_time_school3),coef_unavail +# ,No escorting,, +util_child1_age_16p_noes,Child 1 age 16 years or older - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 15),coef_child_age_16p_noes +util_child2_age_16p_noes,Child 2 age 16 years or older - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 15),coef_child_age_16p_noes +util_child3_age_16p_noes,Child 3 age 16 years or older - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 15),coef_child_age_16p_noes +util_child1_age_6_to_15_noes,Child 1 age 6 to 15 years - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_child_age_6_to_15_noes +util_child2_age_6_to_15_noes,Child 2 age 6 to 15 years - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_child_age_6_to_15_noes +util_child3_age_6_to_15_noes,Child 3 age 6 to 15 years - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_child_age_6_to_15_noes +util_child1_age_u5_noes,Child 1 age 5 years or younger - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_child_age_u5_noes +util_child2_age_u5_noes,Child 2 age 5 years or younger - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_child_age_u5_noes +util_child3_age_u5_noes,Child 3 age 5 years or younger - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_child_age_u5_noes +util_ln_dist_from_school_child1_noes,Logged distance to school for Child 1 - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * np.log(1 + df.dist_school_to_home1),coef_ln_dist_from_school_noes +util_ln_dist_from_school_child2_noes,Logged distance to school for Child 2 - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * np.log(1 + df.dist_school_to_home2),coef_ln_dist_from_school_noes +util_ln_dist_from_school_child3_noes,Logged distance to school for Child 3 - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * np.log(1 + df.dist_school_to_home3),coef_ln_dist_from_school_noes +util_ln_dist_from_school_child1_u6_noes,Logged distance to school for Child 1 under 6 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 < 6) * np.log(1 + df.dist_school_to_home1),coef_ln_dist_from_school_u6_noes +util_ln_dist_from_school_child2_u6_noes,Logged distance to school for Child 2 under 6 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 < 6) * np.log(1 + df.dist_school_to_home2),coef_ln_dist_from_school_u6_noes +util_ln_dist_from_school_child3_u6_noes,Logged distance to school for Child 3 under 6 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 < 6) * np.log(1 + df.dist_school_to_home3),coef_ln_dist_from_school_u6_noes +util_ln_dist_from_school_child1_6to9_noes,Logged distance to school for Child 1 6 to 9 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 > 5) * (df.age_child1 < 10) * np.log(1 + df.dist_school_to_home1),coef_ln_dist_from_school_6to9_noes +util_ln_dist_from_school_child2_6to9_noes,Logged distance to school for Child 2 6 to 9 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 > 5) * (df.age_child2 < 10) * np.log(1 + df.dist_school_to_home2),coef_ln_dist_from_school_6to9_noes +util_ln_dist_from_school_child3_6to9_noes,Logged distance to school for Child 3 6 to 9 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 > 5) * (df.age_child3 < 10) * np.log(1 + df.dist_school_to_home3),coef_ln_dist_from_school_6to9_noes +# ,Ride Sharing,, +util_child1_age_16p_rs,Child 1 age 16 years or older - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_child_age_16p_rs +util_child2_age_16p_rs,Child 2 age 16 years or older - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_child_age_16p_rs +util_child3_age_16p_rs,Child 3 age 16 years or older - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_child_age_16p_rs +util_child1_age_10to15_rs,Child 1 age 10 to 15 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10to15_rs +util_child2_age_10to15_rs,Child 2 age 10 to 15 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10to15_rs +util_child3_age_10to15_rs,Child 3 age 10 to 15 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10to15_rs +util_child1_age_6to9_rs,Child 1 age 6 to 9 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 10),coef_child_age_6to9_rs +util_child2_age_6to9_rs,Child 2 age 6 to 9 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 10),coef_child_age_6to9_rs +util_child3_age_6to9_rs,Child 3 age 6 to 9 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 10),coef_child_age_6to9_rs +util_child1_age_u6_rs,Child 1 age under 6 years old - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_child_age_u6_rs +util_child2_age_u6_rs,Child 2 age under 6 years old - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_child_age_u6_rs +util_child3_age_u6_rs,Child 3 age under 6 years old - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_child_age_u6_rs +# ,Pure Escort Distance,, +util_child1_dist_pe_over30miles,Child 1 Pure escorting not allowed if over 30 miles,((chauf1 == 2) | (chauf1 == 4)) & (dist_school_to_home1 > 30),coef_unavail +util_child2_dist_pe_over30miles,Child 2 Pure escorting not allowed if over 30 miles,((chauf2 == 2) | (chauf2 == 4)) & (dist_school_to_home2 > 30),coef_unavail +util_child3_dist_pe_over30miles,Child 3 Pure escorting not allowed if over 30 miles,((chauf3 == 2) | (chauf3 == 4)) & (dist_school_to_home3 > 30),coef_unavail +# ,Household Interactions,, +util_hh_inc_u25k_noes,Household income less than 25k - No Escorting,(income <= 25000) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_u25k_noes +util_hh_inc_25to50k_noes,Household income between 25 and 50k - No Escorting,((income > 25000) & (income <= 50000)) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_25to50k_noes +util_zero_auto_hh_noes,Zero cars in the household - No Escorting,(auto_ownership == 0) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_zero_auto_hh_noes +util_cars_lt_workers_rs,Cars fewer than household workers - Ride Share,(auto_ownership < num_workers) & ((chauf1 % 2 == 1) | (chauf2 % 2 == 1) | (chauf3 % 2 == 1)),coef_cars_lt_workers_rs +util_cars_lt_workers_pe,Cars fewer than household workers - Pure escort,(auto_ownership < num_workers) & ((chauf1 % 2 == 0) | (chauf2 % 2 == 0) | (chauf3 % 2 == 0)),coef_cars_lt_workers_pe +# ,Chauffer Interactions,, +util_chauf1_female_rs,Chauffeur 1 Female - Ride share,(gender_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_female_rs +util_chauf1_male_rs,Chauffeur 1 Male - Ride share,(gender_chauf1 == 1) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_male_rs +util_chauf1_female_pe,Chauffeur 1 Female - Pure Escort,(gender_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_female_pe +util_chauf1_male_pe,Chauffeur 1 Male - Pure Escort,(gender_chauf1 == 1) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_male_pe +util_chauf2_female_rs,Chauffeur 2 Female - Ride share,(gender_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_female_rs +util_chauf2_male_rs,Chauffeur 2 Male - Ride share,(gender_chauf2 == 1) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_male_rs +util_chauf2_female_pe,Chauffeur 2 Female - Pure Escort,(gender_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_female_pe +util_chauf2_male_pe,Chauffeur 2 Male - Pure Escort,(gender_chauf2 == 1) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_male_pe +util_chauf1_pt_worker_rs,Chauffer 1 part time worker - Ride share,(ptype_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_pt_worker_rs +util_chauf1_pt_worker_pe,Chauffer 1 part time worker - Pure escort,(ptype_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_pt_worker_pe +util_chauf2_pt_worker_rs,Chauffer 2 part time worker - Ride share,(ptype_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_pt_worker_rs +util_chauf2_pt_worker_pe,Chauffer 2 part time worker - Pure escort,(ptype_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_pt_worker_pe +util_chauf1_non_worker_pe,Chauffer 1 non worker - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_non_worker_pe +util_chauf2_non_worker_pe,Chauffer 2 non worker - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_non_worker_pe +util_chauf1_univ_stud_re,Chauffer 1 university student - Ride Share,(ptype_chauf1 == 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_univ_stud_re +util_chauf2_univ_stud_re,Chauffer 2 university student - Ride Share,(ptype_chauf2 == 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_univ_stud_re +util_chauf1_age_u35_pe,Chauffer 1 Age 35 years or younger - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_age_u35_pe +util_chauf2_age_u35_pe,Chauffer 2 Age 35 years or younger - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_age_u35_pe +util_chauf1_time_to_work_or_univ_rs,Chauffer 1 Auto time to work or university - Ride Share,time_mand_to_home1 * ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_time_to_work_or_univ_rs +util_chauf2_time_to_work_or_univ_rs,Chauffer 2 Auto time to work or university - Ride Share,time_mand_to_home2 * ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_time_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_rs,Chauffer 1 Walk dist to work or university - Ride Share,(dist_mand1_to_home < 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf2_walk_dist_to_work_or_univ_rs,Chauffer 2 Walk dist to work or university - Ride Share,(dist_mand2_to_home < 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_pe,Chauffer 1 Walk dist to work or university - Pure Escort,(dist_home_to_mand1 < 3) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_walk_dist_to_work_or_univ_pe +util_chauf2_walk_dist_to_work_or_univ_pe,Chauffer 2 Walk dist to work or university - Pure Escort,(dist_home_to_mand2 < 3) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_walk_dist_to_work_or_univ_pe +# ,Chauffer deviation,, +util_chauf1_abs_dev_rs_child1only,Chauffer 1 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child1_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child1only,Chauffer 2 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child1_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child2only,Chauffer 1 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child2_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child2only,Chauffer 2 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child2_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child3only,Chauffer 1 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_in_child3_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child3only,Chauffer 2 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_in_child3_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child12,Chauffer 1 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child12_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child12,Chauffer 2 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child12_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child13,Chauffer 1 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child13_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child13,Chauffer 2 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child13_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child23,Chauffer 1 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child23_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child23,Chauffer 2 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child23_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child123,Chauffer 1 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child123_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child123,Chauffer 2 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child123_chauf2,coef_abs_dev_distance +util_chauf1_rel_dev_rs_child1only,Chauffer 1 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child1_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child1only,Chauffer 2 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child1_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child2only,Chauffer 1 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child2_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child2only,Chauffer 2 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child2_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child3only,Chauffer 1 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_in_child3_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child3only,Chauffer 2 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_in_child3_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child12,Chauffer 1 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child12_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child12,Chauffer 2 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child12_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child13,Chauffer 1 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child13_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child13,Chauffer 2 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child13_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child23,Chauffer 1 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_in_child23_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child23,Chauffer 2 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_in_child23_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child123,Chauffer 1 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_in_child123_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child123,Chauffer 2 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_in_child123_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +# ,Same TAZ,, +util_same_taz_child12_escort,Same school TAZ for child 1 and 2 - Escorting,(bundle1 == bundle2) & (bundle1 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child13_escort,Same school TAZ for child 1 and 3 - Escorting,(bundle1 == bundle3) & (bundle1 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child32_escort,Same school TAZ for child 3 and 2 - Escorting,(bundle3 == bundle2) & (bundle2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_escort +util_same_taz_child12_no_escort,Same school TAZ for child 1 and 2 - No Escorting,(bundle1 == 0) & (bundle2 == 0) & (child_id1 > 0) & (child_id2 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child13_no_escort,Same school TAZ for child 1 and 3 - No Escorting,(bundle1 == 0) & (bundle3 == 0) & (child_id1 > 0) & (child_id3 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child32_no_escort,Same school TAZ for child 3 and 2 - No Escorting,(bundle3 == 0) & (bundle2 == 0) & (child_id3 > 0) & (child_id2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_no_escort +# ,Outbound Terms,, +util_no_escort_outbound_child1,No escorting in outbound direction - Child 1,(bundle1 == 0) & (child_id1 > 0) & (bundle1_outbound == 0),coef_no_escort_outbound +util_no_escort_outbound_child2,No escorting in outbound direction - Child 2,(bundle2 == 0) & (child_id2 > 0) & (bundle2_outbound == 0),coef_no_escort_outbound +util_no_escort_outbound_child3,No escorting in outbound direction - Child 3,(bundle3 == 0) & (child_id3 > 0) & (bundle3_outbound == 0),coef_no_escort_outbound +util_outbound_rs_child1,Ride sharing in the outbound direction - Child 1,((chauf1 == 1) | (chauf1 == 3)) & ((chauf1_outbound == 1) | (chauf1_outbound == 3)),coef_outbound_rs +util_outbound_rs_child2,Ride sharing in the outbound direction - Child 2,((chauf2 == 1) | (chauf2 == 3)) & ((chauf2_outbound == 1) | (chauf2_outbound == 3)),coef_outbound_rs +util_outbound_rs_child3,Ride sharing in the outbound direction - Child 3,((chauf3 == 1) | (chauf3 == 3)) & ((chauf3_outbound == 1) | (chauf3_outbound == 3)),coef_outbound_rs +util_same_chauf1,Same chauffeur in both directions (not child specific) - chauf 1 inbound & outbound,((chauf1 == 1) | (chauf1 == 2) | (chauf2 == 1) | (chauf2 == 2) | (chauf3 == 1) | (chauf3 == 2)) & ((nbund1_outbound > 0)),coef_same_chauf +util_same_chauf2,Same chauffeur in both directions (not child specific) - chauf 2 inbound & outbound,((chauf1 == 3) | (chauf1 == 4) | (chauf2 == 3) | (chauf2 == 4) | (chauf3 == 3) | (chauf3 == 4)) & ((nbund2_outbound > 0)),coef_same_chauf +# ,Calibration Constants,, +util_calib_child1_age_u6_rs,Child 1 age under 6 - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_calib_child_age_u6_rs +util_calib_child2_age_u6_rs,Child 2 age under 6 - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_calib_child_age_u6_rs +util_calib_child3_age_u6_rs,Child 3 age under 6 - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_calib_child_age_u6_rs +util_calib_child1_age_16p_rs,Child 1 age 16 years or older - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_calib_child_age_16p_rs +util_calib_child2_age_16p_rs,Child 2 age 16 years or older - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_calib_child_age_16p_rs +util_calib_child3_age_16p_rs,Child 3 age 16 years or older - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_calib_child_age_16p_rs +util_calib_child1_age_6to15_noes,Child 1 age 6 to 15 years - Calibration constant - No Escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_noes +util_calib_child2_age_6to15_noes,Child 2 age 6 to 15 years - Calibration constant - No Escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_noes +util_calib_child3_age_6to15_noes,Child 3 age 6 to 15 years - Calibration constant - No Escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_noes +util_calib_child1_age_u6_noes,Child 1 age 5 years or younger - Calibration constant - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_calib_child_age_u6_noes +util_calib_child2_age_u6_noes,Child 2 age 5 years or younger - Calibration constant - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_calib_child_age_u6_noes +util_calib_child3_age_u6_noes,Child 3 age 5 years or younger - Calibration constant - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_calib_child_age_u6_noes +util_calib_child1_age_6to15_rs,Child 1 age 6 to 15 years - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_rs +util_calib_child2_age_6to15_rs,Child 2 age 6 to 15 years - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_rs +util_calib_child3_age_6to15_rs,Child 3 age 6 to 15 years - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_rs diff --git a/configs/resident/school_escorting_outbound.csv b/configs/resident/school_escorting_outbound.csv new file mode 100644 index 0000000..82a1c66 --- /dev/null +++ b/configs/resident/school_escorting_outbound.csv @@ -0,0 +1,163 @@ +Label,Description,Expression,Coefficient +# ,Availability Conditions,, +util_one_child_to_school,Availability based on number of eligible children,((bundle2 + bundle3) > 0) & (num_children_going_to_school==1),coef_unavail +util_two_children_to_school,Availability based on number of eligible children,(bundle3 > 0) & (num_children_going_to_school == 2),coef_unavail +util_one_potential_chauffeur,Availability based on number of eligible chauffeurs,((bundle1 + bundle2 + bundle3) > 0) & (num_potential_chauffeurs == 0),coef_unavail +util_two_potential_chauffeurs,Availability based on number of eligible chauffeurs,((chauf1 > 2) | (chauf2 > 2) | (chauf3 > 2)) & (num_potential_chauffeurs == 1),coef_unavail +util_avail_rs_cdap_child1_chauf1,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child1_chauf2,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf1,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf2,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf1,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf2,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_sync_child1_chauf1,Availability for RideSharing by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 1) & (df.pref_depart_time_chauf1 > 0) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child1_chauf2,Availability for RideSharing by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 3) & (df.pref_depart_time_chauf2 > 0) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf1,Availability for RideSharing by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 1) & (df.pref_depart_time_chauf1 > 0) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf2,Availability for RideSharing by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 3) & (df.pref_depart_time_chauf2 > 0) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf1,Availability for RideSharing by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 1) & (df.pref_depart_time_chauf1 > 0) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf2,Availability for RideSharing by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 3) & (df.pref_depart_time_chauf2 > 0) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_cdap_child1_chauf1,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child1_chauf2,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf1,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf2,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf1,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf2,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_sync_child1_chauf1,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child1_chauf2,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf1,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf2,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf1,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf2,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and2,Availability for bundling child 1 and child 2,@(df.bundle1 == df.bundle2) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and3,Availability for bundling child 1 and child 3,@(df.bundle1 == df.bundle3) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_2and3,Availability for bundling child 2 and child 3,@(df.bundle2 == df.bundle3) & (df.bundle2 > 0) & (np.abs(df.pref_depart_time_school2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_mult_bundles_outbound1,Availability Chauffeur 1 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs1 + npe1) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_mult_bundles_outbound2,Availability Chauffeur 2 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs2 + npe2) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_pe_during_mand_child1_chauf1,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child1_chauf2,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child2_chauf1,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child2_chauf2,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child3_chauf1,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child3_chauf2,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +# ,No escorting,, +util_child1_age_16p_noes,Child 1 age 16 years or older - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 15),coef_child_age_16p_noes +util_child2_age_16p_noes,Child 2 age 16 years or older - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 15),coef_child_age_16p_noes +util_child3_age_16p_noes,Child 3 age 16 years or older - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 15),coef_child_age_16p_noes +util_child1_age_10_to_15_noes,Child 1 age 10 to 15 years - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10_to_15_noes +util_child2_age_10_to_15_noes,Child 2 age 10 to 15 years - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10_to_15_noes +util_child3_age_10_to_15_noes,Child 3 age 10 to 15 years - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10_to_15_noes +util_child1_age_u9_noes,Child 1 age 9 years or younger - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 10),coef_child_age_u9_noes +util_child2_age_u9_noes,Child 2 age 9 years or younger - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 10),coef_child_age_u9_noes +util_child3_age_u9_noes,Child 3 age 9 years or younger - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 10),coef_child_age_u9_noes +util_ln_dist_to_school_child1_noes,Logged distance to school for Child 1 - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child2_noes,Logged distance to school for Child 2 - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child3_noes,Logged distance to school for Child 3 - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child1_u6_noes,Logged distance to school for Child 1 under 6 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 < 6) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child2_u6_noes,Logged distance to school for Child 2 under 6 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 < 6) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child3_u6_noes,Logged distance to school for Child 3 under 6 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 < 6) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child1_6to9_noes,Logged distance to school for Child 1 6 to 9 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 > 5) * (df.age_child1 < 10) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child2_6to9_noes,Logged distance to school for Child 2 6 to 9 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 > 5) * (df.age_child2 < 10) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child3_6to9_noes,Logged distance to school for Child 3 6 to 9 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 > 5) * (df.age_child3 < 10) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_6to9_noes +# ,Ride Sharing,, +util_child1_age_16p_rs,Child 1 age 16 years or older - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_child_age_16p_rs +util_child2_age_16p_rs,Child 2 age 16 years or older - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_child_age_16p_rs +util_child3_age_16p_rs,Child 3 age 16 years or older - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_child_age_16p_rs +util_child1_age_10to15_rs,Child 1 age 10 to 15 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10to15_rs +util_child2_age_10to15_rs,Child 2 age 10 to 15 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10to15_rs +util_child3_age_10to15_rs,Child 3 age 10 to 15 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10to15_rs +util_child1_age_6to9_rs,Child 1 age 6 to 9 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 10),coef_child_age_6to9_rs +util_child2_age_6to9_rs,Child 2 age 6 to 9 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 10),coef_child_age_6to9_rs +util_child3_age_6to9_rs,Child 3 age 6 to 9 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 10),coef_child_age_6to9_rs +util_child1_age_u6_rs,Child 1 age under 6 years old - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_child_age_u6_rs +util_child2_age_u6_rs,Child 2 age under 6 years old - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_child_age_u6_rs +util_child3_age_u6_rs,Child 3 age under 6 years old - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_child_age_u6_rs +# ,Pure Escort Distance,, +util_child1_dist_pe,Child 1 distance to school - Pure escorting,((chauf1 == 2) | (chauf1 == 4)) * dist_home_to_school1,coef_child_dist_pe +util_child2_dist_pe,Child 2 distance to school - Pure escorting,((chauf2 == 2) | (chauf2 == 4)) * dist_home_to_school2,coef_child_dist_pe +util_child3_dist_pe,Child 3 distance to school - Pure escorting,((chauf3 == 2) | (chauf3 == 4)) * dist_home_to_school3,coef_child_dist_pe +util_child1_dist_pe_over30miles,Child 1 Pure escorting not allowed if over 30 miles,((chauf1 == 2) | (chauf1 == 4)) & (dist_home_to_school1 > 30),coef_unavail +util_child2_dist_pe_over30miles,Child 2 Pure escorting not allowed if over 30 miles,((chauf2 == 2) | (chauf2 == 4)) & (dist_home_to_school2 > 30),coef_unavail +util_child3_dist_pe_over30miles,Child 3 Pure escorting not allowed if over 30 miles,((chauf3 == 2) | (chauf3 == 4)) & (dist_home_to_school3 > 30),coef_unavail +# ,Household Interactions,, +util_hh_inc_u25k_noes,Household income less than 25k - No Escorting,(income <= 25000) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_u25k_noes +util_hh_inc_25to50k_noes,Household income between 25 and 50k - No Escorting,((income > 25000) & (income <= 50000)) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_25to50k_noes +util_zero_auto_hh_noes,Zero cars in the household - No Escorting,(auto_ownership == 0) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_zero_auto_hh_noes +util_cars_lt_workers_rs,Cars fewer than household workers - Ride Share,(auto_ownership < num_workers) & ((chauf1 % 2 == 1) | (chauf2 % 2 == 1) | (chauf3 % 2 == 1)),coef_cars_lt_workers_rs +util_cars_lt_workers_pe,Cars fewer than household workers - Pure escort,(auto_ownership < num_workers) & ((chauf1 % 2 == 0) | (chauf2 % 2 == 0) | (chauf3 % 2 == 0)),coef_cars_lt_workers_pe +# ,Chauffer Interactions,, +util_chauf1_female_rs,Chauffeur 1 Female - Ride share,(gender_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_female_rs +util_chauf1_male_rs,Chauffeur 1 Male - Ride share,(gender_chauf1 == 1) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_male_rs +util_chauf1_female_pe,Chauffeur 1 Female - Pure Escort,(gender_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_female_pe +util_chauf1_male_pe,Chauffeur 1 Male - Pure Escort,(gender_chauf1 == 1) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_male_pe +util_chauf2_female_rs,Chauffeur 2 Female - Ride share,(gender_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_female_rs +util_chauf2_male_rs,Chauffeur 2 Male - Ride share,(gender_chauf2 == 1) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_male_rs +util_chauf2_female_pe,Chauffeur 2 Female - Pure Escort,(gender_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_female_pe +util_chauf2_male_pe,Chauffeur 2 Male - Pure Escort,(gender_chauf2 == 1) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_male_pe +util_chauf1_pt_worker_rs,Chauffer 1 part time worker - Ride share,(ptype_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_pt_worker_rs +util_chauf1_pt_worker_pe,Chauffer 1 part time worker - Pure escort,(ptype_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_pt_worker_pe +util_chauf2_pt_worker_rs,Chauffer 2 part time worker - Ride share,(ptype_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_pt_worker_rs +util_chauf2_pt_worker_pe,Chauffer 2 part time worker - Pure escort,(ptype_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_pt_worker_pe +util_chauf1_non_worker_pe,Chauffer 1 non worker - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_non_worker_pe +util_chauf2_non_worker_pe,Chauffer 2 non worker - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_non_worker_pe +util_chauf1_univ_stud_re,Chauffer 1 university student - Ride Share,(ptype_chauf1 == 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_univ_stud_re +util_chauf2_univ_stud_re,Chauffer 2 university student - Ride Share,(ptype_chauf2 == 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_univ_stud_re +util_chauf1_age_u35_pe,Chauffer 1 Age 35 years or younger - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_age_u35_pe +util_chauf2_age_u35_pe,Chauffer 2 Age 35 years or younger - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_age_u35_pe +util_chauf1_time_to_work_or_univ_rs,Chauffer 1 Auto time to work or university - Ride Share,time_home_to_mand1 * ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_time_to_work_or_univ_rs +util_chauf2_time_to_work_or_univ_rs,Chauffer 2 Auto time to work or university - Ride Share,time_home_to_mand2 * ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_time_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_rs,Chauffer 1 Walk dist to work or university - Ride Share,(dist_home_to_mand1 < 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf2_walk_dist_to_work_or_univ_rs,Chauffer 2 Walk dist to work or university - Ride Share,(dist_home_to_mand2 < 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_pe,Chauffer 1 Walk dist to work or university - Pure Escort,(dist_home_to_mand1 < 3) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_walk_dist_to_work_or_univ_pe +util_chauf2_walk_dist_to_work_or_univ_pe,Chauffer 2 Walk dist to work or university - Pure Escort,(dist_home_to_mand2 < 3) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_walk_dist_to_work_or_univ_pe +# ,Chauffer deviation,, +util_chauf1_abs_dev_rs_child1only,Chauffer 1 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child1only,Chauffer 2 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child2only,Chauffer 1 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child2only,Chauffer 2 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child3only,Chauffer 1 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child3only,Chauffer 2 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child12,Chauffer 1 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child12,Chauffer 2 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child13,Chauffer 1 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child13,Chauffer 2 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child23,Chauffer 1 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child23,Chauffer 2 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child123,Chauffer 1 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child123,Chauffer 2 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2,coef_abs_dev_distance +util_chauf1_rel_dev_rs_child1only,Chauffer 1 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child1only,Chauffer 2 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child2only,Chauffer 1 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child2only,Chauffer 2 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child3only,Chauffer 1 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child3only,Chauffer 2 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child12,Chauffer 1 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child12,Chauffer 2 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child13,Chauffer 1 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child13,Chauffer 2 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child23,Chauffer 1 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child23,Chauffer 2 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child123,Chauffer 1 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child123,Chauffer 2 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +# ,Same TAZ,, +util_same_taz_child12_escort,Same school TAZ for child 1 and 2 - Escorting,(bundle1 == bundle2) & (bundle1 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child13_escort,Same school TAZ for child 1 and 3 - Escorting,(bundle1 == bundle3) & (bundle1 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child32_escort,Same school TAZ for child 3 and 2 - Escorting,(bundle3 == bundle2) & (bundle2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_escort +util_same_taz_child12_no_escort,Same school TAZ for child 1 and 2 - No Escorting,(bundle1 == 0) & (bundle2 == 0) & (child_id1 > 0) & (child_id2 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child13_no_escort,Same school TAZ for child 1 and 3 - No Escorting,(bundle1 == 0) & (bundle3 == 0) & (child_id1 > 0) & (child_id3 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child32_no_escort,Same school TAZ for child 3 and 2 - No Escorting,(bundle3 == 0) & (bundle2 == 0) & (child_id3 > 0) & (child_id2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_no_escort_23 +# ,Calibration constants,, +util_calib_child1_age_u6_rs,Child 1 age under 6 - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_calib_child_age_u6_rs +util_calib_child2_age_u6_rs,Child 2 age under 6 - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_calib_child_age_u6_rs +util_calib_child3_age_u6_rs,Child 3 age under 6 - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_calib_child_age_u6_rs +util_calib_child1_age_16p_rs,Child 1 age 16 years or older - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_calib_child_age_16p_rs +util_calib_child2_age_16p_rs,Child 2 age 16 years or older - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_calib_child_age_16p_rs +util_calib_child3_age_16p_rs,Child 3 age 16 years or older - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_calib_child_age_16p_rs +util_calib_child1_age_6to15_noes,Child 1 age 6 to 15 years - Calibration constant - No Escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_noes +util_calib_child2_age_6to15_noes,Child 2 age 6 to 15 years - Calibration constant - No Escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_noes +util_calib_child3_age_6to15_noes,Child 3 age 6 to 15 years - Calibration constant - No Escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_noes +util_calib_child1_age_6to15_rs,Child 1 age 6 to 15 years - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_rs +util_calib_child2_age_6to15_rs,Child 2 age 6 to 15 years - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_rs +util_calib_child3_age_6to15_rs,Child 3 age 6 to 15 years - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_rs +util_calib_child1_age_u6_noes,Child 1 age 6 years or younger - Calibration constant - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_calib_child_age_u6_noes +util_calib_child2_age_u6_noes,Child 2 age 6 years or younger - Calibration constant - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_calib_child_age_u6_noes +util_calib_child3_age_u6_noes,Child 3 age 6 years or younger - Calibration constant - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_calib_child_age_u6_noes diff --git a/configs/resident/school_escorting_outbound_cond.csv b/configs/resident/school_escorting_outbound_cond.csv new file mode 100644 index 0000000..5d890c7 --- /dev/null +++ b/configs/resident/school_escorting_outbound_cond.csv @@ -0,0 +1,169 @@ +Label,Description,Expression,Coefficient +# ,Availability Conditions,, +util_one_child_to_school,Availability based on number of eligible children,((bundle2 + bundle3) > 0) & (num_children_going_to_school==1),coef_unavail +util_two_children_to_school,Availability based on number of eligible children,(bundle3 > 0) & (num_children_going_to_school == 2),coef_unavail +util_one_potential_chauffeur,Availability based on number of eligible chauffeurs,((bundle1 + bundle2 + bundle3) > 0) & (num_potential_chauffeurs == 0),coef_unavail +util_two_potential_chauffeurs,Availability based on number of eligible chauffeurs,((chauf1 > 2) | (chauf2 > 2) | (chauf3 > 2)) & (num_potential_chauffeurs == 1),coef_unavail +util_avail_rs_cdap_child1_chauf1,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child1_chauf2,Availability for RideSharing by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf1,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child2_chauf2,Availability for RideSharing by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf1,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 1) & (cdap_chauf1 != 'M'),coef_unavail +util_avail_rs_cdap_child3_chauf2,Availability for RideSharing by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 3) & (cdap_chauf2 != 'M'),coef_unavail +util_avail_rs_sync_child1_chauf1,Availability for RideSharing by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 1) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child1_chauf2,Availability for RideSharing by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 3) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf1,Availability for RideSharing by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 1) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child2_chauf2,Availability for RideSharing by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 3) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf1,Availability for RideSharing by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 1) & (np.abs(df.pref_depart_time_chauf1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_rs_sync_child3_chauf2,Availability for RideSharing by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 3) & (np.abs(df.pref_depart_time_chauf2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_cdap_child1_chauf1,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 1,(bundle1 > 0) & (chauf1 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child1_chauf2,Availability for Pure-Escorting by daily pattern - Child 1 and Chauffeur 2,(bundle1 > 0) & (chauf1 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf1,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 1,(bundle2 > 0) & (chauf2 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child2_chauf2,Availability for Pure-Escorting by daily pattern - Child 2 and Chauffeur 2,(bundle2 > 0) & (chauf2 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf1,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 1,(bundle3 > 0) & (chauf3 == 2) & (ptype_chauf1 < 6) & (cdap_chauf1 == 'H'),coef_unavail +util_avail_pe_cdap_child3_chauf2,Availability for Pure-Escorting by daily pattern - Child 3 and Chauffeur 2,(bundle3 > 0) & (chauf3 == 4) & (ptype_chauf2 < 6) & (cdap_chauf2 == 'H'),coef_unavail +util_avail_pe_sync_child1_chauf1,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child1_chauf2,Availability for Pure-Escorting by synchronization - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school1 + ((df.time_home_to_school1 + df.time_school_to_home1) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf1,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child2_chauf2,Availability for Pure-Escorting by synchronization - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school2 + ((df.time_home_to_school2 + df.time_school_to_home2) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf1,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.ptype_chauf1 < 4) & (df.cdap_chauf1 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf1) > max_bin_difference_between_departure_times),coef_unavail +util_avail_pe_sync_child3_chauf2,Availability for Pure-Escorting by synchronization - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.ptype_chauf2 < 4) & (df.cdap_chauf2 == 'H') & (np.abs(df.pref_depart_time_school3 + ((df.time_home_to_school3 + df.time_school_to_home3) / mins_per_time_bin) - df.pref_depart_time_chauf2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and2,Availability for bundling child 1 and child 2,@(df.bundle1 == df.bundle2) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school2) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_1and3,Availability for bundling child 1 and child 3,@(df.bundle1 == df.bundle3) & (df.bundle1 > 0) & (np.abs(df.pref_depart_time_school1 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_bundle_child_2and3,Availability for bundling child 2 and child 3,@(df.bundle2 == df.bundle3) & (df.bundle2 > 0) & (np.abs(df.pref_depart_time_school2 - df.pref_depart_time_school3) > max_bin_difference_between_departure_times),coef_unavail +util_avail_mult_bundles_outbound1,Availability Chauffeur 1 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs1 + npe1) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_mult_bundles_outbound2,Availability Chauffeur 2 - Expected arrival from previous pure escort must be before departure for subsequent escort,((nrs2 + npe2) > 1) & ~avail_multiple_bundles,coef_unavail +util_avail_pe_during_mand_child1_chauf1,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 1,@(df.bundle1 > 0) & (df.chauf1 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child1_chauf2,Availability pure escort tour must take place before mandatory tour - Child 1 and Chauffeur 2,@(df.bundle1 > 0) & (df.chauf1 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school1),coef_unavail +util_avail_pe_during_mand_child2_chauf1,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 1,@(df.bundle2 > 0) & (df.chauf2 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child2_chauf2,Availability pure escort tour must take place before mandatory tour - Child 2 and Chauffeur 2,@(df.bundle2 > 0) & (df.chauf2 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school2),coef_unavail +util_avail_pe_during_mand_child3_chauf1,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 1,@(df.bundle3 > 0) & (df.chauf3 == 2) & (df.cdap_chauf1 == 'M') & ((df.pref_depart_time_chauf1 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +util_avail_pe_during_mand_child3_chauf2,Availability pure escort tour must take place before mandatory tour - Child 3 and Chauffeur 2,@(df.bundle3 > 0) & (df.chauf3 == 4) & (df.cdap_chauf2 == 'M') & ((df.pref_depart_time_chauf2 - max_bin_difference_between_departure_times) <= df.pref_depart_time_school3),coef_unavail +# ,No escorting,, +util_child1_age_16p_noes,Child 1 age 16 years or older - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 15),coef_child_age_16p_noes +util_child2_age_16p_noes,Child 2 age 16 years or older - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 15),coef_child_age_16p_noes +util_child3_age_16p_noes,Child 3 age 16 years or older - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 15),coef_child_age_16p_noes +util_child1_age_10_to_15_noes,Child 1 age 10 to 15 years - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10_to_15_noes +util_child2_age_10_to_15_noes,Child 2 age 10 to 15 years - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10_to_15_noes +util_child3_age_10_to_15_noes,Child 3 age 10 to 15 years - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10_to_15_noes +util_child1_age_u9_noes,Child 1 age 9 years or younger - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 10),coef_child_age_u9_noes +util_child2_age_u9_noes,Child 2 age 9 years or younger - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 10),coef_child_age_u9_noes +util_child3_age_u9_noes,Child 3 age 9 years or younger - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 10),coef_child_age_u9_noes +util_ln_dist_to_school_child1_noes,Logged distance to school for Child 1 - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child2_noes,Logged distance to school for Child 2 - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child3_noes,Logged distance to school for Child 3 - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_noes +util_ln_dist_to_school_child1_u6_noes,Logged distance to school for Child 1 under 6 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 < 6) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child2_u6_noes,Logged distance to school for Child 2 under 6 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 < 6) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child3_u6_noes,Logged distance to school for Child 3 under 6 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 < 6) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_u6_noes +util_ln_dist_to_school_child1_6to9_noes,Logged distance to school for Child 1 6 to 9 years old - No escort,@(df.bundle1 == 0) * (df.child_id1 > 0) * (df.age_child1 > 5) * (df.age_child1 < 10) * np.log(1 + df.dist_home_to_school1),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child2_6to9_noes,Logged distance to school for Child 2 6 to 9 years old - No escort,@(df.bundle2 == 0) * (df.child_id2 > 0) * (df.age_child2 > 5) * (df.age_child2 < 10) * np.log(1 + df.dist_home_to_school2),coef_ln_dist_to_school_6to9_noes +util_ln_dist_to_school_child3_6to9_noes,Logged distance to school for Child 3 6 to 9 years old - No escort,@(df.bundle3 == 0) * (df.child_id3 > 0) * (df.age_child3 > 5) * (df.age_child3 < 10) * np.log(1 + df.dist_home_to_school3),coef_ln_dist_to_school_6to9_noes +# ,Ride Sharing,, +util_child1_age_16p_rs,Child 1 age 16 years or older - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_child_age_16p_rs +util_child2_age_16p_rs,Child 2 age 16 years or older - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_child_age_16p_rs +util_child3_age_16p_rs,Child 3 age 16 years or older - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_child_age_16p_rs +util_child1_age_10to15_rs,Child 1 age 10 to 15 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 9) & (age_child1 < 16),coef_child_age_10to15_rs +util_child2_age_10to15_rs,Child 2 age 10 to 15 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 9) & (age_child2 < 16),coef_child_age_10to15_rs +util_child3_age_10to15_rs,Child 3 age 10 to 15 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 9) & (age_child3 < 16),coef_child_age_10to15_rs +util_child1_age_6to9_rs,Child 1 age 6 to 9 years - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 10),coef_child_age_6to9_rs +util_child2_age_6to9_rs,Child 2 age 6 to 9 years - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 10),coef_child_age_6to9_rs +util_child3_age_6to9_rs,Child 3 age 6 to 9 years - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 10),coef_child_age_6to9_rs +util_child1_age_u6_rs,Child 1 age under 6 years old - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_child_age_u6_rs +util_child2_age_u6_rs,Child 2 age under 6 years old - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_child_age_u6_rs +util_child3_age_u6_rs,Child 3 age under 6 years old - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_child_age_u6_rs +# ,Pure Escort Distance,, +util_child1_dist_pe,Child 1 distance to school - Pure escorting,((chauf1 == 2) | (chauf1 == 4)) * dist_home_to_school1,coef_child_dist_pe +util_child2_dist_pe,Child 2 distance to school - Pure escorting,((chauf2 == 2) | (chauf2 == 4)) * dist_home_to_school2,coef_child_dist_pe +util_child3_dist_pe,Child 3 distance to school - Pure escorting,((chauf3 == 2) | (chauf3 == 4)) * dist_home_to_school3,coef_child_dist_pe +util_child1_dist_pe_over30miles,Child 1 Pure escorting not allowed if over 30 miles,((chauf1 == 2) | (chauf1 == 4)) & (dist_home_to_school1 > 30),coef_unavail +util_child2_dist_pe_over30miles,Child 2 Pure escorting not allowed if over 30 miles,((chauf2 == 2) | (chauf2 == 4)) & (dist_home_to_school2 > 30),coef_unavail +util_child3_dist_pe_over30miles,Child 3 Pure escorting not allowed if over 30 miles,((chauf3 == 2) | (chauf3 == 4)) & (dist_home_to_school3 > 30),coef_unavail +# ,Household Interactions,, +util_hh_inc_u25k_noes,Household income less than 25k - No Escorting,(income <= 25000) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_u25k_noes +util_hh_inc_25to50k_noes,Household income between 25 and 50k - No Escorting,((income > 25000) & (income <= 50000)) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_hh_inc_25to50k_noes +util_zero_auto_hh_noes,Zero cars in the household - No Escorting,(auto_ownership == 0) & ((bundle1 == 0) | ((bundle2 == 0) & (num_children_going_to_school > 1)) | ((bundle3 == 0) & (num_children_going_to_school > 2))),coef_zero_auto_hh_noes +util_cars_lt_workers_rs,Cars fewer than household workers - Ride Share,(auto_ownership < num_workers) & ((chauf1 % 2 == 1) | (chauf2 % 2 == 1) | (chauf3 % 2 == 1)),coef_cars_lt_workers_rs +util_cars_lt_workers_pe,Cars fewer than household workers - Pure escort,(auto_ownership < num_workers) & ((chauf1 % 2 == 0) | (chauf2 % 2 == 0) | (chauf3 % 2 == 0)),coef_cars_lt_workers_pe +# ,Chauffer Interactions,, +util_chauf1_female_rs,Chauffeur 1 Female - Ride share,(gender_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_female_rs +util_chauf1_male_rs,Chauffeur 1 Male - Ride share,(gender_chauf1 == 1) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_male_rs +util_chauf1_female_pe,Chauffeur 1 Female - Pure Escort,(gender_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_female_pe +util_chauf1_male_pe,Chauffeur 1 Male - Pure Escort,(gender_chauf1 == 1) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_male_pe +util_chauf2_female_rs,Chauffeur 2 Female - Ride share,(gender_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_female_rs +util_chauf2_male_rs,Chauffeur 2 Male - Ride share,(gender_chauf2 == 1) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_male_rs +util_chauf2_female_pe,Chauffeur 2 Female - Pure Escort,(gender_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_female_pe +util_chauf2_male_pe,Chauffeur 2 Male - Pure Escort,(gender_chauf2 == 1) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_male_pe +util_chauf1_pt_worker_rs,Chauffer 1 part time worker - Ride share,(ptype_chauf1 == 2) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_pt_worker_rs +util_chauf1_pt_worker_pe,Chauffer 1 part time worker - Pure escort,(ptype_chauf1 == 2) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_pt_worker_pe +util_chauf2_pt_worker_rs,Chauffer 2 part time worker - Ride share,(ptype_chauf2 == 2) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_pt_worker_rs +util_chauf2_pt_worker_pe,Chauffer 2 part time worker - Pure escort,(ptype_chauf2 == 2) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_pt_worker_pe +util_chauf1_non_worker_pe,Chauffer 1 non worker - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_non_worker_pe +util_chauf2_non_worker_pe,Chauffer 2 non worker - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_non_worker_pe +util_chauf1_univ_stud_re,Chauffer 1 university student - Ride Share,(ptype_chauf1 == 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_univ_stud_re +util_chauf2_univ_stud_re,Chauffer 2 university student - Ride Share,(ptype_chauf2 == 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_univ_stud_re +util_chauf1_age_u35_pe,Chauffer 1 Age 35 years or younger - Pure escort,(ptype_chauf1 == 4) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_age_u35_pe +util_chauf2_age_u35_pe,Chauffer 2 Age 35 years or younger - Pure escort,(ptype_chauf2 == 4) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_age_u35_pe +util_chauf1_time_to_work_or_univ_rs,Chauffer 1 Auto time to work or university - Ride Share,time_home_to_mand1 * ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_time_to_work_or_univ_rs +util_chauf2_time_to_work_or_univ_rs,Chauffer 2 Auto time to work or university - Ride Share,time_home_to_mand2 * ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_time_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_rs,Chauffer 1 Walk dist to work or university - Ride Share,(dist_home_to_mand1 < 3) & ((chauf1 == 1) | (chauf2 == 1) | (chauf3 == 1)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf2_walk_dist_to_work_or_univ_rs,Chauffer 2 Walk dist to work or university - Ride Share,(dist_home_to_mand2 < 3) & ((chauf1 == 3) | (chauf2 == 3) | (chauf3 == 3)),coef_chauf_walk_dist_to_work_or_univ_rs +util_chauf1_walk_dist_to_work_or_univ_pe,Chauffer 1 Walk dist to work or university - Pure Escort,(dist_home_to_mand1 < 3) & ((chauf1 == 2) | (chauf2 == 2) | (chauf3 == 2)),coef_chauf_walk_dist_to_work_or_univ_pe +util_chauf2_walk_dist_to_work_or_univ_pe,Chauffer 2 Walk dist to work or university - Pure Escort,(dist_home_to_mand2 < 3) & ((chauf1 == 4) | (chauf2 == 4) | (chauf3 == 4)),coef_chauf_walk_dist_to_work_or_univ_pe +# ,Chauffer deviation,, +util_chauf1_abs_dev_rs_child1only,Chauffer 1 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child1only,Chauffer 2 Absolute deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child2only,Chauffer 1 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child2only,Chauffer 2 Absolute deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child3only,Chauffer 1 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child3only,Chauffer 2 Absolute deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child12,Chauffer 1 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child12,Chauffer 2 Absolute deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child13,Chauffer 1 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child13,Chauffer 2 Absolute deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child23,Chauffer 1 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child23,Chauffer 2 Absolute deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2,coef_abs_dev_distance +util_chauf1_abs_dev_rs_child123,Chauffer 1 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1,coef_abs_dev_distance +util_chauf2_abs_dev_rs_child123,Chauffer 2 Absolute deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2,coef_abs_dev_distance +util_chauf1_rel_dev_rs_child1only,Chauffer 1 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child1_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child1only,Chauffer 2 Relative deviation ride share - child 1 only,((bundle1 != bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child1_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child2only,Chauffer 1 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child2_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child2only,Chauffer 2 Relative deviation ride share - child 2 only,((bundle1 != bundle2) & (bundle2 != bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child2_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child3only,Chauffer 1 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 1)) * abs_dev_dist_out_child3_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child3only,Chauffer 2 Relative deviation ride share - child 3 only,((bundle1 != bundle3) & (bundle2 != bundle3) & (chauf3 == 3)) * abs_dev_dist_out_child3_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child12,Chauffer 1 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child12_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child12,Chauffer 2 Relative deviation ride share - child 1 & 2,((bundle1 == bundle2) & (bundle1 != bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child12_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child13,Chauffer 1 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child13_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child13,Chauffer 2 Relative deviation ride share - child 1 & 3,((bundle1 != bundle2) & (bundle1 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child13_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child23,Chauffer 1 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 1)) * abs_dev_dist_out_child23_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child23,Chauffer 2 Relative deviation ride share - child 2 & 3,((bundle1 != bundle2) & (bundle2 == bundle3) & (chauf2 == 3)) * abs_dev_dist_out_child23_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +util_chauf1_rel_dev_rs_child123,Chauffer 1 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 1)) * abs_dev_dist_out_child123_chauf1 / (dist_home_to_mand1 + 0.001),coef_rel_dev_distance +util_chauf2_rel_dev_rs_child123,Chauffer 2 Relative deviation ride share - child 1 & 2 & 3,((bundle1 == bundle2) & (bundle2 == bundle3) & (chauf1 == 3)) * abs_dev_dist_out_child123_chauf2 / (dist_home_to_mand2 + 0.001),coef_rel_dev_distance +# ,Same TAZ,, +util_same_taz_child12_escort,Same school TAZ for child 1 and 2 - Escorting,(bundle1 == bundle2) & (bundle1 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child13_escort,Same school TAZ for child 1 and 3 - Escorting,(bundle1 == bundle3) & (bundle1 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_escort +util_same_taz_child32_escort,Same school TAZ for child 3 and 2 - Escorting,(bundle3 == bundle2) & (bundle2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_escort +util_same_taz_child12_no_escort,Same school TAZ for child 1 and 2 - No Escorting,(bundle1 == 0) & (bundle2 == 0) & (child_id1 > 0) & (child_id2 > 0) & (school_location_child1 == school_location_child2) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child13_no_escort,Same school TAZ for child 1 and 3 - No Escorting,(bundle1 == 0) & (bundle3 == 0) & (child_id1 > 0) & (child_id3 > 0) & (school_location_child1 == school_location_child3) & (school_location_child1 > 0),coef_same_taz_no_escort +util_same_taz_child32_no_escort,Same school TAZ for child 3 and 2 - No Escorting,(bundle3 == 0) & (bundle2 == 0) & (child_id3 > 0) & (child_id2 > 0) & (school_location_child3 == school_location_child2) & (school_location_child2 > 0),coef_same_taz_no_escort +# ,Constants related to inbound choice,, +util_no_escort_inbound_child1,No escorting in inbound direction - Child 1,(bundle1 == 0) & (child_id1 > 0) & (bundle1_inbound == 0),coef_no_escort_inbound +util_no_escort_inbound_child2,No escorting in inbound direction - Child 2,(bundle2 == 0) & (child_id2 > 0) & (bundle2_inbound == 0),coef_no_escort_inbound +util_no_escort_inbound_child3,No escorting in inbound direction - Child 3,(bundle3 == 0) & (child_id3 > 0) & (bundle3_inbound == 0),coef_no_escort_inbound +util_same_chauf1,Same chauffeur in both directions (not child specific) - chauf 1 inbound & inbound,((chauf1 == 1) | (chauf1 == 2) | (chauf2 == 1) | (chauf2 == 2) | (chauf3 == 1) | (chauf3 == 2)) & ((nbund1_inbound > 0)),coef_same_chauf +util_same_chauf2,Same chauffeur in both directions (not child specific) - chauf 2 inbound & inbound,((chauf1 == 3) | (chauf1 == 4) | (chauf2 == 3) | (chauf2 == 4) | (chauf3 == 3) | (chauf3 == 4)) & ((nbund2_inbound > 0)),coef_same_chauf +# ,Calibration constants,, +util_calib_child1_age_u6_rs,Child 1 age under 6 - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 < 6),coef_calib_child_age_u6_rs +util_calib_child2_age_u6_rs,Child 2 age under 6 - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 < 6),coef_calib_child_age_u6_rs +util_calib_child3_age_u6_rs,Child 3 age under 6 - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 < 6),coef_calib_child_age_u6_rs +util_calib_child1_age_16p_rs,Child 1 age 16 years or older - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 15),coef_calib_child_age_16p_rs +util_calib_child2_age_16p_rs,Child 2 age 16 years or older - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 15),coef_calib_child_age_16p_rs +util_calib_child3_age_16p_rs,Child 3 age 16 years or older - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 15),coef_calib_child_age_16p_rs +util_calib_child1_age_6to15_noes,Child 1 age 6 to 15 years - Calibration constant - No Escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_noes +util_calib_child2_age_6to15_noes,Child 2 age 6 to 15 years - Calibration constant - No Escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_noes +util_calib_child3_age_6to15_noes,Child 3 age 6 to 15 years - Calibration constant - No Escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_noes +util_calib_child1_age_6to15_rs,Child 1 age 6 to 15 years - Calibration constant - Ride Share,((chauf1 == 1) | (chauf1 == 3)) & (age_child1 > 5) & (age_child1 < 16),coef_calib_child_age_6to15_rs +util_calib_child2_age_6to15_rs,Child 2 age 6 to 15 years - Calibration constant - Ride Share,((chauf2 == 1) | (chauf2 == 3)) & (age_child2 > 5) & (age_child2 < 16),coef_calib_child_age_6to15_rs +util_calib_child3_age_6to15_rs,Child 3 age 6 to 15 years - Calibration constant - Ride Share,((chauf3 == 1) | (chauf3 == 3)) & (age_child3 > 5) & (age_child3 < 16),coef_calib_child_age_6to15_rs +util_calib_child1_age_u6_noes,Child 1 age 6 years or younger - Calibration constant - No escort,(bundle1 == 0) & (child_id1 > 0) & (age_child1 < 6),coef_calib_child_age_u6_noes +util_calib_child2_age_u6_noes,Child 2 age 6 years or younger - Calibration constant - No escort,(bundle2 == 0) & (child_id2 > 0) & (age_child2 < 6),coef_calib_child_age_u6_noes +util_calib_child3_age_u6_noes,Child 3 age 6 years or younger - Calibration constant - No escort,(bundle3 == 0) & (child_id3 > 0) & (age_child3 < 6),coef_calib_child_age_u6_noes diff --git a/configs/resident/school_escorting_preprocessor_inbound.csv b/configs/resident/school_escorting_preprocessor_inbound.csv new file mode 100644 index 0000000..9832ae9 --- /dev/null +++ b/configs/resident/school_escorting_preprocessor_inbound.csv @@ -0,0 +1,148 @@ +Description,Target,Expression +Number of children going to school,num_children_going_to_school,"np.where(df['child_id1'] > 0, 1, 0) + np.where(df['child_id2'] > 0, 1, 0) + np.where(df['child_id3'] > 0,1,0)" +Number of potential chauffeurs,num_potential_chauffeurs,"np.where(df['chauf_id1'] > 0, 1, 0) + np.where(df['chauf_id2'] > 0, 1, 0)" +Person Type - chauffer 1,ptype_chauf1,"reindex(persons.ptype, df.chauf_id1)" +Person Type - chauffer 2,ptype_chauf2,"reindex(persons.ptype, df.chauf_id2)" +Gender - chauffer 1,gender_chauf1,"reindex(persons.sex, df.chauf_id1)" +Gender - chauffer 2,gender_chauf2,"reindex(persons.sex, df.chauf_id2)" +Age - chauffer 1,age_chauf1,"reindex(persons.age, df.chauf_id1)" +Age - chauffer 2,age_chauf2,"reindex(persons.age, df.chauf_id2)" +Daily activity pattern - chauffer 1,cdap_chauf1,"reindex(persons.cdap_activity, df.chauf_id1)" +Daily activity pattern - chauffer 2,cdap_chauf2,"reindex(persons.cdap_activity, df.chauf_id2)" +Age - child 1,age_child1,"reindex(persons.age, df.child_id1)" +Age - child 2,age_child2,"reindex(persons.age, df.child_id2)" +Age - child 3,age_child3,"reindex(persons.age, df.child_id3)" +# Departure times from school and work ,, +Preferred departure time from school - child 1,pref_depart_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id1)" +Preferred departure time from school - child 2,pref_depart_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id2)" +Preferred departure time from school - child 3,pref_depart_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id3)" +# setting preffered departure time for chauffer to the last mandatory tour of the day for inbound escorting ,, +Preferred departure time from work / univ - chauffer 1 - tour 1,pref_depart_time_chauf1_tour1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id1)" +Preferred departure time from work / univ - chauffer 2 - tour 1,pref_depart_time_chauf2_tour1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id2)" +Preferred departure time from work / univ - chauffer 1 - tour 2,pref_depart_time_chauf1_tour2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 2)].set_index('person_id').end, df.chauf_id1)" +Preferred departure time from work / univ - chauffer 2 - tour 2,pref_depart_time_chauf2_tour2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 2)].set_index('person_id').end, df.chauf_id2)" +Preferred departure time from work / univ - chauffer 1,pref_depart_time_chauf1,"np.where(pref_depart_time_chauf1_tour2 > pref_depart_time_chauf1_tour1, pref_depart_time_chauf1_tour2, pref_depart_time_chauf1_tour1)" +Preferred departure time from work / univ - chauffer 1,pref_depart_time_chauf2,"np.where(pref_depart_time_chauf2_tour2 > pref_depart_time_chauf2_tour1, pref_depart_time_chauf2_tour2, pref_depart_time_chauf2_tour1)" +# Distances and times to school and work ,, +School location - child 1,school_location_child1,"reindex(persons.school_zone_id, df.child_id1)" +School location - child 2,school_location_child2,"reindex(persons.school_zone_id, df.child_id2)" +School location - child 3,school_location_child3,"reindex(persons.school_zone_id, df.child_id3)" +School location - chauffer 1,_school_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +School location - chauffer 2,_school_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Work location - chauffer 1,_work_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +Work location - chauffer 2,_work_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Mandatory tour location - chauffer 1,_mandatory_location_chauf1,"_school_location_chauf1.where(ptype_chauf1 == 3, _work_location_chauf1)" +Mandatory tour location - chauffer 2,_mandatory_location_chauf2,"_school_location_chauf1.where(ptype_chauf2 == 3, _work_location_chauf2)" +# creating valid school locations to pass to skim_dict,, +,_valid_school_location_child1,school_location_child1.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child2,school_location_child2.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child3,school_location_child3.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_mandatory_location_chauf1,_mandatory_location_chauf1.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +,_valid_mandatory_location_chauf2,_mandatory_location_chauf2.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +Auto time home to school - child 1,time_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('HOV2_M_TIME', 'PM')), 0)" +Auto time home to school - child 2,time_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('HOV2_M_TIME', 'PM')), 0)" +Auto time home to school - child 3,time_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('HOV2_M_TIME', 'PM')), 0)" +Auto time school to home - child 1,time_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('HOV2_M_TIME', 'PM')), 0)" +Auto time school to home - child 2,time_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('HOV2_M_TIME', 'PM')), 0)" +Auto time school to home - child 3,time_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('HOV2_M_TIME', 'PM')), 0)" +Auto dist home to school - child 1,dist_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('HOV2_M_DIST', 'PM')), 0)" +Auto dist home to school - child 2,dist_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('HOV2_M_DIST', 'PM')), 0)" +Auto dist home to school - child 3,dist_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('HOV2_M_DIST', 'PM')), 0)" +Auto dist intra school taz - child 1,dist_intra_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('HOV2_M_DIST', 'PM')), 0)" +Auto dist intra school taz - child 2,dist_intra_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('HOV2_M_DIST', 'PM')), 0)" +Auto dist intra school taz - child 3,dist_intra_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('HOV2_M_DIST', 'PM')), 0)" +Auto time home to work or university - chauffer 1,time_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('HOV2_M_TIME', 'PM')), 0)" +Auto time home to work or university - chauffer 2,time_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('HOV2_M_TIME', 'PM')), 0)" +Auto time work or university to home - chauffer 1,time_mand_to_home1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(_valid_mandatory_location_chauf1, df.home_zone_id, ('HOV2_M_TIME', 'PM')), 0)" +Auto time work or university to home - chauffer 2,time_mand_to_home2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(_valid_mandatory_location_chauf2, df.home_zone_id, ('HOV2_M_TIME', 'PM')), 0)" +Auto dist home to work or university - chauffer 1,dist_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('HOV2_M_DIST', 'PM')), 0)" +Auto dist home to work or university - chauffer 2,dist_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('HOV2_M_DIST', 'PM')), 0)" +Auto dist work or university to home - chauffer 1,dist_mand1_to_home,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(_valid_mandatory_location_chauf1, df.home_zone_id, ('HOV2_M_DIST', 'PM')), 0)" +Auto dist work or university to home - chauffer 2,dist_mand2_to_home,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(_valid_mandatory_location_chauf2, df.home_zone_id, ('HOV2_M_DIST', 'PM')), 0)" +# inbound distance combinations between chauffeurs and children,, +Distance from chauffeur 1 mandatory location to child 1 school,time_mand1_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf1, ('HOV2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 2 school,time_mand1_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf1, ('HOV2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 3 school,time_mand1_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf1, ('HOV2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 1 school,time_mand2_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf2, ('HOV2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 2 school,time_mand2_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf2, ('HOV2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 3 school,time_mand2_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf2, ('HOV2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 1 school,_dist_mand1_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_mandatory_location_chauf1, _valid_school_location_child1, ('HOV2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 1 school,_dist_mand2_to_school1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_mandatory_location_chauf2, _valid_school_location_child1, ('HOV2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 2 school,_dist_mand1_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_mandatory_location_chauf1, _valid_school_location_child2, ('HOV2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 2 school,_dist_mand2_to_school2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_mandatory_location_chauf2, _valid_school_location_child2, ('HOV2_M_TIME', 'PM')), 0)" +Distance from chauffeur 1 mandatory location to child 3 school,_dist_mand1_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_mandatory_location_chauf1, _valid_school_location_child3, ('HOV2_M_TIME', 'PM')), 0)" +Distance from chauffeur 2 mandatory location to child 3 school,_dist_mand2_to_school3,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_mandatory_location_chauf2, _valid_school_location_child3, ('HOV2_M_TIME', 'PM')), 0)" +Distance from child 1 school to child 2 school,_dist_school1_to_school2,"np.where((school_location_child1 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child2, ('HOV2_M_TIME', 'PM')), 0)" +Distance from child 1 school to child 3 school,_dist_school1_to_school3,"np.where((school_location_child1 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child3, ('HOV2_M_TIME', 'PM')), 0)" +Distance from child 2 school to child 3 school,_dist_school2_to_school3,"np.where((school_location_child2 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child3, ('HOV2_M_TIME', 'PM')), 0)" +Distance from child 2 school to child 1 school,_dist_school2_to_school1,"np.where((school_location_child2 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child1, ('HOV2_M_TIME', 'PM')), 0)" +Distance from child 3 school to child 1 school,_dist_school3_to_school1,"np.where((school_location_child3 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child1, ('HOV2_M_TIME', 'PM')), 0)" +Distance from child 3 school to child 2 school,_dist_school3_to_school2,"np.where((school_location_child3 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child2, ('HOV2_M_TIME', 'PM')), 0)" +,_return_min_taking_child1,"(pref_depart_time_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,_return_min_taking_child2,"(pref_depart_time_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,_return_min_taking_child3,"(pref_depart_time_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +Availability of taking child 1 and then 2,_avail_child1_then_child2,"(_return_min_taking_child1 < (pref_depart_time_school2 * mins_per_time_bin))" +Availability of taking child 2 and then 1,_avail_child2_then_child1,"(_return_min_taking_child2 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 1 and then 3,_avail_child1_then_child3,"(_return_min_taking_child1 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 1,_avail_child3_then_child1,"(_return_min_taking_child3 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 2 and then 3,_avail_child2_then_child3,"(_return_min_taking_child2 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 2,_avail_child3_then_child2,"(_return_min_taking_child3 < (pref_depart_time_school2 * mins_per_time_bin))" +multiple_bundle_availability,avail_multiple_bundles,(_avail_child1_then_child2 | _avail_child2_then_child1 | _avail_child1_then_child3 | _avail_child3_then_child1 | _avail_child2_then_child3 | _avail_child3_then_child2) +# absolute deviation distance inbound,, +Absolute deviation inbound distance Child 1 Chauffer 1,abs_dev_dist_in_child1_chauf1,"np.maximum(_dist_mand1_to_school1 + dist_school_to_home1 - dist_mand1_to_home,0)" +Absolute deviation inbound distance Child 1 Chauffer 2,abs_dev_dist_in_child1_chauf2,"np.maximum(_dist_mand2_to_school1 + dist_school_to_home1 - dist_mand2_to_home,0)" +Absolute deviation inbound distance Child 2 Chauffer 1,abs_dev_dist_in_child2_chauf1,"np.maximum(_dist_mand1_to_school2 + dist_school_to_home2 - dist_mand1_to_home,0)" +Absolute deviation inbound distance Child 2 Chauffer 2,abs_dev_dist_in_child2_chauf2,"np.maximum(_dist_mand2_to_school2 + dist_school_to_home2 - dist_mand2_to_home,0)" +Absolute deviation inbound distance Child 3 Chauffer 1,abs_dev_dist_in_child3_chauf1,"np.maximum(_dist_mand1_to_school3 + dist_school_to_home3 - dist_mand1_to_home,0)" +Absolute deviation inbound distance Child 3 Chauffer 2,abs_dev_dist_in_child3_chauf2,"np.maximum(_dist_mand2_to_school3 + dist_school_to_home3 - dist_mand2_to_home,0)" +Absolute deviation inbound distance child12 Chauffer 1,abs_dev_dist_in_child12_chauf1,"np.maximum(np.minimum(_dist_mand1_to_school1 + _dist_school1_to_school2 + dist_school_to_home2, _dist_mand1_to_school2 + _dist_school2_to_school1 + dist_school_to_home1) - dist_mand1_to_home, 0)" +Absolute deviation inbound distance child12 Chauffer 2,abs_dev_dist_in_child12_chauf2,"np.maximum(np.minimum(_dist_mand2_to_school1 + _dist_school1_to_school2 + dist_school_to_home2, _dist_mand2_to_school2 + _dist_school2_to_school1 + dist_school_to_home1) - dist_mand2_to_home, 0)" +Absolute deviation inbound distance child13 Chauffer 1,abs_dev_dist_in_child13_chauf1,"np.maximum(np.minimum(_dist_mand1_to_school1 + _dist_school1_to_school3 + dist_school_to_home2, _dist_mand1_to_school3 + _dist_school3_to_school1 + dist_school_to_home3) - dist_mand1_to_home, 0)" +Absolute deviation inbound distance child13 Chauffer 2,abs_dev_dist_in_child13_chauf2,"np.maximum(np.minimum(_dist_mand2_to_school1 + _dist_school1_to_school3 + dist_school_to_home2, _dist_mand2_to_school3 + _dist_school3_to_school1 + dist_school_to_home3) - dist_mand2_to_home, 0)" +Absolute deviation inbound distance child23 Chauffer 1,abs_dev_dist_in_child23_chauf1,"np.maximum(np.minimum(_dist_mand1_to_school1 + _dist_school2_to_school3 + dist_school_to_home3, _dist_mand1_to_school3 + _dist_school3_to_school2 + dist_school_to_home2) - dist_mand1_to_home, 0)" +Absolute deviation inbound distance child23 Chauffer 2,abs_dev_dist_in_child23_chauf2,"np.maximum(np.minimum(_dist_mand2_to_school1 + _dist_school2_to_school3 + dist_school_to_home3, _dist_mand2_to_school3 + _dist_school3_to_school2 + dist_school_to_home2) - dist_mand2_to_home, 0)" +,_dist_mand1_school1_school2_school3,"_dist_mand1_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + dist_school_to_home1" +,_dist_mand1_school1_school3_school2,"_dist_mand1_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + dist_school_to_home1" +,_dist_mand1_school2_school1_school3,"_dist_mand1_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + dist_school_to_home2" +,_dist_mand1_school2_school3_school1,"_dist_mand1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + dist_school_to_home2" +,_dist_mand1_school3_school1_school2,"_dist_mand1_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + dist_school_to_home3" +,_dist_mand1_school3_school2_school1,"_dist_mand1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + dist_school_to_home3" +,_min_dist_dropoff_order_in_child123_chauf1,_dist_mand1_school1_school2_school3 +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school1_school3_school2 > 0) & (_dist_mand1_school1_school3_school2 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school1_school3_school2, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school2_school1_school3 > 0) & (_dist_mand1_school2_school1_school3 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school2_school1_school3, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school2_school3_school1 > 0) & (_dist_mand1_school2_school3_school1 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school2_school3_school1, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school3_school1_school2 > 0) & (_dist_mand1_school3_school1_school2 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school3_school1_school2, _min_dist_dropoff_order_in_child123_chauf1)" +,_min_dist_dropoff_order_in_child123_chauf1,"np.where((_dist_mand1_school3_school2_school1 > 0) & (_dist_mand1_school3_school2_school1 < _min_dist_dropoff_order_in_child123_chauf1), _dist_mand1_school3_school2_school1, _min_dist_dropoff_order_in_child123_chauf1)" +Absolute deviation inbound distance child123 Chauffer 1,abs_dev_dist_in_child123_chauf1,"np.maximum(_min_dist_dropoff_order_in_child123_chauf1 - dist_home_to_mand1, 0)" +,_dist_mand2_school1_school2_school3,"_dist_mand2_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + dist_school_to_home1" +,_dist_mand2_school1_school3_school2,"_dist_mand2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + dist_school_to_home1" +,_dist_mand2_school2_school1_school3,"_dist_mand2_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + dist_school_to_home2" +,_dist_mand2_school2_school3_school1,"_dist_mand2_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + dist_school_to_home2" +,_dist_mand2_school3_school1_school2,"_dist_mand2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + dist_school_to_home3" +,_dist_mand2_school3_school2_school1,"_dist_mand2_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + dist_school_to_home3" +,_min_dist_dropoff_order_in_child123_chauf2,_dist_mand2_school1_school2_school3 +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school1_school3_school2 > 0) & (_dist_mand2_school1_school3_school2 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school1_school3_school2, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school2_school1_school3 > 0) & (_dist_mand2_school2_school1_school3 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school2_school1_school3, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school2_school3_school1 > 0) & (_dist_mand2_school2_school3_school1 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school2_school3_school1, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school3_school1_school2 > 0) & (_dist_mand2_school3_school1_school2 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school3_school1_school2, _min_dist_dropoff_order_in_child123_chauf2)" +,_min_dist_dropoff_order_in_child123_chauf2,"np.where((_dist_mand2_school3_school2_school1 > 0) & (_dist_mand2_school3_school2_school1 < _min_dist_dropoff_order_in_child123_chauf2), _dist_mand2_school3_school2_school1, _min_dist_dropoff_order_in_child123_chauf2)" +Absolute deviation inbound distance child123 Chauffer 2,abs_dev_dist_in_child123_chauf2,"np.maximum(_min_dist_dropoff_order_in_child123_chauf2 - dist_home_to_mand1, 0)" +# overlapping time windows from outbound escorting,, +Preferred departure time to school - child 1,_pref_depart_time_to_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id1)" +Preferred departure time to school - child 2,_pref_depart_time_to_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id2)" +Preferred departure time to school - child 3,_pref_depart_time_to_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id3)" +,return_min_taking_outbound_child1_pe,"(_pref_depart_time_to_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,return_min_taking_outbound_child2_pe,"(_pref_depart_time_to_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,return_min_taking_outbound_child3_pe,"(_pref_depart_time_to_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +finding latest time chauffer 1 returns from outbound pure escort tour,outbound_pe_return_time_home_chauf1,-1 +,outbound_pe_return_time_home_chauf1,"np.where((df.chauf1_outbound == 2) & (return_min_taking_outbound_child1_pe > outbound_pe_return_time_home_chauf1), return_min_taking_outbound_child1_pe, outbound_pe_return_time_home_chauf1)" +,outbound_pe_return_time_home_chauf1,"np.where((df.chauf2_outbound == 2) & (return_min_taking_outbound_child2_pe > outbound_pe_return_time_home_chauf1), return_min_taking_outbound_child2_pe, outbound_pe_return_time_home_chauf1)" +,outbound_pe_return_time_home_chauf1,"np.where((df.chauf3_outbound == 2) & (return_min_taking_outbound_child3_pe > outbound_pe_return_time_home_chauf1), return_min_taking_outbound_child3_pe, outbound_pe_return_time_home_chauf1)" +finding latest time chauffer 2 returns from outbound pure escort tour,outbound_pe_return_time_home_chauf2,-1 +,outbound_pe_return_time_home_chauf2,"np.where((df.chauf1_outbound == 4) & (return_min_taking_outbound_child1_pe > outbound_pe_return_time_home_chauf2), return_min_taking_outbound_child1_pe, outbound_pe_return_time_home_chauf2)" +,outbound_pe_return_time_home_chauf2,"np.where((df.chauf2_outbound == 4) & (return_min_taking_outbound_child2_pe > outbound_pe_return_time_home_chauf2), return_min_taking_outbound_child2_pe, outbound_pe_return_time_home_chauf2)" +,outbound_pe_return_time_home_chauf2,"np.where((df.chauf3_outbound == 4) & (return_min_taking_outbound_child3_pe > outbound_pe_return_time_home_chauf2), return_min_taking_outbound_child3_pe, outbound_pe_return_time_home_chauf2)" +finding latest time chauffer 1 returns from outbound ride share tour,outbound_rs_return_time_home_chauf1,pref_depart_time_chauf1 +finding latest time chauffer 2 returns from outbound ride share tour,outbound_rs_return_time_home_chauf2,pref_depart_time_chauf2 +return time of outbound school escoring tour - chauffeur 1,return_bin_outbound_school_escorting1,"np.where(df.nrs1_outbound > 0, pref_depart_time_chauf1, outbound_pe_return_time_home_chauf1 / mins_per_time_bin)" +return time of outbound school escoring tour - chauffeur 2,return_bin_outbound_school_escorting2,"np.where(df.nrs2_outbound > 0, pref_depart_time_chauf2, outbound_pe_return_time_home_chauf2 / mins_per_time_bin)" diff --git a/configs/resident/school_escorting_preprocessor_outbound.csv b/configs/resident/school_escorting_preprocessor_outbound.csv new file mode 100644 index 0000000..40a12be --- /dev/null +++ b/configs/resident/school_escorting_preprocessor_outbound.csv @@ -0,0 +1,122 @@ +Description,Target,Expression +Number of children going to school,num_children_going_to_school,"np.where(df['child_id1'] > 0, 1, 0) + np.where(df['child_id2'] > 0, 1, 0) + np.where(df['child_id3'] > 0,1,0)" +Number of potential chauffeurs,num_potential_chauffeurs,"np.where(df['chauf_id1'] > 0, 1, 0) + np.where(df['chauf_id2'] > 0, 1, 0)" +Person Type - chauffer 1,ptype_chauf1,"reindex(persons.ptype, df.chauf_id1)" +Person Type - chauffer 2,ptype_chauf2,"reindex(persons.ptype, df.chauf_id2)" +Gender - chauffer 1,gender_chauf1,"reindex(persons.sex, df.chauf_id1)" +Gender - chauffer 2,gender_chauf2,"reindex(persons.sex, df.chauf_id2)" +Age - chauffer 1,age_chauf1,"reindex(persons.age, df.chauf_id1)" +Age - chauffer 2,age_chauf2,"reindex(persons.age, df.chauf_id2)" +Daily activity pattern - chauffer 1,cdap_chauf1,"reindex(persons.cdap_activity, df.chauf_id1)" +Daily activity pattern - chauffer 2,cdap_chauf2,"reindex(persons.cdap_activity, df.chauf_id2)" +Age - child 1,age_child1,"reindex(persons.age, df.child_id1)" +Age - child 2,age_child2,"reindex(persons.age, df.child_id2)" +Age - child 3,age_child3,"reindex(persons.age, df.child_id3)" +# Departure times to school and work +Preferred departure time to school - child 1,pref_depart_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id1)" +Preferred departure time to school - child 2,pref_depart_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id2)" +Preferred departure time to school - child 3,pref_depart_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id3)" +Preferred departure time to work / univ - chauffer 1,pref_depart_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id1)" +Preferred departure time to work / univ - chauffer 2,pref_depart_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id2)" +# Distances and times to school and work +School location - child 1,school_location_child1,"reindex(persons.school_zone_id, df.child_id1)" +School location - child 2,school_location_child2,"reindex(persons.school_zone_id, df.child_id2)" +School location - child 3,school_location_child3,"reindex(persons.school_zone_id, df.child_id3)" +School location - chauffer 1,_school_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +School location - chauffer 2,_school_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Work location - chauffer 1,_work_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +Work location - chauffer 2,_work_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Mandatory tour location - chauffer 1,_mandatory_location_chauf1,"_school_location_chauf1.where(ptype_chauf1 == 3, _work_location_chauf1)" +Mandatory tour location - chauffer 2,_mandatory_location_chauf2,"_school_location_chauf1.where(ptype_chauf2 == 3, _work_location_chauf2)" +# creating valid school locations to pass to skim_dict,, +,_valid_school_location_child1,school_location_child1.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child2,school_location_child2.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child3,school_location_child3.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_mandatory_location_chauf1,_mandatory_location_chauf1.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +,_valid_mandatory_location_chauf2,_mandatory_location_chauf2.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +Auto time home to school - child 1,time_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('HOV2_M_TIME', 'AM')), 0)" +Auto time home to school - child 2,time_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('HOV2_M_TIME', 'AM')), 0)" +Auto time home to school - child 3,time_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('HOV2_M_TIME', 'AM')), 0)" +Auto time school to home - child 1,time_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('HOV2_M_TIME', 'AM')), 0)" +Auto time school to home - child 2,time_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('HOV2_M_TIME', 'AM')), 0)" +Auto time school to home - child 3,time_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('HOV2_M_TIME', 'AM')), 0)" +Auto dist home to school - child 1,dist_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('HOV2_M_DIST', 'AM')), 0)" +Auto dist home to school - child 2,dist_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('HOV2_M_DIST', 'AM')), 0)" +Auto dist home to school - child 3,dist_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('HOV2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 1,dist_intra_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('HOV2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 2,dist_intra_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('HOV2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 3,dist_intra_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('HOV2_M_DIST', 'AM')), 0)" +Auto time home to work or university - chauffer 1,time_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('HOV2_M_TIME', 'AM')), 0)" +Auto time home to work or university - chauffer 2,time_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('HOV2_M_TIME', 'AM')), 0)" +Auto dist home to work or university - chauffer 1,dist_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('HOV2_M_DIST', 'AM')), 0)" +Auto dist home to work or university - chauffer 2,dist_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('HOV2_M_DIST', 'AM')), 0)" +# outbound distance combinations between chauffeurs and children,, +Distance from child 1 school to chauffeur 1 mandatory location,_dist_school1_to_mand1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf1, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 1 school to chauffeur 2 mandatory location,_dist_school1_to_mand2,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf2, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 1 mandatory location,_dist_school2_to_mand1,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf1, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 2 mandatory location,_dist_school2_to_mand2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf2, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 1 mandatory location,_dist_school3_to_mand1,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf1, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 2 mandatory location,_dist_school3_to_mand2,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf2, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 1 school to child 2 school,_dist_school1_to_school2,"np.where((school_location_child1 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child2, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 1 school to child 3 school,_dist_school1_to_school3,"np.where((school_location_child1 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child3, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 2 school to child 3 school,_dist_school2_to_school3,"np.where((school_location_child2 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child3, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 2 school to child 1 school,_dist_school2_to_school1,"np.where((school_location_child2 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child1, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 3 school to child 1 school,_dist_school3_to_school1,"np.where((school_location_child3 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child1, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 3 school to child 2 school,_dist_school3_to_school2,"np.where((school_location_child3 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child2, ('HOV2_M_TIME', 'AM')), 0)" +# absolute deviation distance outbound,, +Absolute deviation outbound distance Child 1 Chauffer 1,abs_dev_dist_out_child1_chauf1,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 1 Chauffer 2,abs_dev_dist_out_child1_chauf2,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 2 Chauffer 1,abs_dev_dist_out_child2_chauf1,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 2 Chauffer 2,abs_dev_dist_out_child2_chauf2,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 3 Chauffer 1,abs_dev_dist_out_child3_chauf1,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 3 Chauffer 2,abs_dev_dist_out_child3_chauf2,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance child12 Chauffer 1,abs_dev_dist_out_child12_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child12 Chauffer 2,abs_dev_dist_out_child12_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child13 Chauffer 1,abs_dev_dist_out_child13_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child13 Chauffer 2,abs_dev_dist_out_child13_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child23 Chauffer 1,abs_dev_dist_out_child23_chauf1,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child23 Chauffer 2,abs_dev_dist_out_child23_chauf2,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2) - dist_home_to_mand2, 0)" +,_dist_school1_school2_school3_mand1,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1" +,_dist_school1_school3_school2_mand1,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1" +,_dist_school2_school1_school3_mand1,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1" +,_dist_school2_school3_school1_mand1,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1" +,_dist_school3_school1_school2_mand1,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1" +,_dist_school3_school2_school1_mand1,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1" +,_min_dist_dropoff_order_out_child123_chauf1,_dist_school1_school2_school3_mand1 +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school1_school3_school2_mand1 > 0) & (_dist_school1_school3_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school1_school3_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school1_school3_mand1 > 0) & (_dist_school2_school1_school3_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school1_school3_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school3_school1_mand1 > 0) & (_dist_school2_school3_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school3_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school1_school2_mand1 > 0) & (_dist_school3_school1_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school1_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school2_school1_mand1 > 0) & (_dist_school3_school2_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school2_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf1,"np.maximum(_min_dist_dropoff_order_out_child123_chauf1 - dist_home_to_mand1, 0)" +,_dist_school1_school2_school3_mand2,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2" +,_dist_school1_school3_school2_mand2,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2" +,_dist_school2_school1_school3_mand2,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2" +,_dist_school2_school3_school1_mand2,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2" +,_dist_school3_school1_school2_mand2,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2" +,_dist_school3_school2_school1_mand2,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2" +,_min_dist_dropoff_order_out_child123_chauf2,_dist_school1_school2_school3_mand2 +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school1_school3_school2_mand2 > 0) & (_dist_school1_school3_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school1_school3_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school1_school3_mand2 > 0) & (_dist_school2_school1_school3_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school1_school3_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school3_school1_mand2 > 0) & (_dist_school2_school3_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school3_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school1_school2_mand2 > 0) & (_dist_school3_school1_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school1_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school2_school1_mand2 > 0) & (_dist_school3_school2_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school2_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf2,"np.maximum(_min_dist_dropoff_order_out_child123_chauf2 - dist_home_to_mand2, 0)" +# Availability for multiple bundles,, +,_return_min_taking_child1,"(pref_depart_time_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,_return_min_taking_child2,"(pref_depart_time_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,_return_min_taking_child3,"(pref_depart_time_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +Availability of taking child 1 and then 2,_avail_child1_then_child2,"(_return_min_taking_child1 < (pref_depart_time_school2 * mins_per_time_bin))" +Availability of taking child 2 and then 1,_avail_child2_then_child1,"(_return_min_taking_child2 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 1 and then 3,_avail_child1_then_child3,"(_return_min_taking_child1 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 1,_avail_child3_then_child1,"(_return_min_taking_child3 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 2 and then 3,_avail_child2_then_child3,"(_return_min_taking_child2 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 2,_avail_child3_then_child2,"(_return_min_taking_child3 < (pref_depart_time_school2 * mins_per_time_bin))" +multiple_bundle_availability,avail_multiple_bundles,(_avail_child1_then_child2 | _avail_child2_then_child1 | _avail_child1_then_child3 | _avail_child3_then_child1 | _avail_child2_then_child3 | _avail_child3_then_child2) +# ,, +# Inbound specific terms ,, +Preferred return time from school - child 1,pref_return_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id1)" +Preferred return time from school - child 2,pref_return_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id2)" +Preferred return time from school - child 3,pref_return_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id3)" +Preferred return time from work / univ - chauffer 1,pref_return_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id1)" +Preferred return time from work / univ - chauffer 2,pref_return_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id2)" diff --git a/configs/resident/school_escorting_preprocessor_outbound_cond.csv b/configs/resident/school_escorting_preprocessor_outbound_cond.csv new file mode 100644 index 0000000..80d59cf --- /dev/null +++ b/configs/resident/school_escorting_preprocessor_outbound_cond.csv @@ -0,0 +1,138 @@ +Description,Target,Expression +Number of children going to school,num_children_going_to_school,"np.where(df['child_id1'] > 0, 1, 0) + np.where(df['child_id2'] > 0, 1, 0) + np.where(df['child_id3'] > 0,1,0)" +Number of potential chauffeurs,num_potential_chauffeurs,"np.where(df['chauf_id1'] > 0, 1, 0) + np.where(df['chauf_id2'] > 0, 1, 0)" +Person Type - chauffer 1,ptype_chauf1,"reindex(persons.ptype, df.chauf_id1)" +Person Type - chauffer 2,ptype_chauf2,"reindex(persons.ptype, df.chauf_id2)" +Gender - chauffer 1,gender_chauf1,"reindex(persons.sex, df.chauf_id1)" +Gender - chauffer 2,gender_chauf2,"reindex(persons.sex, df.chauf_id2)" +Age - chauffer 1,age_chauf1,"reindex(persons.age, df.chauf_id1)" +Age - chauffer 2,age_chauf2,"reindex(persons.age, df.chauf_id2)" +Daily activity pattern - chauffer 1,cdap_chauf1,"reindex(persons.cdap_activity, df.chauf_id1)" +Daily activity pattern - chauffer 2,cdap_chauf2,"reindex(persons.cdap_activity, df.chauf_id2)" +Age - child 1,age_child1,"reindex(persons.age, df.child_id1)" +Age - child 2,age_child2,"reindex(persons.age, df.child_id2)" +Age - child 3,age_child3,"reindex(persons.age, df.child_id3)" +# Departure times to school and work +Preferred departure time to school - child 1,pref_depart_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id1)" +Preferred departure time to school - child 2,pref_depart_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id2)" +Preferred departure time to school - child 3,pref_depart_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').start, df.child_id3)" +Preferred departure time to work / univ - chauffer 1,pref_depart_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id1)" +Preferred departure time to work / univ - chauffer 2,pref_depart_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').start, df.chauf_id2)" +# Distances and times to school and work +School location - child 1,school_location_child1,"reindex(persons.school_zone_id, df.child_id1)" +School location - child 2,school_location_child2,"reindex(persons.school_zone_id, df.child_id2)" +School location - child 3,school_location_child3,"reindex(persons.school_zone_id, df.child_id3)" +School location - chauffer 1,_school_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +School location - chauffer 2,_school_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Work location - chauffer 1,_work_location_chauf1,"reindex(persons.workplace_zone_id, df.chauf_id1)" +Work location - chauffer 2,_work_location_chauf2,"reindex(persons.workplace_zone_id, df.chauf_id2)" +Mandatory tour location - chauffer 1,_mandatory_location_chauf1,"_school_location_chauf1.where(ptype_chauf1 == 3, _work_location_chauf1)" +Mandatory tour location - chauffer 2,_mandatory_location_chauf2,"_school_location_chauf1.where(ptype_chauf2 == 3, _work_location_chauf2)" +# creating valid school locations to pass to skim_dict,, +,_valid_school_location_child1,school_location_child1.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child2,school_location_child2.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_school_location_child3,school_location_child3.fillna(persons[persons.school_zone_id > 0].school_zone_id.mode()[0]) +,_valid_mandatory_location_chauf1,_mandatory_location_chauf1.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +,_valid_mandatory_location_chauf2,_mandatory_location_chauf2.fillna(persons[persons.workplace_zone_id > 0].workplace_zone_id.mode()[0]) +Auto time home to school - child 1,time_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('HOV2_M_TIME', 'AM')), 0)" +Auto time home to school - child 2,time_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('HOV2_M_TIME', 'AM')), 0)" +Auto time home to school - child 3,time_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('HOV2_M_TIME', 'AM')), 0)" +Auto time school to home - child 1,time_school_to_home1,"np.where(school_location_child1 > 0, skim_dict.lookup(_valid_school_location_child1, df.home_zone_id, ('HOV2_M_TIME', 'AM')), 0)" +Auto time school to home - child 2,time_school_to_home2,"np.where(school_location_child2 > 0, skim_dict.lookup(_valid_school_location_child2, df.home_zone_id, ('HOV2_M_TIME', 'AM')), 0)" +Auto time school to home - child 3,time_school_to_home3,"np.where(school_location_child3 > 0, skim_dict.lookup(_valid_school_location_child3, df.home_zone_id, ('HOV2_M_TIME', 'AM')), 0)" +Auto dist home to school - child 1,dist_home_to_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('HOV2_M_DIST', 'AM')), 0)" +Auto dist home to school - child 2,dist_home_to_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('HOV2_M_DIST', 'AM')), 0)" +Auto dist home to school - child 3,dist_home_to_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('HOV2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 1,dist_intra_school1,"np.where(school_location_child1 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child1, ('HOV2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 2,dist_intra_school2,"np.where(school_location_child2 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child2, ('HOV2_M_DIST', 'AM')), 0)" +Auto dist intra school taz - child 3,dist_intra_school3,"np.where(school_location_child3 > 0, skim_dict.lookup(df.home_zone_id, _valid_school_location_child3, ('HOV2_M_DIST', 'AM')), 0)" +Auto time home to work or university - chauffer 1,time_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('HOV2_M_TIME', 'AM')), 0)" +Auto time home to work or university - chauffer 2,time_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('HOV2_M_TIME', 'AM')), 0)" +Auto dist home to work or university - chauffer 1,dist_home_to_mand1,"np.where(_mandatory_location_chauf1 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf1, ('HOV2_M_DIST', 'AM')), 0)" +Auto dist home to work or university - chauffer 2,dist_home_to_mand2,"np.where(_mandatory_location_chauf2 > 0, skim_dict.lookup(df.home_zone_id, _valid_mandatory_location_chauf2, ('HOV2_M_DIST', 'AM')), 0)" +# outbound distance combinations between chauffeurs and children,, +Distance from child 1 school to chauffeur 1 mandatory location,_dist_school1_to_mand1,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf1, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 1 school to chauffeur 2 mandatory location,_dist_school1_to_mand2,"np.where((school_location_child1 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_mandatory_location_chauf2, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 1 mandatory location,_dist_school2_to_mand1,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf1, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 2 school to chauffeur 2 mandatory location,_dist_school2_to_mand2,"np.where((school_location_child2 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_mandatory_location_chauf2, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 1 mandatory location,_dist_school3_to_mand1,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf1, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 3 school to chauffeur 2 mandatory location,_dist_school3_to_mand2,"np.where((school_location_child3 > 0) & (_mandatory_location_chauf2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_mandatory_location_chauf2, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 1 school to child 2 school,_dist_school1_to_school2,"np.where((school_location_child1 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child2, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 1 school to child 3 school,_dist_school1_to_school3,"np.where((school_location_child1 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child1, _valid_school_location_child3, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 2 school to child 3 school,_dist_school2_to_school3,"np.where((school_location_child2 > 0) & (school_location_child3 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child3, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 2 school to child 1 school,_dist_school2_to_school1,"np.where((school_location_child2 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child2, _valid_school_location_child1, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 3 school to child 1 school,_dist_school3_to_school1,"np.where((school_location_child3 > 0) & (school_location_child1 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child1, ('HOV2_M_TIME', 'AM')), 0)" +Distance from child 3 school to child 2 school,_dist_school3_to_school2,"np.where((school_location_child3 > 0) & (school_location_child2 > 0), skim_dict.lookup(_valid_school_location_child3, _valid_school_location_child2, ('HOV2_M_TIME', 'AM')), 0)" +# absolute deviation distance outbound,, +Absolute deviation outbound distance Child 1 Chauffer 1,abs_dev_dist_out_child1_chauf1,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 1 Chauffer 2,abs_dev_dist_out_child1_chauf2,"np.maximum(dist_home_to_school1 + _dist_school1_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 2 Chauffer 1,abs_dev_dist_out_child2_chauf1,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 2 Chauffer 2,abs_dev_dist_out_child2_chauf2,"np.maximum(dist_home_to_school2 + _dist_school2_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance Child 3 Chauffer 1,abs_dev_dist_out_child3_chauf1,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand1 - dist_home_to_mand1,0)" +Absolute deviation outbound distance Child 3 Chauffer 2,abs_dev_dist_out_child3_chauf2,"np.maximum(dist_home_to_school3 + _dist_school3_to_mand2 - dist_home_to_mand2,0)" +Absolute deviation outbound distance child12 Chauffer 1,abs_dev_dist_out_child12_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child12 Chauffer 2,abs_dev_dist_out_child12_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2, dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child13 Chauffer 1,abs_dev_dist_out_child13_chauf1,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child13 Chauffer 2,abs_dev_dist_out_child13_chauf2,"np.maximum(np.minimum(dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2) - dist_home_to_mand2, 0)" +Absolute deviation outbound distance child23 Chauffer 1,abs_dev_dist_out_child23_chauf1,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1) - dist_home_to_mand1, 0)" +Absolute deviation outbound distance child23 Chauffer 2,abs_dev_dist_out_child23_chauf2,"np.maximum(np.minimum(dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2, dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2) - dist_home_to_mand2, 0)" +,_dist_school1_school2_school3_mand1,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand1" +,_dist_school1_school3_school2_mand1,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand1" +,_dist_school2_school1_school3_mand1,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand1" +,_dist_school2_school3_school1_mand1,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand1" +,_dist_school3_school1_school2_mand1,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand1" +,_dist_school3_school2_school1_mand1,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand1" +,_min_dist_dropoff_order_out_child123_chauf1,_dist_school1_school2_school3_mand1 +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school1_school3_school2_mand1 > 0) & (_dist_school1_school3_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school1_school3_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school1_school3_mand1 > 0) & (_dist_school2_school1_school3_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school1_school3_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school2_school3_school1_mand1 > 0) & (_dist_school2_school3_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school2_school3_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school1_school2_mand1 > 0) & (_dist_school3_school1_school2_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school1_school2_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +,_min_dist_dropoff_order_out_child123_chauf1,"np.where((_dist_school3_school2_school1_mand1 > 0) & (_dist_school3_school2_school1_mand1 < _min_dist_dropoff_order_out_child123_chauf1), _dist_school3_school2_school1_mand1, _min_dist_dropoff_order_out_child123_chauf1)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf1,"np.maximum(_min_dist_dropoff_order_out_child123_chauf1 - dist_home_to_mand1, 0)" +,_dist_school1_school2_school3_mand2,"dist_home_to_school1 + _dist_school1_to_school2 + _dist_school2_to_school3 + _dist_school3_to_mand2" +,_dist_school1_school3_school2_mand2,"dist_home_to_school1 + _dist_school1_to_school3 + _dist_school3_to_school2 + _dist_school2_to_mand2" +,_dist_school2_school1_school3_mand2,"dist_home_to_school2 + _dist_school2_to_school1 + _dist_school1_to_school3 + _dist_school3_to_mand2" +,_dist_school2_school3_school1_mand2,"dist_home_to_school2 + _dist_school2_to_school3 + _dist_school3_to_school1 + _dist_school1_to_mand2" +,_dist_school3_school1_school2_mand2,"dist_home_to_school3 + _dist_school3_to_school1 + _dist_school1_to_school2 + _dist_school2_to_mand2" +,_dist_school3_school2_school1_mand2,"dist_home_to_school3 + _dist_school3_to_school2 + _dist_school2_to_school1 + _dist_school1_to_mand2" +,_min_dist_dropoff_order_out_child123_chauf2,_dist_school1_school2_school3_mand2 +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school1_school3_school2_mand2 > 0) & (_dist_school1_school3_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school1_school3_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school1_school3_mand2 > 0) & (_dist_school2_school1_school3_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school1_school3_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school2_school3_school1_mand2 > 0) & (_dist_school2_school3_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school2_school3_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school1_school2_mand2 > 0) & (_dist_school3_school1_school2_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school1_school2_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +,_min_dist_dropoff_order_out_child123_chauf2,"np.where((_dist_school3_school2_school1_mand2 > 0) & (_dist_school3_school2_school1_mand2 < _min_dist_dropoff_order_out_child123_chauf2), _dist_school3_school2_school1_mand2, _min_dist_dropoff_order_out_child123_chauf2)" +Absolute deviation outbound distance child123 Chauffer 1,abs_dev_dist_out_child123_chauf2,"np.maximum(_min_dist_dropoff_order_out_child123_chauf2 - dist_home_to_mand2, 0)" +# Availability for multiple bundles,, +,_return_min_taking_child1,"(pref_depart_time_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +,_return_min_taking_child2,"(pref_depart_time_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +,_return_min_taking_child3,"(pref_depart_time_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +Availability of taking child 1 and then 2,_avail_child1_then_child2,"(_return_min_taking_child1 < (pref_depart_time_school2 * mins_per_time_bin))" +Availability of taking child 2 and then 1,_avail_child2_then_child1,"(_return_min_taking_child2 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 1 and then 3,_avail_child1_then_child3,"(_return_min_taking_child1 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 1,_avail_child3_then_child1,"(_return_min_taking_child3 < (pref_depart_time_school1 * mins_per_time_bin))" +Availability of taking child 2 and then 3,_avail_child2_then_child3,"(_return_min_taking_child2 < (pref_depart_time_school3 * mins_per_time_bin))" +Availability of taking child 3 and then 2,_avail_child3_then_child2,"(_return_min_taking_child3 < (pref_depart_time_school2 * mins_per_time_bin))" +multiple_bundle_availability,avail_multiple_bundles,(_avail_child1_then_child2 | _avail_child2_then_child1 | _avail_child1_then_child3 | _avail_child3_then_child1 | _avail_child2_then_child3 | _avail_child3_then_child2) +# ,, +# Inbound specific terms ,, +Preferred return time from school - child 1,pref_return_time_school1,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id1)" +Preferred return time from school - child 2,pref_return_time_school2,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id2)" +Preferred return time from school - child 3,pref_return_time_school3,"reindex(tours[(tours.tour_type == 'school') & (tours.tour_num == 1)].set_index('person_id').end, df.child_id3)" +Preferred return time from work / univ - chauffer 1,pref_return_time_chauf1,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id1)" +Preferred return time from work / univ - chauffer 2,pref_return_time_chauf2,"reindex(tours[(tours.tour_category == 'mandatory') & (tours.tour_num == 1)].set_index('person_id').end, df.chauf_id2)" +# overlapping time windows from inbound escorting,, +# ,_return_min_taking_inbound_child1_pe,"(_pref_depart_time_to_school1 * mins_per_time_bin) + time_home_to_school1 + time_school_to_home1" +# ,_return_min_taking_inbound_child2_pe,"(_pref_depart_time_to_school2 * mins_per_time_bin) + time_home_to_school2 + time_school_to_home2" +# ,_return_min_taking_inbound_child3_pe,"(_pref_depart_time_to_school3 * mins_per_time_bin) + time_home_to_school3 + time_school_to_home3" +# finding earliest time chauffer 1 returns from inbound pure escort tour,inbound_pe_return_time_home_chauf1,-1 +# ,inbound_pe_return_time_home_chauf1,"np.where((df.chauf1_inbound == 2) & (_return_min_taking_inbound_child1_pe > inbound_pe_return_time_home_chauf1), _return_min_taking_inbound_child1_pe, inbound_pe_return_time_home_chauf1)" +# ,inbound_pe_return_time_home_chauf1,"np.where((df.chauf2_inbound == 2) & (_return_min_taking_inbound_child2_pe > inbound_pe_return_time_home_chauf1), _return_min_taking_inbound_child2_pe, inbound_pe_return_time_home_chauf1)" +# ,inbound_pe_return_time_home_chauf1,"np.where((df.chauf3_inbound == 2) & (_return_min_taking_inbound_child3_pe > inbound_pe_return_time_home_chauf1), _return_min_taking_inbound_child3_pe, inbound_pe_return_time_home_chauf1)" +# finding earliest time chauffer 2 returns from inbound pure escort tour,inbound_pe_return_time_home_chauf2,-1 +# ,inbound_pe_return_time_home_chauf2,"np.where((df.chauf1_inbound == 2) & (_return_min_taking_inbound_child1_pe > inbound_pe_return_time_home_chauf2), _return_min_taking_inbound_child1_pe, inbound_pe_return_time_home_chauf2)" +# ,inbound_pe_return_time_home_chauf2,"np.where((df.chauf2_inbound == 2) & (_return_min_taking_inbound_child2_pe > inbound_pe_return_time_home_chauf2), _return_min_taking_inbound_child2_pe, inbound_pe_return_time_home_chauf2)" +# ,inbound_pe_return_time_home_chauf2,"np.where((df.chauf3_inbound == 2) & (_return_min_taking_inbound_child3_pe > inbound_pe_return_time_home_chauf2), _return_min_taking_inbound_child3_pe, inbound_pe_return_time_home_chauf2)" +# finding latest time chauffer 1 returns from inbound ride share tour,inbound_rs_return_time_home_chauf1,pref_depart_time_chauf1 +# finding latest time chauffer 2 returns from inbound ride share tour,inbound_rs_return_time_home_chauf2,pref_depart_time_chauf2 +# return time of inbound school escoring tour - chauffeur 1,return_bin_inbound_school_escorting1,"np.where(df.nrs1_inbound > 0, pref_depart_time_chauf1, inbound_pe_return_time_home_chauf1)" +# return time of inbound school escoring tour - chauffeur 2,return_bin_inbound_school_escorting2,"np.where(df.nrs2_inbound > 0, pref_depart_time_chauf2, inbound_pe_return_time_home_chauf2)" diff --git a/configs/resident/school_location.csv b/configs/resident/school_location.csv new file mode 100644 index 0000000..906636c --- /dev/null +++ b/configs/resident/school_location.csv @@ -0,0 +1,29 @@ +Label,Description,Expression,university,highschool,gradeschool,preschool +local_dist,,_DIST@skims['DIST'],1,1,1,1 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mclogsum_univ,coef_mclogsum_hsch,coef_mclogsum_gs,coef_mclogsum_ps +util_Distance,Distance,@_DIST.clip(upper=50),coef_dist_univ,coef_dist_hsch,coef_dist_gs,coef_dist_ps +util_log_Distance,log_Distance,@np.log(_DIST.clip(upper=50)+1),coef_lndist_univ,coef_zero,coef_zero,coef_lndist_ps +util_Distance_squareroot,Squareroot of distance,@_DIST.clip(upper=50)**0.5,coef_sqrtdist_univ,coef_sqrtdist_hsch,coef_sqrtdist_gs,coef_sqrtdist_ps +util_Distance_squared,Distance_squared,@_DIST.clip(upper=50)**2,coef_sqrddist_univ,coef_sqrddist_hsch,coef_sqrddist_gs,coef_sqrddist_ps +util_Distance_cubed,Distance_cubed,@_DIST.clip(upper=50)**3,coef_cubeddist_univ,coef_cubeddist_hsch,coef_cubeddist_gs,coef_cubeddist_ps +util_Distance_worker_univ,Distance for a worker_university specific,"@np.where(df.ptype==1, _DIST.clip(upper=50), 0)",coef_workerdist_univ,coef_zero,coef_zero,coef_zero +util_Distance_largeuniversity_univ,Distance for large university enrollment,"@np.where(df.collegeenroll>5000, _DIST.clip(upper=50), 0)",coef_univenrol_dist_univ,coef_zero,coef_zero,coef_zero +util_Distance _lowincome_prek,Distance - low income,"@np.where(df.income<60000, _DIST.clip(upper=50), 0)",coef_zero,coef_zero,coef_zero,coef_lowincdist_ps +util_Distance - age03_prek,Distance - age 0 to 3,"@np.where(df.age<3,_DIST.clip(upper=50),0)",coef_zero,coef_zero,coef_zero,coef_age03dist_ps +util_LoggedSize,Logged Size variable - University specific,@df['size_term'].apply(np.log1p),coef_lnSize,coef_lnSize,coef_lnSize,coef_lnSize +util_no_attractions,no attractions if logged university size is zero,@df['size_term']==0,-999,-999,-999,-999 +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1 +util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 +#,,,,,, +util_ABM2calibration_0-1miles,ABM2 calibration_0-1miles,@(_DIST<1),coef_zero,coef_abmcalib_01miles,coef_abmcalib_01miles,coef_abmcalib_01miles +util_ABM2calibration_1-2miles,ABM2 calibration_1-2miles,@(_DIST<2) * (_DIST>=1),coef_zero,coef_abmcalib_12miles,coef_abmcalib_12miles,coef_abmcalib_12miles +util_ABM2calibration_2-3miles,ABM2 calibration_2-3miles,@(_DIST<3) * (_DIST>=2),coef_zero,coef_abmcalib_23miles,coef_abmcalib_23miles,coef_abmcalib_23miles +util_ABM2calibration_0-20miles,ABM2 calibration_0-20miles,@(_DIST<20) * (_DIST),coef_zero,coef_abmcalib_20miles,coef_abmcalib_20miles,coef_abmcalib_20miles +#,,,,,, +#calibration constants,,, +util_Calibration 0-2 - miles,Calibration 0-2 - miles,@_DIST<=2,,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_Calibration 2-5 - miles,Calibration 2-5 - miles,@(_DIST>2) * (_DIST<=5),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_Calibration 5-10 - miles,Calibration 5-10 - miles,@(_DIST>5) * (_DIST<=10),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_ABM2 calibration 10-20 - miles,ABM3 calibration 10-20 - miles,@(_DIST>10) * (_DIST<=20),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_ABM2 calibration 20-30 - miles,ABM3 calibration 20-30 - miles,@(_DIST>20) * (_DIST<=30),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles +util_ABM2 calibration >30 - miles,ABM3 calibration >30 - miles,@(_DIST>30),,coef_distance_0_2miles,coef_distance_2_5miles,coef_distance_5_10miles diff --git a/configs/resident/school_location.yaml b/configs/resident/school_location.yaml new file mode 100644 index 0000000..96c98a5 --- /dev/null +++ b/configs/resident/school_location.yaml @@ -0,0 +1,74 @@ +SAMPLE_SIZE: 30 +ESTIMATION_SAMPLE_SIZE: 10 + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - school_segment + - household_id + - ptype + - collegeenroll + - income + - age + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: school_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: school_location_logsum +# comment out MODE_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +MODE_CHOICE_LOGSUM_COLUMN_NAME: school_modechoice_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: school_location_sample + + +SAMPLE_SPEC: school_location_sample.csv +SPEC: school_location.csv +COEFFICIENTS: school_location_coefficients.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor + +LOGSUM_TOUR_PURPOSE: + university: univ + highschool: school + gradeschool: school + preschool: school + +annotate_persons: + SPEC: annotate_persons_school + DF: persons + +# - shadow pricing + +# required by initialize_households when creating school_destination_size table +CHOOSER_TABLE_NAME: persons + +# size_terms model_selector +MODEL_SELECTOR: school + +# chooser column with segment_id for this segment type +CHOOSER_SEGMENT_COLUMN_NAME: school_segment + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_student + + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +SEGMENT_IDS: + university: 4 # SCHOOL_SEGMENT_UNIV DEFINED IN constants.yaml + highschool: 3 # SCHOOL_SEGMENT_HIGH + gradeschool: 2 # SCHOOL_SEGMENT_GRADE + preschool: 1 # SCHOOL_SEGMENT_PREK + + +# model adds these tables (informational - not added if commented out) +SHADOW_PRICE_TABLE: school_shadow_prices +MODELED_SIZE_TABLE: school_modeled_size + +# not loaded if commented out +SAVED_SHADOW_PRICE_TABLE_NAME: school_shadow_prices.csv diff --git a/configs/resident/school_location_coefficients.csv b/configs/resident/school_location_coefficients.csv new file mode 100644 index 0000000..06450ed --- /dev/null +++ b/configs/resident/school_location_coefficients.csv @@ -0,0 +1,43 @@ +coefficient_name,value,constrain +coef_zero,0,T +coef_lnSize,1,T +coef_abmcalib_01miles,-0.962352271,T +coef_abmcalib_12miles,-0.6403,T +coef_abmcalib_23miles,-0.1299,T +coef_abmcalib_20miles,0.0307,T +#,, +coef_mclogsum_univ,0.25,F +coef_dist_univ,1.279,F +coef_lndist_univ,10.412,F +coef_sqrtdist_univ,-13.774,F +coef_sqrddist_univ,-0.011,F +coef_cubeddist_univ,0,F +coef_workerdist_univ,0.013,F +coef_univenrol_dist_univ,0.028,F +#,, +coef_mclogsum_hsch,0.25,F +coef_dist_hsch,0.0241,F +coef_sqrtdist_hsch,-2.2566,F +coef_sqrddist_hsch,0.012,F +coef_cubeddist_hsch,-0.0002,F +#,, +coef_mclogsum_gs,0.34935,F +coef_dist_gs,1.4025,F +coef_sqrtdist_gs,-6.6929,F +coef_sqrddist_gs,-0.0245,F +coef_cubeddist_gs,0.0002,F +#,, +coef_mclogsum_ps,0.5,F +coef_dist_ps,0.4333,F +coef_lndist_ps,4.3415,F +coef_sqrtdist_ps,-7.38,F +coef_sqrddist_ps,0.0125,F +coef_cubeddist_ps,-0.0004,F +coef_lowincdist_ps,-0.0956,F +coef_age03dist_ps,-0.007,F +#,, +coef_distance_0_2miles,0.170,F +coef_distance_2_5miles,0.120,F +coef_distance_5_10miles,0.250,F +coef_distance_10_20miles,-0.400,F +coef_distance_20_30miles,-2.900,F diff --git a/configs/resident/school_location_sample.csv b/configs/resident/school_location_sample.csv new file mode 100644 index 0000000..2df8d72 --- /dev/null +++ b/configs/resident/school_location_sample.csv @@ -0,0 +1,14 @@ +Label,Description,Expression,university,highschool,gradeschool,preschool +local_dist,,_DIST@skims['DIST'],1,1,1,1 +util_Distance,Distance,@_DIST.clip(upper=50),coef_dist_univ,coef_dist_hsch,coef_dist_gs,coef_dist_ps +util_log_Distance,log_Distance,@np.log(_DIST.clip(upper=50)+1),coef_lndist_univ,coef_zero,coef_zero,coef_lndist_ps +util_Squareroot_distance,Squareroot_distance,@_DIST.clip(upper=50)**0.5,coef_sqrtdist_univ,coef_sqrtdist_hsch,coef_sqrtdist_gs,coef_sqrtdist_ps +util_Distance_squared,Distance_squared,@_DIST.clip(upper=50)**2,coef_sqrddist_univ,coef_sqrddist_hsch,coef_sqrddist_gs,coef_sqrddist_ps +util_Distance_cubed,Distance_cubed,@_DIST.clip(upper=50)**3,coef_cubeddist_univ,coef_cubeddist_hsch,coef_cubeddist_gs,coef_cubeddist_ps +util_Distance_worker_univspecific,Distance for a worker_university specific,"@np.where(df.ptype==1, _DIST.clip(upper=50), 0)",coef_workerdist_univ,coef_zero,coef_zero,coef_zero +util_Distance_largeuniversity_univspecific,Distance for large university enrollment,"@np.where(df.collegeenroll>5000, _DIST.clip(upper=50), 0)",coef_univenrol_dist_univ,coef_zero,coef_zero,coef_zero +util_Distance _lowincome_prek,Distance - low income,"@np.where(df.income<60000, _DIST.clip(upper=50), 0)",coef_zero,coef_zero,coef_zero,coef_lowincdist_ps +util_Distance - age03_prek,Distance - age 0 to 3,"@np.where(df.age<3,_DIST.clip(upper=50),0)",coef_zero,coef_zero,coef_zero,coef_age03dist_ps +util_LoggedSize,Logged Size variable - University specific,@df['size_term'].apply(np.log1p),coef_lnSize,coef_lnSize,coef_lnSize,coef_lnSize +util_no_attractions,no attractions if logged university size is zero,@df['size_term']==0,-999,-999,-999,-999 +util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1,1 \ No newline at end of file diff --git a/configs/resident/settings.yaml b/configs/resident/settings.yaml new file mode 100644 index 0000000..8c66330 --- /dev/null +++ b/configs/resident/settings.yaml @@ -0,0 +1,189 @@ +model_name: resident + +#inherit_settings: True + +chunk_size: 0 + +# assume enough RAM to not chunk +chunk_training_mode: disabled + +# - tracing + +# turn shadow_pricing on and off for all models (e.g. school and work) +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: True + +memory_profile: False + +# Sample TAZ before MAZ in destination choice +want_dest_choice_presampling: True + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +# trace household id; comment out or leave empty for no trace +# households with all tour types +trace_hh_id: + +chunk_training_mode: disabled + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +#trace_od: + +households_sample_size: 10 + +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + hhid: household_id + persons: hhsize + hworkers: num_workers + veh: auto_ownership + mgra: home_zone_id + hinc: income + hht: HHT + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + - bldgsz + - unittype + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + PERID: person_id + rename_columns: + hhid: household_id + perid: person_id + pnum: PNUM + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + - educ + - naics2_original_code + - soc2 + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + drop_columns: + - i1 + - i2 + - i3 + - i4 + - i5 + - i6 + - i7 + - i8 + - i9 + - hs + - hs_sf + - hs_mf + - hs_mh + - hh_sf + - hh_mf + - hh_mh + - zip09 + - luz_id + +cleanup_pipeline_after_run: True + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + - vehicles + - proto_disaggregate_accessibility + - disaggregate_accessibility + +distributed_time_factor_work_mean: 1.0 +distributed_time_factor_work_stddev: 0.7 +distributed_time_factor_nonwork_mean: 1.0 +distributed_time_factor_nonwork_stddev: 0.6 +distributed_time_factor_min: 0.1 +distributed_time_factor_max: 10 + +resume_after: + +models: + ### mp_init_proto_pop (single process) + - initialize_proto_population # Separate step so proto tables can be split for multiprocess. + ### mp_disaggregate_accessibility + - compute_disaggregate_accessibility + ### mp_initialize_hhs (single process) + - initialize_landuse + - initialize_households + ### mp_accessibility + - compute_accessibility + ### mp_households + - av_ownership + - auto_ownership_simulate + - work_from_home + - external_worker_identification + - external_workplace_location + - school_location + - workplace_location + - transit_pass_subsidy + - transit_pass_ownership + - vehicle_type_choice + - adjust_auto_operating_cost + - transponder_ownership + - free_parking + - telecommute_frequency + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - school_escorting + - joint_tour_frequency_composition + - external_joint_tour_identification + - joint_tour_participation + - joint_tour_destination + - external_joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - external_non_mandatory_identification + - non_mandatory_tour_destination + - external_non_mandatory_destination + - non_mandatory_tour_scheduling + - vehicle_allocation + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - parking_location + ### mp_summarize (single process) + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + - write_tables diff --git a/configs/resident/settings_mp.yaml b/configs/resident/settings_mp.yaml new file mode 100644 index 0000000..3ca3da8 --- /dev/null +++ b/configs/resident/settings_mp.yaml @@ -0,0 +1,109 @@ +inherit_settings: settings.yaml + +multiprocess: True +households_sample_size: 0 +num_processes: 40 +trace_hh_id: +chunk_training_mode: disabled +# chunk_size: 240_000_000_000 + +use_shadow_pricing: True + +memory_profile: False + +# raise error if any sub-process fails without waiting for others to complete +# (Shadow pricing requires fail_fast setting in multiprocessing mode) +fail_fast: True + +resume_after: + +models: + ### mp_init_proto_pop (single process) + - initialize_proto_population # Separate step so proto tables can be split for multiprocess. + ### mp_disaggregate_accessibility + - compute_disaggregate_accessibility + ### mp_initialize_hhs (single process) + - initialize_landuse + - initialize_households + ### mp_accessibility + - compute_accessibility + ### mp_households + - av_ownership + - auto_ownership_simulate + - work_from_home + - external_worker_identification + - external_workplace_location + - school_location + - workplace_location + - transit_pass_subsidy + - transit_pass_ownership + - vehicle_type_choice + - adjust_auto_operating_cost + - transponder_ownership + - free_parking + - telecommute_frequency + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - school_escorting + - joint_tour_frequency_composition + - external_joint_tour_identification + - joint_tour_participation + - joint_tour_destination + - external_joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - external_non_mandatory_identification + - non_mandatory_tour_destination + - external_non_mandatory_destination + - non_mandatory_tour_scheduling + - vehicle_allocation + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - parking_location + ### mp_summarize (single process) + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + # - write_to_datalake + - update_tables + - write_tables + + +multiprocess_steps: + - name: mp_init_proto_pop + begin: initialize_proto_population + - name: mp_disaggregate_accessibility + num_processes: 20 + begin: compute_disaggregate_accessibility + slice: + tables: + - proto_households + - proto_persons + - proto_tours + - name: mp_initialize_hhs + begin: initialize_landuse + - name: mp_accessibility + begin: compute_accessibility + num_processes: 10 + slice: + tables: + - accessibility + except: True # this is needed so landuse (i.e. destinations) doesn't get split + - name: mp_households + begin: av_ownership + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_data_dictionary diff --git a/configs/resident/shadow_pricing.yaml b/configs/resident/shadow_pricing.yaml new file mode 100644 index 0000000..b778c31 --- /dev/null +++ b/configs/resident/shadow_pricing.yaml @@ -0,0 +1,51 @@ +shadow_pricing_models: + school: school_location + workplace: workplace_location + external_workplace: external_workplace_location + +# global switch to enable/disable loading of saved shadow prices +# (ignored if global use_shadow_pricing switch is False) +LOAD_SAVED_SHADOW_PRICES: False + +# write out choices by iteration to trace folder +WRITE_ITERATION_CHOICES: False + +# number of shadow price iterations for cold start +MAX_ITERATIONS: 10 + +# number of shadow price iterations for warm start (after loading saved shadow_prices) +MAX_ITERATIONS_SAVED: 1 + +## Shadow pricing method +# SHADOW_PRICE_METHOD: ctramp +# SHADOW_PRICE_METHOD: daysim +SHADOW_PRICE_METHOD: simulation + +# --- simulation method settings +# ignore criteria for zones smaller than size_threshold +SIZE_THRESHOLD: 10 +# ignore criteria for zones smaller than target_threshold (total employmnet or enrollment) +TARGET_THRESHOLD: 20 +# zone passes if modeled is within percent_tolerance of predicted_size +PERCENT_TOLERANCE: 5 +# max percentage of zones allowed to fail +FAIL_THRESHOLD: 1 +# apply different targets for each segment specified in destination_size_terms.csv +school_segmentation_targets: + # format is segment: land_use_column + university: tot_collegeenroll + highschool: enrollgrade9to12 + gradeschool: enrollgradekto8 + preschool: preschool_target + +# if target names are the same, they will be combined together +workplace_segmentation_targets: + # FIXME: need to use employment with removed external workers + constr_maint: total_emp_revised + health: total_emp_revised + military: total_emp_revised + mngt_busi_scic_arts: total_emp_revised + prod_trans_move: total_emp_revised + sales_office: total_emp_revised + services: total_emp_revised + diff --git a/configs/resident/stop_frequency.yaml b/configs/resident/stop_frequency.yaml new file mode 100644 index 0000000..6118976 --- /dev/null +++ b/configs/resident/stop_frequency.yaml @@ -0,0 +1,77 @@ +# inherit_settings: True +LOGIT_TYPE: MNL + +preprocessor: + SPEC: stop_frequency_annotate_tours_preprocessor + DF: tours_merged + TABLES: + - persons + - land_use + - accessibility + +SEGMENT_COL: primary_purpose + +SPEC_SEGMENTS: + - primary_purpose: work + SPEC: stop_frequency_work.csv + COEFFICIENTS: stop_frequency_coefficients_work.csv + - primary_purpose: school + SPEC: stop_frequency_school.csv + COEFFICIENTS: stop_frequency_coefficients_school.csv + - primary_purpose: univ + SPEC: stop_frequency_univ.csv + COEFFICIENTS: stop_frequency_coefficients_univ.csv + - primary_purpose: social + SPEC: stop_frequency_social.csv + COEFFICIENTS: stop_frequency_coefficients_social.csv + - primary_purpose: shopping + SPEC: stop_frequency_shopping.csv + COEFFICIENTS: stop_frequency_coefficients_shopping.csv + - primary_purpose: eatout + SPEC: stop_frequency_eatout.csv + COEFFICIENTS: stop_frequency_coefficients_eatout.csv + - primary_purpose: escort + SPEC: stop_frequency_escort.csv + COEFFICIENTS: stop_frequency_coefficients_escort.csv + - primary_purpose: othmaint + SPEC: stop_frequency_othmaint.csv + COEFFICIENTS: stop_frequency_coefficients_othmaint.csv + - primary_purpose: othdiscr + SPEC: stop_frequency_othdiscr.csv + COEFFICIENTS: stop_frequency_coefficients_othdiscr.csv + - primary_purpose: atwork + SPEC: stop_frequency_atwork.csv + COEFFICIENTS: stop_frequency_coefficients_atwork.csv + +CONSTANTS: + TRANSIT_MODES: + - WALK_TRANSIT + - PNR_TRANSIT + - KNR_TRANSIT + - TNC_TRANSIT + DRIVE_TO_TRANSIT_MODES: + - PNR_LOC + - KNR_LOC + - TNC_LOC + - PNR_PRM + - KNR_PRM + - TNC_PRM + - PNR_MIX + - KNR_MIX + - TNC_MIX + NONMOTORIZED_MODES: + - WALK + - BIKE + SHOP_TOUR: shopping + MAINT_TOUR: othmaint + SCHOOL_TOUR: school + EATOUT_TOUR: eatout + SOCIAL_TOUR: social + DISCR_TOUR: othdiscr + num_atwork_subtours_map: + no_subtours: 0 + eat: 1 + business1: 1 + maint: 1 + business2: 2 + eat_business: 2 diff --git a/configs/resident/stop_frequency_alternatives.csv b/configs/resident/stop_frequency_alternatives.csv new file mode 100644 index 0000000..72f49a7 --- /dev/null +++ b/configs/resident/stop_frequency_alternatives.csv @@ -0,0 +1,18 @@ +#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,out,in +0out_0in,0,0 +0out_1in,0,1 +0out_2in,0,2 +0out_3in,0,3 +1out_0in,1,0 +1out_1in,1,1 +1out_2in,1,2 +1out_3in,1,3 +2out_0in,2,0 +2out_1in,2,1 +2out_2in,2,2 +2out_3in,2,3 +3out_0in,3,0 +3out_1in,3,1 +3out_2in,3,2 +3out_3in,3,3 diff --git a/configs/resident/stop_frequency_annotate_tours_preprocessor.csv b/configs/resident/stop_frequency_annotate_tours_preprocessor.csv new file mode 100644 index 0000000..fe558b8 --- /dev/null +++ b/configs/resident/stop_frequency_annotate_tours_preprocessor.csv @@ -0,0 +1,56 @@ +Description,Target,Expression +#,, +# define primary_purpose to use for slicing choosers with a value that identifies the spec to be used ,, +# e.g. univ segment means there will be a spec called stop_frequency_univ.csv,, +# so the 'school' tour_type can treat univ and non-univ school tours differently,, +,primary_purpose,"df.tour_type.where((df.tour_type != 'school') | ~df.is_university, 'univ')" +,primary_purpose,"primary_purpose.where(df.tour_category!='atwork', 'atwork')" +#,, +,distance_in_miles,od_skims['DIST'] +#,, +,is_joint,df.tour_category=='joint' +,_HH_PERSON_COUNT,"lambda exp, persons: persons.query(exp).groupby('household_id').size()" +,num_full,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_FULL, persons), df.household_id)" +,num_part,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_PART, persons), df.household_id)" +,num_student,"reindex_i(_HH_PERSON_COUNT('pstudent != %s' % PSTUDENT_NOT, persons), df.household_id)" +Num Kids between 0 and 4 (including) years old,num_age_0_4,"reindex_i(_HH_PERSON_COUNT('age < 5', persons), df.household_id)" +Num kids between 4 and 15 (including) years old,num_age_5_15,"reindex_i(_HH_PERSON_COUNT('(age >= 5) & (age <16)', persons), df.household_id)" +Number of Adults (>= 16 years old),num_adult,"reindex_i(_HH_PERSON_COUNT('age >= 16', persons), df.household_id)" +,more_cars_than_workers,df.auto_ownership >= (num_full + num_part) +,tour_mode_is_transit,df.tour_mode.isin(TRANSIT_MODES) +,tour_mode_is_drive_transit,df.tour_mode.isin(DRIVE_TO_TRANSIT_MODES) +,tour_mode_is_non_motorized,df.tour_mode.isin(NONMOTORIZED_MODES) +,tour_mode_is_schbus,df.tour_mode.isin(['SCH_BUS']) + +#,, +#num_work_tours already defined,, +,num_total_tours,"reindex_i(df.groupby('person_id').size(), df.person_id)" +school but not university,num_school_tours,"reindex_i(df[primary_purpose==SCHOOL_TOUR].groupby('person_id').size(), df.person_id)" +,num_univ_tours,(df.is_university) * num_school_tours +#num_escort_tours already defined,, +# indiv tour counts should not include joint tours by point_person,, +,num_shop_tours,"reindex_i(df[~is_joint & (df.tour_type==SHOP_TOUR)].groupby('person_id').size(), df.person_id)" +,num_maint_tours,"reindex_i(df[~is_joint & (df.tour_type==MAINT_TOUR)].groupby('person_id').size(), df.person_id)" +,num_eatout_tours,"reindex_i(df[~is_joint & (df.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" +,num_social_tours,"reindex_i(df[~is_joint & (df.tour_type==SOCIAL_TOUR)].groupby('person_id').size(), df.person_id)" +,num_discr_tours,"reindex_i(df[~is_joint & (df.tour_type==DISCR_TOUR)].groupby('person_id').size(), df.person_id)" +#,, +Number of subtours in the tour,num_atwork_subtours,"df.atwork_subtour_frequency.map(num_atwork_subtours_map, na_action='ignore').fillna(0).astype(np.int8)" +#,, +,periods_per_hour,2 +,duration_hours,df.duration / periods_per_hour +Number of hh shop tours including joint,num_hh_shop_tours,"reindex_i(df[df.tour_type==SHOP_TOUR].groupby('household_id').size(), df.person_id)" +Number of hh maint tours including joint,num_hh_maint_tours,"reindex_i(df[df.tour_type==MAINT_TOUR].groupby('household_id').size(), df.person_id)" +tourStartsInPeakPeriod,_tour_starts_in_peak,(network_los.skim_time_period_label(df.start) == 'AM') | (network_los.skim_time_period_label(df.start) == 'PM') +AccesibilityAtOrigin fallback,hhacc,0 +AccesibilityAtOrigin if transit,hhacc,"hhacc.where(~tour_mode_is_transit, df.trPkRetail.where(_tour_starts_in_peak, df.trOpRetail))" +AccesibilityAtOrigin if non_motorized,hhacc,"hhacc.where(~tour_mode_is_non_motorized, df.nmRetail)" +AccesibilityADestination fallback,pracc,0 +AccesibilityADestination peak transit,_dest_trPkRetail,"reindex(accessibility.trPkRetail, df.destination)" +AccesibilityADestination off-peak transit,_dest_trOpRetail,"reindex(accessibility.trOpRetail, df.destination)" +AccesibilityAtDestination if transit,pracc,"pracc.where(~tour_mode_is_transit, _dest_trPkRetail.where(_tour_starts_in_peak, _dest_trOpRetail))" +AccesibilityAtDestination if non_motorized,pracc,"pracc.where(~tour_mode_is_non_motorized, reindex(accessibility.nmRetail, df.destination))" +,destination_area_type,"reindex(land_use.pseudomsa, df.destination)" +,tour_mode_is_sov,df.tour_mode == 'SOV' +,tour_mode_is_hov,"df.tour_mode.isin(['HOV2', 'HOV3'])" +,tour_mode_is_taxi,"df.tour_mode.isin(['TAXI', 'TNC_SINGLE', 'TNC_SHARED'])" diff --git a/configs/resident/stop_frequency_atwork.csv b/configs/resident/stop_frequency_atwork.csv new file mode 100644 index 0000000..af6ffa8 --- /dev/null +++ b/configs/resident/stop_frequency_atwork.csv @@ -0,0 +1,9 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_atwork,coef_alternative_specific_constants_0out_2in_atwork,coef_alternative_specific_constants_0out_3in_atwork,coef_alternative_specific_constants_1out_0in_atwork,coef_alternative_specific_constants_1out_1in_atwork,coef_alternative_specific_constants_1out_2in_atwork,coef_alternative_specific_constants_1out_3in_atwork,coef_alternative_specific_constants_2out_0in_atwork,coef_alternative_specific_constants_2out_1in_atwork,coef_alternative_specific_constants_2out_2in_atwork,coef_alternative_specific_constants_2out_3in_atwork,coef_alternative_specific_constants_3out_0in_atwork,coef_alternative_specific_constants_3out_1in_atwork,coef_alternative_specific_constants_3out_2in_atwork,coef_alternative_specific_constants_3out_3in_atwork +util_is_pre_covid,Tour happened before covid hit,@scenarioYear==2016,,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork,coef_is_pre_covid_1plus_stops_atwork +util_num_age_5_15,Number of children in household age 5 to 15,num_age_5_15,,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork,coef_num_age_5_15_1plus_stops_atwork +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork,coef_tour_mode_non_motorized_1plus_stops_atwork +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork,coef_tour_duration_in_hours_1plus_stops_atwork +util_tour_type_eat,Atwork tour type is eat,(tour_type == 'eat'),,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork,coef_tour_type_eat_1plus_stops_atwork +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_LOC','PNR_PRM','PNR_MIX','KNR_LOC','KNR_PRM','KNR_MIX','TNC_LOC','TNC_PRM','TNC_MIX']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.3411831667549546,1.5644482135598068,-0.12240711424725262,-1.9592538168463436,1.1518382833625738,2.9268706664109896,-10.0,-0.8904060015251857,-1.2471095726908494,-10.0,-5.0,-0.8005791109300985,-10.0,-10.0,-5.0,2.613748359446448 diff --git a/configs/resident/stop_frequency_coefficients_atwork.csv b/configs/resident/stop_frequency_coefficients_atwork.csv new file mode 100644 index 0000000..141a532 --- /dev/null +++ b/configs/resident/stop_frequency_coefficients_atwork.csv @@ -0,0 +1,22 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_atwork,-3.092158257967754,F +coef_alternative_specific_constants_0out_2in_atwork,-4.899871765683751,F +coef_alternative_specific_constants_0out_3in_atwork,-5.768845734049241,F +coef_alternative_specific_constants_1out_0in_atwork,-3.546439038144793,F +coef_alternative_specific_constants_1out_1in_atwork,-5.3893910748423615,F +coef_alternative_specific_constants_1out_2in_atwork,-6.542073629348957,F +coef_alternative_specific_constants_1out_3in_atwork,-6.724352088610809,F +coef_alternative_specific_constants_2out_0in_atwork,-5.561245279802787,F +coef_alternative_specific_constants_2out_1in_atwork,-6.254352130392174,F +coef_alternative_specific_constants_2out_2in_atwork,-7.640624157012655,F +coef_alternative_specific_constants_2out_3in_atwork,-7.235168765535057,F +coef_alternative_specific_constants_3out_0in_atwork,-5.768855987819784,F +coef_alternative_specific_constants_3out_1in_atwork,-6.947501714137311,F +coef_alternative_specific_constants_3out_2in_atwork,-8.333869615557205,F +coef_alternative_specific_constants_3out_3in_atwork,-6.542030510182685,F +coef_is_pre_covid_1plus_stops_atwork,0.5756937400481714,F +coef_num_age_5_15_1plus_stops_atwork,0.2843024724548341,F +coef_tour_mode_non_motorized_1plus_stops_atwork,-1.1394252090580002,F +coef_tour_duration_in_hours_1plus_stops_atwork,0.8337318380115536,F +coef_tour_type_eat_1plus_stops_atwork,-1.5126695768515324,F diff --git a/configs/resident/stop_frequency_coefficients_eatout.csv b/configs/resident/stop_frequency_coefficients_eatout.csv new file mode 100644 index 0000000..a32e9fa --- /dev/null +++ b/configs/resident/stop_frequency_coefficients_eatout.csv @@ -0,0 +1,26 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_eatout,-2.719322587570536,F +coef_alternative_specific_constants_0out_2in_eatout,-4.04271514492949,F +coef_alternative_specific_constants_0out_3in_eatout,-4.838544989216345,F +coef_alternative_specific_constants_1out_0in_eatout,-2.8659262240861816,F +coef_alternative_specific_constants_1out_1in_eatout,-3.916842376183419,F +coef_alternative_specific_constants_1out_2in_eatout,-5.230563088163616,F +coef_alternative_specific_constants_1out_3in_eatout,-5.117238989358759,F +coef_alternative_specific_constants_2out_0in_eatout,-4.067401892944042,F +coef_alternative_specific_constants_2out_1in_eatout,-5.31395707633995,F +coef_alternative_specific_constants_2out_2in_eatout,-5.884510389772713,F +coef_alternative_specific_constants_2out_3in_eatout,-7.063117137546255,F +coef_alternative_specific_constants_3out_0in_eatout,-4.811844812901839,F +coef_alternative_specific_constants_3out_1in_eatout,-6.051577660315912,F +coef_alternative_specific_constants_3out_2in_eatout,-6.370029686002297,F +coef_alternative_specific_constants_3out_3in_eatout,-7.3509005055134,F +coef_is_joint_tour_1plus_stops_eatout,-0.7985681547806562,F +coef_number_of_nonwork_tours_1plus_stops_eatout,-0.1427263606567685,F +coef_age_65_79_1plus_stops_eatout,0.256692320705862,F +coef_age_80_plus_1plus_stops_eatout,0.6133960840207545,F +coef_non_mandatory_accessibility_1plus_stops_eatout,0.0385191271941809,F +coef_tour_mode_non_motorized_1plus_stops_eatout,-0.6439075033171243,F +coef_tour_mode_hov_1plus_stops_eatout,0.7283869214461836,F +coef_tour_duration_in_hours_1plus_stops_eatout,0.0711785217183164,F +coef_distance_to_tour_destination_1plus_stops_eatout,0.0419177818028756,F diff --git a/configs/resident/stop_frequency_coefficients_escort.csv b/configs/resident/stop_frequency_coefficients_escort.csv new file mode 100644 index 0000000..da22c76 --- /dev/null +++ b/configs/resident/stop_frequency_coefficients_escort.csv @@ -0,0 +1,28 @@ +coefficient_name,value,constrain +coef_unavailable,-998.9999999850988,T +coef_alternative_specific_constants_0out_1in_escort,-2.4949660926999164,F +coef_alternative_specific_constants_0out_2in_escort,-3.672015130183568,F +coef_alternative_specific_constants_0out_3in_escort,-4.317384022338293,F +coef_alternative_specific_constants_1out_0in_escort,-2.9339040180046654,F +coef_alternative_specific_constants_1out_1in_escort,-3.948109055444319,F +coef_alternative_specific_constants_1out_2in_escort,-4.78515783271987,F +coef_alternative_specific_constants_1out_3in_escort,-5.303964208013312,F +coef_alternative_specific_constants_2out_0in_escort,-4.186671117779487,F +coef_alternative_specific_constants_2out_1in_escort,-5.057105742551855,F +coef_alternative_specific_constants_2out_2in_escort,-6.576948604682893,F +coef_alternative_specific_constants_2out_3in_escort,-6.443397350305856,F +coef_alternative_specific_constants_3out_0in_escort,-4.421518979725427,F +coef_alternative_specific_constants_3out_1in_escort,-5.088849493880071,F +coef_alternative_specific_constants_3out_2in_escort,-6.124959873262966,F +coef_alternative_specific_constants_3out_3in_escort,-6.220285587804925,F +coef_is_student_1plus_stops_escort,0.2161986201032582,F +coef_age_5_18_1plus_stops_escort,0.3727005469727215,F +coef_age_65_79_1plus_stops_escort,0.389659640407008,F +coef_hhinc_less_15_1plus_stops_escort,-0.3864753297575288,F +coef_hhsize_is_3_1plus_stops_escort,-0.6055607644471578,F +coef_hhsize_is_4p_1plus_stops_escort,-0.5839400121244439,F +coef_non_mandatory_accessibility_1plus_stops_escort,0.0692389635383912,F +coef_tour_mode_non_motorized_1plus_stops_escort,-1.2439664098905787,F +coef_start_time_is_afternoon_1plus_stops_escort,0.1764927977315441,F +coef_distance_to_tour_destination_1plus_stops_escort,0.0353903674319862,F +coef_no_stops_to_school_escorting_1plus_stops_escort,0.5908677898396228,F diff --git a/configs/resident/stop_frequency_coefficients_othdiscr.csv b/configs/resident/stop_frequency_coefficients_othdiscr.csv new file mode 100644 index 0000000..b7e0257 --- /dev/null +++ b/configs/resident/stop_frequency_coefficients_othdiscr.csv @@ -0,0 +1,30 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_othdiscr,-2.4864652946738204,F +coef_alternative_specific_constants_0out_2in_othdiscr,-3.630516428961318,F +coef_alternative_specific_constants_0out_3in_othdiscr,-4.214343839043677,F +coef_alternative_specific_constants_1out_0in_othdiscr,-2.754048091372477,F +coef_alternative_specific_constants_1out_1in_othdiscr,-3.521188010752997,F +coef_alternative_specific_constants_1out_2in_othdiscr,-4.612681926260593,F +coef_alternative_specific_constants_1out_3in_othdiscr,-4.851923018137574,F +coef_alternative_specific_constants_2out_0in_othdiscr,-4.020178130616265,F +coef_alternative_specific_constants_2out_1in_othdiscr,-4.7492677704752095,F +coef_alternative_specific_constants_2out_2in_othdiscr,-5.897904534647468,F +coef_alternative_specific_constants_2out_3in_othdiscr,-6.020509324357631,F +coef_alternative_specific_constants_3out_0in_othdiscr,-4.428598584691764,F +coef_alternative_specific_constants_3out_1in_othdiscr,-5.2241670713521176,F +coef_alternative_specific_constants_3out_2in_othdiscr,-5.754805074140499,F +coef_alternative_specific_constants_3out_3in_othdiscr,-6.064960698334528,F +coef_is_joint_tour_1plusstops_othdiscr,-0.8985020250009612,F +coef_number_of_nonwork_tours_1plusstops_othdiscr,-0.2643315301718771,F +coef_age_5_18_1plusstops_othdiscr,-0.5660532740496371,F +coef_age_19_35_1plusstops_othdiscr,-0.2845268067760428,F +coef_age_65_79_1plusstops_othdiscr,0.1695756068531331,F +coef_preschool_child_in_hh_1plusstops_othdiscr,0.2693515451224259,F +coef_has_retiree_in_hh_1plusstops_othdiscr,-0.2169572360551797,F +coef_non_mandatory_accessibility_1plusstops_othdiscr,0.0690683633584472,F +coef_tour_mode_non_motorized_1plusstops_othdiscr,-1.508573161584664,F +coef_tour_mode_hov_1plusstops_othdiscr,0.9061054262736302,F +coef_start_time_is_morning_1plusstops_othdiscr,0.2982113854685047,F +coef_start_time_is_afternoon_1plusstops_othdiscr,0.1819176043755117,F +coef_distance_to_tour_destination_1plusstops_othdiscr,0.0375087318106341,F diff --git a/configs/resident/stop_frequency_coefficients_othmaint.csv b/configs/resident/stop_frequency_coefficients_othmaint.csv new file mode 100644 index 0000000..ac32ba7 --- /dev/null +++ b/configs/resident/stop_frequency_coefficients_othmaint.csv @@ -0,0 +1,27 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_othmaint,-1.5602924404450391,F +coef_alternative_specific_constants_0out_2in_othmaint,-2.384779306812381,F +coef_alternative_specific_constants_0out_3in_othmaint,-2.613794769617,F +coef_alternative_specific_constants_1out_0in_othmaint,-2.307883473311813,F +coef_alternative_specific_constants_1out_1in_othmaint,-2.938077675037544,F +coef_alternative_specific_constants_1out_2in_othmaint,-3.556152461131553,F +coef_alternative_specific_constants_1out_3in_othmaint,-3.3623723352749835,F +coef_alternative_specific_constants_2out_0in_othmaint,-3.1913289639656037,F +coef_alternative_specific_constants_2out_1in_othmaint,-3.8047760031,F +coef_alternative_specific_constants_2out_2in_othmaint,-4.390949985891435,F +coef_alternative_specific_constants_2out_3in_othmaint,-4.092462208845949,F +coef_alternative_specific_constants_3out_0in_othmaint,-3.568806711128329,F +coef_alternative_specific_constants_3out_1in_othmaint,-4.029936250472073,F +coef_alternative_specific_constants_3out_2in_othmaint,-4.599707691979066,F +coef_alternative_specific_constants_3out_3in_othmaint,-4.159153508767797,F +coef_is_joint_tour_1plusstops_othmaint,-1.1415368904713223,F +coef_number_of_children_in_hh_1plusstops_othmaint,0.2355862776646383,F +coef_number_of_nonwork_tours_1plusstops_othmaint,-0.2334086312449428,F +coef_hhsize_is_4p_1plusstops_othmaint,-0.3807340945310137,F +coef_tour_mode_non_motorized_1plusstops_othmaint,-0.6198408634766976,F +coef_tour_mode_hov_1plusstops_othmaint,1.082794990210597,F +coef_tour_duration_in_hours_1plusstops_othmaint,0.0305033478198989,F +coef_start_time_is_morning_1plusstops_othmaint,0.4176418075950055,F +coef_start_time_is_afternoon_1plusstops_othmaint,0.3736907177152264,F +coef_distance_to_tour_destination_1plusstops_othmaint,0.0292475180940679,F diff --git a/configs/resident/stop_frequency_coefficients_school.csv b/configs/resident/stop_frequency_coefficients_school.csv new file mode 100644 index 0000000..946c3a5 --- /dev/null +++ b/configs/resident/stop_frequency_coefficients_school.csv @@ -0,0 +1,32 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_school,-2.270036976078339,F +coef_alternative_specific_constants_0out_2in_school,-3.145965730364082,F +coef_alternative_specific_constants_0out_3in_school,-3.817363247655941,F +coef_alternative_specific_constants_1out_0in_school,-3.2274948701101858,F +coef_alternative_specific_constants_1out_1in_school,-4.844409089801835,F +coef_alternative_specific_constants_1out_2in_school,-5.650069018317617,F +coef_alternative_specific_constants_1out_3in_school,-6.05550780788901,F +coef_alternative_specific_constants_2out_0in_school,-4.928299261107959,F +coef_alternative_specific_constants_2out_1in_school,-7.308311886880356,F +coef_alternative_specific_constants_2out_2in_school,-7.44173289877394,F +coef_alternative_specific_constants_2out_3in_school,-8.28896797269094,F +coef_alternative_specific_constants_3out_0in_school,-5.984327657398892,F +coef_alternative_specific_constants_3out_1in_school,-8.001275620584247,F +coef_alternative_specific_constants_3out_2in_school,-8.693945624974088,F +coef_alternative_specific_constants_3out_3in_school,-8.694474791173255,F +coef_is_pre_covid_0out_1plusin_school,0.7125702463447188,F +coef_number_of_nonwork_tours_0out_1plusin_school,-0.3498536779228892,F +coef_school_child_in_hh_0out_1plusin_school,0.266793257512214,F +coef_non_mandatory_accessibility_0out_1plusin_school,0.0853214914318699,F +coef_tour_mode_non_motorized_0out_1plusin_school,-1.1253582283051773,F +coef_is_pre_covid_1plusout_1plusin_school,1.002629718374584,F +coef_number_of_nonwork_tours_1plusout_1plusin_school,-0.6057535513686793,F +coef_school_child_in_hh_1plusout_1plusin_school,0.8706807230114831,F +coef_non_worker_in_hh_1plusout_1plusin_school,0.3680982964801776,F +coef_non_mandatory_accessibility_1plusout_1plusin_school,0.1310875479177641,F +coef_tour_mode_non_motorized_1plusout_1plusin_school,-3.1117693140257034,F +coef_is_pre_covid_1plusout_0in_school,0.7698140702438344,F +coef_school_child_in_hh_1plusout_0in_school,0.7989949430082903,F +coef_tour_mode_non_motorized_1plusout_0in_school,-2.226875040638454,F +coef_tour_mode_schbus_0out_1plusin_school,-1.719166651447957,F diff --git a/configs/resident/stop_frequency_coefficients_shopping.csv b/configs/resident/stop_frequency_coefficients_shopping.csv new file mode 100644 index 0000000..84fb051 --- /dev/null +++ b/configs/resident/stop_frequency_coefficients_shopping.csv @@ -0,0 +1,28 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_shopping,-1.99478486659318,F +coef_alternative_specific_constants_0out_2in_shopping,-3.0029547936212144,F +coef_alternative_specific_constants_0out_3in_shopping,-3.8162472442792046,F +coef_alternative_specific_constants_1out_0in_shopping,-1.9298112667501943,F +coef_alternative_specific_constants_1out_1in_shopping,-3.081424428417236,F +coef_alternative_specific_constants_1out_2in_shopping,-3.952826067704117,F +coef_alternative_specific_constants_1out_3in_shopping,-4.447523137843972,F +coef_alternative_specific_constants_2out_0in_shopping,-2.899949450341273,F +coef_alternative_specific_constants_2out_1in_shopping,-3.882204113550045,F +coef_alternative_specific_constants_2out_2in_shopping,-5.063709523400166,F +coef_alternative_specific_constants_2out_3in_shopping,-5.063705408442639,F +coef_alternative_specific_constants_3out_0in_shopping,-3.4691848835291794,F +coef_alternative_specific_constants_3out_1in_shopping,-4.042050191513082,F +coef_alternative_specific_constants_3out_2in_shopping,-5.363812433746681,F +coef_alternative_specific_constants_3out_3in_shopping,-5.794596035666434,F +coef_is_joint_tour_1plusstops_shopping,-0.9376746632102424,F +coef_is_pre_covid_1plusstops_shopping,0.1880597722233851,F +coef_is_worker_1plusstops_shopping,-0.2984125841057903,F +coef_number_of_nonwork_tours_1plusstops_shopping,-0.2795952331923437,F +coef_school_child_in_hh_1plusstops_shopping,0.2755159915996485,F +coef_non_mandatory_accessibility_1plusstops_shopping,0.0357694082510162,F +coef_tour_mode_non_motorized_1plusstops_shopping,-1.073202207993033,F +coef_tour_mode_hov_1plusstops_shopping,0.8575809701489939,F +coef_tour_mode_transit_1plusstops_shopping,-0.4963693102721252,F +coef_tour_duration_in_hours_1plusstops_shopping,0.091893516673009,F +coef_distance_to_tour_destination_1plusstops_shopping,0.0911619700854451,F diff --git a/configs/resident/stop_frequency_coefficients_social.csv b/configs/resident/stop_frequency_coefficients_social.csv new file mode 100644 index 0000000..cd075f0 --- /dev/null +++ b/configs/resident/stop_frequency_coefficients_social.csv @@ -0,0 +1,24 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_social,-2.669744941267482,F +coef_alternative_specific_constants_0out_2in_social,-3.948157548888865,F +coef_alternative_specific_constants_0out_3in_social,-5.093324105648871,F +coef_alternative_specific_constants_1out_0in_social,-2.4747854649511694,F +coef_alternative_specific_constants_1out_1in_social,-3.820302803735448,F +coef_alternative_specific_constants_1out_2in_social,-4.842135917765225,F +coef_alternative_specific_constants_1out_3in_social,-5.0934431568988,F +coef_alternative_specific_constants_2out_0in_social,-3.350273969073528,F +coef_alternative_specific_constants_2out_1in_social,-5.429849829581639,F +coef_alternative_specific_constants_2out_2in_social,-6.346124542226189,F +coef_alternative_specific_constants_2out_3in_social,-5.652975455372254,F +coef_alternative_specific_constants_3out_0in_social,-4.84199879613982,F +coef_alternative_specific_constants_3out_1in_social,-5.093366632426675,F +coef_alternative_specific_constants_3out_2in_social,-7.039224828803537,F +coef_alternative_specific_constants_3out_3in_social,-7.039372547075768,F +coef_is_joint_tour_1plus_stops_social,-0.776560245920542,F +coef_number_of_nonwork_tours_1plus_stops_social,-0.2094030767278961,F +coef_age_5_18_1plus_stops_social,-0.7707681157165264,F +coef_non_mandatory_accessibility_1plus_stops_social,0.0399803049577818,F +coef_tour_mode_hov_1plus_stops_social,0.5244598307982651,F +coef_tour_duration_in_hours_1plus_stops_social,0.0731344275787003,F +coef_distance_to_tour_destination_1plus_stops_social,0.0431956613779886,F diff --git a/configs/resident/stop_frequency_coefficients_univ.csv b/configs/resident/stop_frequency_coefficients_univ.csv new file mode 100644 index 0000000..d82f6e1 --- /dev/null +++ b/configs/resident/stop_frequency_coefficients_univ.csv @@ -0,0 +1,20 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_1out_0in_univ,-2.9606821036239173,F +coef_alternative_specific_constants_1out_1in_univ,-2.994590126632717,F +coef_alternative_specific_constants_1out_2in_univ,-3.964059230970261,F +coef_alternative_specific_constants_1out_3in_univ,-4.110765202022077,F +coef_alternative_specific_constants_2out_0in_univ,-3.653878365193867,F +coef_alternative_specific_constants_2out_1in_univ,-4.570265363130149,F +coef_alternative_specific_constants_2out_2in_univ,-5.109276446867356,F +coef_alternative_specific_constants_2out_3in_univ,-5.109225424254904,F +coef_alternative_specific_constants_3out_0in_univ,-3.836259451583652,F +coef_alternative_specific_constants_3out_1in_univ,-4.416165484960151,F +coef_alternative_specific_constants_3out_2in_univ,-4.975726090153633,F +coef_alternative_specific_constants_3out_3in_univ,-4.858027864893195,F +coef_alternative_specific_constants_0out_1plusin_univ,-2.9441205060555693,F +coef_preschool_child_in_hh_1plusstops_univ,0.7353602959988254,F +coef_tour_mode_non_motorized_1plusstops_univ,-0.7149209302739166,F +coef_tour_mode_hov_1plusstops_univ,0.621498744143073,F +coef_tour_mode_transit_1plusstops_univ,0.4356060774156569,F +coef_tour_duration_in_hours_1plusstops_univ,0.1838966207472205,F diff --git a/configs/resident/stop_frequency_coefficients_work.csv b/configs/resident/stop_frequency_coefficients_work.csv new file mode 100644 index 0000000..2ab4ccf --- /dev/null +++ b/configs/resident/stop_frequency_coefficients_work.csv @@ -0,0 +1,67 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_alternative_specific_constants_0out_1in_work,-2.8447418642103286,F +coef_alternative_specific_constants_0out_2in_work,-5.074469232999513,F +coef_alternative_specific_constants_0out_3in_work,-7.356348978101946,F +coef_alternative_specific_constants_1out_0in_work,-3.998672291447317,F +coef_alternative_specific_constants_1out_1in_work,-5.340917046762127,F +coef_alternative_specific_constants_1out_2in_work,-7.293230160779633,F +coef_alternative_specific_constants_1out_3in_work,-8.644343958007005,F +coef_alternative_specific_constants_2out_0in_work,-5.706113217470375,F +coef_alternative_specific_constants_2out_1in_work,-7.153817173561018,F +coef_alternative_specific_constants_3out_0in_work,-6.7780536846962365,F +coef_alternative_specific_constants_3out_1in_work,-8.192357372172,F +coef_preschool_child_in_hh_1out_0in_work,0.4918316153479145,F +coef_school_child_in_hh_1out_0in_work,0.5231248472492089,F +coef_school_child_in_hh_1out_1in_work,0.281812148150233,F +coef_school_child_in_hh_1out_2in_work,0.5109837440498461,F +coef_school_child_in_hh_1out_3in_work,0.5146518187296193,F +coef_non_worker_in_hh_0out_1in_work,-0.2499539606769114,F +coef_non_worker_in_hh_0out_2in_work,-0.3895812012559245,F +coef_non_worker_in_hh_0out_3in_work,-0.7028004655351838,F +coef_tour_mode_sov_0out_1in_work,0.6663968126601825,F +coef_tour_mode_sov_0out_2in_work,0.8490983927253895,F +coef_tour_mode_sov_0out_3in_work,1.019470744148101,F +coef_tour_mode_hov_0out_1in_work,1.753765085617916,F +coef_tour_mode_hov_0out_2in_work,2.425463128045174,F +coef_tour_mode_hov_0out_3in_work,3.083984260465549,F +coef_tour_mode_hov_1out_0in_work,1.8810986245847765,F +coef_tour_mode_hov_1out_1in_work,2.903102643291588,F +coef_tour_mode_hov_1out_2in_work,3.0104904515059983,F +coef_tour_mode_hov_1out_3in_work,3.4462649672615124,F +coef_tour_mode_hov_2out_0in_work,2.3579589023621974,F +coef_tour_mode_hov_2out_1in_work,2.896537717160822,F +coef_tour_mode_hov_3out_0in_work,2.367639329734867,F +coef_tour_mode_hov_3out_1in_work,3.1434258750404256,F +coef_tour_duration_in_hours_0out_1in_work,0.0968419497302259,F +coef_tour_duration_in_hours_0out_2in_work,0.1904355233462784,F +coef_tour_duration_in_hours_0out_3in_work,0.3367138795904639,F +coef_tour_duration_in_hours_1out_0in_work,0.066650821483439,F +coef_tour_duration_in_hours_1out_1in_work,0.1646547411229061,F +coef_tour_duration_in_hours_1out_2in_work,0.2551530816688599,F +coef_tour_duration_in_hours_1out_3in_work,0.3252548498168742,F +coef_tour_duration_in_hours_2out_1in_work,0.1961700400992169,F +coef_tour_duration_in_hours_3out_1in_work,0.1501823000654714,F +coef_alternative_specific_constants_2plusout_2plusin_work,-9.526330213144162,F +coef_tour_mode_hov_2plusout_2plusin_work,3.7049086577656944,F +coef_tour_duration_in_hours_2plusout_2plusin_work,0.2609768272005556,F +coef_is_parttime_worker_1plusout_1plusin_work,0.3300187195960226,F +coef_is_parttime_worker_0out_1plusin_work,0.2855160872348049,F +coef_non_worker_in_hh_1plusout_1plusin_work,-0.4641234561873718,F +coef_preschool_child_in_hh_2plusout_0in_work,0.5136768446923758,F +coef_school_child_in_hh_2plusout_0in_work,0.715230225338151,F +coef_tour_mode_sov_1plusout_0in_work,0.3208347112170486,F +coef_tour_mode_sov_1plusout_1plusin_work,0.4540272237487077,F +coef_tour_duration_in_hours_2plusout_0in_work,0.0594361573692384,F +coef_preschool_child_in_hh_1plusout_1plusin_work,0.4336604925767433,F +coef_school_child_in_hh_2plusout_1plusin_work,0.3953971625740665,F +coef_age_35_to_44_1plusout_1plusin_work,0.2864843022250448,F +coef_age_55_to_64_0out_1plusin_work,0.173801196295945,F +coef_age_55_to_64_1plusout_0in_work,0.2187960088158339,F +coef_age_45_to_54_1plusout_1plusin_work,0.3495514458182455,F +coef_age_55_to_64_1plusout_1plusin_work,0.3141273930142538,F +coef_non_mandatory_accessibility_1plusout_0in_work,0.0555899473533157,F +coef_distance_to_tour_destination_0out_1plusin_work,0.0075317302289467,F +coef_distance_to_tour_destination_1plusout_1plusin_work,0.0103740905129598,F +coef_distance_to_tour_destination_1plusout_0in_work,0.0122085315777465,F +coef_telecommute_2_4_days_week_1plusout_1plusin_work,0.6007761919718061,F diff --git a/configs/resident/stop_frequency_eatout.csv b/configs/resident/stop_frequency_eatout.csv new file mode 100644 index 0000000..bdce8b5 --- /dev/null +++ b/configs/resident/stop_frequency_eatout.csv @@ -0,0 +1,14 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_eatout,coef_alternative_specific_constants_0out_2in_eatout,coef_alternative_specific_constants_0out_3in_eatout,coef_alternative_specific_constants_1out_0in_eatout,coef_alternative_specific_constants_1out_1in_eatout,coef_alternative_specific_constants_1out_2in_eatout,coef_alternative_specific_constants_1out_3in_eatout,coef_alternative_specific_constants_2out_0in_eatout,coef_alternative_specific_constants_2out_1in_eatout,coef_alternative_specific_constants_2out_2in_eatout,coef_alternative_specific_constants_2out_3in_eatout,coef_alternative_specific_constants_3out_0in_eatout,coef_alternative_specific_constants_3out_1in_eatout,coef_alternative_specific_constants_3out_2in_eatout,coef_alternative_specific_constants_3out_3in_eatout +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout,coef_is_joint_tour_1plus_stops_eatout +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout,coef_number_of_nonwork_tours_1plus_stops_eatout +util_age_65_79,Age Group - 65 yrs to 79 yrs,(age >= 65) & (age < 80),,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout,coef_age_65_79_1plus_stops_eatout +util_age_80_plus,Age Group - 80 yrs and older,(age >= 80),,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout,coef_age_80_plus_1plus_stops_eatout +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout,coef_non_mandatory_accessibility_1plus_stops_eatout +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout,coef_tour_mode_non_motorized_1plus_stops_eatout +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout,coef_tour_mode_hov_1plus_stops_eatout +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout,coef_tour_duration_in_hours_1plus_stops_eatout +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout,coef_distance_to_tour_destination_1plus_stops_eatout +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_LOC','PNR_PRM','PNR_MIX','KNR_LOC','KNR_PRM','KNR_MIX','TNC_LOC','TNC_PRM','TNC_MIX']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.10349,-0.5178,1.3724,-2.4904,0.10798,-0.03633,0.8058,-1.9377,0.5571,-2.5862,-0.7659,-0.1902,0.0788,-1.9865,-10.0,3.70084 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-0.12758,-0.6326,-1.13646,-1.9196,1.4714,0.7681,-10.0,-0.4150,-0.5229,-5.0,-0.04229,-10.0,-5.0,-5.0,1.3845,-5.0 diff --git a/configs/resident/stop_frequency_escort.csv b/configs/resident/stop_frequency_escort.csv new file mode 100644 index 0000000..5b667ac --- /dev/null +++ b/configs/resident/stop_frequency_escort.csv @@ -0,0 +1,16 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_escort,coef_alternative_specific_constants_0out_2in_escort,coef_alternative_specific_constants_0out_3in_escort,coef_alternative_specific_constants_1out_0in_escort,coef_alternative_specific_constants_1out_1in_escort,coef_alternative_specific_constants_1out_2in_escort,coef_alternative_specific_constants_1out_3in_escort,coef_alternative_specific_constants_2out_0in_escort,coef_alternative_specific_constants_2out_1in_escort,coef_alternative_specific_constants_2out_2in_escort,coef_alternative_specific_constants_2out_3in_escort,coef_alternative_specific_constants_3out_0in_escort,coef_alternative_specific_constants_3out_1in_escort,coef_alternative_specific_constants_3out_2in_escort,coef_alternative_specific_constants_3out_3in_escort +util_is_student,person is student,is_student,,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort,coef_is_student_1plus_stops_escort +util_age_5_18,Age Group - 5 yrs to 18 yrs,(age >= 5) & (age < 19),,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort,coef_age_5_18_1plus_stops_escort +util_age_65_79,Age Group - 65 yrs to 79 yrs,(age >= 65) & (age < 80),,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort,coef_age_65_79_1plus_stops_escort +util_hhinc_less_15,Income less than 15k,(income < 15000),,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort,coef_hhinc_less_15_1plus_stops_escort +util_hhsize_is_3,Household size is 3,(hhsize == 3),,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort,coef_hhsize_is_3_1plus_stops_escort +util_hhsize_is_4p,Household size is 4p,(hhsize >= 4),,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort,coef_hhsize_is_4p_1plus_stops_escort +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort,coef_non_mandatory_accessibility_1plus_stops_escort +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort,coef_tour_mode_non_motorized_1plus_stops_escort +util_start_time_is_afternoon,Tour start time between noon and 5pm,(start >= 19) & (start < 29),,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort,coef_start_time_is_afternoon_1plus_stops_escort +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort,coef_distance_to_tour_destination_1plus_stops_escort +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_unavailable,coef_unavailable,coef_unavailable,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_unavailable,coef_unavailable,coef_unavailable,coef_no_stops_to_school_escorting_1plus_stops_escort,coef_unavailable,coef_unavailable,coef_unavailable +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_LOC','PNR_PRM','PNR_MIX','KNR_LOC','KNR_PRM','KNR_MIX','TNC_LOC','TNC_PRM','TNC_MIX']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,0.01523,0.1329,-0.2217,-0.5655,-1.06194,-0.9030,0.7036,-2.4331,-0.6172,0.7406,1.7755,-4.1892,0.2658,-4.7708,-0.9725,3.5835 diff --git a/configs/resident/stop_frequency_othdiscr.csv b/configs/resident/stop_frequency_othdiscr.csv new file mode 100644 index 0000000..02fe01a --- /dev/null +++ b/configs/resident/stop_frequency_othdiscr.csv @@ -0,0 +1,18 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_othdiscr,coef_alternative_specific_constants_0out_2in_othdiscr,coef_alternative_specific_constants_0out_3in_othdiscr,coef_alternative_specific_constants_1out_0in_othdiscr,coef_alternative_specific_constants_1out_1in_othdiscr,coef_alternative_specific_constants_1out_2in_othdiscr,coef_alternative_specific_constants_1out_3in_othdiscr,coef_alternative_specific_constants_2out_0in_othdiscr,coef_alternative_specific_constants_2out_1in_othdiscr,coef_alternative_specific_constants_2out_2in_othdiscr,coef_alternative_specific_constants_2out_3in_othdiscr,coef_alternative_specific_constants_3out_0in_othdiscr,coef_alternative_specific_constants_3out_1in_othdiscr,coef_alternative_specific_constants_3out_2in_othdiscr,coef_alternative_specific_constants_3out_3in_othdiscr +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr,coef_is_joint_tour_1plusstops_othdiscr +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr,coef_number_of_nonwork_tours_1plusstops_othdiscr +util_age_5_18,Age Group - 5 yrs to 18 yrs,(age >= 5) & (age < 19),,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr,coef_age_5_18_1plusstops_othdiscr +util_age_19_35,Age Group - 19 yrs to 34 yrs,(age >= 19) & (age < 35),,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr,coef_age_19_35_1plusstops_othdiscr +util_age_65_79,Age Group - 65 yrs to 79 yrs,(age >= 65) & (age < 80),,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr,coef_age_65_79_1plusstops_othdiscr +util_preschool_child_in_hh,Presence of preschooler in household other than self,has_preschool_kid,,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr,coef_preschool_child_in_hh_1plusstops_othdiscr +util_has_retiree_in_hh,Presence of reitred peopl in household other than self,has_retiree,,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr,coef_has_retiree_in_hh_1plusstops_othdiscr +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr,coef_non_mandatory_accessibility_1plusstops_othdiscr +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr,coef_tour_mode_non_motorized_1plusstops_othdiscr +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr,coef_tour_mode_hov_1plusstops_othdiscr +util_start_time_is_morning,Tour start time is before noon,(start < 19),,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr,coef_start_time_is_morning_1plusstops_othdiscr +util_start_time_is_afternoon,Tour start time between noon and 5pm,(start >= 19) & (start < 29),,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr,coef_start_time_is_afternoon_1plusstops_othdiscr +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr,coef_distance_to_tour_destination_1plusstops_othdiscr +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_LOC','PNR_PRM','PNR_MIX','KNR_LOC','KNR_PRM','KNR_MIX','TNC_LOC','TNC_PRM','TNC_MIX']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.7203,1.5385,1.3997,1.5604,-0.2187,-1.6251,0.4121,-5.1473,-2.0148,-3.6239,-3.7608,-10.0,-1.2510,-2.3088,-2.5209,2.5864 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0,-25.0 diff --git a/configs/resident/stop_frequency_othmaint.csv b/configs/resident/stop_frequency_othmaint.csv new file mode 100644 index 0000000..c5b90b7 --- /dev/null +++ b/configs/resident/stop_frequency_othmaint.csv @@ -0,0 +1,15 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_othmaint,coef_alternative_specific_constants_0out_2in_othmaint,coef_alternative_specific_constants_0out_3in_othmaint,coef_alternative_specific_constants_1out_0in_othmaint,coef_alternative_specific_constants_1out_1in_othmaint,coef_alternative_specific_constants_1out_2in_othmaint,coef_alternative_specific_constants_1out_3in_othmaint,coef_alternative_specific_constants_2out_0in_othmaint,coef_alternative_specific_constants_2out_1in_othmaint,coef_alternative_specific_constants_2out_2in_othmaint,coef_alternative_specific_constants_2out_3in_othmaint,coef_alternative_specific_constants_3out_0in_othmaint,coef_alternative_specific_constants_3out_1in_othmaint,coef_alternative_specific_constants_3out_2in_othmaint,coef_alternative_specific_constants_3out_3in_othmaint +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint,coef_is_joint_tour_1plusstops_othmaint +util_number_of_children_in_hh,Number of children in HH,num_children,,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint,coef_number_of_children_in_hh_1plusstops_othmaint +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint,coef_number_of_nonwork_tours_1plusstops_othmaint +util_hhsize_is_4p,Household size is 4p,(hhsize >= 4),,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint,coef_hhsize_is_4p_1plusstops_othmaint +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint,coef_tour_mode_non_motorized_1plusstops_othmaint +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint,coef_tour_mode_hov_1plusstops_othmaint +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint,coef_tour_duration_in_hours_1plusstops_othmaint +util_start_time_is_morning,Tour start time is before noon,(start < 19),,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint,coef_start_time_is_morning_1plusstops_othmaint +util_start_time_is_afternoon,Tour start time between noon and 5pm,(start >= 19) & (start < 29),,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint,coef_start_time_is_afternoon_1plusstops_othmaint +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint,coef_distance_to_tour_destination_1plusstops_othmaint +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_LOC','PNR_PRM','PNR_MIX','KNR_LOC','KNR_PRM','KNR_MIX','TNC_LOC','TNC_PRM','TNC_MIX']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.2656123444029792,0.15348593472541502,-0.3788741163797237,0.123283919762546,0.22954639797160692,0.5755515179272356,-0.21495880583644267,-0.6257626266582341,1.003382155880165,0.28582196724350073,0.0002870161481544698,-1.718504862855789,-2.145372675786179,-2.2250799871962363,1.1522061332946996,1.8970899388634406 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-1.3355,0.9021,0.5683,-1.7962,1.50029,-0.9014,-1.3765,-10.0,-3.4045,0.7563,-10.0,-1.7586,-0.04717,-0.5280,-10.0,3.9129 diff --git a/configs/resident/stop_frequency_school.csv b/configs/resident/stop_frequency_school.csv new file mode 100644 index 0000000..dac0cd7 --- /dev/null +++ b/configs/resident/stop_frequency_school.csv @@ -0,0 +1,12 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_school,coef_alternative_specific_constants_0out_2in_school,coef_alternative_specific_constants_0out_3in_school,coef_alternative_specific_constants_1out_0in_school,coef_alternative_specific_constants_1out_1in_school,coef_alternative_specific_constants_1out_2in_school,coef_alternative_specific_constants_1out_3in_school,coef_alternative_specific_constants_2out_0in_school,coef_alternative_specific_constants_2out_1in_school,coef_alternative_specific_constants_2out_2in_school,coef_alternative_specific_constants_2out_3in_school,coef_alternative_specific_constants_3out_0in_school,coef_alternative_specific_constants_3out_1in_school,coef_alternative_specific_constants_3out_2in_school,coef_alternative_specific_constants_3out_3in_school +util_is_pre_covid,Tour happened before covid hit,@scenarioYear==2016,,coef_is_pre_covid_0out_1plusin_school,coef_is_pre_covid_0out_1plusin_school,coef_is_pre_covid_0out_1plusin_school,coef_is_pre_covid_1plusout_0in_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_0in_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_0in_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school,coef_is_pre_covid_1plusout_1plusin_school +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_0out_1plusin_school,coef_number_of_nonwork_tours_0out_1plusin_school,coef_number_of_nonwork_tours_0out_1plusin_school,,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school,coef_number_of_nonwork_tours_1plusout_1plusin_school +util_school_child_in_hh,Presence of school age child in household other than self,has_school_kid,,coef_school_child_in_hh_0out_1plusin_school,coef_school_child_in_hh_0out_1plusin_school,coef_school_child_in_hh_0out_1plusin_school,coef_school_child_in_hh_1plusout_0in_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_0in_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_0in_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school,coef_school_child_in_hh_1plusout_1plusin_school +util_non_worker_in_hh,Presence of non workers in household other than self,has_non_worker,,,,,,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school,coef_non_worker_in_hh_1plusout_1plusin_school +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_0out_1plusin_school,coef_non_mandatory_accessibility_0out_1plusin_school,coef_non_mandatory_accessibility_0out_1plusin_school,,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school,coef_non_mandatory_accessibility_1plusout_1plusin_school +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_0out_1plusin_school,coef_tour_mode_non_motorized_0out_1plusin_school,coef_tour_mode_non_motorized_0out_1plusin_school,coef_tour_mode_non_motorized_1plusout_0in_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_0in_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_0in_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school,coef_tour_mode_non_motorized_1plusout_1plusin_school +util_tour_mode_schoolbus,Tour mode is schoolbus,tour_mode_is_schbus,,coef_tour_mode_schbus_0out_1plusin_school,coef_tour_mode_schbus_0out_1plusin_school,coef_tour_mode_schbus_0out_1plusin_school,,,,,,,,,,,, +util_nostopschooltour,no stops on school tours,tour_mode_is_schbus,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_LOC','PNR_PRM','PNR_MIX','KNR_LOC','KNR_PRM','KNR_MIX','TNC_LOC','TNC_PRM','TNC_MIX']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-4.1165,1.3926,0.5790,0.9793,4.6426,2.2195,3.4805,2.7292,2.4262,3.10758,-25.0,-25.0,-2.7718,0.2813,-10.0,3.9258 diff --git a/configs/resident/stop_frequency_shopping.csv b/configs/resident/stop_frequency_shopping.csv new file mode 100644 index 0000000..7b015cc --- /dev/null +++ b/configs/resident/stop_frequency_shopping.csv @@ -0,0 +1,16 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_shopping,coef_alternative_specific_constants_0out_2in_shopping,coef_alternative_specific_constants_0out_3in_shopping,coef_alternative_specific_constants_1out_0in_shopping,coef_alternative_specific_constants_1out_1in_shopping,coef_alternative_specific_constants_1out_2in_shopping,coef_alternative_specific_constants_1out_3in_shopping,coef_alternative_specific_constants_2out_0in_shopping,coef_alternative_specific_constants_2out_1in_shopping,coef_alternative_specific_constants_2out_2in_shopping,coef_alternative_specific_constants_2out_3in_shopping,coef_alternative_specific_constants_3out_0in_shopping,coef_alternative_specific_constants_3out_1in_shopping,coef_alternative_specific_constants_3out_2in_shopping,coef_alternative_specific_constants_3out_3in_shopping +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping,coef_is_joint_tour_1plusstops_shopping +util_is_pre_covid,Tour happened before covid hit,@scenarioYear==2016,,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping,coef_is_pre_covid_1plusstops_shopping +util_is_worker,Person is worker,is_worker,,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping,coef_is_worker_1plusstops_shopping +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping,coef_number_of_nonwork_tours_1plusstops_shopping +util_school_child_in_hh,Presence of school age child in household other than self,has_school_kid,,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping,coef_school_child_in_hh_1plusstops_shopping +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping,coef_non_mandatory_accessibility_1plusstops_shopping +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping,coef_tour_mode_non_motorized_1plusstops_shopping +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping,coef_tour_mode_hov_1plusstops_shopping +util_tour_mode_transit,Tour mode is transit,tour_mode_is_transit,,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping,coef_tour_mode_transit_1plusstops_shopping +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping,coef_tour_duration_in_hours_1plusstops_shopping +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping,coef_distance_to_tour_destination_1plusstops_shopping +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_LOC','PNR_PRM','PNR_MIX','KNR_LOC','KNR_PRM','KNR_MIX','TNC_LOC','TNC_PRM','TNC_MIX']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.2500906598537606,0.057193649152341756,0.4804144838454415,-0.261395884078163,0.22894326452057048,0.5910095505581588,-0.8981616524559722,-4.199029251095443,0.09230772468214696,0.5169317692363429,-0.9915339032679815,-1.1533584002576693,-0.42731015383922333,-2.6985663726233104,1.9854294273438238,3.3854166649272117 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-0.2533,1.3037,0.7569,-1.6520,-0.7370,0.8157,0.006162,-10.0,-1.1890,-10.0,-10.0,-5.0,-0.0569,-10.0,-5.0,3.9856 diff --git a/configs/resident/stop_frequency_social.csv b/configs/resident/stop_frequency_social.csv new file mode 100644 index 0000000..c3f8336 --- /dev/null +++ b/configs/resident/stop_frequency_social.csv @@ -0,0 +1,12 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_social,coef_alternative_specific_constants_0out_2in_social,coef_alternative_specific_constants_0out_3in_social,coef_alternative_specific_constants_1out_0in_social,coef_alternative_specific_constants_1out_1in_social,coef_alternative_specific_constants_1out_2in_social,coef_alternative_specific_constants_1out_3in_social,coef_alternative_specific_constants_2out_0in_social,coef_alternative_specific_constants_2out_1in_social,coef_alternative_specific_constants_2out_2in_social,coef_alternative_specific_constants_2out_3in_social,coef_alternative_specific_constants_3out_0in_social,coef_alternative_specific_constants_3out_1in_social,coef_alternative_specific_constants_3out_2in_social,coef_alternative_specific_constants_3out_3in_social +util_is_joint_tour,Is joint tour,is_joint,,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social,coef_is_joint_tour_1plus_stops_social +util_number_of_nonwork_tours,Number of non-work tours,(num_total_tours - num_work_tours),,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social,coef_number_of_nonwork_tours_1plus_stops_social +util_age_5_18,Age Group - 5 yrs to 18 yrs,(age >= 5) & (age < 19),,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social,coef_age_5_18_1plus_stops_social +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social,coef_non_mandatory_accessibility_1plus_stops_social +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social,coef_tour_mode_hov_1plus_stops_social +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social,coef_tour_duration_in_hours_1plus_stops_social +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social,coef_distance_to_tour_destination_1plus_stops_social +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_LOC','PNR_PRM','PNR_MIX','KNR_LOC','KNR_PRM','KNR_MIX','TNC_LOC','TNC_PRM','TNC_MIX']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,0.11096680817187969,-0.5366374837582596,0.49515245428835686,-0.639202818836102,-0.6855014200561323,-0.10103468501475253,-1.2010426433613348,-3.4876529906722045,-0.5781845322079998,-0.7324835700455633,-2.204662008303293,-5.0,-0.21810513280268298,-0.4631769418491418,-4.458065289588038,3.811850876178379 +util_abm3_calibration_constants_joint,joint tour calibration constants,is_joint,-0.1786,0.3422,0.8914,-3.7763,-2.4629,-2.3188,-1.9052,3.2893,-10.0,-10.0,-5.0,-5.0,-0.6356,-5.0,-5.0,-5.0 diff --git a/configs/resident/stop_frequency_univ.csv b/configs/resident/stop_frequency_univ.csv new file mode 100644 index 0000000..3980977 --- /dev/null +++ b/configs/resident/stop_frequency_univ.csv @@ -0,0 +1,11 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1plusin_univ,coef_alternative_specific_constants_0out_1plusin_univ,coef_alternative_specific_constants_0out_1plusin_univ,coef_alternative_specific_constants_1out_0in_univ,coef_alternative_specific_constants_1out_1in_univ,coef_alternative_specific_constants_1out_2in_univ,coef_alternative_specific_constants_1out_3in_univ,coef_alternative_specific_constants_2out_0in_univ,coef_alternative_specific_constants_2out_1in_univ,coef_alternative_specific_constants_2out_2in_univ,coef_alternative_specific_constants_2out_3in_univ,coef_alternative_specific_constants_3out_0in_univ,coef_alternative_specific_constants_3out_1in_univ,coef_alternative_specific_constants_3out_2in_univ,coef_alternative_specific_constants_3out_3in_univ +util_preschool_child_in_hh,Presence of preschooler in household other than self,has_preschool_kid,,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ,coef_preschool_child_in_hh_1plusstops_univ +util_tour_mode_non_motorized,Tour mode is non-motorized,tour_mode_is_non_motorized,,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ,coef_tour_mode_non_motorized_1plusstops_univ +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ,coef_tour_mode_hov_1plusstops_univ +util_tour_mode_transit,Tour mode is transit,tour_mode_is_transit,,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ,coef_tour_mode_transit_1plusstops_univ +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ,coef_tour_duration_in_hours_1plusstops_univ +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_LOC','PNR_PRM','PNR_MIX','KNR_LOC','KNR_PRM','KNR_MIX','TNC_LOC','TNC_PRM','TNC_MIX']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,0.37144,-0.03316,-0.64012,-5.3232,0.6633,-1.8947,-3.6886,-3.3084,0.3413,-15.5376,-20.0,-10.0,-0.2133,-25.0,-25.0,1.4841 diff --git a/configs/resident/stop_frequency_work.csv b/configs/resident/stop_frequency_work.csv new file mode 100644 index 0000000..9a8b541 --- /dev/null +++ b/configs/resident/stop_frequency_work.csv @@ -0,0 +1,19 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_alternative_specific_constants,Alternative specific constants,1,,coef_alternative_specific_constants_0out_1in_work,coef_alternative_specific_constants_0out_2in_work,coef_alternative_specific_constants_0out_3in_work,coef_alternative_specific_constants_1out_0in_work,coef_alternative_specific_constants_1out_1in_work,coef_alternative_specific_constants_1out_2in_work,coef_alternative_specific_constants_1out_3in_work,coef_alternative_specific_constants_2out_0in_work,coef_alternative_specific_constants_2out_1in_work,coef_alternative_specific_constants_2plusout_2plusin_work,coef_alternative_specific_constants_2plusout_2plusin_work,coef_alternative_specific_constants_3out_0in_work,coef_alternative_specific_constants_3out_1in_work,coef_alternative_specific_constants_2plusout_2plusin_work,coef_alternative_specific_constants_2plusout_2plusin_work +util_is_parttime_worker,Person is part-time worker,(ptype == 2),,coef_is_parttime_worker_0out_1plusin_work,coef_is_parttime_worker_0out_1plusin_work,coef_is_parttime_worker_0out_1plusin_work,,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work,coef_is_parttime_worker_1plusout_1plusin_work +util_age_35_to_44,Age Group - 35 yrs to 44 yrs,(age >= 35) & (age < 45),,,,,,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work,coef_age_35_to_44_1plusout_1plusin_work +util_age_45_to_54,Age Group - 45 yrs to 54 yrs,(age >= 45) & (age < 55),,,,,,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work,coef_age_45_to_54_1plusout_1plusin_work +util_age_55_to_64,Age Group - 55 yrs to 64 yrs,(age >= 55) & (age < 65),,coef_age_55_to_64_0out_1plusin_work,coef_age_55_to_64_0out_1plusin_work,coef_age_55_to_64_0out_1plusin_work,coef_age_55_to_64_1plusout_0in_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_0in_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_0in_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work,coef_age_55_to_64_1plusout_1plusin_work +util_preschool_child_in_hh,Presence of preschooler in household other than self,has_preschool_kid,,,,,coef_preschool_child_in_hh_1out_0in_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_2plusout_0in_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_2plusout_0in_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work,coef_preschool_child_in_hh_1plusout_1plusin_work +util_school_child_in_hh,Presence of school age child in household other than self,has_school_kid,,,,,coef_school_child_in_hh_1out_0in_work,coef_school_child_in_hh_1out_1in_work,coef_school_child_in_hh_1out_2in_work,coef_school_child_in_hh_1out_3in_work,coef_school_child_in_hh_2plusout_0in_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_0in_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_1plusin_work,coef_school_child_in_hh_2plusout_1plusin_work +util_non_worker_in_hh,Presence of non workers in household other than self,has_non_worker,,coef_non_worker_in_hh_0out_1in_work,coef_non_worker_in_hh_0out_2in_work,coef_non_worker_in_hh_0out_3in_work,,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work,coef_non_worker_in_hh_1plusout_1plusin_work +util_non_mandatory_accessibility,Disaggreage accessibility at home location to non-mandatory location,(othdiscr_accessibility + shopping_accessibility) / 2,,,,,coef_non_mandatory_accessibility_1plusout_0in_work,,,,coef_non_mandatory_accessibility_1plusout_0in_work,,,,coef_non_mandatory_accessibility_1plusout_0in_work,,, +util_tour_mode_sov,Tour mode is sov,tour_mode_is_sov,,coef_tour_mode_sov_0out_1in_work,coef_tour_mode_sov_0out_2in_work,coef_tour_mode_sov_0out_3in_work,coef_tour_mode_sov_1plusout_0in_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_0in_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_0in_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work,coef_tour_mode_sov_1plusout_1plusin_work +util_tour_mode_hov,Tour mode is hov,tour_mode_is_hov,,coef_tour_mode_hov_0out_1in_work,coef_tour_mode_hov_0out_2in_work,coef_tour_mode_hov_0out_3in_work,coef_tour_mode_hov_1out_0in_work,coef_tour_mode_hov_1out_1in_work,coef_tour_mode_hov_1out_2in_work,coef_tour_mode_hov_1out_3in_work,coef_tour_mode_hov_2out_0in_work,coef_tour_mode_hov_2out_1in_work,coef_tour_mode_hov_2plusout_2plusin_work,coef_tour_mode_hov_2plusout_2plusin_work,coef_tour_mode_hov_3out_0in_work,coef_tour_mode_hov_3out_1in_work,coef_tour_mode_hov_2plusout_2plusin_work,coef_tour_mode_hov_2plusout_2plusin_work +util_tour_duration_in_hours,Tour duration in hours,(duration / 2),,coef_tour_duration_in_hours_0out_1in_work,coef_tour_duration_in_hours_0out_2in_work,coef_tour_duration_in_hours_0out_3in_work,coef_tour_duration_in_hours_1out_0in_work,coef_tour_duration_in_hours_1out_1in_work,coef_tour_duration_in_hours_1out_2in_work,coef_tour_duration_in_hours_1out_3in_work,coef_tour_duration_in_hours_2plusout_0in_work,coef_tour_duration_in_hours_2out_1in_work,coef_tour_duration_in_hours_2plusout_2plusin_work,coef_tour_duration_in_hours_2plusout_2plusin_work,coef_tour_duration_in_hours_2plusout_0in_work,coef_tour_duration_in_hours_3out_1in_work,coef_tour_duration_in_hours_2plusout_2plusin_work,coef_tour_duration_in_hours_2plusout_2plusin_work +util_distance_to_tour_destination,Distance from origin to tour destination in miles,distance_in_miles,,coef_distance_to_tour_destination_0out_1plusin_work,coef_distance_to_tour_destination_0out_1plusin_work,coef_distance_to_tour_destination_0out_1plusin_work,coef_distance_to_tour_destination_1plusout_0in_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_0in_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_0in_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work,coef_distance_to_tour_destination_1plusout_1plusin_work +util_telecommute_2_4_days_week,Person telecommutes 2 to 4 days,(telecommute_frequency == '2_4_days_week'),,,,,,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work,coef_telecommute_2_4_days_week_1plusout_1plusin_work +util_no_stops_to_school_escorting,Do not allow stops for school escort half-tour -- outbound,"(school_esc_outbound.isin(['ride_share', 'pure_escort']))",,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_no_stops_from_school_escorting,Do not allow stops for school escort half-tour -- inbound,"(school_esc_inbound.isin(['ride_share', 'pure_escort']))",,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable +util_nostop_drive_transit,no stops on pnr knr tnc transit tours,"@(df.tour_mode.isin(['PNR_LOC','PNR_PRM','PNR_MIX','KNR_LOC','KNR_PRM','KNR_MIX','TNC_LOC','TNC_PRM','TNC_MIX']))",,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_abm3_calibration_constants,calibration constants,is_joint==0,-0.1946,0.5187,1.00488,0.29090,-0.46260,-0.40521,0.90284,0.009135,-1.9785,0.6807,2.2795,2.3279,-1.1649,0.7008,-7.2781,2.91850 diff --git a/configs/resident/telecommute_frequency.csv b/configs/resident/telecommute_frequency.csv new file mode 100644 index 0000000..871e065 --- /dev/null +++ b/configs/resident/telecommute_frequency.csv @@ -0,0 +1,32 @@ +Label,Description,Expression,No_Telecommute,1_day_week,2_3_days_week,4_days_week +util_HasChildren0to5,Has children 0 to 5 years old,@df.num_young_children>0,,coef_HasChildren0to5_1day,coef_HasChildren0to5_234day,coef_HasChildren0to5_234day +util_HasChildren6to12,Has children 6 to 12 years old,@df.num_children_6_to_12>0,,coef_HasChildren6to12_1day,coef_HasChildren6to12_23day,coef_HasChildren6to12_4day +util_OneAdultInHH,One adult in hh,@df.num_adults==1,,coef_OneAdultInHH_1day,coef_OneAdultInHH_23day,coef_OneAdultInHH_4day +util_Female,female,@df.female,,coef_Female_1234day,coef_Female_1234day,coef_Female_1234day +util_PartTimeWorker,Part-time worker,@df.pemploy==2,,coef_PartTimeWorker_1234day,coef_PartTimeWorker_1234day,coef_PartTimeWorker_1234day +util_Income60to100k,Income 60-100k,"@df.income.between(60000, 100000)",,coef_Income60to100k_1day,coef_Income60to100k_23day,coef_Income60to100k_4day +util_Income100to150k,Income 100-150k,"@df.income.between(100000, 150000)",,coef_Income100to150k_1day,coef_Income100to150k_234day,coef_Income100to150k_234day +util_Income150kplus,Income 150k+,@df.income > 150000,,coef_Income150kplus_1day,coef_Income150kplus_23day,coef_Income150kplus_4day +util_0Autos,0 Autos,@df.auto_ownership==0,,coef_0Autos_1day,coef_0Autos_234day,coef_0Autos_234day +util_1Auto,1 Auto,@df.auto_ownership==1,,coef_1Auto_1day,coef_1Auto_234day,coef_1Auto_234day +util_3plusAutos,3+ Autos,@df.auto_ownership>=3,,coef_3plusAutos_1day,coef_3plusAutos_23day,coef_3plusAutos_4day +util_DistanceToWork,Distance to work,@df.distance_to_work,,coef_DistanceToWork_1day,coef_DistanceToWork_234day,coef_DistanceToWork_234day +util_calib_2020,scenario year is 2020,@scenarioYear==2020,,coef_calib_2020_1day,coef_calib_2020_23day,coef_calib_2020_4day +util_calib_2025,scenario year is 2025,@scenarioYear==2025,,coef_calib_2025_1day,coef_calib_2025_23day,coef_calib_2025_4day +util_calib_2035,scenario year is 2035,@scenarioYear==2035,,coef_calib_2035_1day,coef_calib_2035_23day,coef_calib_2035_4day +util_calib_2050,scenario year is 2050,@scenarioYear==2050,,coef_calib_2050_1day,coef_calib_2050_23day,coef_calib_2050_4day +util_2016,Model year is 2016,@PRE_COVID,,coef_2016_1day,coef_2016_23day,coef_2016_4day +util_accomodation,Accomodation industry,@df.naics_code==721,,coef_accomodation_1234day,coef_accomodation_1234day,coef_accomodation_1234day +util_agriculture,Agriculture industry,@df.naics_code==11,,coef_agriculture_1234day,coef_agriculture_1234day,coef_agriculture_1234day +util_business_srv,Business services industry,@df.naics_code==54,,coef_business_srv_1day,coef_business_srv_23day,coef_business_srv_4day +util_construction,Construction industry,@df.naics_code==23,,coef_construction_1day,coef_construction_234day,coef_construction_234day +util_education,Education industry,@df.naics_code==61,,coef_education_1234day,coef_education_1234day,coef_education_1234day +util_entertainment,Entertainment industry,@df.naics_code==71,,coef_entertainment_1day,coef_entertainment_23day,coef_entertainment_4day +util_food_srv,Food services industry,@df.naics_code==722,,coef_food_srv_1234day,coef_food_srv_1234day,coef_food_srv_1234day +util_government,Government industry,@df.naics_code==92,,coef_government_1day,coef_government_234day,coef_government_234day +util_healthcare,Healthcare industry,@df.naics_code==62,,coef_healthcare_1234day,coef_healthcare_1234day,coef_healthcare_1234day +util_manufacturing,Manufacturing industry,"@df.naics_code.isin([31,32,33])",,coef_manufacturing_1day,coef_manufacturing_234day,coef_manufacturing_234day +util_mgmt_srv,Management services industry,@df.naics_code==55,,coef_mgmt_srv_1day,coef_mgmt_srv_23day,coef_mgmt_srv_4day +util_military,Miliary industry,@df.naics_code==9000,,coef_military_1day,coef_military_234day,coef_military_234day +util_retail,Retail industry,"@df.naics_code.isin([44,45])",,coef_retail_1day,coef_retail_23day,coef_retail_4day +util_asc,Alternative specific constant,1,,asc_1day,asc_23day,asc_4day diff --git a/configs/resident/telecommute_frequency.yaml b/configs/resident/telecommute_frequency.yaml new file mode 100644 index 0000000..162066e --- /dev/null +++ b/configs/resident/telecommute_frequency.yaml @@ -0,0 +1,9 @@ + +# borrowed from free parking model + +SPEC: telecommute_frequency.csv +COEFFICIENTS: telecommute_frequency_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + diff --git a/configs/resident/telecommute_frequency_coeffs.csv b/configs/resident/telecommute_frequency_coeffs.csv new file mode 100644 index 0000000..5d1b9f0 --- /dev/null +++ b/configs/resident/telecommute_frequency_coeffs.csv @@ -0,0 +1,100 @@ +coefficient_name,value,constrain +coef_Services_1day,0.0,F +coef_SalesOffice_1day,0.0,F +coef_ResourceConstruct_1day,0.0,F +coef_TransportMat_1day,0.0,F +coef_HasChildren0to5_1day,0.0,T +coef_HasChildren6to12_1day,0.0,T +coef_OneAdultInHH_1day,0.2307987638695857,F +coef_2plusAdultsInHH_1day,0.0,F +coef_Female_1234day,0.0,T +coef_PartTimeWorker_1234day,-0.1647657054647985,F +coef_PaysToPark_1day,0.0,F +coef_Income60to100k_1day,0.3549582138983352,F +coef_Income100to150k_1day,0.0,T +coef_Income150kplus_1day,0.0,T +coef_0Autos_1day,0.0,T +coef_1Auto_1day,-0.2037126296304744,F +coef_3plusAutos_1day,-0.2366548258122986,F +coef_DistanceToWork_1day,0.0065817284180437,F +coef_Services_23day,0.0,F +coef_SalesOffice_23day,0.0,F +coef_ResourceConstruct_23day,0.0,F +coef_TransportMat_23day,0.0,F +coef_HasChildren0to5_234day,-0.3282697175018726,F +coef_HasChildren6to12_23day,0.0,T +coef_OneAdultInHH_23day,0.0,T +coef_2plusAdultsInHH_23day,0.0,F +coef_PaysToPark_23day,0.0,F +coef_Income60to100k_23day,0.0,T +coef_Income100to150k_234day,0.0,T +coef_Income150kplus_23day,0.0,T +coef_0Autos_234day,0.0,T +coef_1Auto_234day,0.0,T +coef_3plusAutos_23day,-0.2673431713437322,F +coef_DistanceToWork_234day,0.011270292473366,F +coef_HasChildren6to12_4day,0.0,T +coef_OneAdultInHH_4day,0.0,T +coef_2plusAdultsInHH_4day,0.0,F +coef_PaysToPark_4day,0.0,F +coef_Income60to100k_4day,0.0,T +coef_Income100to150k_4day,0.0,T +coef_Income150kplus_4day,0.0,T +coef_3plusAutos_4day,-0.5086281851316395,F +coef_calib_2020_1day,0.0,T +coef_calib_2025_1day,0.0,T +coef_calib_2035_1day,0.0,T +coef_calib_2050_1day,0.0,T +coef_calib_2020_23day,0.0,T +coef_calib_2025_23day,0.0,T +coef_calib_2035_23day,0.0,T +coef_calib_2050_23day,0.0,T +coef_calib_2020_4day,0.0,T +coef_calib_2025_4day,0.0,T +coef_calib_2035_4day,0.0,T +coef_calib_2050_4day,0.0,T +coef_2016_1day,-0.1247850665609524,F +coef_accomodation_1234day,-1.1672033007413072,F +coef_agriculture_1234day,0.0,T +coef_business_srv_1day,0.8048779013830238,F +coef_construction_1day,0.0,T +coef_education_1234day,-0.0387511815872183,F +coef_entertainment_1day,0.0,T +coef_food_srv_1day,0.0,F +coef_government_1day,0.4503818768273094,F +coef_healthcare_1day,0.0,F +coef_manufacturing_1day,0.0,T +coef_mgmt_srv_1day,0.0,T +coef_military_1day,0.0,T +coef_retail_1day,-0.8320341753416274,F +coef_2016_23day,-1.138520687534541,F +coef_accomodation_23day,0.0,F +coef_agriculture_23day,0.0,F +coef_business_srv_23day,0.9228435480770156,F +coef_construction_234day,0.0,T +coef_education_23day,0.0,F +coef_entertainment_23day,0.0,T +coef_food_srv_1234day,-1.6057467385857538,F +coef_government_234day,0.0,T +coef_healthcare_1234day,-0.2529974908266847,F +coef_manufacturing_234day,0.0,T +coef_mgmt_srv_23day,0.5723496481424273,F +coef_military_234day,-0.512646790150494,F +coef_retail_23day,-0.7576291501959187,F +coef_2016_4day,-3.7059529398972826,F +coef_accomodation_4day,0.0,F +coef_agriculture_4day,0.0,F +coef_business_srv_4day,1.117777186998694,F +coef_construction_4day,0.0,F +coef_education_4day,0.0,F +coef_entertainment_4day,0.0,T +coef_food_srv_4day,0.0,F +coef_government_4day,0.0,F +coef_healthcare_4day,0.0,F +coef_manufacturing_4day,0.0,F +coef_mgmt_srv_4day,0.970302573177628,F +coef_military_4day,0.0,F +coef_retail_4day,-2.2338993784519388,F +asc_1day,-2.5488707215613267,F +asc_23day,-1.5339620734289143,F +asc_4day,-1.947902150190833,F diff --git a/configs/resident/tour_departure_and_duration_alternatives.csv b/configs/resident/tour_departure_and_duration_alternatives.csv new file mode 100644 index 0000000..30ff417 --- /dev/null +++ b/configs/resident/tour_departure_and_duration_alternatives.csv @@ -0,0 +1,1177 @@ +start,end +1,1 +1,2 +1,3 +1,4 +1,5 +1,6 +1,7 +1,8 +1,9 +1,10 +1,11 +1,12 +1,13 +1,14 +1,15 +1,16 +1,17 +1,18 +1,19 +1,20 +1,21 +1,22 +1,23 +1,24 +1,25 +1,26 +1,27 +1,28 +1,29 +1,30 +1,31 +1,32 +1,33 +1,34 +1,35 +1,36 +1,37 +1,38 +1,39 +1,40 +1,41 +1,42 +1,43 +1,44 +1,45 +1,46 +1,47 +1,48 +2,2 +2,3 +2,4 +2,5 +2,6 +2,7 +2,8 +2,9 +2,10 +2,11 +2,12 +2,13 +2,14 +2,15 +2,16 +2,17 +2,18 +2,19 +2,20 +2,21 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+shopping,MD,13,24 +shopping,PM,25,27 +shopping,EV,33,35 +#,,, +escort,EA,1,6 +escort,AM,9,10 +escort,MD,13,24 +escort,PM,25,27 +escort,EV,33,35 +#,,, +othmaint,EA,1,6 +othmaint,AM,9,10 +othmaint,MD,13,24 +othmaint,PM,25,27 +othmaint,EV,33,35 +#,,, +othdiscr,EA,1,6 +othdiscr,AM,9,10 +othdiscr,MD,13,24 +othdiscr,PM,25,27 +othdiscr,EV,33,35 +#,,, +social,EA,1,6 +social,AM,9,10 +social,MD,13,24 +social,PM,25,27 +social,EV,33,35 +#,,, +eatout,EA,1,6 +eatout,AM,9,10 +eatout,MD,13,24 +eatout,PM,25,27 +eatout,EV,33,35 +#,,, +atwork,EA,1,6 +atwork,AM,9,10 +atwork,MD,13,24 +atwork,PM,25,27 +atwork,EV,33,35 diff --git a/configs/resident/tour_mode_choice.csv b/configs/resident/tour_mode_choice.csv new file mode 100644 index 0000000..26e799f --- /dev/null +++ b/configs/resident/tour_mode_choice.csv @@ -0,0 +1,373 @@ +Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_LOC,WALK_PRM,WALK_MIX,PNR_LOC,PNR_PRM,PNR_MIX,KNR_LOC,KNR_PRM,KNR_MIX,TNC_LOC,TNC_PRM,TNC_MIX,TAXI,TNC_SINGLE,TNC_SHARED,SCH_BUS,EBIKE,ESCOOTER +#,drivealone,,,,,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable,DRIVEALONE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - In-vehicle time,Drive alone - In-vehicle time,@(df.da_time_skims_inb + df.da_time_skims_out) * df.autoIVTFactor * df.time_factor,coef_ivt,,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - reliability - inb,Drive alone - reliability - inbound,"@(df.da_rel_skims_inb/np.minimum(df.da_dist_skims_inb,0.1)) * df.time_factor",coef_rel_inb,,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - reliability - out,Drive alone - reliability - outbound,"@(df.da_rel_skims_out/np.minimum(df.da_dist_skims_out,0.1)) * df.time_factor",coef_rel_out,,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - cost - inb,Drive alone - cost - inbound,@(df.sov_auto_op_cost * df.autoCPMFactor * df.da_dist_skims_inb + df.da_cost_skims_inb)/df.cost_sensitivity,coef_income,,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - cost - out,Drive alone - cost - outbound,@(df.sov_auto_op_cost * df.autoCPMFactor * df.da_dist_skims_out + df.da_cost_skims_out)/df.cost_sensitivity,coef_income,,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - Parking cost ,Drive alone - Parking cost ,@(df.parkingCost*df.autoParkingCostFactor)/df.cost_sensitivity,coef_income,,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - Terminal Time - acc,Drive alone - Terminal Time ,@(df.oTermTime + df.dTermTime) * df.autoTermTimeFactor * df.time_factor,coef_acctime,,,,,,,,,,,,,,,,,,,,,, +#,Shared ride2,,,,,,,,,,,,,,,,,,,,,,,, +util_Shared2_Unavailable,Shared ride 2 - Unavailable,sr2_available == False,,-999,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 - In -vehicle time,Shared ride 2 - In -vehicle time,@(df.s2_time_skims_inb + df.s2_time_skims_out) * df.autoIVTFactor * df.time_factor,,coef_ivt,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 - reliability - inb,Shared ride 2 - reliability - inbound,"@(df.s2_rel_skims_inb/np.minimum(df.s2_dist_skims_inb,0.1)) * df.time_factor",,coef_rel_inb,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 - reliability - out,Shared ride 2 - reliability - outband,"@(df.s2_rel_skims_out/np.minimum(df.s2_dist_skims_out,0.1))* df.time_factor",,coef_rel_out,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 - cost - inb,Shared ride 2 - cost - inbound,@(df.sr2_auto_op_cost * df.autoCPMFactor * df.s2_dist_skims_inb + df.s2_cost_skims_inb)/df.cost_sensitivity,,coef_income,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 - cost - out,Shared ride 2 - cost - outbound,@(df.sr2_auto_op_cost * df.autoCPMFactor* df.s2_dist_skims_out + df.s2_cost_skims_out)/df.cost_sensitivity,,coef_income,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 - Parking cost ,Shared ride 2 - Parking cost ,@(df.parkingCost * df.costFactorS2 * df.autoParkingCostFactor)/df.cost_sensitivity,,coef_income,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - Terminal Time - acc,Shared ride 2 - Terminal Time - access,@(df.oTermTime + df.dTermTime) * df.autoTermTimeFactor * df.time_factor,,coef_acctime,,,,,,,,,,,,,,,,,,,,, +util_SHARED2_Two_person_household,Shared ride 2- One person household,@(df.hhsize == 2),,coef_hhsize2_sr2,,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDE 2_Three_person_household,Shared ride 2 - Two person household,@(df.hhsize == 3),,coef_hhsize3_sr2,,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDE 2_Three+_person_household,Shared ride 2 - Two person household,@(df.hhsize > 3),,coef_hhsize4p_sr2,,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDE 2_Person_is_16_24_years_old_or_older,Shared ride 2- Person is 16 to 24 years old or older,@(df.age >= 16) & (df.age <= 24),,coef_age1624_sr2,,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDE 2_Person_is_41_55_years_old_or_older,Shared ride 2- Person is 41 to 55 years old or older,@(df.age >= 41) & (df.age <= 55),,coef_age4155_sr2,,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDE 2_Person_is_56_64_years_old_or_older,Shared ride 2- Person is 56 to 64 years old or older,@(df.age >= 56) & (df.age <= 64),,coef_age5664_sr2,,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDE 2_Person_is_65+_years_old_or_older,Shared ride 2- Person is more than 65 years old or older,@(df.age >= 65),,coef_age65pl_sr2,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 - Female,Shared ride 2 - female,@(df.female == 1),,coef_female_sr2,,,,,,,,,,,,,,,,,,,,, +#,shared ride 3,,,,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - In -vehicle time,Shared ride 3 - In -vehicle time,@(df.s3_time_skims_out + df.s3_time_skims_inb) * df.autoIVTFactor * df.time_factor,,,coef_ivt,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - reliability - inb,Shared ride 3 - reliability - inbound,"@(df.s3_rel_skims_inb/np.minimum(df.s3_dist_skims_inb,0.1)) * df.time_factor",,,coef_rel_inb,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - reliability - out,Shared ride 3 - reliability - outbound,"@(df.s3_rel_skims_out/np.minimum(df.s3_dist_skims_out,0.1)) * df.time_factor",,,coef_rel_out,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - cost - inb,Shared ride 3 - cost - inbound,@(df.sr3p_auto_op_cost * df.s3_dist_skims_inb * df.autoCPMFactor + df.s3_cost_skims_inb)/df.cost_sensitivity,,,coef_income,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - cost - out,Shared ride 3 - cost - outbound,@(df.sr3p_auto_op_cost * df.s3_dist_skims_out * df.autoCPMFactor + df.s3_cost_skims_out)/df.cost_sensitivity,,,coef_income,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - Parking cost ,Shared ride 3 - Parking cost ,@(df.parkingCost * df.costFactorS3 * df.autoParkingCostFactor)/df.cost_sensitivity,,,coef_income,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - Terminal Time - acc,Shared ride 3 - Terminal Time - acc,@(df.oTermTime + df.dTermTime) * df.autoTermTimeFactor * df.time_factor,,,coef_acctime,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDe 3_Two_person_household,Shared ride 3- One person household,@(df.hhsize == 2),,,coef_hhsize2_sr3p,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDe 3_Three_person_household,Shared ride 3 - Two person household,@(df.hhsize == 3),,,coef_hhsize3_sr3p,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDe 3_Three+_person_household,Shared ride 3 - Two person household,@(df.hhsize > 3),,,coef_hhsize4p_sr3p,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDe 3_Person_is_16_24_years_old_or_older,Shared ride 3 - Person is 16 to 24 years old or older,@(df.age >= 16) & (df.age <= 24),,,coef_age1624_sr3p,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDe 3_Person_is_41_55_years_old_or_older,Shared ride 3 - Person is 41 to 55 years old or older,@(df.age >= 41) & (df.age <= 55),,,coef_age4155_sr3p,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDe 3_Person_is_56_64_years_old_or_older,Shared ride 3 - Person is 56 to 64 years old or older,@(df.age >= 56) & (df.age <= 64),,,coef_age5664_sr3p,,,,,,,,,,,,,,,,,,,, +util_SHARED RIDe 3_Person_is_65+_years_old_or_older,Shared ride 3 - Person is more than 65 years old or older,@(df.age >= 65),,,coef_age65pl_sr3p,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - Female,Shared ride 3 - female,@(df.female == 1),,,coef_female_sr3p,,,,,,,,,,,,,,,,,,,, +#,Walk,,,,,,,,,,,,,,,,,,,,,,,, +util_Walk - Unavailable,Walk - Unavailable,walkAvailable==0,,,,-999,,,,,,,,,,,,,,,,,,, +util_Walk - Age under 16,Walk - Age under 16,@(df.age < 16),,,,coef_ageund16_walk,,,,,,,,,,,,,,,,,,, +util_Walk - Age 16 to 24,Walk - Age 16 to 24,@(df.age > 15) & (df.age < 25) ,,,,coef_age1624_walk,,,,,,,,,,,,,,,,,,, +util_Walk - Age 41 to 55,Walk - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,coef_age4155_walk,,,,,,,,,,,,,,,,,,, +util_Walk - Age 56 to 64,Walk - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,coef_age5664_walk,,,,,,,,,,,,,,,,,,, +util_Walk - Age 65 plus,Walk - Age 65 plus,@(df.age > 64),,,,coef_age65pl_walk,,,,,,,,,,,,,,,,,,, +util_Walk - Female,Walk - Female,@df.female,,,,coef_female_walk,,,,,,,,,,,,,,,,,,, +util_Walk - Income 60k to 100k,Walk - Income 60k to 100k,@(df.income > 59999) * (df.income < 100000),,,,coef_inc60100_walk,,,,,,,,,,,,,,,,,,, +util_Walk - Normalized Landuse Variable Sum [Origin Intersection + DU],Walk - Normalized Landuse Variable Sum [Origin Intersection + DU],LUVarsNormalized_walk,,,,coef_LUnorm_walk,,,,,,,,,,,,,,,,,,, +"util_Walk - Normalized Destination Employment Density, walk","Walk - Normalized Destination Employment Density, walk",dMGRAEmpDenNorm_walk,,,,coef_dEmpNorm_walk,,,,,,,,,,,,,,,,,,, +util_Walk - time coefficient,Walk - time coefficient,@(df.walk_time_skims_inb + df.walk_time_skims_out) * df.time_factor,,,,coef_walktime,,,,,,,,,,,,,,,,,,, +#,Bike,,,,,,,,,,,,,,,,,,,,,,,, +util_BIKE_Mode unavailable,Bike - Mode unavailable,bikeAvailable == 0,,,,,-999,,,,,,,,,,,,,,,,,, +"util_BIKE_Age under 16, bike","Bike - Age under 16, bike",@(df.age < 16),,,,,coef_ageund16_bike,,,,,,,,,,,,,,,,,, +"util_BIKE_Age 16 to 24, bike","Bike - Age 16 to 24, bike",@(df.age > 15) & (df.age < 25),,,,,coef_age1624_bike,,,,,,,,,,,,,,,,,, +"util_BIKE_Age 41 to 55, bike","Bike - Age 41 to 55, bike",@(df.age > 40) & (df.age < 56),,,,,coef_age4155_bike,,,,,,,,,,,,,,,,,, +"util_BIKE_Age 56 to 64, bike","Bike - Age 56 to 64, bike",@(df.age > 55) & (df.age < 65),,,,,coef_age5664_bike,,,,,,,,,,,,,,,,,, +"util_BIKE_Age 65 plus, bike","Bike - Age 65 plus, bike",@(df.age > 64),,,,,coef_age65pl_bike,,,,,,,,,,,,,,,,,, +"util_BIKE_Female, bike","Bike - Female, bike",@df.female,,,,,coef_female_bike,,,,,,,,,,,,,,,,,, +"util_BIKE_Income 100k plus, bike","Bike - Income 100k plus, bike",@(df.income > 99999),,,,,coef_inc100plus_bike,,,,,,,,,,,,,,,,,, +util_BIKE_Normalized Landuse Variable [Origin Employment + DU],Bike - - Normalized Landuse Variable [Origin Employment + DU],LUVarsNormalized_bike,,,,,coef_LUnorm_bike,,,,,,,,,,,,,,,,,, +util_BIKE_Bike - logsum,Bike - logsum,bikeLSI + bikeLSO,,,,,coef_bikeLogsum,,,,,,,,,,,,,,,,,, +util_BIKE_Miles to coast from origin MGRA,Bike - Miles to coast from origin MGRA,DistanceToCoast,,,,,coef_oMlCoast_bike,,,,,,,,,,,,,,,,,, +util_BIKE_Miles greater than 2 to coast from origin MGRA,Bike - Miles greater than 2 to coast from origin MGRA,"@(np.maximum(df.DistanceToCoast-2,0))",,,,,coef_oMlCoast2p_bike,,,,,,,,,,,,,,,,,, +util_BIKE_Miles greater than 5 to coast from origin MGRA,Bike - Miles greater than 5 to coast from origin MGRA,"@(np.maximum(df.DistanceToCoast-5,0))",,,,,coef_oMlCoast5p_bike,,,,,,,,,,,,,,,,,, +#,Walk_LOC,,,,,,,,,,,,,,,,,,,,,,,, +util_WALK_LOC_Unavailable,WalkTransit_Available,walk_local_available == 0,,,,,,-999,,,,,,,,,,,,,,,,, +util_WALK_LOC_In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WALK_LOC_TOTALIVTT'] + dot_skims['WALK_LOC_TOTALIVTT']) * df.time_factor,,,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_WALK_LOC_iwait_time,WALK_LOC - wait time,@(odt_skims['WALK_LOC_FIRSTWAIT'] + dot_skims['WALK_LOC_FIRSTWAIT']) * df.time_factor,,,,,,coef_wait,,,,,,,,,,,,,,,,, +util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@(odt_skims['WALK_LOC_XFERWAIT'] + dot_skims['WALK_LOC_XFERWAIT'])* df.time_factor,,,,,,coef_xwait,,,,,,,,,,,,,,,,, +util_WALK_LOC_transfer_waLK_time,WALK_LOC - transfer walk time,@(odt_skims['WALK_LOC_XFERWALK'] + dot_skims['WALK_LOC_XFERWALK'])* df.time_factor,,,,,,coef_xwalk,,,,,,,,,,,,,,,,, +util_WALK_LOC_transfers_penalty,WALK_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WALK_LOC_XFERS'], a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['WALK_LOC_XFERS'], a_min=None,a_max=4)))*df.time_factor",,,,,,coef_xfer,,,,,,,,,,,,,,,,, +util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access + egress time,@2 * (df.origin_local_time + df.dest_local_time)* df.time_factor,,,,,,coef_acctime,,,,,,,,,,,,,,,,, +util_WALK_LOC_Fare,WALK_LOC - Fare,@df.transitSubsidyPassDiscount*(odt_skims['WALK_LOC_FARE'] + dot_skims['WALK_LOC_FARE'])*100/df.cost_sensitivity,,,,,,coef_income,,,,,,,,,,,,,,,,, +util_WALK_LOC - Age 16 to 24,WALK_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,coef_age1624_tran,,,,,,,,,,,,,,,,, +util_WALK_LOC - Age 41 to 55,WALK_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,coef_age4155_tran,,,,,,,,,,,,,,,,, +util_WALK_LOC - Age 56 to 64,WALK_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,coef_age5664_tran,,,,,,,,,,,,,,,,, +util_WALK_LOC - Age 65+,WALK_LOC - Age 65+,@(df.age > 64),,,,,,coef_age65pl_tran,,,,,,,,,,,,,,,,, +util_WALK_LOC - Female,WALK_LOC - Female,@(df.female),,,,,,coef_female_tran,,,,,,,,,,,,,,,,, +util_WALK_LOC - Origin Mix,WALK_LOC - Origin Mix,oMGRAMix,,,,,,coef_oMix_wTran,,,,,,,,,,,,,,,,, +util_WALK_LOC - Origin Intersection Density,WALK_LOC - Origin Intersection Density,oMGRATotInt,,,,,,coef_oIntDen_wTran,,,,,,,,,,,,,,,,, +util_WALK_LOC - Destination Employment Density,WALK_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,coef_dEmpDen_wTran,,,,,,,,,,,,,,,,, +#,WALK_PRM,,,,,,,,,,,,,,,,,,,,,,,, +util_WalkTransit_Unavailable,WalkTransit_Available,walk_premium_available == 0,,,,,,,-999,,,,,,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time,WALK_PRM - In-vehicle time,@(odt_skims['WALK_PRM_TOTALIVTT'] + dot_skims['WALK_PRM_TOTALIVTT']) * df.time_factor,,,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_LRT,WALK_PRM - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['WALK_PRM_LRTIVTT'] + dot_skims['WALK_PRM_LRTIVTT'])* df.time_factor,,,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_CMR,WALK_PRM - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['WALK_PRM_CMRIVTT'] + dot_skims['WALK_PRM_CMRIVTT'])* df.time_factor,,,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_LTD_EXP,WALK_PRM - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['WALK_PRM_LTDEXPIVTT'] + dot_skims['WALK_PRM_LTDEXPIVTT'] + odt_skims['WALK_PRM_EXPIVTT'] + dot_skims['WALK_PRM_EXPIVTT'])* df.time_factor,,,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_URB,WALK_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['WALK_PRM_BRTIVTT'] + dot_skims['WALK_PRM_BRTIVTT'])* df.time_factor,,,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_WALK_PRM_wait_time,WALK_PRM - wait time,@(odt_skims['WALK_PRM_FIRSTWAIT'] + dot_skims['WALK_PRM_FIRSTWAIT']) * df.time_factor,,,,,,,coef_wait,,,,,,,,,,,,,,,, +util_WALK_PRM_transfer_wait_time,WALK_PRM - transfer wait time,@(odt_skims['WALK_PRM_XFERWAIT'] + dot_skims['WALK_PRM_XFERWAIT'])* df.time_factor,,,,,,,coef_xwait,,,,,,,,,,,,,,,, +util_WALK_PRM_transfer_waLK_time,WALK_PRM - transfer walk time,@(odt_skims['WALK_PRM_XFERWALK'] + dot_skims['WALK_PRM_XFERWALK'])* df.time_factor,,,,,,,coef_xwalk,,,,,,,,,,,,,,,, +util_WALK_PRM_transfers_penalty,WALK_PRM - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WALK_PRM_XFERS'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['WALK_PRM_XFERS'] + df.mtnev_egr_xfer, a_min=None,a_max=4)))*df.time_factor",,,,,,,coef_xfer,,,,,,,,,,,,,,,, +util_WALK_PRM_Walk_access_time,WALK_PRM - Walk access,@2 * df.origin_prm_time * df.time_factor,,,,,,,coef_acctime,,,,,,,,,,,,,,,, +util_WALK_PRM_Walk_egress_time,WALK_PRM - egress time,"@np.where(df.nev_egress_available, df.nev_egress_time, np.where(df.microtransit_egress_available, df.microtransit_egress_time, 2*df.dest_prm_time)) * df.time_factor",,,,,,,coef_acctime,,,,,,,,,,,,,,,, +util_WALK_PRM_Fare,WALK_PRM - Fare,@df.transitSubsidyPassDiscount*(odt_skims['WALK_PRM_FARE'] + dot_skims['WALK_PRM_FARE'])*100/df.cost_sensitivity,,,,,,,coef_income,,,,,,,,,,,,,,,, +util_WALK_PRM_ASC,WALK_PRM - Alternative-specific constant,(wlk_prm_out_asc + wlk_prm_inb_asc) * time_factor,,,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_WALK_PRM - Age 16 to 24,WALK_PRM - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,coef_age1624_tran,,,,,,,,,,,,,,,, +util_WALK_PRM - Age 41 to 55,WALK_PRM - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,coef_age4155_tran,,,,,,,,,,,,,,,, +util_WALK_PRM - Age 56 to 64,WALK_PRM - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,coef_age5664_tran,,,,,,,,,,,,,,,, +util_WALK_PRM - Age 65+,WALK_PRM - Age 65+,@(df.age > 64),,,,,,,coef_age65pl_tran,,,,,,,,,,,,,,,, +util_WALK_PRM - Female,WALK_PRM - Female,@(df.female),,,,,,,coef_female_tran,,,,,,,,,,,,,,,, +util_WALK_PRM - Origin Mix,WALK_PRM - Origin Mix,oMGRAMix,,,,,,,coef_oMix_wTran,,,,,,,,,,,,,,,, +util_WALK_PRM - Origin Intersection Density,WALK_PRM - Origin Intersection Density,oMGRATotInt,,,,,,,coef_oIntDen_wTran,,,,,,,,,,,,,,,, +util_WALK_PRM - Destination Employment Density,WALK_PRM - Destination Employment Density,dMGRAEmpDen,,,,,,,coef_dEmpDen_wTran,,,,,,,,,,,,,,,, +#,WALK_MIX,,,,,,,,,,,,,,,,,,,,,,,, +util_WALK_MIX_Unavailable,WALK_MIX - Unavailable,walk_mix_available == False,,,,,,,,-999,,,,,,,,,,,,,,, +util_WALK_MIX_In_vehicle_time,WALK_MIX - In-vehicle time,@(odt_skims['WALK_MIX_TOTALIVTT'] + dot_skims['WALK_MIX_TOTALIVTT']) *df.time_factor,,,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_LRT,WALK_MIX - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['WALK_MIX_LRTIVTT'] + dot_skims['WALK_MIX_LRTIVTT'])*df.time_factor,,,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_CMR,WALK_MIX - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['WALK_MIX_CMRIVTT'] + dot_skims['WALK_MIX_CMRIVTT'])*df.time_factor,,,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_LTD_EXP,WALK_MIX - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['WALK_MIX_LTDEXPIVTT'] + dot_skims['WALK_MIX_LTDEXPIVTT'] + odt_skims['WALK_MIX_EXPIVTT'] + dot_skims['WALK_MIX_EXPIVTT'])*df.time_factor,,,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_URB,WALK_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['WALK_MIX_BRTIVTT'] + dot_skims['WALK_MIX_BRTIVTT'])*df.time_factor,,,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_WALK_MIX_FIRST_wait_time,WALK_MIX - First wait time,@(odt_skims['WALK_MIX_FIRSTWAIT']) + (dot_skims['WALK_MIX_FIRSTWAIT'])*df.time_factor,,,,,,,,coef_wait,,,,,,,,,,,,,,, +util_WALK_MIX_transfer_wait_time,WALK_MIX - transfer wait time,@(odt_skims['WALK_MIX_XFERWAIT'] + dot_skims['WALK_MIX_XFERWAIT'])*df.time_factor,,,,,,,,coef_xwait,,,,,,,,,,,,,,, +util_WALK_PRM_transfer_waLK_time,WALK_PRM - transfer walk time,@(odt_skims['WALK_MIX_XFERWALK'] + dot_skims['WALK_MIX_XFERWALK']) *df.time_factor,,,,,,,,coef_xwalk,,,,,,,,,,,,,,, +util_WALK_MIX_number_of_transfers,WALK_MIX - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WALK_MIX_XFERS'], a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['WALK_MIX_XFERS'], a_min=None,a_max=4)))*df.time_factor",,,,,,,,coef_xfer,,,,,,,,,,,,,,, +util_WALK_MIX_Walk_access_time,WALK_MIX - Walk access + egress time,@2 *(df.origin_mix_time + df.dest_mix_time)*df.time_factor,,,,,,,,coef_acctime,,,,,,,,,,,,,,, +util_WALK_MIX_Fare,WALK_MIX - Fare,@df.transitSubsidyPassDiscount*(odt_skims['WALK_MIX_FARE'] + dot_skims['WALK_MIX_FARE'])*100/df.cost_sensitivity,,,,,,,,coef_income,,,,,,,,,,,,,,, +util_WALK_MIX_ASC,WALK_MIX - Alternative-specific constant,(wlk_mix_out_asc + wlk_mix_inb_asc) * time_factor,,,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_WALK_MIX - Age 16 to 24,WALK_MIX - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,coef_age1624_tran,,,,,,,,,,,,,,, +util_WALK_MIX - Age 41 to 55,WALK_MIX - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,coef_age4155_tran,,,,,,,,,,,,,,, +util_WALK_MIX - Age 56 to 64,WALK_MIX - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,coef_age5664_tran,,,,,,,,,,,,,,, +util_WALK_MIX - Age 65+,WALK_MIX - Age 65+,@(df.age > 64),,,,,,,,coef_age65pl_tran,,,,,,,,,,,,,,, +util_WALK_MIX - Female,WALK_MIX - Female,@(df.female),,,,,,,,coef_female_tran,,,,,,,,,,,,,,, +util_WALK_MIX - Origin Mix,WALK_MIX - Origin Mix,oMGRAMix,,,,,,,,coef_oMix_wTran,,,,,,,,,,,,,,, +util_WALK_MIX - Origin Intersection Density,WALK_MIX - Origin Intersection Density,oMGRATotInt,,,,,,,,coef_oIntDen_wTran,,,,,,,,,,,,,,, +util_WALK_MIX - Destination Employment Density,WALK_MIX - Destination Employment Density,dMGRAEmpDen,,,,,,,,coef_dEmpDen_wTran,,,,,,,,,,,,,,, +#,PNR_LOC,,,,,,,,,,,,,,,,,,,,,,,, +util_PNR_LOC_Unavailable,PNR_LOC - Unavailable,"@(df.pnr_local_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,,-999,,,,,,,,,,,,,, +util_PNRTransit_0Auto,PNRTransit_0Auto,@(df.auto_ownership==0),,,,,,,,,-999,,,,,,,,,,,,,, +util_PNR_LOC_Unavailable_for_persons_less_than_16,PNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,-999,,,,,,,,,,,,,, +util_PNR_LOC_In_vehicle_time,PNR_LOC - In-vehicle time,@(odt_skims['PNROUT_LOC_TOTALIVTT'] + dot_skims['PNRIN_LOC_TOTALIVTT'])*df.time_factor,,,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_PNR_LOC_iwait_time,PNR_LOC - First iwait time,@(odt_skims['PNROUT_LOC_FIRSTWAIT']) + (dot_skims['PNRIN_LOC_FIRSTWAIT'])*df.time_factor,,,,,,,,,coef_wait,,,,,,,,,,,,,, +util_PNR_LOC_transfer_wait_time,PNR_LOC - transfer wait time,@(odt_skims['PNROUT_LOC_XFERWAIT'] + dot_skims['PNRIN_LOC_XFERWAIT'])*df.time_factor,,,,,,,,,coef_xwait,,,,,,,,,,,,,, +util_PNR_LOC_number_of_transfers,PNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNROUT_LOC_XFERS'], a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['PNRIN_LOC_XFERS'], a_min=None,a_max=4))) *df.time_factor",,,,,,,,,coef_xferdrive,,,,,,,,,,,,,, +util_PNR_LOC_PNR_time,PNR_LOC - PNR time,@(odt_skims['PNROUT_LOC_ACC'] + dot_skims['PNRIN_LOC_EGR']) *df.time_factor,,,,,,,,,coef_acctime,,,,,,,,,,,,,, +util_PNR_LOC_PNR_cost,PNR_LOC - PNR cost,@(df.sov_auto_op_cost * (odt_skims['PNROUT_LOC_ACC'] + dot_skims['PNRIN_LOC_EGR']) *driveSpeed )/df.cost_sensitivity,,,,,,,,,coef_income,,,,,,,,,,,,,, +util_PNR_LOC_Walk_egress_time_(at_attraction_end),PNR_LOC - Walk egress time (at attraction end),@2 *df.dest_local_time* df.time_factor,,,,,,,,,coef_acctime,,,,,,,,,,,,,, +util_PNR_LOC_Walk_other_time,PNR_LOC - Walk other time,@(odt_skims['PNROUT_LOC_XFERWALK'] + dot_skims['PNRIN_LOC_XFERWALK'])*df.time_factor,,,,,,,,,coef_xwalk,,,,,,,,,,,,,, +util_PNR_LOC_Fare_and_operating_cost,PNR_LOC - Fare ,@df.transitSubsidyPassDiscount*(odt_skims['PNROUT_LOC_FARE'] + dot_skims['PNRIN_LOC_FARE'])*100/df.cost_sensitivity,,,,,,,,,coef_income,,,,,,,,,,,,,, +util_PNR_LOC - Age 16 to 24,PNR_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,,coef_age1624_tran,,,,,,,,,,,,,, +util_PNR_LOC - Age 41 to 55,PNR_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,,coef_age4155_tran,,,,,,,,,,,,,, +util_PNR_LOC - Age 56 to 64,PNR_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,,coef_age5664_tran,,,,,,,,,,,,,, +util_PNR_LOC - Age 65+,PNR_LOC - Age 65+,@(df.age > 65),,,,,,,,,coef_age65pl_tran,,,,,,,,,,,,,, +util_PNR_LOC - Female,PNR_LOC - Female,@(df.female),,,,,,,,,coef_female_tran,,,,,,,,,,,,,, +util_PNR_LOC - Destination Employment Density,PNR_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,,,coef_dEmpDen_dTran,,,,,,,,,,,,,, +#,PNR_PRM,,,,,,,,,,,,,,,,,,,,,,,, +util_PNR_PRM_Unavailable,PNR_PRM - Unavailable,"@(df.pnr_premium_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,,,-999,,,,,,,,,,,,, +util_PNR_PRM_Unavailable_for_persons_less_than_16,PNR_PRM - Unavailable for persons less than 16,@df.age < 16,,,,,,,,,,-999,,,,,,,,,,,,, +util_PNR_PRM_In_vehicle_time,PNR_PRM - In-vehicle time,@(odt_skims['PNROUT_PRM_TOTALIVTT'] + dot_skims['PNRIN_PRM_TOTALIVTT']) *df.time_factor,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_LRT,PNR_PRM - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['PNROUT_PRM_LRTIVTT'] + dot_skims['PNRIN_PRM_LRTIVTT']) *df.time_factor,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_CMR,PNR_PRM - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['PNROUT_PRM_CMRIVTT'] + dot_skims['PNRIN_PRM_CMRIVTT']) *df.time_factor,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_LTD_EXP,PNR_PRM - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['PNROUT_PRM_LTDEXPIVTT'] + dot_skims['PNRIN_PRM_LTDEXPIVTT'] + odt_skims['PNROUT_PRM_EXPIVTT'] + dot_skims['PNRIN_PRM_EXPIVTT']) *df.time_factor,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_URB,PNR_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['PNROUT_PRM_BRTIVTT'] + dot_skims['PNRIN_PRM_BRTIVTT']) *df.time_factor,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM_FIRST_iwait_time,PNR_PRM - First iwait time,@(odt_skims['WALK_PRM_XFERWAIT'] + dot_skims['WALK_PRM_XFERWAIT'])* df.time_factor,,,,,,,,,,coef_wait,,,,,,,,,,,,, +util_PNR_PRM_transfer_wait_time,PNR_PRM - transfer wait time,@(odt_skims['PNROUT_PRM_XFERWAIT'] + dot_skims['PNRIN_PRM_XFERWAIT'])*df.time_factor,,,,,,,,,,coef_xwait,,,,,,,,,,,,, +util_PNR_PRM_number_of_transfers,PNR_PRM - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNROUT_PRM_XFERS'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['PNRIN_PRM_XFERS'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) *df.time_factor",,,,,,,,,,coef_xferdrive,,,,,,,,,,,,, +util_PNR_PRM_PNR_time,PNR_PRM - PNR time,@(odt_skims['PNROUT_PRM_ACC'] + dot_skims['PNRIN_PRM_EGR']),,,,,,,,,,coef_acctime,,,,,,,,,,,,, +util_PNR_PRM_PNR_cost,PNR_PRM - PNR cost,@(df.sov_auto_op_cost * (odt_skims['PNROUT_PRM_ACC'] + dot_skims['PNRIN_PRM_EGR']) *driveSpeed )/df.cost_sensitivity,,,,,,,,,,coef_income,,,,,,,,,,,,, +util_PNR_PRM_Walk_egress_time_(at_attraction_end),PNR_PRM - Walk egress time (at attraction end),"@np.where(df.nev_egress_available, df.nev_egress_time, np.where(df.microtransit_egress_available, df.microtransit_egress_time, 2*df.dest_prm_time)) * df.time_factor",,,,,,,,,,coef_acctime,,,,,,,,,,,,, +util_PNR_PRM_Walk_other_time,PNR_PRM - Walk other time,@(odt_skims['PNROUT_PRM_XFERWALK'] + dot_skims['PNRIN_PRM_XFERWALK']) *df.time_factor,,,,,,,,,,coef_xwalk,,,,,,,,,,,,, +util_PNR_PRM_Fare,PNR_PRM - Fare,@df.transitSubsidyPassDiscount*(odt_skims['PNROUT_PRM_FARE'] + dot_skims['PNRIN_PRM_FARE'])*100/df.cost_sensitivity,,,,,,,,,,coef_income,,,,,,,,,,,,, +util_PNR_PRM_ASC,PNR_PRM - Alternative-specific constant,(pnr_prm_out_asc + pnr_prm_inb_asc) * time_factor,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM - Age 16 to 24,PNR_PRM - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,,,coef_age1624_tran,,,,,,,,,,,,, +util_PNR_PRM - Age 41 to 55,PNR_PRM - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,,,coef_age4155_tran,,,,,,,,,,,,, +util_PNR_PRM - Age 56 to 64,PNR_PRM - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,,,coef_age5664_tran,,,,,,,,,,,,, +util_PNR_PRM - Age 65+,PNR_PRM - Age 65+,@(df.age > 64),,,,,,,,,,coef_age65pl_tran,,,,,,,,,,,,, +util_PNR_PRM - Female,PNR_PRM - Female,@(df.female),,,,,,,,,,coef_female_tran,,,,,,,,,,,,, +util_PNR_PRM - Destination Employment Density,PNR_PRM - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,coef_dEmpDen_dTran,,,,,,,,,,,,, +#,PNR_MIX,,,,,,,,,,,,,,,,,,,,,,,, +util_PNR_MIX_Unavailable,PNR_mix - Unavailable,"@(df.pnr_mix_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,,,,-999,,,,,,,,,,,, +util_PNR_MIX_Transit_0Auto,PNRTransit_0Auto,@(df.auto_ownership==0),,,,,,,,,,,-999,,,,,,,,,,,, +util_PNR_PRM_Unavailable_for_persons_less_than_16,PNR_mix - Unavailable for persons less than 16,@df.age < 16,,,,,,,,,,,-999,,,,,,,,,,,, +util_PNR_MIX_In_vehicle_time,PNR_MIX - In-vehicle time,@(odt_skims['PNROUT_MIX_TOTALIVTT'] + dot_skims['PNRIN_MIX_TOTALIVTT']) *df.time_factor,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX_In_vehicle_time_on_LRT,PNR_MIX - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['PNROUT_MIX_LRTIVTT'] + dot_skims['PNRIN_MIX_LRTIVTT']) *df.time_factor,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX_In_vehicle_time_on_CMR,PNR_MIX - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['PNROUT_MIX_CMRIVTT'] + dot_skims['PNRIN_MIX_CMRIVTT']) *df.time_factor,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX_In_vehicle_time_on_LTD_EXP,PNR_MIX - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['PNROUT_MIX_LTDEXPIVTT'] + dot_skims['PNRIN_MIX_LTDEXPIVTT'] + odt_skims['PNROUT_MIX_EXPIVTT'] + dot_skims['PNRIN_MIX_EXPIVTT'])*df.time_factor,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX_In_vehicle_time_on_URB,PNR_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['PNROUT_MIX_BRTIVTT'] + dot_skims['PNRIN_MIX_BRTIVTT'])*df.time_factor,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX_FIRST_iwait_time,PNR_MIX - First iwait time,@(odt_skims['PNROUT_MIX_FIRSTWAIT'] + dot_skims['PNRIN_MIX_FIRSTWAIT']) *df.time_factor,,,,,,,,,,,coef_wait,,,,,,,,,,,, +util_PNR_MIX_transfer_wait_time,PNR_MIX - transfer wait time,@(odt_skims['PNROUT_MIX_XFERWAIT'] + dot_skims['PNRIN_MIX_XFERWAIT'])*df.time_factor,,,,,,,,,,,coef_xwait,,,,,,,,,,,, +util_PNR_MIX_number_of_transfers,PNR_MIX - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNROUT_MIX_XFERS'], a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['PNRIN_MIX_XFERS'], a_min=None,a_max=4))) *df.time_factor",,,,,,,,,,,coef_xferdrive,,,,,,,,,,,, +util_PNR_MIX_PNR_time,PNR_MIX - PNR time,@(odt_skims['PNROUT_MIX_ACC'] + dot_skims['PNRIN_MIX_EGR'])*df.time_factor,,,,,,,,,,,coef_acctime,,,,,,,,,,,, +util_PNR_MIX_PNR_cost,PNR_MIX - PNR cost,@(df.sov_auto_op_cost * (odt_skims['PNROUT_MIX_ACC'] + dot_skims['PNRIN_MIX_EGR']) *driveSpeed )/df.cost_sensitivity,,,,,,,,,,,coef_income,,,,,,,,,,,, +util_PNR_MIX_Walk_egress_time_(at_attraction_end),PNR_MIX - Walk egress time (at attraction end),@2 * df.dest_mix_time* df.time_factor,,,,,,,,,,,coef_acctime,,,,,,,,,,,, +util_PNR_MIX_Walk_other_time,PNR_MIX - Walk other time,@(odt_skims['PNROUT_MIX_XFERWALK'] + dot_skims['PNRIN_MIX_XFERWALK']) *df.time_factor,,,,,,,,,,,coef_xwalk,,,,,,,,,,,, +util_PNR_MIX_Fare,PNR_MIX - Fare,@df.transitSubsidyPassDiscount*(odt_skims['PNROUT_MIX_FARE'] + dot_skims['PNRIN_MIX_FARE'])*100/df.cost_sensitivity,,,,,,,,,,,coef_income,,,,,,,,,,,, +util_PNR_MIX_ASC,PNR_MIX - Alternative-specific constant,(pnr_mix_out_asc + pnr_mix_inb_asc) * time_factor,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX - Age 16 to 24,PNR_MIX - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,,,,coef_age1624_tran,,,,,,,,,,,, +util_PNR_MIX - Age 41 to 55,PNR_MIX - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,,,,coef_age4155_tran,,,,,,,,,,,, +util_PNR_MIX - Age 56 to 64,PNR_MIX - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,,,,coef_age5664_tran,,,,,,,,,,,, +util_PNR_MIX - Age 65+,PNR_MIX - Age 65+,@(df.age > 64),,,,,,,,,,,coef_age65pl_tran,,,,,,,,,,,, +util_PNR_MIX - Female,PNR_MIX - Female,@(df.female),,,,,,,,,,,coef_female_tran,,,,,,,,,,,, +util_PNR_MIX - Destination Employment Density,PNR_MIX - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,coef_dEmpDen_dTran,,,,,,,,,,,, +#,,,,,,,,,,,,,,,,,,,,,,,,, +util_KNR_LOC_Unavailable,KNR_LOC - Unavailable,"@(df.knr_local_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,,,,,-999,,,,,,,,,,, +util_KNR_LOC_In_vehicle_time,KNR_LOC - In-vehicle time,@(odt_skims['KNROUT_LOC_TOTALIVTT'] + dot_skims['KNRIN_LOC_TOTALIVTT']) *df.time_factor,,,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_KNR_LOC_iwait_time,KNR_LOC - First iwait time,@(odt_skims['KNROUT_LOC_FIRSTWAIT']) + (dot_skims['KNRIN_LOC_FIRSTWAIT'])*df.time_factor,,,,,,,,,,,,coef_wait,,,,,,,,,,, +util_KNR_LOC_transfer_wait_time,KNR_LOC - transfer wait time,@(odt_skims['KNROUT_LOC_XFERWAIT'] + dot_skims['KNRIN_LOC_XFERWAIT'])*df.time_factor,,,,,,,,,,,,coef_xwait,,,,,,,,,,, +util_KNR_LOC_number_of_transfers,KNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KNROUT_LOC_XFERS'], a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['KNRIN_LOC_XFERS'], a_min=None,a_max=4))) *df.time_factor",,,,,,,,,,,,coef_xferdrive,,,,,,,,,,, +util_KNR_LOC_KNR_time,KNR_LOC - KNR time,@(odt_skims['KNROUT_LOC_ACC'] + dot_skims['KNRIN_LOC_EGR'])*df.time_factor,,,,,,,,,,,,coef_acctime,,,,,,,,,,, +util_KNR_LOC_KNR_cost,KNR_LOC - KNR cost,@(df.sr2_auto_op_cost * (odt_skims['KNROUT_LOC_ACC'] + dot_skims['KNRIN_LOC_EGR']) *driveSpeed )/df.cost_sensitivity,,,,,,,,,,,,coef_income,,,,,,,,,,, +util_KNR_LOC_Walk_egress_time_(at_attraction_end),KNR_LOC - Walk egress time (at attraction end),@2 * df.dest_local_time* df.time_factor,,,,,,,,,,,,coef_acctime,,,,,,,,,,, +util_KNR_LOC_Walk_other_time,KNR_LOC - Walk other time,@(odt_skims['KNROUT_LOC_XFERWALK'] + dot_skims['KNRIN_LOC_XFERWALK'])*df.time_factor,,,,,,,,,,,,coef_xwalk,,,,,,,,,,, +util_KNR_LOC_Fare_and_operating_cost,KNR_LOC - Fare ,@df.transitSubsidyPassDiscount*(odt_skims['KNROUT_LOC_FARE'] + dot_skims['KNRIN_LOC_FARE'])*100/df.cost_sensitivity,,,,,,,,,,,,coef_income,,,,,,,,,,, +util_KNR_LOC - Age 16 to 24,KNR_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,,,,,coef_age1624_tran,,,,,,,,,,, +util_KNR_LOC - Age 41 to 55,KNR_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,,,,,coef_age4155_tran,,,,,,,,,,, +util_KNR_LOC - Age 56 to 64,KNR_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,,,,,coef_age5664_tran,,,,,,,,,,, +util_KNR_LOC - Age 65+,KNR_LOC - Age 65+,@(df.age > 64),,,,,,,,,,,,coef_age65pl_tran,,,,,,,,,,, +util_KNR_LOC - Female,KNR_LOC - Female,@(df.female),,,,,,,,,,,,coef_female_tran,,,,,,,,,,, +util_KNR_LOC - Destination Employment Density,KNR_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,,coef_dEmpDen_dTran,,,,,,,,,,, +#,KNR_PRM,,,,,,,,,,,,,,,,,,,,,,,, +util_KNR_PRM_Unavailable,KNR_PRM - Unavailable,"@(df.knr_premium_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,,,,,,-999,,,,,,,,,, +util_KNR_PRM_In_vehicle_time,KNR_PRM - In-vehicle time,@(odt_skims['KNROUT_PRM_TOTALIVTT'] + dot_skims['KNRIN_PRM_TOTALIVTT']) *df.time_factor,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM_In_vehicle_time_on_LRT,KNR_PRM - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['KNROUT_PRM_LRTIVTT'] + dot_skims['KNRIN_PRM_LRTIVTT'])*df.time_factor,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM_In_vehicle_time_on_CMR,KNR_PRM - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['KNROUT_PRM_CMRIVTT'] + dot_skims['KNRIN_PRM_CMRIVTT'])*df.time_factor,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM_In_vehicle_time_on_LTD_EXP,KNR_PRM - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['KNROUT_PRM_LTDEXPIVTT'] + dot_skims['KNRIN_PRM_LTDEXPIVTT'] + odt_skims['KNROUT_PRM_EXPIVTT'] + dot_skims['KNRIN_PRM_EXPIVTT'])*df.time_factor,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM_In_vehicle_time_on_URB,KNR_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['KNROUT_PRM_BRTIVTT'] + dot_skims['KNRIN_PRM_BRTIVTT'])*df.time_factor,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM_FIRST_iwait_time,KNR_PRM - First iwait time,@(odt_skims['KNROUT_PRM_FIRSTWAIT'] + dot_skims['KNRIN_PRM_FIRSTWAIT'])*df.time_factor,,,,,,,,,,,,,coef_wait,,,,,,,,,, +util_KNR_PRM_transfer_wait_time,KNR_PRM - transfer wait time,@(odt_skims['KNROUT_PRM_XFERWAIT'] + dot_skims['KNRIN_PRM_XFERWAIT'])*df.time_factor,,,,,,,,,,,,,coef_xwait,,,,,,,,,, +util_KNR_PRM_number_of_transfers,KNR_PRM - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KNROUT_PRM_XFERS'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['KNRIN_PRM_XFERS'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) *df.time_factor",,,,,,,,,,,,,coef_xferdrive,,,,,,,,,, +util_KNR_PRM_KNR_time,KNR_PRM - KNR time,@(odt_skims['KNROUT_PRM_ACC'] + dot_skims['KNRIN_PRM_EGR']) *df.time_factor,,,,,,,,,,,,,coef_acctime,,,,,,,,,, +util_KNR_PRM_KNR_cost,KNR_PRM - KNR cost,@(df.sr2_auto_op_cost * (odt_skims['KNROUT_PRM_ACC'] + dot_skims['KNRIN_PRM_EGR']) *driveSpeed )/df.cost_sensitivity,,,,,,,,,,,,,coef_income,,,,,,,,,, +util_KNR_PRM_Walk_egress_time_(at_attraction_end),KNR_PRM - Walk egress time (at attraction end),"@np.where(df.nev_egress_available, df.nev_egress_time, np.where(df.microtransit_egress_available, df.microtransit_egress_time, 2*df.dest_prm_time)) * df.time_factor",,,,,,,,,,,,,coef_acctime,,,,,,,,,, +util_KNR_PRM_Walk_other_time,KNR_PRM - Walk other time,@(odt_skims['KNROUT_PRM_XFERWALK'] + dot_skims['KNRIN_PRM_XFERWALK']) *df.time_factor,,,,,,,,,,,,,coef_xwalk,,,,,,,,,, +util_KNR_PRM_Fare,KNR_PRM - Fare,@df.transitSubsidyPassDiscount*(odt_skims['KNROUT_PRM_FARE'] + dot_skims['KNRIN_PRM_FARE'])*100/df.cost_sensitivity,,,,,,,,,,,,,coef_income,,,,,,,,,, +util_KNR_PRM_ASC,KNR_PRM - Alternative-specific constant,(knr_prm_out_asc + knr_prm_inb_asc) * time_factor,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM - Age 16 to 24,KNR_PRM - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,,,,,,coef_age1624_tran,,,,,,,,,, +util_KNR_PRM - Age 41 to 55,KNR_PRM - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,,,,,,coef_age4155_tran,,,,,,,,,, +util_KNR_PRM - Age 56 to 64,KNR_PRM - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,,,,,,coef_age5664_tran,,,,,,,,,, +util_KNR_PRM - Age 65+,KNR_PRM - Age 65+,@(df.age > 64),,,,,,,,,,,,,coef_age65pl_tran,,,,,,,,,, +util_KNR_PRM - Female,KNR_PRM - Female,@(df.female),,,,,,,,,,,,,coef_female_tran,,,,,,,,,, +util_KNR_PRM - Destination Employment Density,KNR_PRM - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,,,coef_dEmpDen_dTran,,,,,,,,,, +#,KNR_MIX,,,,,,,,,,,,,,,,,,,,,,,, +util_KNR_MIX_Unavailable,KNR_mix - Unavailable,"@(df.knr_mix_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,,,,,,,-999,,,,,,,,, +util_KNR_MIX_In_vehicle_time,KNR_MIX - In-vehicle time,@(odt_skims['KNROUT_MIX_TOTALIVTT'] + dot_skims['KNRIN_MIX_TOTALIVTT']) *df.time_factor,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX_In_vehicle_time_on_Bus,KNR_MIX - In-vehicle time on Bus,@(odt_skims['KNROUT_MIX_BUSIVTT'] + dot_skims['KNRIN_MIX_BUSIVTT']) *df.time_factor,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX_In_vehicle_time_on_LRT,KNR_MIX - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['KNROUT_MIX_LRTIVTT'] + dot_skims['KNRIN_MIX_LRTIVTT']) *df.time_factor,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX_In_vehicle_time_on_CMR,KNR_MIX - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['KNROUT_MIX_CMRIVTT'] + dot_skims['KNRIN_MIX_CMRIVTT']) *df.time_factor,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX_In_vehicle_time_on_LTD_EXP,KNR_MIX - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['KNROUT_MIX_LTDEXPIVTT'] + dot_skims['KNRIN_MIX_LTDEXPIVTT'] + odt_skims['KNROUT_MIX_EXPIVTT'] + dot_skims['KNRIN_MIX_EXPIVTT']) *df.time_factor,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX_In_vehicle_time_on_URB,KNR_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['KNROUT_MIX_BRTIVTT'] + dot_skims['KNRIN_MIX_BRTIVTT']) *df.time_factor,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX_FIRST_iwait_time,KNR_MIX - First iwait time,@(odt_skims['KNROUT_MIX_FIRSTWAIT'] + dot_skims['KNRIN_MIX_FIRSTWAIT'])*df.time_factor,,,,,,,,,,,,,,coef_wait,,,,,,,,, +util_KNR_MIX_transfer_wait_time,KNR_MIX - transfer wait time,@(odt_skims['KNROUT_MIX_XFERWAIT'] + dot_skims['KNRIN_MIX_XFERWAIT'])*df.time_factor,,,,,,,,,,,,,,coef_xwait,,,,,,,,, +util_KNR_MIX_number_of_transfers,KNR_MIX - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KNROUT_MIX_XFERS'], a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['KNRIN_MIX_XFERS'], a_min=None,a_max=4))) *df.time_factor",,,,,,,,,,,,,,coef_xferdrive,,,,,,,,, +util_KNR_MIX_KNR_time,KNR_MIX - KNR time,@(odt_skims['KNROUT_MIX_ACC'] + dot_skims['KNRIN_MIX_EGR'])*df.time_factor,,,,,,,,,,,,,,coef_acctime,,,,,,,,, +util_KNR_MIX_KNR_cost,KNR_MIX - KNR cost,@(df.sr2_auto_op_cost * (odt_skims['KNROUT_MIX_ACC'] + dot_skims['KNRIN_MIX_EGR']) *driveSpeed )/df.cost_sensitivity,,,,,,,,,,,,,,coef_income,,,,,,,,, +util_KNR_MIX_Walk_egress_time_(at_attraction_end),KNR_MIX - Walk egress time (at attraction end),@2 *df.dest_mix_time* df.time_factor,,,,,,,,,,,,,,coef_acctime,,,,,,,,, +util_KNR_MIX_Walk_other_time,KNR_MIX - Walk other time,@(odt_skims['KNROUT_MIX_XFERWALK'] + dot_skims['KNRIN_MIX_XFERWALK']) *df.time_factor,,,,,,,,,,,,,,coef_xwalk,,,,,,,,, +util_KNR_MIX_Fare,KNR_MIX - Fare,@(odt_skims['KNROUT_MIX_FARE'] + dot_skims['KNRIN_MIX_FARE'])*100/df.cost_sensitivity,,,,,,,,,,,,,,coef_income,,,,,,,,, +util_KNR_MIX_ASC,KNR_MIX - Alternative-specific constant,(knr_mix_out_asc + knr_mix_inb_asc) * time_factor,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX - Age 16 to 24,KNR_MIX - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,,,,,,,coef_age1624_tran,,,,,,,,, +util_KNR_MIX - Age 41 to 55,KNR_MIX - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,,,,,,,coef_age4155_tran,,,,,,,,, +util_KNR_MIX - Age 56 to 64,KNR_MIX - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,,,,,,,coef_age5664_tran,,,,,,,,, +util_KNR_MIX - Age 65+,KNR_MIX - Age 65+,@(df.age > 64),,,,,,,,,,,,,,coef_age65pl_tran,,,,,,,,, +util_KNR_MIX - Female,KNR_MIX - Female,@(df.female),,,,,,,,,,,,,,coef_female_tran,,,,,,,,, +util_KNR_MIX - Destination Employment Density,KNR_MIX - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,,,,coef_dEmpDen_dTran,,,,,,,,, +#,,,,,,,,,,,,,,,,,,,,,,,,, +util_TNC_LOC_Unavailable,TNC_LOC - Unavailable,"@(df.tnc_local_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,,,,,,,,-999,,,,,,,, +util_TNC_LOC_Unavailable_for_persons_less_than_16,TNC_LOC - Unavailable for persons less than 16,age < 12,,,,,,,,,,,,,,,-999,,,,,,,, +util_TNC_LOC_In_vehicle_time,TNC_LOC - In-vehicle time,@(odt_skims['TNCOUT_LOC_TOTALIVTT'] + dot_skims['TNCIN_LOC_TOTALIVTT']) *df.time_factor,,,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_TNC_LOC_iwait_time,TNC_LOC - First iwait time,@(odt_skims['TNCOUT_LOC_FIRSTWAIT']) + (dot_skims['TNCIN_LOC_FIRSTWAIT'])*df.time_factor,,,,,,,,,,,,,,,coef_wait,,,,,,,, +util_TNC_LOC_transfer_wait_time,TNC_LOC - transfer wait time,@(odt_skims['TNCOUT_LOC_XFERWAIT'] + dot_skims['TNCIN_LOC_XFERWAIT']) *df.time_factor,,,,,,,,,,,,,,,coef_xwait,,,,,,,, +util_TNC_LOC_number_of_transfers,TNC_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['TNCOUT_LOC_XFERS'], a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['TNCIN_MIX_XFERS'], a_min=None,a_max=4))) *df.time_factor",,,,,,,,,,,,,,,coef_xferdrive,,,,,,,, +util_TNC_LOC_TNC_time,TNC_LOC - TNC time,@(odt_skims['TNCOUT_LOC_ACC'] + dot_skims['TNCIN_LOC_EGR'])* df.time_factor,,,,,,,,,,,,,,,coef_acctime,,,,,,,, +util_TNC_LOC_Walk_egress_time_(at_attraction_end),TNC_LOC - Walk egress time (at attraction end),@2 * df.dest_local_time * df.time_factor,,,,,,,,,,,,,,,coef_acctime,,,,,,,, +util_TNC_LOC_Walk_other_time,TNC_LOC - Walk other time,@(odt_skims['TNCOUT_LOC_XFERWALK'] + dot_skims['TNCIN_LOC_XFERWALK']) *df.time_factor,,,,,,,,,,,,,,,coef_xwalk,,,,,,,, +util_TNC_LOC_Fare_and_operating_cost,TNC_LOC - Fare ,@(odt_skims['TNCOUT_LOC_FARE'] + dot_skims['TNCIN_LOC_FARE'])*100/df.cost_sensitivity,,,,,,,,,,,,,,,coef_income,,,,,,,, +util_TNC_LOC - Age 16 to 24,TNC_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,,,,,,,,coef_age1624_tran,,,,,,,, +util_TNC_LOC - Age 41 to 55,TNC_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,,,,,,,,coef_age4155_tran,,,,,,,, +util_TNC_LOC - Age 56 to 64,TNC_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,,,,,,,,coef_age5664_tran,,,,,,,, +util_TNC_LOC - Age 65+,TNC_LOC - Age 65+,@(df.age > 64),,,,,,,,,,,,,,,coef_age65pl_tran,,,,,,,, +util_TNC_LOC - Female,TNC_LOC - Female,@(df.female),,,,,,,,,,,,,,,coef_female_tran,,,,,,,, +util_TNC_LOC - Destination Employment Density,TNC_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,,,,,coef_dEmpDen_dTran,,,,,,,, +#,TNC_PRM,,,,,,,,,,,,,,,,,,,,,,,, +util_TNC_PRM_Unavailable,TNC_PRM - Unavailable,"@(df.tnc_premium_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,,,,,,,,,-999,,,,,,, +util_TNC_PRM_Unavailable_for_persons_less_than_16,TNC_PRM - Unavailable for persons less than 12,@df.age < 12,,,,,,,,,,,,,,,,-999,,,,,,, +util_TNC_PRM_In_vehicle_time,TNC_PRM - In-vehicle time,@(odt_skims['TNCOUT_PRM_TOTALIVTT'] + dot_skims['TNCIN_PRM_TOTALIVTT']) *df.time_factor,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNC_PRM_In_vehicle_time_on_LRT,TNC_PRM - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['TNCOUT_PRM_LRTIVTT'] + dot_skims['TNCIN_PRM_LRTIVTT'])*df.time_factor,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNC_PRM_In_vehicle_time_on_CMR,TNC_PRM - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['TNCOUT_PRM_CMRIVTT'] + dot_skims['TNCIN_PRM_CMRIVTT']) *df.time_factor,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNC_PRM_In_vehicle_time_on_LTD_EXP,TNC_PRM - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['TNCOUT_PRM_LTDEXPIVTT'] + dot_skims['TNCIN_PRM_LTDEXPIVTT'] + odt_skims['TNCOUT_PRM_EXPIVTT'] + dot_skims['TNCIN_PRM_EXPIVTT'])*df.time_factor,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNC_PRM_In_vehicle_time_on_URB,TNC_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['TNCOUT_PRM_BRTIVTT'] + dot_skims['TNCIN_PRM_BRTIVTT'])*df.time_factor,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNC_PRM_FIRST_iwait_time,TNC_PRM - First iwait time,@(odt_skims['TNCOUT_PRM_FIRSTWAIT'] + dot_skims['TNCIN_PRM_FIRSTWAIT']) *df.time_factor,,,,,,,,,,,,,,,,coef_wait,,,,,,, +util_TNC_PRM_transfer_wait_time,TNC_PRM - transfer wait time,@(odt_skims['TNCOUT_PRM_XFERWAIT'] + dot_skims['TNCIN_PRM_XFERWAIT'])*df.time_factor,,,,,,,,,,,,,,,,coef_xwait,,,,,,, +util_TNC_PRM_number_of_transfers,TNC_PRM - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['TNCOUT_PRM_XFERS'] + df.mtnev_acc_xfer + df.mtnev_egr_xfer, a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['TNCIN_PRM_XFERS'] + df.mtnev_acc_xfer + df.mtnev_egr_xfer, a_min=None,a_max=4))) *df.time_factor",,,,,,,,,,,,,,,,coef_xferdrive,,,,,,, +util_TNC_PRM_TNC_time,TNC_PRM - TNC time,"@np.where(df.nev_access_available, df.nev_access_time, np.where(df.microtransit_access_available, df.microtransit_access_time, odt_skims['TNCOUT_PRM_ACC'] + dot_skims['TNCIN_PRM_EGR'])) *df.time_factor",,,,,,,,,,,,,,,,coef_acctime,,,,,,, +util_TNC_PRM_Walk_egress_time_(at_attraction_end),TNC_PRM - Walk egress time (at attraction end),"@np.where(df.nev_egress_available, df.nev_egress_time, np.where(df.microtransit_egress_available, df.microtransit_egress_time, 2*df.dest_prm_time)) * df.time_factor",,,,,,,,,,,,,,,,coef_acctime,,,,,,, +util_TNC_PRM_Walk_other_time,TNC_PRM - Walk other time,@ (odt_skims['TNCOUT_PRM_XFERWALK'] + dot_skims['TNCIN_PRM_XFERWALK']) *df.time_factor,,,,,,,,,,,,,,,,coef_xwalk,,,,,,, +util_TNC_PRM_Fare,TNC_PRM - Fare,"@np.where(df.microtransit_access_available | df.nev_access_available, df.transitSubsidyPassDiscount*(odt_skims['KNROUT_PRM_FARE'] + dot_skims['KNRIN_PRM_FARE']), df.transitSubsidyPassDiscount*(odt_skims['TNCOUT_PRM_FARE'] + dot_skims['TNCIN_PRM_FARE']))*100/df.cost_sensitivity",,,,,,,,,,,,,,,,coef_income,,,,,,, +util_TNC_PRM_ASC,TNC_PRM - Alternative-specific constant,(tnc_prm_out_asc + tnc_prm_inb_asc) * time_factor,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNC_PRM - Age 16 to 24,TNC_PRM - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,,,,,,,,,coef_age1624_tran,,,,,,, +util_TNC_PRM - Age 41 to 55,TNC_PRM - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,,,,,,,,,coef_age4155_tran,,,,,,, +util_TNC_PRM - Age 56 to 64,TNC_PRM - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,,,,,,,,,coef_age5664_tran,,,,,,, +util_TNC_PRM - Age 65+,TNC_PRM - Age 65+,@(df.age > 64),,,,,,,,,,,,,,,,coef_age65pl_tran,,,,,,, +util_TNC_PRM - Female,TNC_PRM - Female,@(df.female),,,,,,,,,,,,,,,,coef_female_tran,,,,,,, +util_TNC_PRM - Destination Employment Density,TNC_PRM - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,,,,,,coef_dEmpDen_dTran,,,,,,, +#,TNC_MIX,,,,,,,,,,,,,,,,,,,,,,,, +util_TNC_MIX_Unavailable,TNC_mix - Unavailable,"@(df.tnc_mix_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,,,,,,,,,,,-999,,,,,, +util_TNC_PRM_Unavailable_for_persons_less_than_16,TNC_mix - Unavailable for persons less than 16,@df.age < 12,,,,,,,,,,,,,,,,,-999,,,,,, +util_TNC_MIX_In_vehicle_time,TNC_MIX - In-vehicle time,@(odt_skims['TNCOUT_MIX_TOTALIVTT'] + dot_skims['TNCIN_MIX_TOTALIVTT']) *df.time_factor,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNC_MIX_In_vehicle_time_on_LRT,TNC_MIX - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['TNCOUT_MIX_LRTIVTT'] + dot_skims['TNCIN_MIX_LRTIVTT']) *df.time_factor,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNC_MIX_In_vehicle_time_on_CMR,TNC_MIX - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['TNCOUT_MIX_CMRIVTT'] + dot_skims['TNCIN_MIX_CMRIVTT'])*df.time_factor,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNC_MIX_In_vehicle_time_on_LTD_EXP,TNC_MIX - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['TNCOUT_MIX_LTDEXPIVTT'] + dot_skims['TNCIN_MIX_LTDEXPIVTT'] + odt_skims['TNCOUT_MIX_EXPIVTT'] + dot_skims['TNCIN_MIX_EXPIVTT'])*df.time_factor,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNC_MIX_In_vehicle_time_on_URB,TNC_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['TNCOUT_MIX_BRTIVTT'] + dot_skims['TNCIN_MIX_BRTIVTT']) *df.time_factor,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNC_MIX_FIRST_iwait_time,TNC_MIX - First iwait time,@(odt_skims['TNCOUT_MIX_FIRSTWAIT'] + dot_skims['TNCIN_MIX_FIRSTWAIT']) *df.time_factor,,,,,,,,,,,,,,,,,coef_wait,,,,,, +util_TNC_MIX_transfer_wait_time,TNC_MIX - transfer wait time,@(odt_skims['TNCOUT_MIX_XFERWAIT'] + dot_skims['TNCIN_MIX_XFERWAIT']) *df.time_factor,,,,,,,,,,,,,,,,,coef_xwait,,,,,, +util_TNC_MIX_number_of_transfers,TNC_MIX - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['TNCOUT_MIX_XFERS'], a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['TNCIN_MIX_XFERS'], a_min=None,a_max=4))) *df.time_factor",,,,,,,,,,,,,,,,,coef_xferdrive,,,,,, +util_TNC_MIX_TNC_time,TNC_MIX - TNC time,@(odt_skims['TNCOUT_MIX_ACC'] + dot_skims['TNCIN_MIX_EGR'])*df.time_factor,,,,,,,,,,,,,,,,,coef_acctime,,,,,, +util_TNC_MIX_Walk_egress_time_(at_attraction_end),TNC_MIX - Walk egress time (at attraction end),@2 * df.dest_mix_time * df.time_factor,,,,,,,,,,,,,,,,,coef_acctime,,,,,, +util_TNC_MIX_Walk_other_time,TNC_MIX - Walk other time,@(odt_skims['TNCOUT_MIX_XFERWALK'] + dot_skims['TNCIN_MIX_XFERWALK']) *df.time_factor,,,,,,,,,,,,,,,,,coef_xwalk,,,,,, +util_TNC_MIX_Fare,TNC_MIX - Fare,@(odt_skims['TNCOUT_MIX_FARE'] + dot_skims['TNCIN_MIX_FARE'])*100/df.cost_sensitivity,,,,,,,,,,,,,,,,,coef_income,,,,,, +util_TNC_MIX_ASC,TNC_MIX - Alternative-specific constant,(tnc_mix_out_asc + tnc_mix_inb_asc) * time_factor,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNC_MIX - Age 16 to 24,TNC_MIX - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,,,,,,,,,,,coef_age1624_tran,,,,,, +util_TNC_MIX - Age 41 to 55,TNC_MIX - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,,,,,,,,,,,coef_age4155_tran,,,,,, +util_TNC_MIX - Age 56 to 64,TNC_MIX - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,,,,,,,,,,,coef_age5664_tran,,,,,, +util_TNC_MIX - Age 65+,TNC_MIX - Age 65+,@(df.age > 64),,,,,,,,,,,,,,,,,coef_age65pl_tran,,,,,, +util_TNC_MIX - Female,TNC_MIX - Female,@(df.female),,,,,,,,,,,,,,,,,coef_female_tran,,,,,, +util_TNC_MIX - Destination Employment Density,TNC_MIX - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,,,,,,,coef_dEmpDen_dTran,,,,,, +#,Taxi,,,,,,,,,,,,,,,,,,,,,,,, +util_Taxi - In-vehicle time,Taxi - In-vehicle time,@(df.s2_time_skims_inb + df.s2_time_skims_out) * df.time_factor,,,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_Taxi - Wait time,Taxi - Wait time,@df.totalWaitTaxi * df.time_factor,,,,,,,,,,,,,,,,,,coef_wait,,,,, +util_Taxi - Fare,Taxi - Fare,@((Taxi_baseFare*2 + (df.s2_dist_skims_out + df.s2_dist_skims_inb) * Taxi_costPerMile + (df.s2_time_skims_out + df.s2_time_skims_inb) * Taxi_costPerMinute)*100 + df.s2_cost_skims_out + df.s2_cost_skims_inb)/df.cost_sensitivity,,,,,,,,,,,,,,,,,,coef_income,,,,, +#,TNC Single,,,,,,,,,,,,,,,,,,,,,,,, +util_TNC Single - In-vehicle time,TNC Single - In-vehicle time,@(df.s2_time_skims_out + df.s2_time_skims_inb) * df.time_factor,,,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_TNC Single - Wait time,TNC Single - Wait time,@df.totalWaitSingleTNC * df.time_factor,,,,,,,,,,,,,,,,,,,coef_wait,,,, +util_TNC Single - Cost,TNC Single - Cost,"@(np.maximum(TNC_single_baseFare*2 + (df.s2_dist_skims_out + df.s2_dist_skims_inb) * TNC_single_costPerMile + (df.s2_time_skims_out + df.s2_time_skims_inb) * TNC_single_costPerMinute, TNC_single_costMinimum*2)*100 + df.s2_cost_skims_out + df.s2_cost_skims_inb)/df.cost_sensitivity",,,,,,,,,,,,,,,,,,,coef_income,,,, +#,TNC Shared,,,,,,,,,,,,,,,,,,,,,,,, +util_TNC Shared - In-vehicle time,TNC Shared - In-vehicle time,"@(np.where(df.nev_available, df.nev_time, np.where(df.microtransit_available, df.microtransit_time, (df.s2_time_skims_out + df.s2_time_skims_inb)))) * TNC_shared_IVTFactor * df.time_factor",,,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_TNC Shared - Wait time,TNC Shared - Wait time,"@np.where(df.nev_available, 2*nevWaitTime, np.where(df.microtransit_available, 2*microtransitWaitTime, df.totalWaitSharedTNC)) * df.time_factor",,,,,,,,,,,,,,,,,,,,coef_wait,,, +util_TNC Shared - Cost,TNC Shared - Cost,"@np.where(df.nev_available, 2*nevCost, np.where(df.microtransit_available, 2*microtransitCost, (np.maximum(TNC_shared_baseFare*2 + (df.s2_dist_skims_out + df.s2_dist_skims_inb) * TNC_shared_costPerMile + (df.s2_time_skims_out + df.s2_time_skims_inb)* TNC_shared_costPerMinute, TNC_shared_costMinimum*2)*100 + df.s2_cost_skims_out + df.s2_cost_skims_inb)))/df.cost_sensitivity",,,,,,,,,,,,,,,,,,,,coef_income,,, +#,School bus,,,,,,,,,,,,,,,,,,,,,,,, +util_School Bus - Unavailable,School Bus - Unavailable,SCHBUS_available==0,,,,,,,,,,,,,,,,,,,,,-999,, +util_School Bus - In-vehicle Time at 20 miles per hour,School Bus - In-vehicle Time at 20 miles per hour,(da_dist_skims_out + da_dist_skims_inb)*3 * time_factor,,,,,,,,,,,,,,,,,,,,,coef_ivt,, +util_School Bus - Wait Time (asserted),School Bus - Wait Time (asserted) * c_fwt,10 * time_factor,,,,,,,,,,,,,,,,,,,,,coef_wait,, +util_School Bus - Walk Time (asserted),School Bus - Walk Time (asserted),10 * time_factor,,,,,,,,,,,,,,,,,,,,,coef_acctime,, +util_School Bus - Age 1 to 5,School Bus - Age 1 to 5,@(df.age > 0) & (df.age < 6),,,,,,,,,,,,,,,,,,,,,coef_age0105_schb,, +util_School Bus - Age 6 to 12,School Bus - Age 6 to 12,@(df.age > 5) & (df.age < 13),,,,,,,,,,,,,,,,,,,,,coef_age0612_schb,, +util_School Bus - Age 13 to 15,School Bus - Age 13 to 15,@(df.age > 12) & (df.age < 16),,,,,,,,,,,,,,,,,,,,,coef_age1315_schb,, +util_School Bus - Female,School Bus - Female,@(df.female),,,,,,,,,,,,,,,,,,,,,coef_female_schb,, +#calibration,,,,,,,,,,,,,,,,,,,,,,,,, +util_calib_KNR Transit - Distance Parameter,abm2+ calibration constant,"@np.maximum((10-df.da_dist_skims_out),0)",,,,,,,,,,,,coef_calib_distance_KNR_TRANSIT,coef_calib_distance_KNR_TRANSIT,coef_calib_distance_KNR_TRANSIT,coef_calib_distance_TNC_TRANSIT,coef_calib_distance_TNC_TRANSIT,coef_calib_distance_TNC_TRANSIT,,,,,, +util_calib_Walk-Transit - Distance Parameter,abm2+ calibration constant,"@np.maximum((200+(-133*df.da_dist_skims_out)),0)",,,,,,coef_calib_distance_WALK_TRANSIT,coef_calib_distance_WALK_TRANSIT,coef_calib_distance_WALK_TRANSIT,,,,,,,,,,,,,,, +util_calib_PNR Transit - Distance Parameter ,abm2+ calibration constant,"@np.maximum((45+(-2.5*df.da_dist_skims_out)),0)",,,,,,,,,coef_calib_distance_PNR_TRANSIT,coef_calib_distance_PNR_TRANSIT,coef_calib_distance_PNR_TRANSIT,,,,,,,,,,,, +util_calib_parkingdistrict,abm2+ calibration constant,parkingConstrainedArea==1,,,,,,coef_calib_parkingconst_WLK_TRANSIT,coef_calib_parkingconst_WLK_TRANSIT,coef_calib_parkingconst_WLK_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT,coef_calib_parkingconst_DRV_TRANSIT,,,,,, +util_calib_probikedistrict,abm2+ calibration constant,(district27==8) | (district27==9),,,,,coef_calib_probikedistrict_BIKE,,,,,,,,,,,,,,,,,, +#util_calib_zeroautohh,abm2+ calibration constant,@(df.auto_ownership == 0),coef_calib_zeroautohh_DRIVEALONE,coef_calib_zeroautohh_SHARED2,coef_calib_zeroautohh_SHARED3,coef_calib_zeroautohh_WALK,coef_calib_zeroautohh_BIKE,coef_calib_zeroautohh_WALK_TRANSIT,coef_calib_zeroautohh_WALK_TRANSIT,coef_calib_zeroautohh_WALK_TRANSIT,coef_calib_zeroautohh_PNR_TRANSIT,coef_calib_zeroautohh_PNR_TRANSIT,coef_calib_zeroautohh_PNR_TRANSIT,coef_calib_zeroautohh_KNR_TRANSIT,coef_calib_zeroautohh_KNR_TRANSIT,coef_calib_zeroautohh_KNR_TRANSIT,coef_calib_zeroautohh_TNC_TRANSIT,coef_calib_zeroautohh_TNC_TRANSIT,coef_calib_zeroautohh_TNC_TRANSIT,coef_calib_zeroautohh_TAXI,coef_calib_zeroautohh_TNC_SINGLE,coef_calib_zeroautohh_TNC_SHARED,coef_calib_zeroautohh_SCH_BUS,, +#util_calib_autodeficienthh,abm2+ calibration constant,@((df.auto_ownership < df.num_adults) & (df.auto_ownership > 0)),,coef_calib_autodeficienthh_SHARED2,coef_calib_autodeficienthh_SHARED3,coef_calib_autodeficienthh_WALK,coef_calib_autodeficienthh_BIKE,coef_calib_autodeficienthh_WALK_TRANSIT,coef_calib_autodeficienthh_WALK_TRANSIT,coef_calib_autodeficienthh_WALK_TRANSIT,coef_calib_autodeficienthh_PNR_TRANSIT,coef_calib_autodeficienthh_PNR_TRANSIT,coef_calib_autodeficienthh_PNR_TRANSIT,coef_calib_autodeficienthh_KNR_TRANSIT,coef_calib_autodeficienthh_KNR_TRANSIT,coef_calib_autodeficienthh_KNR_TRANSIT,coef_calib_autodeficienthh_TNC_TRANSIT,coef_calib_autodeficienthh_TNC_TRANSIT,coef_calib_autodeficienthh_TNC_TRANSIT,coef_calib_autodeficienthh_TAXI,coef_calib_autodeficienthh_TNC_SINGLE,coef_calib_autodeficienthh_TNC_SHARED,coef_calib_autodeficienthh_SCH_BUS,, +#util_calib_autosufficienth,abm2+ calibration constant,@((df.auto_ownership >= df.num_adults) & (df.auto_ownership > 0)),,coef_calib_autosufficienthh_SHARED2,coef_calib_autosufficienthh_SHARED3,coef_calib_autosufficienthh_WALK,coef_calib_autosufficienthh_BIKE,coef_calib_autosufficienthh_WALK_TRANSIT,coef_calib_autosufficienthh_WALK_TRANSIT,coef_calib_autosufficienthh_WALK_TRANSIT,coef_calib_autosufficienthh_PNR_TRANSIT,coef_calib_autosufficienthh_PNR_TRANSIT,coef_calib_autosufficienthh_PNR_TRANSIT,coef_calib_autosufficienthh_KNR_TRANSIT,coef_calib_autosufficienthh_KNR_TRANSIT,coef_calib_autosufficienthh_KNR_TRANSIT,coef_calib_autosufficienthh_TNC_TRANSIT,coef_calib_autosufficienthh_TNC_TRANSIT,coef_calib_autosufficienthh_TNC_TRANSIT,coef_calib_autosufficienthh_TAXI,coef_calib_autosufficienthh_TNC_SINGLE,coef_calib_autosufficienthh_TNC_SHARED,coef_calib_autosufficienthh_SCH_BUS,, +util_calib_zeroautohhindiv,abm2+ calibration constant,@(df.is_indiv & (df.auto_ownership == 0)),,coef_calib_zeroautohhindivtou_SHARED2,coef_calib_zeroautohhindivtou_SHARED3,coef_calib_zeroautohhindivtou_WALK,coef_calib_zeroautohhindivtou_BIKE,coef_calib_zeroautohhindivtou_WALK_TRANSIT,coef_calib_zeroautohhindivtou_WALK_TRANSIT,coef_calib_zeroautohhindivtou_WALK_TRANSIT,coef_calib_zeroautohhindivtou_PNR_TRANSIT,coef_calib_zeroautohhindivtou_PNR_TRANSIT,coef_calib_zeroautohhindivtou_PNR_TRANSIT,coef_calib_zeroautohhindivtou_KNR_TRANSIT,coef_calib_zeroautohhindivtou_KNR_TRANSIT,coef_calib_zeroautohhindivtou_KNR_TRANSIT,coef_calib_zeroautohhindivtou_TNC_TRANSIT,coef_calib_zeroautohhindivtou_TNC_TRANSIT,coef_calib_zeroautohhindivtou_TNC_TRANSIT,coef_calib_zeroautohhindivtou_TAXI,coef_calib_zeroautohhindivtou_TNC_SINGLE,coef_calib_zeroautohhindivtou_TNC_SHARED,coef_calib_zeroautohhindivtou_SCH_BUS,coef_calib_zeroautohhindivtou_EBIKE,coef_calib_zeroautohhindivtou_ESCOOTER +util_calib_autodeficienthh,abm2+ calibration constant,@(df.is_indiv & (df.auto_ownership < df.num_adults) & (df.auto_ownership > 0)),,coef_calib_autodeficienthhind_SHARED2,coef_calib_autodeficienthhind_SHARED3,coef_calib_autodeficienthhind_WALK,coef_calib_autodeficienthhind_BIKE,coef_calib_autodeficienthhind_WALK_TRANSIT,coef_calib_autodeficienthhind_WALK_TRANSIT,coef_calib_autodeficienthhind_WALK_TRANSIT,coef_calib_autodeficienthhind_PNR_TRANSIT,coef_calib_autodeficienthhind_PNR_TRANSIT,coef_calib_autodeficienthhind_PNR_TRANSIT,coef_calib_autodeficienthhind_KNR_TRANSIT,coef_calib_autodeficienthhind_KNR_TRANSIT,coef_calib_autodeficienthhind_KNR_TRANSIT,coef_calib_autodeficienthhind_TNC_TRANSIT,coef_calib_autodeficienthhind_TNC_TRANSIT,coef_calib_autodeficienthhind_TNC_TRANSIT,coef_calib_autodeficienthhind_TAXI,coef_calib_autodeficienthhind_TNC_SINGLE,coef_calib_autodeficienthhind_TNC_SHARED,coef_calib_autodeficienthhind_SCH_BUS,coef_calib_autodeficienthhind_EBIKE,coef_calib_autodeficienthhind_ESCOOTER +util_calib_autosufficienth,abm2+ calibration constant,@(df.is_indiv & (df.auto_ownership >= df.num_adults) & (df.auto_ownership > 0)),,coef_calib_autosufficienthhin_SHARED2,coef_calib_autosufficienthhin_SHARED3,coef_calib_autosufficienthhin_WALK,coef_calib_autosufficienthhin_BIKE,coef_calib_autosufficienthhin_WALK_TRANSIT,coef_calib_autosufficienthhin_WALK_TRANSIT,coef_calib_autosufficienthhin_WALK_TRANSIT,coef_calib_autosufficienthhin_PNR_TRANSIT,coef_calib_autosufficienthhin_PNR_TRANSIT,coef_calib_autosufficienthhin_PNR_TRANSIT,coef_calib_autosufficienthhin_KNR_TRANSIT,coef_calib_autosufficienthhin_KNR_TRANSIT,coef_calib_autosufficienthhin_KNR_TRANSIT,coef_calib_autosufficienthhin_TNC_TRANSIT,coef_calib_autosufficienthhin_TNC_TRANSIT,coef_calib_autosufficienthhin_TNC_TRANSIT,coef_calib_autosufficienthhin_TAXI,coef_calib_autosufficienthhin_TNC_SINGLE,coef_calib_autosufficienthhin_TNC_SHARED,coef_calib_autosufficienthhin_SCH_BUS,coef_calib_autosufficienthhin_EBIKE,coef_calib_autosufficienthhin_ESCOOTER +util_calib_zeroautohhjoint,abm2+ calibration constant,@(df.is_joint & (df.auto_ownership == 0)),,,coef_calib_zeroautohhjointtou_SHARED3,coef_calib_zeroautohhjointtou_WALK,coef_calib_zeroautohhjointtou_BIKE,coef_calib_zeroautohhjointtou_WALK_TRANSIT,coef_calib_zeroautohhjointtou_WALK_TRANSIT,coef_calib_zeroautohhjointtou_WALK_TRANSIT,coef_calib_zeroautohhjointtou_PNR_TRANSIT,coef_calib_zeroautohhjointtou_PNR_TRANSIT,coef_calib_zeroautohhjointtou_PNR_TRANSIT,coef_calib_zeroautohhjointtou_KNR_TRANSIT,coef_calib_zeroautohhjointtou_KNR_TRANSIT,coef_calib_zeroautohhjointtou_KNR_TRANSIT,coef_calib_zeroautohhjointtou_TNC_TRANSIT,coef_calib_zeroautohhjointtou_TNC_TRANSIT,coef_calib_zeroautohhjointtou_TNC_TRANSIT,coef_calib_zeroautohhjointtou_TAXI,coef_calib_zeroautohhjointtou_TNC_SINGLE,coef_calib_zeroautohhjointtou_TNC_SHARED,,coef_calib_zeroautohhjointtou_EBIKE,coef_calib_zeroautohhjointtou_ESCOOTER +util_calib_autodeficienthh,abm2+ calibration constant,@(df.is_joint & (df.auto_ownership < df.num_adults) & (df.auto_ownership > 0)),,,coef_calib_autodeficienthhjoi_SHARED3,coef_calib_autodeficienthhjoi_WALK,coef_calib_autodeficienthhjoi_BIKE,coef_calib_autodeficienthhjoi_WALK_TRANSIT,coef_calib_autodeficienthhjoi_WALK_TRANSIT,coef_calib_autodeficienthhjoi_WALK_TRANSIT,coef_calib_autodeficienthhjoi_PNR_TRANSIT,coef_calib_autodeficienthhjoi_PNR_TRANSIT,coef_calib_autodeficienthhjoi_PNR_TRANSIT,coef_calib_autodeficienthhjoi_KNR_TRANSIT,coef_calib_autodeficienthhjoi_KNR_TRANSIT,coef_calib_autodeficienthhjoi_KNR_TRANSIT,coef_calib_autodeficienthhjoi_TNC_TRANSIT,coef_calib_autodeficienthhjoi_TNC_TRANSIT,coef_calib_autodeficienthhjoi_TNC_TRANSIT,coef_calib_autodeficienthhjoi_TAXI,coef_calib_autodeficienthhjoi_TNC_SINGLE,coef_calib_autodeficienthhjoi_TNC_SHARED,,coef_calib_autodeficienthhjoi_EBIKE,coef_calib_autodeficienthhjoi_ESCOOTER +util_calib_autosufficienth,abm2+ calibration constant,@(df.is_joint & (df.auto_ownership >= df.num_adults) & (df.auto_ownership > 0)),,,coef_calib_autosufficienthhjo_SHARED3,coef_calib_autosufficienthhjo_WALK,coef_calib_autosufficienthhjo_BIKE,coef_calib_autosufficienthhjo_WALK_TRANSIT,coef_calib_autosufficienthhjo_WALK_TRANSIT,coef_calib_autosufficienthhjo_WALK_TRANSIT,coef_calib_autosufficienthhjo_PNR_TRANSIT,coef_calib_autosufficienthhjo_PNR_TRANSIT,coef_calib_autosufficienthhjo_PNR_TRANSIT,coef_calib_autosufficienthhjo_KNR_TRANSIT,coef_calib_autosufficienthhjo_KNR_TRANSIT,coef_calib_autosufficienthhjo_KNR_TRANSIT,coef_calib_autosufficienthhjo_TNC_TRANSIT,coef_calib_autosufficienthhjo_TNC_TRANSIT,coef_calib_autosufficienthhjo_TNC_TRANSIT,coef_calib_autosufficienthhjo_TAXI,coef_calib_autosufficienthhjo_TNC_SINGLE,coef_calib_autosufficienthhjo_TNC_SHARED,,coef_calib_autosufficienthhjo_EBIKE,coef_calib_autosufficienthhjo_ESCOOTER +#util_calib_c_ivtebikeowner,abm2+ calibration constant,time_factor*(ebikeOwnership*(maxEbikeBenefit*(-1))),,,,,coef_calib_civtebikeownership_BIKE,,,,,,,,,,,,,,,,,, +util_calib_escorttour,abm2+ calibration constant,tour_type == 'escort',,,,coef_calib_escorttour_WALK,coef_calib_escorttour_BIKE,coef_calib_escorttour_WALK_TRANSIT,coef_calib_escorttour_WALK_TRANSIT,coef_calib_escorttour_WALK_TRANSIT,coef_calib_escorttour_PNR_TRANSIT,coef_calib_escorttour_PNR_TRANSIT,coef_calib_escorttour_PNR_TRANSIT,coef_calib_escorttour_KNR_TRANSIT,coef_calib_escorttour_KNR_TRANSIT,coef_calib_escorttour_KNR_TRANSIT,coef_calib_escorttour_TNC_TRANSIT,coef_calib_escorttour_TNC_TRANSIT,coef_calib_escorttour_TNC_TRANSIT,,,,,, +#, School Escorting eligibility-odd looking where/isnan is to allow this to work with numba fastmath,,,,,,,,,,,,,,,,,,,,,,,, +util_one_or_more_school_escort,No SOV if on school escort tour,"@(np.where(np.isnan(df.get('num_escortees', 0)), 0 , df.get('num_escortees', 0)) >= 1)",-999,,,,,,,,,,,,,,,,,,,,,, +util_two_or_more_school_escort,Can't take HOV2 if taking two children and yourself,"@(np.where(np.isnan(df.get('num_escortees', 0)), 0 , df.get('num_escortees', 0)) >= 2)",,-999,,,,,,,,,,,,,,,,,,,,, +#,Micromobility (e-scooter/e-bike),,,,,,,,,,,,,,,,,,,,,,,, +util_micromobility_long_access,Shut off micromobility if access time > threshold,"@((df.micro_access_out > microAccessThreshold) | (df.micro_access_inb > microAccessThreshold)|(df.get('num_escortees', 0)>0))",,,,,,,,,,,,,,,,,,,,,,-999,-999 +util_micromobility_long_trip,Shut off ebike if distance > threshold,ebikeMaxDistance,,,,,,,,,,,,,,,,,,,,,,-999, +util_micromobility_long_trip,Shut off escooter if distance > threshold,escooterMaxDistance,,,,,,,,,,,,,,,,,,,,,,,-999 +util_ebike_ivt,Ebike utility for in-vehicle time,@(df.ebike_time_inb + df.ebike_time_out)*df.time_factor,,,,,,,,,,,,,,,,,,,,,,coef_ivt, +util_ebike_access,Ebike utility for access/egress time,@(microConstant + (~df.ebike_owner)&(microRentTime + df.micro_access_inb + df.micro_access_out))*df.time_factor,,,,,,,,,,,,,,,,,,,,,,coef_acctime, +util_ebike_cost_inb,Ebike utility for inbound cost,@((~df.ebike_owner)&((microFixedCost + microVarCost*df.ebike_time_inb)/df.cost_sensitivity)),,,,,,,,,,,,,,,,,,,,,,coef_income, +util_ebike_cost_out,Ebike utility for outbound cost,@((~df.ebike_owner)&((microFixedCost + microVarCost*df.ebike_time_out)/df.cost_sensitivity)),,,,,,,,,,,,,,,,,,,,,,coef_income, +util_escooter_ivt,escooter utility for in-vehicle time,@(df.escooter_time_inb + df.escooter_time_out)*df.time_factor,,,,,,,,,,,,,,,,,,,,,,,coef_ivt +util_escooter_access,escooter utility for in-vehicle time,@(microConstant + microRentTime + df.micro_access_inb + df.micro_access_out)*df.time_factor,,,,,,,,,,,,,,,,,,,,,,,coef_acctime +util_escooter_cost_inb,escooter utility for inbound cost,@((microFixedCost + microVarCost*df.escooter_time_inb)/df.cost_sensitivity),,,,,,,,,,,,,,,,,,,,,,,coef_income +util_escooter_cost_out,escooter utility for outbound cost,@((microFixedCost + microVarCost*df.escooter_time_out)/df.cost_sensitivity),,,,,,,,,,,,,,,,,,,,,,,coef_income diff --git a/configs/resident/tour_mode_choice.yaml b/configs/resident/tour_mode_choice.yaml new file mode 100644 index 0000000..7be6c6a --- /dev/null +++ b/configs/resident/tour_mode_choice.yaml @@ -0,0 +1,114 @@ +LOGIT_TYPE: NL +#LOGIT_TYPE: MNL + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - DRIVEALONE + - SHARED2 + - SHARED3 + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: MICROMOBILITY + coefficient: coef_nest_MICROMOBILITY + alternatives: + - EBIKE + - ESCOOTER + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - name: WALKACCESS + coefficient: coef_nest_TRANSIT_WALKACCESS + alternatives: + - WALK_LOC + - WALK_PRM + - WALK_MIX + - name: PNRACCESS + coefficient: coef_nest_TRANSIT_PNRACCESS + alternatives: + - PNR_LOC + - PNR_PRM + - PNR_MIX + - name: KNRACCESS + coefficient: coef_nest_TRANSIT_KNRACCESS + alternatives: + - KNR_LOC + - KNR_PRM + - KNR_MIX + - name: TNCACCESS + coefficient: coef_nest_TRANSIT_TNCACCESS + alternatives: + - TNC_LOC + - TNC_PRM + - TNC_MIX + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + - name: SCHOOL_BUS + coefficient: coef_nest_SCHOOL_BUS + alternatives: + - SCH_BUS + +SPEC: tour_mode_choice.csv +COEFFICIENTS: tour_mode_choice_coefficients.csv +COEFFICIENT_TEMPLATE: tour_mode_choice_coefficients_template.csv + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - tours + - vehicles + - households + - persons + +nontour_preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + +annotate_tours: + SPEC: annotate_tours_tour_mode_choice + DF: choosers + TABLES: + - tours + + +# to reduce memory needs filter chooser table to these fields +LOGSUM_CHOOSER_COLUMNS: + - tour_type + - hhsize + - density_index + - age + - sex + - auto_ownership + - num_adults + - ptype + - number_of_participants + - tour_category + - value_of_time + - free_parking_at_work + - income_segment + - income + - time_factor_work + - time_factor_nonwork + - num_escortees + - district27 + - ebike_owner + - transit_pass_subsidy + - transit_pass_ownership + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/configs/resident/tour_mode_choice_annotate_choosers_preprocessor.csv b/configs/resident/tour_mode_choice_annotate_choosers_preprocessor.csv new file mode 100644 index 0000000..3915d35 --- /dev/null +++ b/configs/resident/tour_mode_choice_annotate_choosers_preprocessor.csv @@ -0,0 +1,302 @@ +Description,Target,Expression, +#,,, +#placeholder for walk time skim,ownsTransponder,1, +#Auto IVT Factor,autoIVTFactor,"np.where(useAV==0,1.0,autoIVTFactorAV)", +placeholder Auto IVT Factor,autoIVTFactor,1, +#Auto Parking Cost Factor,autoParkingCostFactor,"np.where(useAV==0,1.0,autoParkingCostFactorAV)", +placeholder for missing autoParkingCostFactorAV,autoParkingCostFactor,1, +#Auto CPM Factor,autoCPMFactor,"np.where(useAV==0,1.0,autoCostPerMileFactorAV)", +placeholder for missing autoCostPerMileFactorAV,autoCPMFactor,1, +#Auto Terminal Time Factor,autoTermTimeFactor,"np.where(useAV==0,1.0,autoTerminalTimeFactorAV)", +placeholder for missing autoTerminalTimeFactorAV,autoTermTimeFactor,1, +#MinimumAgeDriveAlone,minimumAgeDA,"np.where(useAV==0,16,minAgeDriveAloneAV)", +placeholder for missing minAgeDriveAloneAV,minimumAgeDA,16, +freeParkingEligibility placeholder,freeParkingEligibility,0, +reimburseProportion placeholder,reimburseProportion,0, +"Tour duration, in hours",tourDuration,df.duration/2, +#,,, +local,_DF_IS_TOUR,'tour_type' in df.columns, +,_num_participants,df.number_of_participants if _DF_IS_TOUR else 1, +,is_joint,(df.tour_category=='joint') if _DF_IS_TOUR else False, +treat tours as work if tour_type not yet decided,tour_type,"df.get('tour_type', default='work')", +#,,, +,is_mandatory,(df.tour_category=='mandatory') if 'tour_category' in df.columns else False, +,is_indiv,~is_joint, +,is_escort,(tour_type == 'escort') if _DF_IS_TOUR else False, +# Auto operating costs,,, +,is_atwork_subtour,(df.tour_category == 'atwork') if _DF_IS_TOUR else False, +,_parent_tour_veh,"reindex(tours['selected_vehicle'], df['parent_tour_id']) if 'selected_vehicle' in df.columns else np.nan", +,_parent_tour_mode,"reindex(tours['tour_mode'], df['parent_tour_id']) if 'tour_mode' in df.columns else np.nan", +,sov_veh_option,"np.where(is_atwork_subtour, _parent_tour_veh, df.get('vehicle_occup_1', np.nan))", +,sr2_veh_option,"np.where(is_atwork_subtour, _parent_tour_veh, df.get('vehicle_occup_2', np.nan))", +,sr3p_veh_option,"np.where(is_atwork_subtour, _parent_tour_veh, df.get('vehicle_occup_3.5', np.nan))", +,sov_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sov_veh_option, df.index)) if 'vehicle_occup_1' in df.columns else np.nan", +,sov_auto_op_cost,"np.where(sov_auto_op_cost.isna() | (pd.Series(sov_veh_option, df.index) == 'non_hh_veh'), costPerMile, sov_auto_op_cost)", +,sr2_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sr2_veh_option, df.index)) if 'vehicle_occup_2' in df.columns else np.nan", +,sr2_auto_op_cost,"np.where(sr2_auto_op_cost.isna() | (pd.Series(sr2_veh_option, df.index) == 'non_hh_veh'), costPerMile, sr2_auto_op_cost)", +,sr3p_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sr3p_veh_option, df.index)) if 'vehicle_occup_3.5' in df.columns else np.nan", +,sr3p_auto_op_cost,"np.where(sr3p_auto_op_cost.isna() | (pd.Series(sr3p_veh_option, df.index) == 'non_hh_veh'), costPerMile, sr3p_auto_op_cost)", +# RIDEHAIL,,, +household_density calculated in annotate_landuse in acres and is converted to sq miles here,_origin_density_measure,"reindex(land_use.PopEmpDenPerMi, df[orig_col_name])", +employment_density calculated in annotate_landuse in acres and is converted to sq miles here,_dest_density_measure,"reindex(land_use.PopEmpDenPerMi, df[dest_col_name])", +,origin_density,"pd.cut(_origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,dest_density,"pd.cut(_dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +# ,, Note that the mean and standard deviation are not the values for the distribution itself but of the underlying normal distribution it is derived from +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime, +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime, +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime, +#parking,,, +#,_free_parking_available,(tour_type == 'work') & df.free_parking_at_work if _DF_IS_TOUR else False, +#,_dest_hourly_peak_parking_cost,"reindex(land_use.exp_hourly, df[dest_col_name])", +#,_dest_hourly_offpeak_parking_cost,"reindex(land_use.exp_hourly, df[dest_col_name])", +#,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)", +#,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)", +#,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost * df.duration, _hourly_offpeak_parking_cost * df.duration)", +# Result of employer parking provision and reimbursement model,freeParkingEligibility,"df.freeParkingEligibility*np.where(is_indiv,1,0)", +,free_parking_available,(tour_type == 'work') & df.free_parking_at_work if _DF_IS_TOUR else False, +person has free on-site parking at workplace,freeOnsite,"(free_parking_available)*np.where(is_indiv,1,0)", +# new reimbursement amount,reimburseProportion,"df.reimburseProportion*np.where(is_indiv,1,0)", +new daily parking cost with reimbursement,parkingCostDayDollars,"reindex(land_use.exp_daily, df[dest_col_name])", +new hourly parking cost with reimbursement,parkingCostHourDollars,"reindex(land_use.exp_hourly, df[dest_col_name])", +new monthly parking cost with reimbursement,parkingCostMonthDollars,"reindex(land_use.exp_monthly, df[dest_col_name])", +Parking area,parkingConstrainedArea,"np.where(reindex(land_use.parking_type, df[dest_col_name]) == 1, 1, 0)", +daily cost converted to cents,parkingCostDay,parkingCostDayDollars*100, +hourly cost converted to cents,parkingCostHour,parkingCostHourDollars*100, +monthly cost converted to cents,parkingCostMonth,parkingCostMonthDollars*100, +Tour parking cost for full-time workers and university students,_parkingCostBeforeReimb,"df.ptype.isin([1,3]) * is_indiv * np.minimum(parkingCostMonth/22, parkingCostDay)", +Tour parking cost for full-time workers and university students,_parkingCostBeforeReimb,"df.ptype.isin([1,3]) * is_indiv * np.minimum(_parkingCostBeforeReimb, parkingCostHour * tourDuration)", +Tour parking cost for other person types,parkingCostBeforeReimb,"np.where((~df.ptype.isin([1,3]) * is_indiv) | (is_joint), np.minimum(parkingCostDay, parkingCostHour * tourDuration), _parkingCostBeforeReimb)", +Reimbursement applies to this tour purpose,reimbursePurpose,df.tour_type == 'work' if _DF_IS_TOUR else False, +Effective parking cost for free parkers,_parkingCost,"0 * np.where(is_indiv*reimbursePurpose*freeOnsite,1,0)", +Effective parking cost for reimbursed parkers,_parkingCost,"np.where(is_indiv*reimbursePurpose*(1-freeOnsite), np.maximum((1-reimburseProportion) * parkingCostBeforeReimb, 0),_parkingCost)", +Effective parking cost applied to tour purpose,parkingCost,"np.where(is_joint+is_indiv*(1-reimbursePurpose), parkingCostBeforeReimb,_parkingCost)", +# cost coef,,, +,_income_exponent,"np.where(tour_type == 'work', 0.6, 0.5)", +,cost_sensitivity,"np.maximum(df.income,1000).pow(_income_exponent)", +,c_cost,coef_income / cost_sensitivity, +# ivt coef,,, +,time_factor,"np.where(tour_type=='work', df.time_factor_work, df.time_factor_nonwork)", +,c_ivt,coef_ivt * time_factor, +#,,, +,vot_da,c_ivt / c_cost * 0.6, +,vot_s2,vot_da / cost_share_s2, +,vot_s3,vot_da / cost_share_s3, +,_vot_bin_da,"np.where(vot_da < vot_threshold_low, 1, np.where(vot_da < vot_threshold_med, 2, 3))", +,_vot_bin_s2,"np.where(vot_s2 < vot_threshold_low, 1, np.where(vot_s2 < vot_threshold_med, 2, 3))", +,_vot_bin_s3,"np.where(vot_s3 < vot_threshold_low, 1, np.where(vot_s3 < vot_threshold_med, 2, 3))", +# vot-indexed skims,,, +,ownsTransponder,df.transponder_ownership if 'transponder_ownership' in df else 0, +DA skim based on Transponder ownership,da_dist_skims_out,"(np.where(ownsTransponder==1, odt_skims['SOV_TR_L_DIST'], odt_skims['SOV_NT_L_DIST']) * (_vot_bin_da == 1)) + (np.where(ownsTransponder==1, odt_skims['SOV_TR_M_DIST'], odt_skims['SOV_NT_M_DIST']) * (_vot_bin_da == 2)) + (np.where(ownsTransponder==1, odt_skims['SOV_TR_H_DIST'], odt_skims['SOV_NT_H_DIST']) * (_vot_bin_da == 3))", +,da_dist_skims_inb,"(np.where(ownsTransponder==1, dot_skims['SOV_TR_L_DIST'], dot_skims['SOV_NT_L_DIST']) * (_vot_bin_da == 1)) + (np.where(ownsTransponder==1, dot_skims['SOV_TR_M_DIST'], dot_skims['SOV_NT_M_DIST']) * (_vot_bin_da == 2)) + (np.where(ownsTransponder==1, dot_skims['SOV_TR_H_DIST'], dot_skims['SOV_NT_H_DIST']) * (_vot_bin_da == 3))", +,da_cost_skims_out,"(np.where(ownsTransponder==1, odt_skims['SOV_TR_L_TOLLCOST'], odt_skims['SOV_NT_L_TOLLCOST']) * (_vot_bin_da == 1)) + (np.where(ownsTransponder==1, odt_skims['SOV_TR_M_TOLLCOST'], odt_skims['SOV_NT_M_TOLLCOST']) * (_vot_bin_da == 2)) + (np.where(ownsTransponder==1, odt_skims['SOV_TR_H_TOLLCOST'], odt_skims['SOV_NT_H_TOLLCOST']) * (_vot_bin_da == 3))", +,da_cost_skims_inb,"(np.where(ownsTransponder==1, dot_skims['SOV_TR_L_TOLLCOST'], dot_skims['SOV_NT_L_TOLLCOST']) * (_vot_bin_da == 1)) + (np.where(ownsTransponder==1, dot_skims['SOV_TR_M_TOLLCOST'], dot_skims['SOV_NT_M_TOLLCOST']) * (_vot_bin_da == 2)) + (np.where(ownsTransponder==1, dot_skims['SOV_TR_H_TOLLCOST'], dot_skims['SOV_NT_H_TOLLCOST']) * (_vot_bin_da == 3))", +,da_time_skims_out,"(np.where(ownsTransponder==1, odt_skims['SOV_TR_L_TIME'], odt_skims['SOV_NT_L_TIME']) * (_vot_bin_da == 1)) + (np.where(ownsTransponder==1, odt_skims['SOV_TR_M_TIME'], odt_skims['SOV_NT_M_TIME']) * (_vot_bin_da == 2)) + (np.where(ownsTransponder==1, odt_skims['SOV_TR_H_TIME'], odt_skims['SOV_NT_H_TIME']) * (_vot_bin_da == 3))", +,da_time_skims_inb,"(np.where(ownsTransponder==1, dot_skims['SOV_TR_L_TIME'], dot_skims['SOV_NT_L_TIME']) * (_vot_bin_da == 1)) + (np.where(ownsTransponder==1, dot_skims['SOV_TR_M_TIME'], dot_skims['SOV_NT_M_TIME']) * (_vot_bin_da == 2)) + (np.where(ownsTransponder==1, dot_skims['SOV_TR_H_TIME'], dot_skims['SOV_NT_H_TIME']) * (_vot_bin_da == 3))", +,da_rel_skims_out,(((odt_skims['SOV_NT_L_REL']) * (_vot_bin_da == 1)) + ((odt_skims['SOV_NT_M_REL']) * (_vot_bin_da == 2)) + ((odt_skims['SOV_NT_H_REL']) * (_vot_bin_da == 3))), +,da_rel_skims_inb,(((dot_skims['SOV_NT_L_REL']) * (_vot_bin_da == 1)) + ((dot_skims['SOV_NT_M_REL']) * (_vot_bin_da == 2)) + ((dot_skims['SOV_NT_H_REL']) * (_vot_bin_da == 3))), +,s2_dist_skims_out,(((odt_skims['HOV2_L_DIST']) * (_vot_bin_s2 == 1)) + ((odt_skims['HOV2_M_DIST']) * (_vot_bin_s2 == 2)) + ((odt_skims['HOV2_H_DIST']) * (_vot_bin_s2 == 3))), +,s2_dist_skims_inb,(((dot_skims['HOV2_L_DIST']) * (_vot_bin_s2 == 1)) + ((dot_skims['HOV2_M_DIST']) * (_vot_bin_s2 == 2)) + ((dot_skims['HOV2_H_DIST']) * (_vot_bin_s2 == 3))), +,s2_cost_skims_out,(((odt_skims['HOV2_L_TOLLCOST']) * (_vot_bin_s2 == 1)) + ((odt_skims['HOV2_M_TOLLCOST']) * (_vot_bin_s2 == 2)) + ((odt_skims['HOV2_H_TOLLCOST']) * (_vot_bin_s2 == 3))), +,s2_cost_skims_inb,(((dot_skims['HOV2_L_TOLLCOST']) * (_vot_bin_s2 == 1)) + ((dot_skims['HOV2_M_TOLLCOST']) * (_vot_bin_s2 == 2)) + ((dot_skims['HOV2_H_TOLLCOST']) * (_vot_bin_s2 == 3))), +,s2_time_skims_out,(((odt_skims['HOV2_L_TIME']) * (_vot_bin_s2 == 1)) + ((odt_skims['HOV2_M_TIME']) * (_vot_bin_s2 == 2)) + ((odt_skims['HOV2_H_TIME']) * (_vot_bin_s2 == 3))), +,s2_time_skims_inb,(((dot_skims['HOV2_L_TIME']) * (_vot_bin_s2 == 1)) + ((dot_skims['HOV2_M_TIME']) * (_vot_bin_s2 == 2)) + ((dot_skims['HOV2_H_TIME']) * (_vot_bin_s2 == 3))), +,s2_rel_skims_out,(((odt_skims['HOV2_L_REL']) * (_vot_bin_s2 == 1)) + ((odt_skims['HOV2_M_REL']) * (_vot_bin_s2 == 2)) + ((odt_skims['HOV2_H_REL']) * (_vot_bin_s2 == 3))), +,s2_rel_skims_inb,(((dot_skims['HOV2_L_REL']) * (_vot_bin_s2 == 1)) + ((dot_skims['HOV2_M_REL']) * (_vot_bin_s2 == 2)) + ((dot_skims['HOV2_H_REL']) * (_vot_bin_s2 == 3))), +,s3_dist_skims_out,(((odt_skims['HOV3_L_DIST']) * (_vot_bin_s2 == 1)) + ((odt_skims['HOV3_M_DIST']) * (_vot_bin_s2 == 2)) + ((odt_skims['HOV3_H_DIST']) * (_vot_bin_s2 == 3))), +,s3_dist_skims_inb,(((dot_skims['HOV3_L_DIST']) * (_vot_bin_s3 == 1)) + ((dot_skims['HOV3_M_DIST']) * (_vot_bin_s3 == 2)) + ((dot_skims['HOV3_H_DIST']) * (_vot_bin_s3 == 3))), +,s3_cost_skims_out,(((odt_skims['HOV3_L_TOLLCOST']) * (_vot_bin_s3 == 1)) + ((odt_skims['HOV3_M_TOLLCOST']) * (_vot_bin_s3 == 2)) + ((odt_skims['HOV3_H_TOLLCOST']) * (_vot_bin_s3 == 3))), +,s3_cost_skims_inb,(((dot_skims['HOV3_L_TOLLCOST']) * (_vot_bin_s3 == 1)) + ((dot_skims['HOV3_M_TOLLCOST']) * (_vot_bin_s3 == 2)) + ((dot_skims['HOV3_H_TOLLCOST']) * (_vot_bin_s3 == 3))), +,s3_time_skims_out,(((odt_skims['HOV3_L_TIME']) * (_vot_bin_s3 == 1)) + ((odt_skims['HOV3_M_TIME']) * (_vot_bin_s3 == 2)) + ((odt_skims['HOV3_H_TIME']) * (_vot_bin_s3 == 3))), +,s3_time_skims_inb,(((dot_skims['HOV3_L_TIME']) * (_vot_bin_s3 == 1)) + ((dot_skims['HOV3_M_TIME']) * (_vot_bin_s3 == 2)) + ((dot_skims['HOV3_H_TIME']) * (_vot_bin_s3 == 3))), +,s3_rel_skims_out,(((odt_skims['HOV3_L_REL']) * (_vot_bin_s3 == 1)) + ((odt_skims['HOV3_M_REL']) * (_vot_bin_s3 == 2)) + ((odt_skims['HOV3_H_REL']) * (_vot_bin_s3 == 3))), +,s3_rel_skims_inb,(((dot_skims['HOV3_L_REL']) * (_vot_bin_s3 == 1)) + ((dot_skims['HOV3_M_REL']) * (_vot_bin_s3 == 2)) + ((dot_skims['HOV3_H_REL']) * (_vot_bin_s3 == 3))), +,walk_time_skims_out,od_skims['walkTime'], +assuming walktime symmetry,walk_time_skims_inb,od_skims['walkTime'], +#placeholder for walk time skim,walk_time_skims_out,odt_skims['SOV_TR_L_DIST'] / 3 * 60, +#placeholder for walk time skim,walk_time_skims_inb,dot_skims['SOV_TR_L_DIST'] / 3 * 60, +#,,, +Determining Tour Origin,origin,df.origin if 'origin' in df.columns else df.home_zone_id if 'home_zone_id' in df.columns else df.workplace_zone_id, +#,,, +DistanceToCoast,DistanceToCoast,"reindex(land_use.milestocoast,origin)", +#,,, +Person age,age,df.age, +# Household income (in dollars),income,df.incomeInDollars, +#,,, +Joint tour,is_joint,"df.get('tour_category', default=False) == 'joint'", +Determining Tour Destination,destination,df.destination if 'destination' in df.columns else df.alt_dest, +Origin MGRA Dwelling Unit Density,oMGRADUDen,"reindex(land_use.duden,origin)", +Origin MGRA Employment Density,oMGRAEmpDen,"reindex(land_use.empden,origin)", +Origin MGRA Total Intersections,oMGRATotInt,"reindex(land_use.totint,origin)", +Destination MGRA Dwelling Unit Density,dMGRADUDen,"reindex(land_use.duden,destination)", +Destination MGRA Employment Density,dMGRAEmpDen,"reindex(land_use.empden,destination)", +Destination MGRA Total Intersections,dMGRATotInt,"reindex(land_use.totint,destination)", +Origin MGRA Mix,oMGRAMix,"np.where(oMGRADUDen+oMGRAEmpDen > 0, (oMGRADUDen*oMGRAEmpDen)/(oMGRADUDen+oMGRAEmpDen),0)", +Destination MGRA Mix,dMGRAMix,"np.where(dMGRADUDen+dMGRAEmpDen > 0, (dMGRADUDen*dMGRAEmpDen)/(dMGRADUDen+dMGRAEmpDen),0)", +Origin MGRA Dwelling Unit Density Normalized by Average in Bike Estimation Sample,oMGRADUDenNorm_bike,oMGRADUDen/6.77, +Origin MGRA Employment Density Normalized by Average in Bike Estimation Sample,oMGRAEmpDenNorm_bike,oMGRAEmpDen/6.47, +Origin MGRA Dwelling Unit Density Normalized by Average in Walk Estimation Sample,oMGRADUDenNorm_walk,oMGRADUDen/7.18, +Origin MGRA Total Intersections Normalized by Average in Walk Estimation Sample,oMGRATotIntNorm_walk,oMGRATotInt/56.19, +Destination MGRA Employment Density Normalized by Average in Walk Estimation Sample,dMGRAEmpDenNorm_walk,dMGRAEmpDen/13.16, +Normalized Landuse Variable Sum [Origin Employment + DU],LUVarsNormalized_bike,oMGRADUDenNorm_bike + oMGRAEmpDenNorm_bike, +Normalized Landuse Variable Sum [Origin Intersection + DU],LUVarsNormalized_walk,oMGRADUDenNorm_walk + oMGRATotIntNorm_walk, +persontype,personType,"df.ptype*np.where(is_indiv,1,0)", +Origin Terminal Time,oTermTime,"reindex(land_use.terminal_time,origin)", +Destination Terminal Time,dTermTime,"reindex(land_use.terminal_time,destination)", +#,,, +# Below bike info is not defined and is turned off!!,,, +Person is female,female,(df.sex == 2), +bike logsum inbound,bikeLSI,odt_skims['BIKE_LOGSUM'], +bike logsum outbound (same as inbound),bikeLSO,dot_skims['BIKE_LOGSUM'], +bike availability,bikeAvailable,(bikeLSI > -300) & (bikeLSO > -300) & (od_skims['DIST'] < max_bike_distance), +bike time inbound,bike_time_inb,dot_skims['BIKE_TIME'], +bike time outbound,bike_time_out,odt_skims['BIKE_TIME'], +#,,, +"Cost factor for shared 2 tours, 1/(2^0.8)",costFactorS2,0.57, +"Cost factor for shared 3+ tours, 1/(3.5^0.8)",costFactorS3,0.37, +,sov_available,"(age>=minimumAgeDA) * np.where(is_joint,0,1) * np.where(is_atwork_subtour==0,1,np.where(_parent_tour_mode=='DRIVEALONE',1,0))", +,sr2_available,"np.where(is_joint==0,1,np.where(_num_participants>=3,0,1))", +,walkAvailable,"np.where(walk_time_skims_out < max_walk_time, 1,0) * np.where(walk_time_skims_inb < max_walk_time, 1,0)", +,SCHBUS_available,(tour_type =='school') & (personType!=3), +#,,, +Determining Tour Origin,origin,df.origin if 'origin' in df.columns else df.home_zone_id if 'home_zone_id' in df.columns else df.workplace_zone_id, +Determining Tour Destination,destination,df.destination if 'destination' in df.columns else df.alt_dest, +#access egress distances,,, +,origin_local_dist,"reindex(land_use.walk_dist_local_bus, origin)", +,origin_prm_dist,"reindex(land_use.walk_dist_premium_transit, origin)", +,origin_mix_dist,"np.minimum(origin_local_dist, origin_prm_dist)", +,origin_micro_prm_dist,"reindex(land_use.micro_dist_premium_transit, origin)", +,dest_local_dist,"reindex(land_use.walk_dist_local_bus, destination)", +,dest_prm_dist,"reindex(land_use.walk_dist_premium_transit, destination)", +,dest_mix_dist,"np.minimum(dest_local_dist, dest_prm_dist)", +,dest_micro_prm_dist,"reindex(land_use.micro_dist_premium_transit, destination)", +#access egress times,,, +,origin_local_time,origin_local_dist * 60/walk_speed, +,origin_prm_time,origin_prm_dist * 60/walk_speed, +,origin_mix_time,origin_mix_dist * 60/walk_speed, +,dest_local_time,dest_local_dist * 60/walk_speed, +,dest_prm_time,dest_prm_dist * 60/walk_speed, +,dest_mix_time,dest_mix_dist * 60/walk_speed, +#,,, +,walk_local_available,(odt_skims['WALK_LOC_TOTALIVTT']>0) & (dot_skims['WALK_LOC_TOTALIVTT']>0), +,walk_premium_available,(odt_skims['WALK_PRM_TOTALIVTT']>0) & (dot_skims['WALK_PRM_TOTALIVTT']>0), +,walk_mix_available,(odt_skims['WALK_MIX_TOTALIVTT']>0) & (dot_skims['WALK_MIX_TOTALIVTT']>0) & (odt_skims['WALK_MIX_XFERS']>0) & (dot_skims['WALK_MIX_XFERS']>0), +,pnr_local_available,(odt_skims['PNROUT_LOC_TOTALIVTT']>0) & (dot_skims['PNRIN_LOC_TOTALIVTT']>0), +,pnr_premium_available,(odt_skims['PNROUT_PRM_TOTALIVTT']>0) & (dot_skims['PNRIN_PRM_TOTALIVTT']>0), +,pnr_mix_available,(odt_skims['PNROUT_MIX_TOTALIVTT']>0) & (dot_skims['PNRIN_MIX_TOTALIVTT']>0) & (odt_skims['PNROUT_MIX_XFERS']>0) & (dot_skims['PNRIN_MIX_XFERS']>0), +,knr_local_available,(odt_skims['KNROUT_LOC_TOTALIVTT']>0) & (dot_skims['KNRIN_LOC_TOTALIVTT']>0), +,knr_premium_available,(odt_skims['KNROUT_PRM_TOTALIVTT']>0) & (dot_skims['KNRIN_PRM_TOTALIVTT']>0), +,knr_mix_available,(odt_skims['KNROUT_MIX_TOTALIVTT']>0) & (dot_skims['KNRIN_MIX_TOTALIVTT']>0) & (odt_skims['KNROUT_MIX_XFERS']>0) & (dot_skims['KNRIN_MIX_XFERS']>0), +,tnc_local_available,(odt_skims['TNCOUT_LOC_TOTALIVTT']>0) & (dot_skims['TNCIN_LOC_TOTALIVTT']>0), +,tnc_premium_available,(odt_skims['TNCOUT_PRM_TOTALIVTT']>0) & (dot_skims['TNCIN_PRM_TOTALIVTT']>0), +,tnc_mix_available,(odt_skims['TNCOUT_MIX_TOTALIVTT']>0) & (dot_skims['TNCIN_MIX_TOTALIVTT']>0) & (odt_skims['TNCOUT_MIX_XFERS']>0) & (dot_skims['TNCIN_MIX_XFERS']>0), +# commuter rail constants based on commuter rail IVTT,,, +,wlk_prm_out_cr_asc,"(-1.0) * (np.minimum(odt_skims['WALK_PRM_CMRIVTT'],60) + np.where(odt_skims['WALK_PRM_CMRIVTT']>40,(np.minimum(odt_skims['WALK_PRM_CMRIVTT'],60)-40)*0.6,0))", +,wlk_prm_inb_cr_asc,"(-1.0) * (np.minimum(dot_skims['WALK_PRM_CMRIVTT'],60) + np.where(dot_skims['WALK_PRM_CMRIVTT']>40,(np.minimum(dot_skims['WALK_PRM_CMRIVTT'],60)-40)*0.6,0))", +,wlk_mix_out_cr_asc,"(-1.0) * (np.minimum(odt_skims['WALK_MIX_CMRIVTT'],60) + np.where(odt_skims['WALK_MIX_CMRIVTT']>40,(np.minimum(odt_skims['WALK_MIX_CMRIVTT'],60)-40)*0.6,0))", +,wlk_mix_inb_cr_asc,"(-1.0) * (np.minimum(dot_skims['WALK_MIX_CMRIVTT'],60) + np.where(dot_skims['WALK_MIX_CMRIVTT']>40,(np.minimum(dot_skims['WALK_MIX_CMRIVTT'],60)-40)*0.6,0))", +,pnr_prm_out_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['PNROUT_PRM_CMRIVTT']>20,(odt_skims['PNROUT_PRM_CMRIVTT']-20),0)+np.where(odt_skims['PNROUT_PRM_CMRIVTT']>40,(odt_skims['PNROUT_PRM_CMRIVTT']-40)*1.5,0)),65)", +,pnr_prm_inb_cr_asc,"(-1.0) * np.minimum((15 + np.where(dot_skims['PNRIN_PRM_CMRIVTT']>20,(dot_skims['PNRIN_PRM_CMRIVTT']-20),0)+np.where(dot_skims['PNRIN_PRM_CMRIVTT']>40,(dot_skims['PNRIN_PRM_CMRIVTT']-40)*1.5,0)),65)", +,pnr_mix_out_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['PNROUT_MIX_CMRIVTT']>20,(odt_skims['PNROUT_MIX_CMRIVTT']-20),0)+np.where(odt_skims['PNROUT_MIX_CMRIVTT']>40,(odt_skims['PNROUT_MIX_CMRIVTT']-40)*1.5,0)),65)", +,pnr_mix_inb_cr_asc,"(-1.0) * np.minimum((15 + np.where(dot_skims['PNRIN_MIX_CMRIVTT']>20,(dot_skims['PNRIN_MIX_CMRIVTT']-20),0)+np.where(dot_skims['PNRIN_MIX_CMRIVTT']>40,(dot_skims['PNRIN_MIX_CMRIVTT']-40)*1.5,0)),65)", +,knr_prm_out_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['KNROUT_PRM_CMRIVTT']>20,(odt_skims['KNROUT_PRM_CMRIVTT']-20),0)+np.where(odt_skims['KNROUT_PRM_CMRIVTT']>40,(odt_skims['KNROUT_PRM_CMRIVTT']-40)*1.5,0)),65)", +,knr_prm_inb_cr_asc,"(-1.0) * np.minimum((15 + np.where(dot_skims['KNRIN_PRM_CMRIVTT']>20,(dot_skims['KNRIN_PRM_CMRIVTT']-20),0)+np.where(dot_skims['KNRIN_PRM_CMRIVTT']>40,(dot_skims['KNRIN_PRM_CMRIVTT']-40)*1.5,0)),65)", +,knr_mix_out_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['KNROUT_MIX_CMRIVTT']>20,(odt_skims['KNROUT_MIX_CMRIVTT']-20),0)+np.where(odt_skims['KNROUT_MIX_CMRIVTT']>40,(odt_skims['KNROUT_MIX_CMRIVTT']-40)*1.5,0)),65)", +,knr_mix_inb_cr_asc,"(-1.0) * np.minimum((15 + np.where(dot_skims['KNRIN_MIX_CMRIVTT']>20,(dot_skims['KNRIN_MIX_CMRIVTT']-20),0)+np.where(dot_skims['KNRIN_MIX_CMRIVTT']>40,(dot_skims['KNRIN_MIX_CMRIVTT']-40)*1.5,0)),65)", +,tnc_prm_out_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['TNCOUT_PRM_CMRIVTT']>20,(odt_skims['TNCOUT_PRM_CMRIVTT']-20),0)+np.where(odt_skims['TNCOUT_PRM_CMRIVTT']>40,(odt_skims['TNCOUT_PRM_CMRIVTT']-40)*1.5,0)),65)", +,tnc_prm_inb_cr_asc,"(-1.0) * np.minimum((15 + np.where(dot_skims['TNCIN_PRM_CMRIVTT']>20,(dot_skims['TNCIN_PRM_CMRIVTT']-20),0)+np.where(dot_skims['TNCIN_PRM_CMRIVTT']>40,(dot_skims['TNCIN_PRM_CMRIVTT']-40)*1.5,0)),65)", +,tnc_mix_out_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['TNCOUT_MIX_CMRIVTT']>20,(odt_skims['TNCOUT_MIX_CMRIVTT']-20),0)+np.where(odt_skims['TNCOUT_MIX_CMRIVTT']>40,(odt_skims['TNCOUT_MIX_CMRIVTT']-40)*1.5,0)),65)", +,tnc_mix_inb_cr_asc,"(-1.0) * np.minimum((15 + np.where(dot_skims['TNCIN_MIX_CMRIVTT']>20,(dot_skims['TNCIN_MIX_CMRIVTT']-20),0)+np.where(dot_skims['TNCIN_MIX_CMRIVTT']>40,(dot_skims['TNCIN_MIX_CMRIVTT']-40)*1.5,0)),65)", +# Note: Hyperloop not implemented in ASC calculations since we arent skimming hyperloop IVTT,,, +,wlk_prm_out_asc,"np.where(odt_skims['WALK_PRM_CMRIVTT'] > 10, wlk_prm_out_cr_asc,np.where(odt_skims['WALK_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['WALK_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['WALK_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))", +,wlk_prm_inb_asc,"np.where(dot_skims['WALK_PRM_CMRIVTT'] > 10, wlk_prm_inb_cr_asc,np.where(dot_skims['WALK_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(dot_skims['WALK_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+dot_skims['WALK_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))", +,pnr_prm_out_asc,"np.where(odt_skims['PNROUT_PRM_CMRIVTT'] > 10, pnr_prm_out_cr_asc,np.where(odt_skims['PNROUT_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['PNROUT_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['PNROUT_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))", +,pnr_prm_inb_asc,"np.where(dot_skims['PNRIN_PRM_CMRIVTT'] > 10, pnr_prm_inb_cr_asc,np.where(dot_skims['PNRIN_PRM_LRTIVTT']>0,pnr_lrt_equiv_asc,np.where(dot_skims['PNRIN_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+dot_skims['PNRIN_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))", +,knr_prm_out_asc,"np.where(odt_skims['KNROUT_PRM_CMRIVTT'] > 10, knr_prm_out_cr_asc,np.where(odt_skims['KNROUT_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['KNROUT_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['KNROUT_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))", +,knr_prm_inb_asc,"np.where(dot_skims['KNRIN_PRM_CMRIVTT'] > 10, knr_prm_inb_cr_asc,np.where(dot_skims['KNRIN_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(dot_skims['KNRIN_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+dot_skims['KNRIN_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))", +,tnc_prm_out_asc,"np.where(odt_skims['TNCOUT_PRM_CMRIVTT'] > 10, tnc_prm_out_cr_asc,np.where(odt_skims['TNCOUT_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['TNCOUT_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['TNCOUT_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))", +,tnc_prm_inb_asc,"np.where(dot_skims['TNCIN_PRM_CMRIVTT'] > 10, tnc_prm_inb_cr_asc,np.where(dot_skims['TNCIN_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(dot_skims['TNCIN_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+dot_skims['TNCIN_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))", +,wlk_mix_out_max_asc,"np.where(odt_skims['WALK_MIX_CMRIVTT'] > 10, wlk_mix_out_cr_asc,np.where(odt_skims['WALK_MIX_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['WALK_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['WALK_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))", +,wlk_mix_inb_max_asc,"np.where(dot_skims['WALK_MIX_CMRIVTT'] > 10, wlk_mix_inb_cr_asc,np.where(dot_skims['WALK_MIX_LRTIVTT']>0,lrt_equiv_asc,np.where(dot_skims['WALK_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+dot_skims['WALK_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))", +,pnr_mix_out_max_asc,"np.where(odt_skims['PNROUT_MIX_CMRIVTT'] > 10, pnr_mix_out_cr_asc,np.where(odt_skims['PNROUT_MIX_LRTIVTT']>0,pnr_lrt_equiv_asc,np.where(odt_skims['PNROUT_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['PNROUT_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))", +,pnr_mix_inb_max_asc,"np.where(dot_skims['PNRIN_MIX_CMRIVTT'] > 10, pnr_mix_inb_cr_asc,np.where(dot_skims['PNRIN_MIX_LRTIVTT']>0,pnr_lrt_equiv_asc,np.where(dot_skims['PNRIN_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+dot_skims['PNRIN_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))", +,knr_mix_out_max_asc,"np.where(odt_skims['KNROUT_MIX_CMRIVTT'] > 10,knr_mix_out_cr_asc,np.where(odt_skims['KNROUT_MIX_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['KNROUT_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['KNROUT_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))", +,knr_mix_inb_max_asc,"np.where(dot_skims['KNRIN_MIX_CMRIVTT'] > 10, knr_mix_inb_cr_asc,np.where(dot_skims['KNRIN_MIX_LRTIVTT']>0,lrt_equiv_asc,np.where(dot_skims['KNRIN_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+dot_skims['KNRIN_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))", +,tnc_mix_out_max_asc,"np.where(odt_skims['TNCOUT_MIX_CMRIVTT'] > 10, tnc_mix_out_cr_asc,np.where(odt_skims['TNCOUT_MIX_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['TNCOUT_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['TNCOUT_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))", +,tnc_mix_inb_max_asc,"np.where(dot_skims['TNCIN_MIX_CMRIVTT'] > 10, tnc_mix_inb_cr_asc,np.where(dot_skims['TNCIN_MIX_LRTIVTT']>0,lrt_equiv_asc,np.where(dot_skims['TNCIN_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+dot_skims['TNCIN_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))", +# reduce by 50% if bus transfer and constant is positive (PRM has no LB service),,, +,wlk_mix_out_asc,"np.where((wlk_mix_out_max_asc > 0) & (odt_skims['WALK_MIX_BUSIVTT'] > 0), 0.5 * wlk_mix_out_max_asc, wlk_mix_out_max_asc)", +,wlk_mix_inb_asc,"np.where((wlk_mix_inb_max_asc > 0) & (dot_skims['WALK_MIX_BUSIVTT'] > 0), 0.5 * wlk_mix_inb_max_asc, wlk_mix_inb_max_asc)", +,pnr_mix_out_asc,"np.where((pnr_mix_out_max_asc > 0) & (odt_skims['PNROUT_MIX_BUSIVTT'] > 0), 0.5 * pnr_mix_out_max_asc, pnr_mix_out_max_asc)", +,pnr_mix_inb_asc,"np.where((pnr_mix_inb_max_asc > 0) & (dot_skims['PNRIN_MIX_BUSIVTT'] > 0), 0.5 * pnr_mix_inb_max_asc, pnr_mix_inb_max_asc)", +,knr_mix_out_asc,"np.where((knr_mix_out_max_asc > 0) & (odt_skims['KNROUT_MIX_BUSIVTT'] > 0), 0.5 * knr_mix_out_max_asc, knr_mix_out_max_asc)", +,knr_mix_inb_asc,"np.where((knr_mix_inb_max_asc > 0) & (dot_skims['KNRIN_MIX_BUSIVTT'] > 0), 0.5 * knr_mix_inb_max_asc, knr_mix_inb_max_asc)", +,tnc_mix_out_asc,"np.where((tnc_mix_out_max_asc > 0) & (odt_skims['TNCOUT_MIX_BUSIVTT'] > 0), 0.5 * tnc_mix_out_max_asc, tnc_mix_out_max_asc)", +,tnc_mix_inb_asc,"np.where((tnc_mix_inb_max_asc > 0) & (dot_skims['TNCIN_MIX_BUSIVTT'] > 0), 0.5 * tnc_mix_inb_max_asc, tnc_mix_inb_max_asc)", +# reduce by 50% if transfers happen between cmr and other prm lines,,, +,wlk_prm_out_asc,"np.where((wlk_prm_out_asc > 0) & (odt_skims['WALK_PRM_XFERS'] > 0), 0.5 * wlk_prm_out_asc, wlk_prm_out_asc)", +,wlk_prm_inb_asc,"np.where((wlk_prm_inb_asc > 0) & (dot_skims['WALK_PRM_XFERS'] > 0), 0.5 * wlk_prm_inb_asc, wlk_prm_inb_asc)", +,pnr_prm_out_asc,"np.where((pnr_prm_out_asc > 0) & (odt_skims['PNROUT_PRM_XFERS'] > 0), 0.5 * pnr_prm_out_asc, pnr_prm_out_asc)", +,pnr_prm_inb_asc,"np.where((pnr_prm_inb_asc > 0) & (dot_skims['PNRIN_PRM_XFERS'] > 0), 0.5 * pnr_prm_inb_asc, pnr_prm_inb_asc)", +,knr_prm_out_asc,"np.where((knr_prm_out_asc > 0) & (odt_skims['KNROUT_PRM_XFERS'] > 0), 0.5 * knr_prm_out_asc, knr_prm_out_asc)", +,knr_prm_inb_asc,"np.where((knr_prm_inb_asc > 0) & (dot_skims['KNRIN_PRM_XFERS'] > 0), 0.5 * knr_prm_inb_asc, knr_prm_inb_asc)", +,tnc_prm_out_asc,"np.where((tnc_prm_out_asc > 0) & (odt_skims['TNCOUT_PRM_XFERS'] > 0), 0.5 * tnc_prm_out_asc, tnc_prm_out_asc)", +,tnc_prm_inb_asc,"np.where((tnc_prm_inb_asc > 0) & (dot_skims['TNCIN_PRM_XFERS'] > 0), 0.5 * tnc_prm_inb_asc, tnc_prm_inb_asc)", +# Micromobility times,,, +ebike time inbound,ebike_time_inb,bike_time_inb * bikeSpeed / ebikeSpeed, +ebike time outbound,ebike_time_out,bike_time_out * bikeSpeed / ebikeSpeed, +escooter time inbound,escooter_time_inb,bike_time_inb * bikeSpeed / escooterSpeed, +escooter time outbound,escooter_time_out,bike_time_out * bikeSpeed / escooterSpeed, +Micromobility access time outbound,micro_access_out,"reindex(land_use.MicroAccessTime,origin)", +Micromobility access time inbound,micro_access_inb,"reindex(land_use.MicroAccessTime,destination)", +ebike max distance availability,ebikeMaxDistance,(od_skims['DIST'] > ebikeMaxDist), +escooter max distance availability,escooterMaxDistance,(od_skims['DIST'] > escooterMaxDist), +# Microtransit and NEV,,, +microtransit available at origin,microtransit_orig,"reindex(land_use.microtransit, df[orig_col_name])", +microtransit available at destination,microtransit_dest,"reindex(land_use.microtransit, df[dest_col_name])", +microtransit available,microtransit_available,(microtransit_orig > 0) & (microtransit_orig == microtransit_dest) & (od_skims['DIST'] < microtransitMaxDist), +microtransit direct time,microtransit_direct_time,"np.maximum(od_skims['DIST']/microtransitSpeed*60, s2_time_skims_out) + np.maximum(od_skims['DIST']/microtransitSpeed*60, s2_time_skims_inb)", +microtransit total time,microtransit_time,"np.maximum(microtransit_direct_time + microtransitDiversionConstant, microtransitDiversionFactor*microtransit_direct_time)", +nev available at origin,nev_orig,"reindex(land_use.nev, df[orig_col_name])", +nev available at destination,nev_dest,"reindex(land_use.nev, df[dest_col_name])", +nev available,nev_available,(nev_orig > 0) & (nev_orig == nev_dest) & (od_skims['DIST'] < nevMaxDist), +nev direct time,nev_direct_time,"np.maximum(od_skims['DIST']/nevSpeed*60, s2_time_skims_out) + np.maximum(od_skims['DIST']/nevSpeed*60, s2_time_skims_inb)", +nev total time,nev_time,"np.maximum(nev_direct_time + nevDiversionConstant, nevDiversionFactor*nev_direct_time)", +# Microtransit and NEV access to transit,,, +microtransit access available,microtransit_access_available,(microtransit_orig>0) & (origin_micro_prm_dist0) & (origin_micro_prm_dist0) & (dest_micro_prm_dist>maxWalkIfMTAccessAvailable) & (dest_micro_prm_dist0) & (dest_micro_prm_dist>maxWalkIfMTAccessAvailable) & (dest_micro_prm_dist19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Female_Departure_after_1230_pm_Linear +util_Parttime_worker_Departure_after_1230_pm__Linear,Part-time worker - Departure after 12:30 pm - Linear,"@np.where(((df.ptype == 2) & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Parttime_worker_Departure_after_1230_pm__Linear +util_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period,Part-time worker - Duration greater than 0.5 hours (depart and arrive in the same period),"@np.where(((df.ptype == 2) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Low income (<25000) - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.is_income_less25K) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Med income (25k to 60k) - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.is_income_25K_to_60K) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Med_income_25k_to_60k_Duration_greater_than_0p5_hours,Med income (25k to 60k) - Duration greater than 0.5 hours,"@np.where(((df.is_income_25K_to_60K) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Med_income_25k_to_60k_Duration_greater_than_0p5_hours +util_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours,Med-high income (60k to 120k) - Duration greater than 0.5 hours,"@np.where(((df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours +#util_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Blue collar - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.work_segment == 5) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +#util_Blue_collar_Duration_greater_than_0p5_hours,Blue collar - Duration greater than 0.5 hours,"@np.where(((df.work_segment == 5) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Blue_collar_Duration_greater_than_0p5_hours +#util_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Health - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.work_segment == 3) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Distance to destination - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where((df.duration<1), ((np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0))) * df.od_distance, 0)",coef_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Distance_to_destination_Duration_greater_than_0p5_hours,Distance to destination - Duration greater than 0.5 hours,"@np.where((df.duration>1), ((np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0))) * df.od_distance, 0)",coef_Distance_to_destination_Duration_greater_than_0p5_hours +util_Subtour_purpose_Business_Departure_before_1200_pm__Linear,Subtour purpose: Business - Departure before 12:00 pm - Linear,"@np.where(((df.tour_type == 'business') & (df.start<19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Business_Departure_before_1200_pm__Linear +util_Subtour_purpose_Business_Departure_after_1230_pm_Linear,Subtour purpose: Business - Departure after 12:30 pm - Linear,"@np.where(((df.tour_type == 'business') & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Business_Departure_after_1230_pm_Linear +util_Subtour_purpose_Business_Duration_greater_than_0p5_hours,Subtour purpose: Business - Duration greater than 0.5 hours,"@np.where(((df.tour_type == 'business') & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Business_Duration_greater_than_0p5_hours +util_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,Subtour purpose: Eat-out - Departure before 12:00 pm - Linear,"@np.where(((df.tour_type == 'eat') & (df.start<19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear +util_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,Subtour purpose: Eat-out - Departure after 12:30 pm - Linear,"@np.where(((df.tour_type == 'eat') & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear +util_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Subtour purpose: Eat-out - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.tour_type == 'eat') & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util__Departure_constants,# Departure constants,,coef__Departure_constants +util_Shift_for_every_30_minutes_before_1030_am_Linear,Shift for every 30 minutes before 10:30 am - Linear,"@np.where((df.start<16), (np.where((df.start< 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start> 21), np.minimum(df.start - 21, 11), 0)), 0)",coef_Shift_for_every_30_minutes_before_1030_am_Linear +util_Before_1100_AM,Before 11:00 AM,@(df.start<17),coef_start_Before_1100_AM +util_1100_AM_1130_AM_start,11:00 AM - 11:30 AM,@(df.start==17),coef_start_1100_AM_1130_AM +util_1130_AM_1200_PM_start,11:30 AM - 12:00 PM,@(df.start==18),coef_start_1130_AM_1200_PM +util_1200_AM_1230_PM_start,12:00 AM - 12:30 PM,@(df.start==19),coef_start_1200_AM_1230_PM +util_1230_PM_0100_PM_start,12:30 PM - 01:00 PM,@(df.start==20),coef_start_1230_PM_0100_PM +util_After_0100_PM,After 01:00 PM,@(df.start>20),coef_start_After_0100_PM +util_Shift_for_every_30_minutes_after_130_pm_Square_Root,Shift for every 30 minutes after 1:30 pm - Square Root,"@np.where((df.start>21), ((np.where((df.start < 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start > 21), np.minimum(df.start - 21, 11), 0))** 0.5), 0)",coef_Shift_for_every_30_minutes_after_130_pm_Square_Root +util__Arrival_constants,# Arrival constants,,coef__Arrival_constants +util_Shift_for_every_30_minutes_before_1130_am_Linear,Shift for every 30 minutes before 11:30 am - Linear,"@np.where((df.end<18), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_before_1130_am_Linear +util_Before_1200_PM,Before 12:00 PM,@(df.end<19),coef_end_Before_1200_PM +util_1200_AM_1230_PM_end,12:00 AM - 12:30 PM,@(df.end==19),coef_end_1200_AM_1230_PM +util_1230_PM_0100_PM_end,12:30 PM - 01:00 PM,@(df.end==20),coef_end_1230_PM_0100_PM +util_0100_PM_0130_PM_end,01:00 PM - 01:30 PM,@(df.end==21),coef_end_0100_PM_0130_PM +util_0130_PM_0200_PM_end,01:30 PM - 02:00 PM,@(df.end==22),coef_end_0130_PM_0200_PM +util_0200_PM_0230_PM_end,02:00 PM - 02:30 PM,@(df.end==23),coef_end_0200_PM_0230_PM +util_After_0230_PM,After 02:30 PM,@(df.end>23),coef_end_After_0230_PM +util_Shift_for_every_30_minutes_after_300_pm_Linear,Shift for every 30 minutes after 3:00 pm - Linear,"@np.where((df.end>24), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_after_300_pm_Linear +util__Duration_constants,# Duration constants,,coef__Duration_constants +util_0_hrs,0 hrs,@(df.duration==0),coef_0_hrs +util_0p5_hrs,0.5 hrs,@(df.duration==1),coef_0p5_hrs +util_1_hrs,1 hrs,@(df.duration==2),coef_1_hrs +util_1p5hrs,1.5hrs,@(df.duration==3),coef_1p5hrs +util_2_hrs,2 hrs,@(df.duration==4),coef_2_hrs +util_Longer_than_2_hrs,Longer than 2 hrs,@(df.duration>4),coef_Longer_than_2_hrs +util_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root,Shift for every 30 minutes more than 2.5 hrs - Square Root,"@np.where((df.duration>5), ((np.where((df.duration < 0), np.minimum(0 - df.duration, 47), 0) + np.where((df.duration > 5), np.minimum(df.duration - 5, 13), 0)) ** 0.5), 0)",coef_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root +util_Calibration_Constant_Departure_eq_18,Calibration Constant - Departure = 18,@(df.start==18),coef_Calibration_Constant_Departure_eq_18 +util_Calibration_Constant_Departure_eq_19,Calibration Constant - Departure = 19,@(df.start==19),coef_Calibration_Constant_Departure_eq_19 +util_Calibration_Constant_Arrival_eq_20,Calibration Constant - Arrival = 20,@(df.end==20),coef_Calibration_Constant_Arrival_eq_20 +util_Calibration_Constant_Arrival_eq_21,Calibration Constant - Arrival = 21,@(df.end==21),coef_Calibration_Constant_Arrival_eq_21 diff --git a/configs/resident/tour_scheduling_atwork.yaml b/configs/resident/tour_scheduling_atwork.yaml new file mode 100644 index 0000000..f04f93b --- /dev/null +++ b/configs/resident/tour_scheduling_atwork.yaml @@ -0,0 +1,16 @@ + +SPEC: tour_scheduling_atwork.csv +COEFFICIENTS: tour_scheduling_atwork_coeffs.csv + +preprocessor: + SPEC: tour_scheduling_atwork_preprocessor + DF: df +# TABLES: +# - land_use +# - tours + +SIMULATE_CHOOSER_COLUMNS: + - od_distance + +CONSTANTS: + time_cap: 30 \ No newline at end of file diff --git a/configs/resident/tour_scheduling_atwork_coefficients.csv b/configs/resident/tour_scheduling_atwork_coefficients.csv new file mode 100644 index 0000000..dd09d22 --- /dev/null +++ b/configs/resident/tour_scheduling_atwork_coefficients.csv @@ -0,0 +1,50 @@ +coefficient_name,value,constrain +coef_early_start_at_5,-7.765548476,F +coef_am_peak_start_at_6,-6.156717827,F +coef_am_peak_start_at_7,-4.061708142,F +coef_am_peak_start_at_8,-2.330535201,F +coef_am_peak_start_at_9,-1.881593386,F +coef_midday_start_at_10_11_12,0,T +coef_midday_start_at_13_14_15,-0.77502158,F +coef_pm_peak_start_at_16_17_18,-0.227528489,F +coef_evening_start_at_19_20_21,-1.015090023,F +coef_late_start_at_22_23,-0.737570054,F +coef_early_end_at_5_6,-2.928312295,F +coef_am_peak_end,-2.928312295,F +coef_midday_end_at_10_11_12,-2.297264374,F +coef_midday_end_at_13_14,0,T +coef_pm_peak_end_at_15,-0.578344457,F +coef_pm_peak_end_at_16,-1.09408722,F +coef_pm_peak_end_at_17,-1.1658466,F +coef_pm_peak_end_at_18,-1.496131081,F +coef_evening_end_at_19_20_21,-2.31998226,F +coef_late_end_at_22_23,-2.31998226,F +coef_duration_of_0_hours,-0.906681512,F +coef_duration_of_1_hour,0,T +coef_duration_of_2_to_3_hours,-1.362175802,F +coef_duration_of_4_to_5_hours,-0.819617616,F +coef_duration_of_6_to_7_hours,1.088111072,F +coef_duration_of_8_to_10_hours,1.734038505,F +coef_duration_of_11_to_13_hours,0.3,F +coef_duration_of_14_to_18_hours,0,T +coef_start_shift_for_outbound_auto_travel_time_off_peak,0.00065,F +coef_start_shift_for_inbound_auto_travel_time_off_peak,0.00065,F +coef_duration_shift_for_outbound_auto_travel_time_off_peak,0.00981,F +coef_duration_shift_for_inbound_auto_travel_time_off_peak,0.00981,F +coef_start_shift_for_business_related_,-0.1113,F +coef_duration_shift_for_business_related_,0.2646,F +coef_start_shift_for_first_sub_tour_of_same_work_tour,-0.5433,F +coef_duration_shift_for_first_sub_tour_of_same_work_tour,-0.3992,F +coef_start_shift_for_subsequent_sub_tour_of_same_work_tour,-0.1844,F +coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour,-0.1844,F +coef_start_shift_for_number_of_mandatory_tours,-0.0193,F +coef_duration_shift_for_number_of_mandatory_tours,-0.7702,F +coef_start_shift_for_number_of_joint_tours,-0.0206,F +coef_duration_shift_for_number_of_joint_tours,-0.2497,F +coef_start_shift_for_number_of_individual_nonmandatory_tours,-0.0128,F +coef_duration_shift_for_number_of_individual_nonmandatory_tours,-0.0422,F +coef_dummy_for_business_related_purpose_and_duration_from_0_to_1,-1.543,F +coef_dummy_for_eating_out_purpose_and_duration_of_1_hour,0.3999,F +coef_dummy_for_eating_out_purpose_and_departure_at_11,1.511,F +coef_dummy_for_eating_out_purpose_and_departure_at_12,2.721,F +coef_dummy_for_eating_out_purpose_and_departure_at_13,2.122,F diff --git a/configs/resident/tour_scheduling_atwork_coeffs.csv b/configs/resident/tour_scheduling_atwork_coeffs.csv new file mode 100644 index 0000000..0e7ab1a --- /dev/null +++ b/configs/resident/tour_scheduling_atwork_coeffs.csv @@ -0,0 +1,47 @@ +coefficient_name,value,constrain +coef_Female_Departure_after_1230_pm_Linear,0.05574558,F +coef_Parttime_worker_Departure_after_1230_pm__Linear,0.129291333,F +coef_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period,0.162008704,F +coef_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.885322446,F +coef_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.526935246,F +coef_Med_income_25k_to_60k_Duration_greater_than_0p5_hours,-0.081917021,F +coef_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours,-0.068358924,F +coef_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,1.191378628,F +coef_Blue_collar_Duration_greater_than_0p5_hours,0.123072852,F +coef_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.791205377,F +coef_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.292363361,F +coef_Distance_to_destination_Duration_greater_than_0p5_hours,0.006885922,F +coef_Subtour_purpose_Business_Departure_before_1200_pm__Linear,0.268963895,F +coef_Subtour_purpose_Business_Departure_after_1230_pm_Linear,0.17631122,F +coef_Subtour_purpose_Business_Duration_greater_than_0p5_hours,0.362189199,F +coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,-0.250770206,F +coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,-0.169861029,F +coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.678939929,F +coef_Shift_for_every_30_minutes_before_1030_am_Linear,-0.731880037,F +coef_start_Before_1100_AM,-2.176744062,F +coef_start_1100_AM_1130_AM,-1.190017952,F +coef_start_1130_AM_1200_PM,-0.198229872,F +coef_start_1200_AM_1230_PM,0,T +coef_start_1230_PM_0100_PM,-0.084950396,F +coef_start_After_0100_PM,-0.205562723,F +coef_Shift_for_every_30_minutes_after_130_pm_Square_Root,0.539088697,F +coef_Shift_for_every_30_minutes_before_1130_am_Linear,0.414546555,F +coef_end_Before_1200_PM,0.279351638,F +coef_end_1200_AM_1230_PM,-0.045281832,F +coef_end_1230_PM_0100_PM,0.214070736,F +coef_end_0100_PM_0130_PM,0,T +coef_end_0130_PM_0200_PM,-0.69742748,F +coef_end_0200_PM_0230_PM,-1.284283533,F +coef_end_After_0230_PM,-2.119733896,F +coef_Shift_for_every_30_minutes_after_300_pm_Linear,-0.508006414,F +coef_0_hrs,-0.969734874,F +coef_0p5_hrs,0,T +coef_1_hrs,0.177457256,F +coef_1p5hrs,-0.171124657,F +coef_2_hrs,-0.4678094,F +coef_Longer_than_2_hrs,-0.523935526,F +coef_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root,-0.424301372,F +coef_Calibration_Constant_Departure_eq_18,-0.045958531,F +coef_Calibration_Constant_Departure_eq_19,-0.099009925,F +coef_Calibration_Constant_Arrival_eq_20,-0.0698094,F +coef_Calibration_Constant_Arrival_eq_21,-0.064355276,F diff --git a/configs/resident/tour_scheduling_atwork_preprocessor.csv b/configs/resident/tour_scheduling_atwork_preprocessor.csv new file mode 100644 index 0000000..60acae2 --- /dev/null +++ b/configs/resident/tour_scheduling_atwork_preprocessor.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +#,, +local scalar distance skim,od_distance,"od_skims['DIST']" diff --git a/configs/resident/tour_scheduling_joint.csv b/configs/resident/tour_scheduling_joint.csv new file mode 100644 index 0000000..c914769 --- /dev/null +++ b/configs/resident/tour_scheduling_joint.csv @@ -0,0 +1,241 @@ +Label,Description,Expression,Coefficient +#SHOPPING,#SHOPPING,,SHOPPING +util_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure before 10:00 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<15)), ((15-df.start)*(df.start<=15) + (df.start-15)*(df.start>15)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear +util_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure after 10:30 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>15)), ((15-df.start)*(df.start<=15) + (df.start-15)*(df.start>15)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear +util_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,SHOPPING - Joint Tours Party Size > 2: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3) & (df.number_of_participants > 2)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs +util_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SHOPPING - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>3) & (df.number_of_participants > 2)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,SHOPPING - Joint Tour with only adults: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3) & (df.composition=='adults')), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs +util_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SHOPPING - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3) & ((df.composition=='children')|(df.composition=='mixed'))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SHOPPING - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>3) & ((df.composition=='children')|(df.composition=='mixed'))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr +util_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs +util_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr +util_shop_Distance_Duration_lt_1p5_hrs,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_shop_Distance_Duration_lt_1p5_hrs +util_shop_Distance_Duration_gt_1p5_hr,SHOPPING - Distance: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_shop_Distance_Duration_gt_1p5_hr +util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear +util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12))**0.5, 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root +util_shop_Departure_Constant_Before_09_00_AM,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<13)),coef_shop_Departure_Constant_Before_09_00_AM +util_shop_Departure_Constant_09_00_AM_09_30_AM,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==13)),coef_shop_Departure_Constant_09_00_AM_09_30_AM +util_shop_Departure_Constant_09_30_AM_10_00_AM,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==14)),coef_shop_Departure_Constant_09_30_AM_10_00_AM +util_shop_Departure_Constant_10_00_AM_10_30_AM,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==15)),coef_shop_Departure_Constant_10_00_AM_10_30_AM +util_shop_Departure_Constant_10_30_AM_11_00_AM,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==16)),coef_shop_Departure_Constant_10_30_AM_11_00_AM +util_shop_Departure_Constant_After_11_00_AM,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>16)),coef_shop_Departure_Constant_After_11_00_AM +util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear +util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared +util_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<19)), ((19-df.end)*(df.end<=19) + (df.end-19)*(df.end>19)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear +util_shop_Arrival_Constant_Before_12_30_PM,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<20)),coef_shop_Arrival_Constant_Before_12_30_PM +util_shop_Arrival_Constant_12_30_PM_03_00_PM,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shop_Arrival_Constant_12_30_PM_03_00_PM +util_shop_Arrival_Constant_03_00_PM_03_30_PM,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==25)),coef_shop_Arrival_Constant_03_00_PM_03_30_PM +util_shop_Arrival_Constant_03_30_PM_04_00_PM,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==26)),coef_shop_Arrival_Constant_03_30_PM_04_00_PM +util_shop_Arrival_Constant_04_00_PM_04_30_PM,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==27)),coef_shop_Arrival_Constant_04_00_PM_04_30_PM +util_shop_Arrival_Constant_04_30_PM_05_00_PM,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==28)),coef_shop_Arrival_Constant_04_30_PM_05_00_PM +util_shop_Arrival_Constant_05_00_PM_05_30_PM,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==29)),coef_shop_Arrival_Constant_05_00_PM_05_30_PM +util_shop_Arrival_Constant_05_30_PM_07_00_PM,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shop_Arrival_Constant_05_30_PM_07_00_PM +util_shop_Arrival_Constant_07_00_PM_09_30_PM,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shop_Arrival_Constant_07_00_PM_09_30_PM +util_shop_Arrival_Constant_After_09_30_PM,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>37)),coef_shop_Arrival_Constant_After_09_30_PM +util_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>38)), ((38-df.end)*(df.end<=38) + (df.end-38)*(df.end>38)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear +util_shop_Duration_Constant_0_hrs,SHOPPING - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Duration_Constant_0_hrs +util_shop_Duration_Constant_0p5_hrs,SHOPPING - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Duration_Constant_0p5_hrs +util_shop_Duration_Constant_1_hrs,SHOPPING - Duration Constant: 1 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Duration_Constant_1_hrs +util_shop_Duration_Constant_1p5hrs,SHOPPING - Duration Constant: 1.5hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==3)),coef_shop_Duration_Constant_1p5hrs +util_shop_Duration_Constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==4)),coef_shop_Duration_Constant_2_hrs +util_shop_Duration_Constant_Longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>4)),coef_shop_Duration_Constant_Longer_than_2_hrs +util_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear +util_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), (((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)) ** 0.5), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root +util_shop_Calibration_Constant_Duration_eq_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Calibration_Constant_Duration_eq_1 +util_shop_Calibration_Constant_Duration_eq_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Calibration_Constant_Duration_eq_2 +util_shop_Calibration_Constant_Duration_eq_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Calibration_Constant_Duration_eq_3 +#MAINTENANCE,#MAINTENANCE,,MAINTENANCE +util_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure before reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<13)), ((13-df.start)*(df.start<=13) + (df.start-13)*(df.start>13)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear +util_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure after reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>13)), ((13-df.start)*(df.start<=13) + (df.start-13)*(df.start>13)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear +util_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tours Party Size > 2: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4) & (df.number_of_participants > 2)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs +util_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,MAINTENANCE - Joint Tours Party Size > 2: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>4) & (df.number_of_participants > 2)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tour with only adults: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4) & (df.composition=='adults')), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs +util_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,MAINTENANCE - Kids in Joint Tour: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,MAINTENANCE - Kids in Joint Tour: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,"MAINTENANCE - Low Income (<=$25,000): Duration > reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr +util_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs +util_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr +util_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr +util_maint_Distance_Duration_lt_1p5_hrs,MAINTENANCE - Distance: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) *(df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_lt_1p5_hrs +util_maint_Distance_Duration_gt_1p5_hr,MAINTENANCE - Distance: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_gt_1p5_hr +util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear +util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), (((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10))** 0.5), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root +util_maint_Departure_Constant_Before_08_00_AM,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<11)),coef_maint_Departure_Constant_Before_08_00_AM +util_maint_Departure_Constant_08_00_AM_08_30_AM,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==11)),coef_maint_Departure_Constant_08_00_AM_08_30_AM +util_maint_Departure_Constant_08_30_AM_09_00_AM,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==12)),coef_maint_Departure_Constant_08_30_AM_09_00_AM +util_maint_Departure_Constant_09_00_AM_09_30_AM,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==13)),coef_maint_Departure_Constant_09_00_AM_09_30_AM +util_maint_Departure_Constant_09_30_AM_10_00_AM,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==14)),coef_maint_Departure_Constant_09_30_AM_10_00_AM +util_maint_Departure_Constant_10_00_AM_10_30_AM,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==15)),coef_maint_Departure_Constant_10_00_AM_10_30_AM +util_maint_Departure_Constant_10_30_AM_11_00_AM,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==16)),coef_maint_Departure_Constant_10_30_AM_11_00_AM +util_maint_Departure_Constant_After_11_00_AM,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>16)),coef_maint_Departure_Constant_After_11_00_AM +util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear +util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared +util_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<15)), ((15-df.end)*(df.end<=15) + (df.end-15)*(df.end>15)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear +util_maint_Arrival_Constant_Before_10_30_AM,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<16)),coef_maint_Arrival_Constant_Before_10_30_AM +util_maint_Arrival_Constant_10_30_AM_11_00_AM,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==16)),coef_maint_Arrival_Constant_10_30_AM_11_00_AM +util_maint_Arrival_Constant_11_00_AM_11_30_AM,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==17)),coef_maint_Arrival_Constant_11_00_AM_11_30_AM +util_maint_Arrival_Constant_11_30_AM_01_30_PM,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maint_Arrival_Constant_11_30_AM_01_30_PM +util_maint_Arrival_Constant_01_30_PM_02_30_PM,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maint_Arrival_Constant_01_30_PM_02_30_PM +util_maint_Arrival_Constant_02_30_PM_04_00_PM,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maint_Arrival_Constant_02_30_PM_04_00_PM +util_maint_Arrival_Constant_04_00_PM_04_30_PM,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==27)),coef_maint_Arrival_Constant_04_00_PM_04_30_PM +util_maint_Arrival_Constant_After_04_30_PM,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>27)),coef_maint_Arrival_Constant_After_04_30_PM +util_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>28)), ((28-df.end)*(df.end<=28) + (df.end-28)*(df.end>28)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear +util_maint_Duration_Constant_0_hrs,MAINTENANCE - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Duration_Constant_0_hrs +util_maint_Duration_Constant_0p5_hrs,MAINTENANCE - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Duration_Constant_0p5_hrs +util_maint_Duration_Constant_Longer_than_0p5_hrs,MAINTENANCE - Duration Constant: Longer than 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>1)),coef_maint_Duration_Constant_Longer_than_0p5_hrs +util_maint_Duration_Constant_Duration_gt_1_hrs_Linear,MAINTENANCE - Duration Constant: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear +util_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,MAINTENANCE - Duration Constant: Duration > 1 hrs - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), (((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2))** 0.5), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root +util_maint_Calibration_Constant_Duration_eq_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Calibration_Constant_Duration_eq_1 +util_maint_Calibration_Constant_Duration_eq_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Calibration_Constant_Duration_eq_2 +util_maint_Calibration_Constant_Duration_eq_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==2)),coef_maint_Calibration_Constant_Duration_eq_3 +util_maint_Calibration_Constant_Duration_eq_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==3)),coef_maint_Calibration_Constant_Duration_eq_4 +util_maint_Calibration_Constant_Duration_eq_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==4)),coef_maint_Calibration_Constant_Duration_eq_5 +#EATOUT,#EAT-OUT,,EATOUT +util_eatout_Distance_to_destination_Duration_lt_1_hrs,EAT-OUT - Distance to destination - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_lt_1_hrs +util_eatout_Distance_to_destination_Duration_gt_1_hrs,EAT-OUT - Distance to destination - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_gt_1_hrs +util_eatout_Low_income_lt25000_Duration_lt_1_hrs,EAT-OUT - Low income (<25000) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Low_income_lt25000_Duration_lt_1_hrs +util_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,EAT-OUT - Medium (25k to 60k) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs +util_eatout_Zero_auto_HH_Duration_gt_1_hrs,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Zero_auto_HH_Duration_gt_1_hrs +util_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,EAT-OUT - Kids in Joint tour - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs +util_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,EAT-OUT - Joint Tours Party Size greater than 2 - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.number_of_participants > 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs +util_eatout_Departure_Constant_11_00_AM_12_00_PM,EAT-OUT - Departure Constant: 11:00 AM - 12:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>=17) & (df.start<=18)),coef_eatout_Departure_Constant_11_00_AM_12_00_PM +util_eatout_Departure_Constant_12_00_PM_12_30_PM,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==19),coef_eatout_Departure_Constant_12_00_PM_12_30_PM +util_eatout_Departure_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Departure Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==20),coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM +util_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<29)), ((29-df.start)*(df.start<=29) + (df.start-29)*(df.start>29)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear +util_eatout_Departure_Constant_Before_05_30_PM,EAT-OUT - Departure Constant: Before 05:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<30),coef_eatout_Departure_Constant_Before_05_30_PM +util_eatout_Departure_Constant_05_30_PM_06_00_PM,EAT-OUT - Departure Constant: 05:30 PM - 06:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==30),coef_eatout_Departure_Constant_05_30_PM_06_00_PM +util_eatout_Departure_Constant_06_00_PM_06_30_PM,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==31),coef_eatout_Departure_Constant_06_00_PM_06_30_PM +util_eatout_Departure_Constant_06_30_PM_07_00_PM,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==32),coef_eatout_Departure_Constant_06_30_PM_07_00_PM +util_eatout_Departure_Constant_07_00_PM_07_30_PM,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==33),coef_eatout_Departure_Constant_07_00_PM_07_30_PM +util_eatout_Departure_Constant_After_07_30_PM,EAT-OUT - Departure Constant: After 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>33),coef_eatout_Departure_Constant_After_07_30_PM +util_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>34)), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear +util_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==20),coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM +util_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==21),coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM +util_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==22),coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM +util_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==23),coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM +util_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<32)), ((32-df.end)*(df.end<=32) + (df.end-32)*(df.end>32)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear +util_eatout_Arrival_Constant_Before_7_00_PM,EAT-OUT - Arrival Constant: Before 7:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<33),coef_eatout_Arrival_Constant_Before_7_00_PM +util_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end==33),coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM +util_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==34),coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM +util_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==35),coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM +util_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==36),coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM +util_eatout_Arrival_Constant_After_09_00_PM,EAT-OUT - Arrival Constant: After 09:00 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end>36),coef_eatout_Arrival_Constant_After_09_00_PM +util_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type== 'eatout') & (df.end>37) & (df.tour_category == 'joint')), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear +util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear +util_eatout_Duration_Constant_0_hours,EAT-OUT - Duration Constant: 0 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Duration_Constant_0_hours +util_eatout_Duration_Constant_0p5_hous,EAT-OUT - Duration Constant: 0.5 hous,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Duration_Constant_0p5_hous +util_eatout_Duration_Constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Duration_Constant_1_hour +util_eatout_Duration_Constant_1p5_hours,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Duration_Constant_1p5_hours +util_eatout_Duration_Constant_2_hours_or_more,EAT-OUT - Duration Constant: 2 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>4),coef_eatout_Duration_Constant_2_hours_or_more +util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear +util_eatout_Calibration_Constant_Duration_eq_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Calibration_Constant_Duration_eq_1 +util_eatout_Calibration_Constant_Duration_eq_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Calibration_Constant_Duration_eq_2 +util_eatout_Calibration_Constant_Duration_eq_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Calibration_Constant_Duration_eq_3 +util_eatout_Calibration_Constant_Duration_eq_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Calibration_Constant_Duration_eq_4 +util_eatout_Calibration_Constant_Departure_eq_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_Calibration_Constant_Departure_eq_1 +util_eatout_Calibration_Constant_Departure_eq_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_Calibration_Constant_Departure_eq_2 +util_eatout_Calibration_Constant_Departure_eq_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_Calibration_Constant_Departure_eq_3 +util_eatout_Calibration_Constant_Departure_eq_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_Calibration_Constant_Departure_eq_17 +util_eatout_Calibration_Constant_Departure_eq_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_Calibration_Constant_Departure_eq_18 +util_eatout_Calibration_Constant_Departure_eq_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_Calibration_Constant_Departure_eq_19 +util_eatout_Calibration_Constant_Departure_eq_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_Calibration_Constant_Departure_eq_20 +util_eatout_Calibration_Constant_Departure_eq_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_Calibration_Constant_Departure_eq_21 +#SOCIAL,#SOCIAL,,SOCIAL +util_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.num_retired_adults == df.hhsize) & (df.tour_type == 'social') & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear +util_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear +util_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear +util_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration < reference - Linear,"@np.where(((df.tour_category == 'joint')&(df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear +util_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration > reference - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear +util_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SOCIAL - Kids in Joint Tour: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SOCIAL - Kids in Joint Tour: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SOCIAL - Joint Tours Party Size > 2: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>4) & (df.number_of_participants > 2)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_social_Auto_Distance_Duration_lt_1_hrs_Linear,SOCIAL - Auto Distance: Duration < reference - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0) ",coef_social_Auto_Distance_Duration_lt_1_hrs_Linear +util_social_Auto_Distance_Duration_gt_1_hrs_Linear,SOCIAL - Auto Distance: Duration > reference - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_social_Auto_Distance_Duration_gt_1_hrs_Linear +util_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear +util_social_Departure_Constant_Before_09_00_AM,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<13),coef_social_Departure_Constant_Before_09_00_AM +util_social_Departure_Constant_09_00_AM_to_09_30_AM,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==13),coef_social_Departure_Constant_09_00_AM_to_09_30_AM +util_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<29), ((29-df.start)*(df.start<=29) + (df.start-29)*(df.start>29)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear +util_social_Departure_Constant_Before_05_30_PM,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<30)),coef_social_Departure_Constant_Before_05_30_PM +util_social_Departure_Constant_05_30_PM_06_00_PM,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==30)),coef_social_Departure_Constant_05_30_PM_06_00_PM +util_social_Departure_Constant_06_00_PM_06_30_PM,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==31)),coef_social_Departure_Constant_06_00_PM_06_30_PM +util_social_Departure_Constant_06_30_PM_07_00_PM,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==32)),coef_social_Departure_Constant_06_30_PM_07_00_PM +util_social_Departure_Constant_07_00_PM_07_30_PM,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==33)),coef_social_Departure_Constant_07_00_PM_07_30_PM +util_social_Departure_Constant_After_07_30_PM,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>33)),coef_social_Departure_Constant_After_07_30_PM +util_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>34), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear +util_social_Arrival_Constant_03_00_PM_to_03_30_PM,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==25)),coef_social_Arrival_Constant_03_00_PM_to_03_30_PM +util_social_Arrival_Constant_03_30_PM_to_04_00_PM,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==26)),coef_social_Arrival_Constant_03_30_PM_to_04_00_PM +util_social_Arrival_Constant_04_00_PM_to_04_30_PM,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==27)),coef_social_Arrival_Constant_04_00_PM_to_04_30_PM +util_social_Arrival_Constant_05_00_PM_to_06_00_PM,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_Arrival_Constant_05_00_PM_to_06_00_PM +util_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<35)), ((35-df.end)*(df.end<=35) + (df.end-35)*(df.end>35)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear +util_social_Arrival_Constant_Before_8_30_PM,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<36)),coef_social_Arrival_Constant_Before_8_30_PM +util_social_Arrival_Constant_8_30_PM_to_9_00_PM,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==36)),coef_social_Arrival_Constant_8_30_PM_to_9_00_PM +util_social_Arrival_Constant_9_00_PM_to_9_30_PM,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==37)),coef_social_Arrival_Constant_9_00_PM_to_9_30_PM +util_social_Arrival_Constant_9_30_PM_to10_00_PM,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==38)),coef_social_Arrival_Constant_9_30_PM_to10_00_PM +util_social_Arrival_Constant_10_00_PM_to_10_30_PM,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==39)),coef_social_Arrival_Constant_10_00_PM_to_10_30_PM +util_social_Arrival_Constant_After_10_30_PM,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>39)),coef_social_Arrival_Constant_After_10_30_PM +util_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>40)), ((40-df.end)*(df.end<=40) + (df.end-40)*(df.end>40)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear +util_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 1.5 hrs - Linear,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3)) * ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)),coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear +util_social_Duration_Constant_Less_than_2_hours,SOCIAL - Duration Constant: Less than 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4)),coef_social_Duration_Constant_Less_than_2_hours +util_social_Duration_Constant_2_hours,SOCIAL - Duration Constant: 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==4)),coef_social_Duration_Constant_2_hours +util_social_Duration_Constant_2p5_hours,SOCIAL - Duration Constant: 2.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==5)),coef_social_Duration_Constant_2p5_hours +util_social_Duration_Constant_3_hours_or_more,SOCIAL - Duration Constant: 3 hours or more,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>5)),coef_social_Duration_Constant_3_hours_or_more +util_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 3.5 hrs - Linear,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>6)) * ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)),coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear +util_social_Calibration_Constant_Duration_eq_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==0)),coef_social_Calibration_Constant_Duration_eq_1 +util_social_Calibration_Constant_Duration_eq_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration == 1)),coef_social_Calibration_Constant_Duration_eq_2 +util_social_Calibration_Constant_Duration_eq_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==2)),coef_social_Calibration_Constant_Duration_eq_3 +util_social_Calibration_Constant_Duration_eq_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==3)),coef_social_Calibration_Constant_Duration_eq_4 +util_social_Calibration_Constant_Duration_eq_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==4)),coef_social_Calibration_Constant_Duration_eq_5 +util_social_Calibration_Constant_Duration_eq_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==5)),coef_social_Calibration_Constant_Duration_eq_6 +util_social_Calibration_Constant_Duration_eq_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==6)),coef_social_Calibration_Constant_Duration_eq_7 +util_social_Calibration_Constant_Duration_eq_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==7)),coef_social_Calibration_Constant_Duration_eq_8 +util_social_Calibration_Constant_Duration_eq_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==8)),coef_social_Calibration_Constant_Duration_eq_9 +#DISCRETIONARY,#DISCRETIONARY,,DISCRETIONARY +util_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.num_retired_adults == df.hhsize) & (df.tour_type == 'othdiscr') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear +util_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear +util_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear +util_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)),0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear +util_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear +util_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,DISCRETIONARY - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,DISCRETIONARY - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,DISCRETIONARY - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.number_of_participants > 2)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_disc_Auto_Distance_Duration_lt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear +util_disc_Auto_Distance_Duration_gt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear +util_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<9)), ((9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear +util_disc_Departure_Constant_Before_7_30_AM_,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<10)),coef_disc_Departure_Constant_Before_7_30_AM_ +util_disc_Departure_Constant_7_30_AM_to_8_00_AM,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==10)),coef_disc_Departure_Constant_7_30_AM_to_8_00_AM +util_disc_Departure_Constant_8_00_AM_to_8_30_AM,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==11)),coef_disc_Departure_Constant_8_00_AM_to_8_30_AM +util_disc_Departure_Constant_8_30_AM_to_9_00_AM,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==12)),coef_disc_Departure_Constant_8_30_AM_to_9_00_AM +util_disc_Departure_Constant_9_00_AM_to_9_30_AM,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==13)),coef_disc_Departure_Constant_9_00_AM_to_9_30_AM +util_disc_Departure_Constant_9_30_AM_to_10_00_AM,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==14)),coef_disc_Departure_Constant_9_30_AM_to_10_00_AM +util_disc_Departure_Constant_10_00_AM_to_10_30_AM,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==15)),coef_disc_Departure_Constant_10_00_AM_to_10_30_AM +util_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<28)), ((28-df.start)*(df.start<=28) + (df.start-28)*(df.start>28)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear +util_disc_Departure_Constant_Before_05_00_PM,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<29)),coef_disc_Departure_Constant_Before_05_00_PM +util_disc_Departure_Constant_05_00_PM_05_30_PM,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Departure_Constant_05_00_PM_05_30_PM +util_disc_Departure_Constant_05_30_PM_06_00_PM,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Departure_Constant_05_30_PM_06_00_PM +util_disc_Departure_Constant_06_00_PM_06_30_PM,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Departure_Constant_06_00_PM_06_30_PM +util_disc_Departure_Constant_06_30_PM_07_00_PM,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Departure_Constant_06_30_PM_07_00_PM +util_disc_Departure_Constant_After_07_00_PM,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>32)),coef_disc_Departure_Constant_After_07_00_PM +util_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>33)), ((33-df.start)*(df.start<=33) + (df.start-33)*(df.start>33)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear +util_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<31)), ((31-df.end)*(df.end<=31) + (df.end-31)*(df.end>31)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear +util_disc_Arrival_Constant_Before_6_30_PM,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<32)),coef_disc_Arrival_Constant_Before_6_30_PM +util_disc_Arrival_Constant_6_30_PM_to_7_00_PM,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==32)),coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM +util_disc_Arrival_Constant_7_00_PM_to_7_30_PM,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==33)),coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM +util_disc_Arrival_Constant_7_30_PM_to_8_00_PM,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==34)),coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM +util_disc_Arrival_Constant_8_00_PM_to_8_30_PM,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==35)),coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM +util_disc_Arrival_Constant_8_30_PM_to_9_00_PM,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==36)),coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM +util_disc_Arrival_Constant_After_9_00_PM,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>36)),coef_disc_Arrival_Constant_After_9_00_PM +util_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>37)), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear +util_disc_Duration_Constant_0_hours,DISCRETIONARY - Duration Constant: 0 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0),coef_disc_Duration_Constant_0_hours +util_disc_Duration_Constant_0p5_hous,DISCRETIONARY - Duration Constant: 0.5 hous,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1),coef_disc_Duration_Constant_0p5_hous +util_disc_Duration_Constant_1_hour,DISCRETIONARY - Duration Constant: 1 hour,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2),coef_disc_Duration_Constant_1_hour +util_disc_Duration_Constant_1p5_hours,DISCRETIONARY - Duration Constant: 1.5 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3),coef_disc_Duration_Constant_1p5_hours +util_disc_Duration_Constant_2_hours,DISCRETIONARY - Duration Constant: 2 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4),coef_disc_Duration_Constant_2_hours +util_disc_Duration_Constant_2p5_hours_or_more,DISCRETIONARY - Duration Constant: 2.5 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>4),coef_disc_Duration_Constant_2p5_hours_or_more +util_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,DISCRETIONARY - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear +util_disc_Calibration_Constant_Duration_eq_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_disc_Calibration_Constant_Duration_eq_4 +util_disc_Calibration_Constant_Duration_eq_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_disc_Calibration_Constant_Duration_eq_5 +util_disc_Calibration_Constant_Departure_eq_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Calibration_Constant_Departure_eq_29 +util_disc_Calibration_Constant_Departure_eq_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Calibration_Constant_Departure_eq_30 +util_disc_Calibration_Constant_Departure_eq_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Calibration_Constant_Departure_eq_31 +util_disc_Calibration_Constant_Departure_eq_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Calibration_Constant_Departure_eq_32 diff --git a/configs/resident/tour_scheduling_joint_coefficients.csv b/configs/resident/tour_scheduling_joint_coefficients.csv new file mode 100644 index 0000000..f1ea1d9 --- /dev/null +++ b/configs/resident/tour_scheduling_joint_coefficients.csv @@ -0,0 +1,307 @@ +coefficient_name,value,constrain +coef_escort_Mode_Choice_Logsum,1.1731730340000002,F +coef_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.335017673,F +coef_escort_Distance_to_destination_Duration_greater_than_0p5_hours,0.005298165,F +coef_escort_Fulltime_worker_Departure_after_8_00_am_Linear,-0.037980109,F +coef_escort_Fulltime_worker_Departure_after_3_00_am_Linear,0.163254125,F +coef_escort_Fulltime_worker_Duration_lt_0p5_hrs,-0.275077482,F +coef_escort_Fulltime_worker_Duration_gt_0p5_hrs,0.051530545,F +coef_escort_University_student_Duration_lt_0p5_hrs,-0.4268027179999999,F +coef_escort_Nondriving_age_student_Duration_gt_0p5_hrs,0.240582361,F +coef_escort_Driving_age_student_Duration_lt_0p5_hrs,-0.5541461910000001,F +coef_escort_Driving_age_student_Duration_gt_0p5_hrs,0.299387708,F +coef_escort_Preschool_kid_Duration_gt_0p5_hrs,0.195482563,F +coef_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs,-0.029281467,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM,0.589083327,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM,0.086690827,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM,0.4775826479999999,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM,-0.2040655019999999,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM,-0.360039254,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM,0.0916141069999999,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM,0.432854268,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM,0.131037275,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.109700265,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM,-0.224568648,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM,-0.357416434,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM,0.629285298,F +coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.0390051479999999,F +coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM,-0.06556611,F +coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM,0.1176809769999999,F +coef_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs,-0.057322708,F +coef_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs,-0.062899692,F +coef_escort_Number_of_joint_tours_Duration_gt_0p5_hrs,-0.048533895,F +coef_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear,-1.4692400019999998,F +coef_escort_Departure_Constant_Before_07_00_AM,-2.070292862,F +coef_escort_Departure_Constant_07_00_AM_07_30_AM,-0.642734296,F +coef_escort_Departure_Constant_07_30_AM_08_00_AM,0.0,T +coef_escort_Departure_Constant_08_00_AM_08_30_AM,-0.2146176669999999,F +coef_escort_Departure_Constant_08_30_AM_09_00_AM,-0.147266606,F +coef_escort_Departure_Constant_After_09_00_AM,-1.356686422,F +coef_escort_Departure_Constant_01_30_PM_02_00_PM,0.368092381,F +coef_escort_Departure_Constant_02_00_PM_02_30_PM,1.166803383,F +coef_escort_Departure_Constant_02_30_PM_03_00_PM,1.28466083,F +coef_escort_Departure_Constant_03_00_PM_03_30_PM,0.581891245,F +coef_escort_Departure_Constant_After_03_30_PM,0.834510243,F +coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,0.175257649,F +coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear,-0.019161202,F +coef_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear,0.44978138,F +coef_escort_Arrival_Constant_Before_07_00_AM,0.549584585,F +coef_escort_Arrival_Constant_07_00_AM_07_30_AM,0.488181278,F +coef_escort_Arrival_Constant_07_30_AM_08_00_AM,0.236447651,F +coef_escort_Arrival_Constant_08_00_AM_08_30_AM,0.0,T +coef_escort_Arrival_Constant_08_30_AM_09_00_AM,-0.6837568009999999,F +coef_escort_Arrival_Constant_After_09_00_AM,-1.428888485,F +coef_escort_Arrival_Constant_02_30_PM_03_00_PM,1.311480662,F +coef_escort_Arrival_Constant_03_00_PM_03_30_PM,1.316883154,F +coef_escort_Arrival_Constant_03_30_PM_04_00_PM,1.3968383919999998,F +coef_escort_Arrival_Constant_04_00_PM_04_30_PM,1.03146139,F +coef_escort_Arrival_Constant_After_04_30_PM,0.907344583,F +coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,-0.148408887,F +coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.389082896,F +coef_escort_Duration_Constant_0_hrs,-0.173757322,F +coef_escort_Duration_Constant_0p5_hrs,0.0,T +coef_escort_Duration_Constant_1_hrs,-0.4312877429999999,F +coef_escort_Duration_Constant_1p5hrs,-0.7004739590000001,F +coef_escort_Duration_Constant_2_hrs,-1.071871358,F +coef_escort_Duration_Constant_Longer_than_2_hrs,-1.691098421,F +coef_escort_Calibration_Constant_Duration_eq_1,-0.047200214,F +coef_escort_Calibration_Constant_Duration_eq_2,0.035611332,F +coef_escort_Calibration_Constant_Departure_eq_9,0.106814756,F +coef_escort_Calibration_Constant_Departure_eq_10,0.2153868639999999,F +coef_escort_Calibration_Constant_Departure_eq_23,-0.255087318,F +coef_escort_Calibration_Constant_Departure_eq_24,-0.296870428,F +coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,0.0,F +coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,0.0,F +coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,0.0,F +coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.0,F +coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,0.0,F +coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0.0,F +coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0.0,F +coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.0,F +coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.0,F +coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,0.0,F +coef_shop_Distance_Duration_lt_1p5_hrs,-0.028607893883491196,F +coef_shop_Distance_Duration_gt_1p5_hr,0.017461281006706703,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-1.0512104309818944,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,0.0,F +coef_shop_Departure_Constant_Before_09_00_AM,0.0,F +coef_shop_Departure_Constant_09_00_AM_09_30_AM,0.0,F +coef_shop_Departure_Constant_09_30_AM_10_00_AM,0.0,F +coef_shop_Departure_Constant_10_00_AM_10_30_AM,0.0,T +coef_shop_Departure_Constant_10_30_AM_11_00_AM,0.0,F +coef_shop_Departure_Constant_After_11_00_AM,0.0,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,0.0,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,-0.00408426422243028,F +coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,-0.17368025954187133,F +coef_shop_Arrival_Constant_Before_12_30_PM,3.1418509688037584,F +coef_shop_Arrival_Constant_12_30_PM_03_00_PM,2.9684267424496387,F +coef_shop_Arrival_Constant_03_00_PM_03_30_PM,3.0135292397053517,F +coef_shop_Arrival_Constant_03_30_PM_04_00_PM,2.540812806437939,F +coef_shop_Arrival_Constant_04_00_PM_04_30_PM,2.634709629448098,T +coef_shop_Arrival_Constant_04_30_PM_05_00_PM,2.8095914129852653,F +coef_shop_Arrival_Constant_05_00_PM_05_30_PM,2.9949590441118166,F +coef_shop_Arrival_Constant_05_30_PM_07_00_PM,2.703929925055346,F +coef_shop_Arrival_Constant_07_00_PM_09_30_PM,0.0,F +coef_shop_Arrival_Constant_After_09_30_PM,1.4252235053772202,F +coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,-0.7628329902801455,F +coef_shop_Duration_Constant_0_hrs,-1.4315217235346374,F +coef_shop_Duration_Constant_0p5_hrs,0.0,F +coef_shop_Duration_Constant_1_hrs,0.0,T +coef_shop_Duration_Constant_1p5hrs,0.5674112263052391,F +coef_shop_Duration_Constant_2_hrs,0.33353948408736633,F +coef_shop_Duration_Constant_Longer_than_2_hrs,0.0,F +coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,-0.5010185517563869,F +coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,0.0,F +coef_shop_Calibration_Constant_Duration_eq_1,0.0,F +coef_shop_Calibration_Constant_Duration_eq_2,0.0,F +coef_shop_Calibration_Constant_Duration_eq_3,0.0,F +coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,-0.7266678344083689,F +coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,0.4158223149950908,F +coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,0.0,F +coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.0,F +coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,0.0,F +coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0.0,F +coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0.0,F +coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.0,F +coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.0,F +coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,0.0,T +coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,0.0,F +coef_maint_Distance_Duration_lt_1p5_hrs,-0.10895512058986324,F +coef_maint_Distance_Duration_gt_1p5_hr,0.0076226279567043346,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,0.0,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,0.0,F +coef_maint_Departure_Constant_Before_08_00_AM,0.0,F +coef_maint_Departure_Constant_08_00_AM_08_30_AM,0.0,F +coef_maint_Departure_Constant_08_30_AM_09_00_AM,0.0,F +coef_maint_Departure_Constant_09_00_AM_09_30_AM,0.0,F +coef_maint_Departure_Constant_09_30_AM_10_00_AM,0.0,F +coef_maint_Departure_Constant_10_00_AM_10_30_AM,0.0,T +coef_maint_Departure_Constant_10_30_AM_11_00_AM,0.0,F +coef_maint_Departure_Constant_After_11_00_AM,0.0,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,0.0,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,0.0,F +coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,0.0,F +coef_maint_Arrival_Constant_Before_10_30_AM,4.343768880893401,F +coef_maint_Arrival_Constant_10_30_AM_11_00_AM,4.083350521602396,T +coef_maint_Arrival_Constant_11_00_AM_11_30_AM,3.8938125537705606,F +coef_maint_Arrival_Constant_11_30_AM_01_30_PM,2.2487727466277105,F +coef_maint_Arrival_Constant_01_30_PM_02_30_PM,1.2142794905263539,F +coef_maint_Arrival_Constant_02_30_PM_04_00_PM,0.0,F +coef_maint_Arrival_Constant_04_00_PM_04_30_PM,-0.6951930245707939,F +coef_maint_Arrival_Constant_After_04_30_PM,-0.8895314765132972,F +coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.6960130813905834,F +coef_maint_Duration_Constant_0_hrs,0.0,F +coef_maint_Duration_Constant_0p5_hrs,0.0,T +coef_maint_Duration_Constant_Longer_than_0p5_hrs,0.0,F +coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear,0.15548429306392084,F +coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,0.0,F +coef_maint_Calibration_Constant_Duration_eq_1,0.0,F +coef_maint_Calibration_Constant_Duration_eq_2,0.0,F +coef_maint_Calibration_Constant_Duration_eq_3,0.0,F +coef_maint_Calibration_Constant_Duration_eq_4,0.0,F +coef_maint_Calibration_Constant_Duration_eq_5,0.0,F +coef_eatout_Distance_to_destination_Duration_lt_1_hrs,-0.19108787133440508,F +coef_eatout_Distance_to_destination_Duration_gt_1_hrs,0.023006152654857007,F +coef_eatout_Low_income_lt25000_Duration_lt_1_hrs,0.0,F +coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,-1.180918837464798,F +coef_eatout_Zero_auto_HH_Duration_gt_1_hrs,0.0,F +coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,1.3374472139338802,F +coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,-1.0580259127605358,F +coef_eatout_Departure_Constant_11_00_AM_12_00_PM,0.9434750816323397,F +coef_eatout_Departure_Constant_12_00_PM_12_30_PM,0.6554959058136046,F +coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM,0.0,F +coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.9675269394009861,F +coef_eatout_Departure_Constant_Before_05_30_PM,-1.302314187436131,F +coef_eatout_Departure_Constant_05_30_PM_06_00_PM,0.0,F +coef_eatout_Departure_Constant_06_00_PM_06_30_PM,0.0,T +coef_eatout_Departure_Constant_06_30_PM_07_00_PM,1.6531878364152586,F +coef_eatout_Departure_Constant_07_00_PM_07_30_PM,2.8956060466853746,F +coef_eatout_Departure_Constant_After_07_30_PM,2.8296134095518095,F +coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,0.48366538096929135,F +coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,0.0,F +coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,1.0896358400995683,F +coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,0.9881110200110167,F +coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,0.0,F +coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,0.8009605390373155,F +coef_eatout_Arrival_Constant_Before_7_00_PM,4.585410018832126,F +coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,3.4838111763141164,F +coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,2.9063054928957155,F +coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,0.0,T +coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,0.7383205534380342,F +coef_eatout_Arrival_Constant_After_09_00_PM,0.0,F +coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-1.322397671171091,F +coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,0.0,F +coef_eatout_Duration_Constant_0_hours,-2.938143755725757,F +coef_eatout_Duration_Constant_0p5_hous,-0.8272449464493281,F +coef_eatout_Duration_Constant_1_hour,0.0,T +coef_eatout_Duration_Constant_1p5_hours,0.5471544500112203,F +coef_eatout_Duration_Constant_2_hours_or_more,1.6524845358696236,F +coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,0.0,F +coef_eatout_Calibration_Constant_Duration_eq_1,0.0,F +coef_eatout_Calibration_Constant_Duration_eq_2,0.0,F +coef_eatout_Calibration_Constant_Duration_eq_3,0.0,F +coef_eatout_Calibration_Constant_Duration_eq_4,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_1,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_2,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_3,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_17,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_18,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_19,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_20,0.0,F +coef_eatout_Calibration_Constant_Departure_eq_21,0.0,F +coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,0.0,F +coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,0.0,F +coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.0,F +coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.0,F +coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.0,F +coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0.0,F +coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0.0,F +coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.0,F +coef_social_Auto_Distance_Duration_lt_1_hrs_Linear,-0.1028458537293977,F +coef_social_Auto_Distance_Duration_gt_1_hrs_Linear,0.007031551489618816,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-1.2628280327123185,F +coef_social_Departure_Constant_Before_09_00_AM,0.0,F +coef_social_Departure_Constant_09_00_AM_to_09_30_AM,0.0,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.8559017615401381,F +coef_social_Departure_Constant_Before_05_30_PM,-2.6714616893962466,F +coef_social_Departure_Constant_05_30_PM_06_00_PM,-0.9852611855634258,F +coef_social_Departure_Constant_06_00_PM_06_30_PM,0.0,T +coef_social_Departure_Constant_06_30_PM_07_00_PM,0.0,F +coef_social_Departure_Constant_07_00_PM_07_30_PM,1.3469930919944328,F +coef_social_Departure_Constant_After_07_30_PM,1.9688176180344494,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,0.0,F +coef_social_Arrival_Constant_03_00_PM_to_03_30_PM,0.0,F +coef_social_Arrival_Constant_03_30_PM_to_04_00_PM,0.0,F +coef_social_Arrival_Constant_04_00_PM_to_04_30_PM,0.0,F +coef_social_Arrival_Constant_05_00_PM_to_06_00_PM,0.0,F +coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,0.850025482539915,F +coef_social_Arrival_Constant_Before_8_30_PM,3.8598870393892537,F +coef_social_Arrival_Constant_8_30_PM_to_9_00_PM,2.8509434322380205,F +coef_social_Arrival_Constant_9_00_PM_to_9_30_PM,2.4329664354881664,F +coef_social_Arrival_Constant_9_30_PM_to10_00_PM,0.0,T +coef_social_Arrival_Constant_10_00_PM_to_10_30_PM,0.0,F +coef_social_Arrival_Constant_After_10_30_PM,-1.5468997489955005,F +coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,-1.2916125250149844,F +coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,-1.0969745826065251,F +coef_social_Duration_Constant_Less_than_2_hours,0.0,F +coef_social_Duration_Constant_2_hours,0.0,F +coef_social_Duration_Constant_2p5_hours,1.2748916919490108,T +coef_social_Duration_Constant_3_hours_or_more,1.9047173259221608,F +coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,0.5841656016243181,F +coef_social_Calibration_Constant_Duration_eq_1,0.0,F +coef_social_Calibration_Constant_Duration_eq_2,0.0,F +coef_social_Calibration_Constant_Duration_eq_3,0.0,F +coef_social_Calibration_Constant_Duration_eq_4,0.0,F +coef_social_Calibration_Constant_Duration_eq_5,0.0,F +coef_social_Calibration_Constant_Duration_eq_6,0.0,F +coef_social_Calibration_Constant_Duration_eq_7,0.0,F +coef_social_Calibration_Constant_Duration_eq_8,0.0,F +coef_social_Calibration_Constant_Duration_eq_9,0.0,F +coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,0.0,F +coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,0.0,F +coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.24546994148668883,F +coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.0,F +coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.0,F +coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0.0,F +coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0.0,F +coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.0,F +coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear,-0.320622923534169,F +coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear,0.00675561463982907,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,-0.5186347200161862,F +coef_disc_Departure_Constant_Before_7_30_AM_,0.0,F +coef_disc_Departure_Constant_7_30_AM_to_8_00_AM,0.0,F +coef_disc_Departure_Constant_8_00_AM_to_8_30_AM,0.635421144057047,F +coef_disc_Departure_Constant_8_30_AM_to_9_00_AM,1.71269871512322,F +coef_disc_Departure_Constant_9_00_AM_to_9_30_AM,1.4799612716000743,F +coef_disc_Departure_Constant_9_30_AM_to_10_00_AM,1.3795958669264374,F +coef_disc_Departure_Constant_10_00_AM_to_10_30_AM,1.3995816792658615,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,-2.6360279383280787,F +coef_disc_Departure_Constant_Before_05_00_PM,-8.336424427296372,F +coef_disc_Departure_Constant_05_00_PM_05_30_PM,-5.57379327428183,F +coef_disc_Departure_Constant_05_30_PM_06_00_PM,-2.6690034899691253,F +coef_disc_Departure_Constant_06_00_PM_06_30_PM,0.0,F +coef_disc_Departure_Constant_06_30_PM_07_00_PM,2.3719797586368685,T +coef_disc_Departure_Constant_After_07_00_PM,4.1766375477943045,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,1.9886912851879812,F +coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,2.5709925185002573,F +coef_disc_Arrival_Constant_Before_6_30_PM,12.082668378061866,F +coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM,9.784761393655252,F +coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM,7.3488908585128465,F +coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM,5.142548978840155,F +coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM,2.624682599127591,T +coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM,0.0,F +coef_disc_Arrival_Constant_After_9_00_PM,-2.912055108760538,F +coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-3.0596989905953733,F +coef_disc_Duration_Constant_0_hours,-8.83825066827061,F +coef_disc_Duration_Constant_0p5_hous,-4.72064177864728,F +coef_disc_Duration_Constant_1_hour,-1.7354740845419863,F +coef_disc_Duration_Constant_1p5_hours,0.0,F +coef_disc_Duration_Constant_2_hours,2.4362046406329068,T +coef_disc_Duration_Constant_2p5_hours_or_more,4.76036400219139,F +coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,2.272588544620332,F +coef_disc_Calibration_Constant_Duration_eq_4,0.0,F +coef_disc_Calibration_Constant_Duration_eq_5,0.0,F +coef_disc_Calibration_Constant_Departure_eq_29,0.0,F +coef_disc_Calibration_Constant_Departure_eq_30,0.0,F +coef_disc_Calibration_Constant_Departure_eq_31,0.0,F +coef_disc_Calibration_Constant_Departure_eq_32,0.0,F diff --git a/configs/resident/tour_scheduling_nonmandatory_eatout.csv b/configs/resident/tour_scheduling_nonmandatory_eatout.csv new file mode 100644 index 0000000..4803d20 --- /dev/null +++ b/configs/resident/tour_scheduling_nonmandatory_eatout.csv @@ -0,0 +1,65 @@ +Label,Description,Expression,Coefficient +#EAT-OUT,#EAT-OUT,,#EAT-OUT +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_eatout_distance_to_destination_duration_less_than_ref,EAT-OUT - Distance to destination - Duration < Reference,"@np.where((df.duration<2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_ref +util_eatout_distance_to_destination_duration_greater_than_ref,EAT-OUT - Distance to destination - Duration > Reference,"@np.where((df.duration>2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_ref +util_eatout_low_income_duration_less_than_ref,EAT-OUT - Low income (<25000) - Duration < Reference,"@np.where(((df.is_income_less25K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_low_income_duration_less_than_ref +util_eatout_medium_income_duration_less_than_ref,EAT-OUT - Medium (25k to 60k) - Duration < Reference,"@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_medium_income_duration_less_than_ref +util_eatout_zeroauto_HH_duration_greater_than_ref,EAT-OUT - Zero auto HH - Duration > Reference,"@np.where(((df.auto_ownership == 0) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_ref +util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.start>31) & (df.ptype == 3)), ((31-df.start)*(df.start<=31) + (df.start-31)*(df.start>31)), 0)",coef_eatout_university_student_departure_after_7_pm_linear +util_eatout_female_duration_less_than_ref,EAT-OUT - Female - Duration < Reference,"@np.where(((df.duration<2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_female_duration_less_than_ref +util_eatout_female_duration_greater_than_ref,EAT-OUT - Female - Duration > Reference,"@np.where(((df.duration>2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_female_duration_greater_than_ref +util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.start<31)), ((31-df.start)*(df.start<=31) + (df.start-31)*(df.start>31)) * (np.log10 (30 *(tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm +util_eatout_time_pressure_duration_less_than_ref,EAT-OUT - Time Pressure - Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_ref +util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am +util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am +util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am +util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm +util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm +util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm +util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm +util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.start<30)), ((30-df.start)*(df.start<=30) + (df.start-30)*(df.start>30)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear +util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.start<31)),coef_eatout_departure_constant_before_6_pm +util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm +util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm +util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm +util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.start>33)),coef_eatout_departure_constant_after_7_30_pm +util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.start>34)), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am +util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm +util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm +util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm +util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm +util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.end<32)), ((32-df.end)*(df.end<=32) + (df.end-32)*(df.end>32)), 0)",coef_eatout_arrival_constant_before_6_30_pm +util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm +util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm +util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm +util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm +util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm +util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.end>36)),coef_eatout_arrival_constant_after_9_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.end>37)), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.duration==0)),coef_eatout_duration_constant_0_hour +util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.duration==1)),coef_eatout_duration_constant_30_minutes +util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.duration==2)),coef_eatout_duration_constant_1_hour +util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes +util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.duration==4)),coef_eatout_duration_constant_2_hours +util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more +util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where((df.duration>5), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.duration==0)),coef_eatout_calibration_constant_duration_1 +util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.duration==1)),coef_eatout_calibration_constant_duration_2 +util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.duration==2)),coef_eatout_calibration_constant_duration_3 +util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.duration==3)),coef_eatout_calibration_constant_duration_4 +util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.start == 1)),coef_eatout_calibration_constant_departure_1 +util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.start == 2)),coef_eatout_calibration_constant_departure_2 +util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.start == 3)),coef_eatout_calibration_constant_departure_3 +util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.start ==17)),coef_eatout_calibration_constant_departure_17 +util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.start ==18)),coef_eatout_calibration_constant_departure_18 +util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.start ==19)),coef_eatout_calibration_constant_departure_19 +util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.start ==20)),coef_eatout_calibration_constant_departure_20 +util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.start ==21)),coef_eatout_calibration_constant_departure_21 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/configs/resident/tour_scheduling_nonmandatory_eatout_coefficients.csv b/configs/resident/tour_scheduling_nonmandatory_eatout_coefficients.csv new file mode 100644 index 0000000..e8232c0 --- /dev/null +++ b/configs/resident/tour_scheduling_nonmandatory_eatout_coefficients.csv @@ -0,0 +1,61 @@ +coefficient_name,value,constrain +coef_eatout_distance_to_destination_duration_less_than_ref,-0.22213088025234518,F +coef_eatout_distance_to_destination_duration_greater_than_ref,0.006882886760254519,F +coef_eatout_low_income_duration_less_than_ref,0.0,F +coef_eatout_medium_income_duration_less_than_ref,0.0,F +coef_eatout_zeroauto_HH_duration_greater_than_ref,0.0,F +coef_eatout_university_student_departure_after_7_pm_linear,0.0,F +coef_eatout_female_duration_less_than_ref,0.0,F +coef_eatout_female_duration_greater_than_ref,0.07587262787357908,F +coef_eatout_time_pressure_departure_before_6_30_pm,0.12152468184322475,F +coef_eatout_time_pressure_duration_less_than_ref,-1.1004669059917198,F +coef_eatout_departure_constant_7_30_am_to_9_am,0.5692134031282525,F +coef_eatout_departure_constant_10_30_am_to_11_am,0.8995439384100027,F +coef_eatout_departure_constant_11_am_to_11_30_am,1.0520049600035701,F +coef_eatout_departure_constant_11_30_am_to_12_pm,1.0552687311323523,F +coef_eatout_departure_constant_12_pm_to_12_30_pm,0.0,F +coef_eatout_departure_constant_12_30_pm_to_1_pm,0.5514341640403775,F +coef_eatout_departure_constant_1_pm_to_1_30_pm,0.0,F +coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,-0.7733197656863885,F +coef_eatout_departure_constant_before_6_pm,1.2101753740729,F +coef_eatout_departure_constant_6_pm_to_6_30_pm,0.0,F +coef_eatout_departure_constant_6_30_pm_to_7_pm,2.01511052873696,T +coef_eatout_departure_constant_7_pm_to_7_30_pm,2.154439497883138,F +coef_eatout_departure_constant_after_7_30_pm,2.3557017509401206,F +coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,0.0,F +coef_eatout_arrival_constant_9_30_am_to_11_am,0.5477594779688595,F +coef_eatout_arrival_constant_12_30_pm_to_1_pm,0.0,F +coef_eatout_arrival_constant_1_pm_to_1_30_pm,0.0,F +coef_eatout_arrival_constant_1_30_pm_to_2_pm,0.6150279185861535,F +coef_eatout_arrival_constant_2_pm_to_2_30_pm,0.0,F +coef_eatout_arrival_constant_2_30_pm_to_3_pm,0.0,F +coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,1.1113521693562283,F +coef_eatout_arrival_constant_before_6_30_pm,0.31077849460990015,F +coef_eatout_arrival_constant_6_30_pm_to_7_pm,1.411751007495926,F +coef_eatout_arrival_constant_7_pm_to_7_30_pm,0.580944990788283,F +coef_eatout_arrival_constant_7_30_pm_to_8_pm,0.8068518512811886,F +coef_eatout_arrival_constant_8_pm_to_8_30_pm,0.0,T +coef_eatout_arrival_constant_8_30_pm_to_9_pm,0.0,F +coef_eatout_arrival_constant_after_9_pm,-0.45642114111135573,F +coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.5535494562837153,F +coef_eatout_duration_constant_0_hour,7.7136294531232235,F +coef_eatout_duration_constant_30_minutes,5.243585849897661,F +coef_eatout_duration_constant_1_hour,0.0,F +coef_eatout_duration_constant_1_hour_30_minutes,1.2046725893713652,T +coef_eatout_duration_constant_2_hours,1.3298595485891556,F +coef_eatout_duration_constant_2_hour_30_minutes_or_more,1.4613341980210828,F +coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,0.0,F +coef_eatout_calibration_constant_duration_1,0.0,F +coef_eatout_calibration_constant_duration_2,0.0,F +coef_eatout_calibration_constant_duration_3,0.0,F +coef_eatout_calibration_constant_duration_4,0.0,F +coef_eatout_calibration_constant_departure_1,0.0,F +coef_eatout_calibration_constant_departure_2,0.0,F +coef_eatout_calibration_constant_departure_3,0.0,F +coef_eatout_calibration_constant_departure_17,0.0,F +coef_eatout_calibration_constant_departure_18,0.0,F +coef_eatout_calibration_constant_departure_19,0.0,F +coef_eatout_calibration_constant_departure_20,0.0,F +coef_eatout_calibration_constant_departure_21,0.0,F +coef_mode_choice_logsum,0.2,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/configs/resident/tour_scheduling_nonmandatory_escort.csv b/configs/resident/tour_scheduling_nonmandatory_escort.csv new file mode 100644 index 0000000..c89079f --- /dev/null +++ b/configs/resident/tour_scheduling_nonmandatory_escort.csv @@ -0,0 +1,80 @@ +Label,Description,Expression,Coefficient +# ESCORT,,, +util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum +"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# +util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.duration<1)), ((df.origin_to_destination_distance) * ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1))), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes +util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.duration>1)), ((df.origin_to_destination_distance) * ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1))), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes +util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after Reference - Linear,"@np.where(((df.ptype == 1) & (df.start>9)), ((9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)),0)",coef_escort_fulltime_worker_departure_after_8_am_linear +"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, +util_escort_fulltime_worker_departure_after_3_pm_linear,ESCORT - Full-time worker - Departure after 3:00 pm - Linear,"@np.where(((df.ptype == 1) & (df.start>24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_fulltime_worker_departure_after_3_pm_linear +util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.ptype == 1) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes +util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.ptype == 1) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes +util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.ptype == 3) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_university_student_duration_less_than_30_minutes +util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where((((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes +util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.ptype == 6) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_driving_age_student_duration_less_than_30_minutes +util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.ptype == 6) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes +"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, +util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.ptype == 8) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes +util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.is_income_60K_to_120K) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_med_high_income_duration_greater_than_30_minutes +util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.num_children == 0) & (df.start<10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_households_with_no_kids_departure_before_7_30_am +util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.num_children == 0) & (df.start>10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_households_with_no_kids_departure_after_8_00_am +util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.num_children == 0) & (df.start<24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm +util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.num_children == 0) & (df.start>24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm +util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.num_children == 0) & (df.end<11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_households_with_no_kids_arrival_before_8_am +util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.num_children == 0) & (df.end>11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am +util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.num_children == 0) & (df.end<25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_households_with_no_kids_arrival_before_3_pm +util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.num_children == 0) & (df.end>25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm +util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes +util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.num_non_escort_tours > 0) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)) * (df.num_non_escort_tours), 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes +util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)) *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes +util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.start<8)), ((8-df.start)*(df.start<=8) + (df.start-8)*(df.start>8)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.start<9)),coef_escort_departure_constant_before_7_am +util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am +util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am +util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am +util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am +util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.start>12)),coef_escort_departure_constant_after_9_am +util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm +util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm +util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm +util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm +util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.start>25)),coef_escort_departure_constant_after_3_30_pm +util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.start>13)), ((13-df.start)*(df.start<=13) + (df.start-13)*(df.start>13)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.start>26)), ((26-df.start)*(df.start<=26) + (df.start-26)*(df.start>26)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear +util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.end<8)), ((8-df.end)*(df.end<=8) + (df.end-8)*(df.end>8)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.end<9)),coef_escort_arrival_constant_before_7_am +util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am +util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am +util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am +util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am +util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.end>12)),coef_escort_arrival_constant_after_9_am +util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm +util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm +util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm +util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm +util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.end>27)),coef_escort_arrival_constant_after_4_30_pm +util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.end>13)), ((13-df.end)*(df.end<=13) + (df.end-13)*(df.end>13)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.end>28)), ((28-df.end)*(df.end<=28) + (df.end-28)*(df.end>28)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.duration==0)),coef_escort_duration_constant_0_hour +util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.duration==1)),coef_escort_duration_constant_30_minutes +util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.duration==2)),coef_escort_duration_constant_1_hour +util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes +util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.duration==4)),coef_escort_duration_constant_2_hours +util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.duration>4)),coef_escort_duration_constant_longer_than_2_hours +util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.duration==0)),coef_escort_calibration_constant_duration_1 +util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@((df.duration==1)),coef_escort_calibration_constant_duration_2 +util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.start==9)),coef_escort_calibration_constant_departure_9 +util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.start==10)),coef_escort_calibration_constant_departure_10 +util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.start==23)),coef_escort_calibration_constant_departure_23 +util_escort_calibration_constant_departure_24,ESCORT - Calibration Constant - Departure = 24,@((df.start==24)),coef_escort_calibration_constant_departure_24 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unlikely +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unlikely +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unlikely \ No newline at end of file diff --git a/configs/resident/tour_scheduling_nonmandatory_escort_coefficients.csv b/configs/resident/tour_scheduling_nonmandatory_escort_coefficients.csv new file mode 100644 index 0000000..d901984 --- /dev/null +++ b/configs/resident/tour_scheduling_nonmandatory_escort_coefficients.csv @@ -0,0 +1,74 @@ +coefficient_name,value,constrain +coef_escort_mode_choice_logsum,0.2,F +coef_escort_distance_to_destination_duration_less_than_30_minutes,-0.4733878369607008,F +coef_escort_distance_to_destination_duration_greater_than_30_minutes,0.008568718107435931,F +coef_escort_fulltime_worker_departure_after_8_am_linear,0.0,F +coef_escort_fulltime_worker_departure_after_3_pm_linear,0.10265282779895883,F +coef_escort_fulltime_worker_duration_less_than_30_minutes,0.48717921412555765,F +coef_escort_fulltime_worker_duration_greater_than_30_minutes,0.06360020624220894,F +coef_escort_university_student_duration_less_than_30_minutes,0.0,F +coef_escort_non_driving_age_student_duration_greater_than_30_minutes,0.13619138148103171,F +coef_escort_driving_age_student_duration_less_than_30_minutes,0.0,F +coef_escort_driving_age_student_duration_greater_than_30_minutes,0.2898061598287889,F +coef_escort_pre_school_kid_duration_greater_than_30_minutes,0.0,F +coef_escort_med_high_income_duration_greater_than_30_minutes,0.0,F +coef_escort_households_with_no_kids_departure_before_7_30_am,0.0,F +coef_escort_households_with_no_kids_departure_after_8_00_am,0.0,F +coef_escort_households_with_no_kids_departure_before_2_30_pm,0.0,F +coef_escort_households_with_no_kids_departure_after_3_00_pm,0.0,F +coef_escort_households_with_no_kids_arrival_before_8_am,0.0,F +coef_escort_households_with_no_kids_arrival_after_8_30_am,0.0,F +coef_escort_households_with_no_kids_arrival_before_3_pm,0.0,F +coef_escort_households_with_no_kids_arrival_after_3_30_pm,0.0,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.2193957525105046,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,-0.33725705669741124,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,-1.843839469217804,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,0.2844617723897163,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.0,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,0.0,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,0.0,F +coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,0.0,F +coef_escort_number_of_individual_tours_duration_greater_than_30_minutes,-0.0620079827984566,F +coef_escort_number_of_joint_tours_duration_greater_than_30_minutes,0.0,F +coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,-1.236018962947682,F +coef_escort_departure_constant_before_7_am,0.7319683480497287,F +coef_escort_departure_constant_7_am_to_7_30_am,0.0,F +coef_escort_departure_constant_7_30_am_to_8_am,4.122740011813239,T +coef_escort_departure_constant_8_am_to_8_30_am,4.835810362294344,F +coef_escort_departure_constant_8_30_am_to_9_am,5.122388952675412,F +coef_escort_departure_constant_after_9_am,4.283724261944397,F +coef_escort_departure_constant_1_30_pm_to_2_pm,0.0,F +coef_escort_departure_constant_2_pm_to_2_30_pm,0.7940143785524454,F +coef_escort_departure_constant_2_30_pm_to_3_pm,1.0810066025980387,F +coef_escort_departure_constant_3_pm_to_3_30_pm,1.326721246058783,F +coef_escort_departure_constant_after_3_30_pm,1.1856245248532122,F +coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,0.19604399657492097,F +coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,0.0,F +coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,0.0,F +coef_escort_arrival_constant_before_7_am,4.009035240130145,F +coef_escort_arrival_constant_7_am_to_7_30_am,4.18385718813814,F +coef_escort_arrival_constant_7_30_am_to_8_am,2.9375790166530416,F +coef_escort_arrival_constant_8_am_to_8_30_am,0.0,T +coef_escort_arrival_constant_8_30_am_to_9_am,0.0,F +coef_escort_arrival_constant_after_9_am,-0.8516924916440327,F +coef_escort_arrival_constant_2_30_pm_to_3_pm,0.8821519927765968,F +coef_escort_arrival_constant_3_pm_to_3_30_pm,1.3755379509692238,F +coef_escort_arrival_constant_3_30_pm_to_4_pm,1.5074800730702913,F +coef_escort_arrival_constant_4_pm_to_4_30_pm,1.6943356568396335,F +coef_escort_arrival_constant_after_4_30_pm,1.2683991826495213,F +coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,-0.2792532861601614,F +coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.17999830126346059,F +coef_escort_duration_constant_0_hour,-0.2939194972058852,F +coef_escort_duration_constant_30_minutes,0.0,T +coef_escort_duration_constant_1_hour,0.0,F +coef_escort_duration_constant_1_hour_30_minutes,-0.717021512011088,F +coef_escort_duration_constant_2_hours,-1.1701511666645092,F +coef_escort_duration_constant_longer_than_2_hours,-1.1702599996232288,F +coef_escort_calibration_constant_duration_1,0.0,F +coef_escort_calibration_constant_duration_2,0.0,F +coef_escort_calibration_constant_departure_9,0.0,F +coef_escort_calibration_constant_departure_10,0.0,F +coef_escort_calibration_constant_departure_23,0.0,F +coef_escort_calibration_constant_departure_24,0.0,F +coef_unavailable,-999.0,T +coef_unlikely,-50.0,T \ No newline at end of file diff --git a/configs/resident/tour_scheduling_nonmandatory_othdiscr.csv b/configs/resident/tour_scheduling_nonmandatory_othdiscr.csv new file mode 100644 index 0000000..bc20b39 --- /dev/null +++ b/configs/resident/tour_scheduling_nonmandatory_othdiscr.csv @@ -0,0 +1,59 @@ +Label,Description,Expression,Coefficient +#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_discretionary_person_less_than_18_years_old_duration_less_than_reference_linear,DISCRETIONARY - Person< 18 years old: Duration < Reference - Linear,"@np.where(((df.duration<4) & (df.age<18)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_reference_linear +util_discretionary_person_less_than_18_years_old_duration_greater_than_reference_linear,DISCRETIONARY - Person< 18 years old: Duration > Reference - Linear,"@np.where(((df.duration>4) & (df.age<18)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_reference_linear +util_discretionary_non_working_senior_retiree_duration_less_than_reference_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < Reference - Linear,"@np.where(((df.duration<4) & (df.ptype == 5)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_reference_linear +util_discretionary_retiree_non_working_senior_only_HH_duration_reference_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < Reference - Linear,"@np.where(((df.retired_adults_only_hh) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_reference_linear +util_discretionary_zero_auto_households_duration_less_than_reference_linear,DISCRETIONARY - Zero auto households: Duration < Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_zero_auto_households_duration_less_than_reference_linear +util_discretionary_zero_auto_households_duration_greater_than_reference_linear,DISCRETIONARY - Zero auto households: Duration > Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_reference_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < Reference - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > Reference - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear +util_discretionary_auto_distance_duration_less_than_reference_linear,DISCRETIONARY - Auto Distance: Duration < Reference,"@np.where(((df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_reference_linear +util_discretionary_auto_distance_duration_greater_than_reference_linear,DISCRETIONARY - Auto Distance: Duration > Reference,"@np.where(((df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_reference_linear +util_discretionary_time_pressure_duration_less_reference,DISCRETIONARY - Time Pressure - Duration < Reference,"@np.where(((df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_less_reference +util_discretionary_time_pressure_duration_greater_reference,DISCRETIONARY - Time Pressure - Duration > Reference,"@np.where(((df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_reference +util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_reference,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < Reference,"@np.where((df.duration<4), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_reference +util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.start<9)), ((9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear +util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.start<10)),coef_discretionary_departure_constant_before_7_30_am +util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am +util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am +util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am +util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am +util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am +util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am +util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.start<28)), ((28-df.start)*(df.start<=28) + (df.start-28)*(df.start>28)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear +util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.start<29)),coef_discretionary_departure_constant_before_5_pm +util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm +util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm +util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm +util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm +util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.start>32)),coef_discretionary_departure_constant_after_7_pm +util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.start>33)), ((33-df.start)*(df.start<=33) + (df.start-33)*(df.start>33)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear +util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.end<31)), ((31-df.end)*(df.end<=31) + (df.end-31)*(df.end>31)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm +util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm +util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm +util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm +util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm +util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm +util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.end>36)),coef_discretionary_arrival_constant_after_9_pm +util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.end>37)), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.duration==0)),coef_discretionary_duration_constant_0_hour +util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.duration==1)),coef_discretionary_duration_constant_30_minutes +util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.duration==2)),coef_discretionary_duration_constant_1_hour +util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes +util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.duration==4)),coef_discretionary_duration_constant_2_hours +util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes +util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more +util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.duration==3)),coef_discretionary_calibration_constant_duration_4 +util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.duration==4)),coef_discretionary_calibration_constant_duration_5 +util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.start==29)),coef_discretionary_calibration_constant_departure_29 +util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.start==30)),coef_discretionary_calibration_constant_departure_30 +util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.start==31)),coef_discretionary_calibration_constant_departure_31 +util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.start==32)),coef_discretionary_calibration_constant_departure_32 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/configs/resident/tour_scheduling_nonmandatory_othdiscr_coefficients.csv b/configs/resident/tour_scheduling_nonmandatory_othdiscr_coefficients.csv new file mode 100644 index 0000000..87e3924 --- /dev/null +++ b/configs/resident/tour_scheduling_nonmandatory_othdiscr_coefficients.csv @@ -0,0 +1,55 @@ +coefficient_name,value,constrain +coef_discretionary_person_less_than_18_years_old_duration_less_than_reference_linear,0.098366285148487,F +coef_discretionary_person_less_than_18_years_old_duration_greater_than_reference_linear,0.0,F +coef_discretionary_non_working_senior_retiree_duration_less_than_reference_linear,-0.1705916260525488,F +coef_discretionary_retiree_non_working_senior_only_HH_duration_reference_linear,0.0,F +coef_discretionary_zero_auto_households_duration_less_than_reference_linear,0.0,F +coef_discretionary_zero_auto_households_duration_greater_than_reference_linear,0.05222497126898865,F +coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,0.0,F +coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,0.0,F +coef_discretionary_auto_distance_duration_less_than_reference_linear,-0.03606825659562718,F +coef_discretionary_auto_distance_duration_greater_than_reference_linear,0.004596600975929468,F +coef_discretionary_time_pressure_duration_less_reference,0.6728960123740134,F +coef_discretionary_time_pressure_duration_greater_reference,0.25402774161065456,F +coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_reference,0.08104208628580123,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,-0.6104747153010481,F +coef_discretionary_departure_constant_before_7_30_am,-0.3390521412898814,F +coef_discretionary_departure_constant_7_30_am_to_8_am,0.0,F +coef_discretionary_departure_constant_8_am_to_8_30_am,0.0,F +coef_discretionary_departure_constant_8_30_am_to_9_am,0.0,F +coef_discretionary_departure_constant_9_am_to_9_30_am,0.33705457332633537,F +coef_discretionary_departure_constant_9_30_am_to_10_am,0.14050652594559154,F +coef_discretionary_departure_constant_10_am_to_10_30_am,0.0,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,2.055051892448251,F +coef_discretionary_departure_constant_before_5_pm,10.078028457555423,F +coef_discretionary_departure_constant_5_pm_to_5_30_pm,8.809104255087856,F +coef_discretionary_departure_constant_5_30_pm_to_6_pm,7.066441289657018,F +coef_discretionary_departure_constant_6_pm_to_6_30_pm,4.909660731901318,F +coef_discretionary_departure_constant_6_30_pm_to_7_pm,2.825818687356497,T +coef_discretionary_departure_constant_after_7_pm,0.0,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,-2.407069142986441,F +coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,-1.9913325130310646,F +coef_discretionary_arrival_constant_before_6_30_pm,-8.318569502929208,F +coef_discretionary_arrival_constant_6_30_pm_to_7_pm,-6.434917972534465,F +coef_discretionary_arrival_constant_7_pm_to_7_30_pm,-4.138799434182262,F +coef_discretionary_arrival_constant_7_30_pm_to_8_pm,-2.1058225272583,F +coef_discretionary_arrival_constant_8_pm_to_8_30_pm,0.0,T +coef_discretionary_arrival_constant_8_30_pm_to_9_pm,2.1493158879532435,F +coef_discretionary_arrival_constant_after_9_pm,3.8558666359863616,F +coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,1.6858826820414816,F +coef_discretionary_duration_constant_0_hour,0.0,F +coef_discretionary_duration_constant_30_minutes,0.0,F +coef_discretionary_duration_constant_1_hour,0.0,F +coef_discretionary_duration_constant_1_hr_30_minutes,0.0,F +coef_discretionary_duration_constant_2_hours,0.0,F +coef_discretionary_duration_constant_2_hr_30_minutes,-2.9894794551717427,T +coef_discretionary_duration_constant_3_hours_or_more,-6.039866672397804,F +coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-2.9211224892215153,F +coef_discretionary_calibration_constant_duration_4,0.0,F +coef_discretionary_calibration_constant_duration_5,0.0,F +coef_discretionary_calibration_constant_departure_29,0.0,F +coef_discretionary_calibration_constant_departure_30,0.0,F +coef_discretionary_calibration_constant_departure_31,0.0,F +coef_discretionary_calibration_constant_departure_32,0.0,F +coef_mode_choice_logsum,0.06338187336512106,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/configs/resident/tour_scheduling_nonmandatory_othmaint.csv b/configs/resident/tour_scheduling_nonmandatory_othmaint.csv new file mode 100644 index 0000000..d9bda3c --- /dev/null +++ b/configs/resident/tour_scheduling_nonmandatory_othmaint.csv @@ -0,0 +1,59 @@ +Label,Description,Expression,Coefficient +#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_maintenance_driving_age_student_duration_greater_than_reference,MAINTENANCE - Driving age student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 6)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_driving_age_student_duration_greater_than_reference +util_maintenance_full_time_worker_duration_greater_than_reference,MAINTENANCE - Full-time worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 1)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_full_time_worker_duration_greater_than_reference +util_maintenance_non_driving_student_duration_greater_than_reference,MAINTENANCE - Non-driving Student: Duration > Reference,"@np.where (((df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_non_driving_student_duration_greater_than_reference +util_maintenance_pre_school_child_duration_less_than_reference,MAINTENANCE - Pre-school Child: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 8)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_pre_school_child_duration_less_than_reference +util_maintenance_part_time_worker_duration_less_than_reference,MAINTENANCE - Part Time Worker: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_part_time_worker_duration_less_than_reference +util_maintenance_part_time_worker_duration_greater_than_reference,MAINTENANCE - Part Time Worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_part_time_worker_duration_greater_than_reference +util_maintenance_retired_duration_less_than_reference,MAINTENANCE - Retired: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_retired_duration_less_than_reference +util_maintenance_retired_duration_greater_than_reference,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_retired_duration_greater_than_reference +util_maintenance_university_student_duration_greater_than_reference,MAINTENANCE - University Student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 3)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_university_student_duration_greater_than_reference +util_maintenance_female_duration_less_than_reference,MAINTENANCE - Female: Duration < Reference,"@np.where(((df.duration<2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_female_duration_less_than_reference +util_maintenance_female_duration_greater_than_reference,MAINTENANCE - Female: Duration > Reference,"@np.where(((df.duration>2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_female_duration_greater_than_reference +util_maintenance_low_income_duration_greater_than_reference,"MAINTENANCE - Low Income (<=$25,000): Duration > Reference","@np.where(((df.is_income_less25K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_low_income_duration_greater_than_reference +util_maintenance_medium_income_duration_less_than_reference,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < Reference","@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_medium_income_duration_less_than_reference +util_maintenance_medium_income_duration_greater_than_reference,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > Reference","@np.where(((df.is_income_25K_to_60K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_medium_income_duration_greater_than_reference +util_maintenance_medium_high_income_duration_greater_than_reference,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > Reference","@np.where(((df.is_income_60K_to_120K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_medium_high_income_duration_greater_than_reference +util_maintenance_distance_duration_less_than_reference,MAINTENANCE - Distance: Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_reference +util_maintenance_distance_duration_greater_than_reference,MAINTENANCE - Distance: Duration > Reference,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_reference +util_maintenance_time_pressure_duration_greater_than_reference,Time Pressure - Duration > Reference,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_reference +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < Reference,"@np.where((df.duration<2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > Reference,"@np.where((df.duration>2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where((df.start<10), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where((df.start<10), (((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root +util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.start<11)),coef_maintenance_departure_constant_before_8_am +util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am +util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am +util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am +util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am +util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am +util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am +util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.start>16)),coef_maintenance_departure_constant_after_11_am +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where((df.start>17), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where((df.start>17), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.end<15)), ((15-df.end)*(df.end<=15) + (df.end-15)*(df.end>15)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear +util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.end<16)),coef_maintenance_arrival_constant_before_10_30_am +util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am +util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am +util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm +util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm +util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm +util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm +util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm +util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.end>28)), ((28-df.end)*(df.end<=28) + (df.end-28)*(df.end>28)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.duration==0)),coef_maintenance_duration_constant_0_hr +util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.duration==1)),coef_maintenance_duration_constant_30_minutes +util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes +util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear +util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.duration>2)), (((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root +util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.duration==0)),coef_maintenance_calibration_constant_duration_1 +util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.duration==1)),coef_maintenance_calibration_constant_duration_2 +util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.duration==2)),coef_maintenance_calibration_constant_duration_3 +util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.duration==3)),coef_maintenance_calibration_constant_duration_4 +util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.duration==4)),coef_maintenance_calibration_constant_duration_5 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/configs/resident/tour_scheduling_nonmandatory_othmaint_coefficients.csv b/configs/resident/tour_scheduling_nonmandatory_othmaint_coefficients.csv new file mode 100644 index 0000000..e5d214b --- /dev/null +++ b/configs/resident/tour_scheduling_nonmandatory_othmaint_coefficients.csv @@ -0,0 +1,55 @@ +coefficient_name,value,constrain +coef_maintenance_driving_age_student_duration_greater_than_reference,0.0,F +coef_maintenance_full_time_worker_duration_greater_than_reference,0.13520704870454775,F +coef_maintenance_non_driving_student_duration_greater_than_reference,0.0,F +coef_maintenance_pre_school_child_duration_less_than_reference,-2.157370739161894,F +coef_maintenance_part_time_worker_duration_less_than_reference,0.0,F +coef_maintenance_part_time_worker_duration_greater_than_reference,0.0,F +coef_maintenance_retired_duration_less_than_reference,0.0,F +coef_maintenance_retired_duration_greater_than_reference,-0.03534027470461993,F +coef_maintenance_university_student_duration_greater_than_reference,0.0,F +coef_maintenance_female_duration_less_than_reference,-0.33642495066387595,F +coef_maintenance_female_duration_greater_than_reference,0.0,F +coef_maintenance_low_income_duration_greater_than_reference,0.040103190219137164,F +coef_maintenance_medium_income_duration_less_than_reference,0.0,F +coef_maintenance_medium_income_duration_greater_than_reference,0.0,T +coef_maintenance_medium_high_income_duration_greater_than_reference,0.0,F +coef_maintenance_distance_duration_less_than_reference,-0.28516479340386963,F +coef_maintenance_distance_duration_greater_than_reference,0.004293750630955166,F +coef_maintenance_time_pressure_duration_greater_than_reference,0.2152799372032978,F +coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,0.0,F +coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,-0.03864547690327943,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,-0.8990260296725098,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,0.9516096316489431,F +coef_maintenance_departure_constant_before_8_am,-0.5598527748241708,F +coef_maintenance_departure_constant_8_am_to_8_30_am,-0.14921181662286023,F +coef_maintenance_departure_constant_8_30_am_to_9_00_am,0.0,F +coef_maintenance_departure_constant_9_am_to_9_30_am,0.0,F +coef_maintenance_departure_constant_9_30_am_to_10_am,0.0,F +coef_maintenance_departure_constant_10_am_to_10_30_am,0.0,T +coef_maintenance_departure_constant_10_30_am_to_11_am,-0.17187147382272855,F +coef_maintenance_departure_constant_after_11_am,-0.3600477839542276,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.02639222500693473,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.0017054145463435359,F +coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,-0.23175571400118172,F +coef_maintenance_arrival_constant_before_10_30_am,0.0,F +coef_maintenance_arrival_constant_10_30_am_to_11_am,0.0,T +coef_maintenance_arrival_constant_11_am_to_11_30_am,0.0,F +coef_maintenance_arrival_constant_11_30_am_to_1_30_pm,0.0,F +coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm,0.0,F +coef_maintenance_arrival_constant_2_30_pm_to_4_pm,0.0,F +coef_maintenance_arrival_constant_4_pm_to_4_30_pm,0.0,F +coef_maintenance_arrival_constant_after_4_30_pm,0.0,F +coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.31053576324304255,F +coef_maintenance_duration_constant_0_hr,5.99100088789488,F +coef_maintenance_duration_constant_30_minutes,6.268122806796934,T +coef_maintenance_duration_constant_longer_than_30_minutes,4.8989465058844175,F +coef_maintenance_duration_constant_duration_greater_than_1_hr_linear,-0.7339031911682701,F +coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root,-0.13942646219715638,F +coef_maintenance_calibration_constant_duration_1,0.0,F +coef_maintenance_calibration_constant_duration_2,0.0,F +coef_maintenance_calibration_constant_duration_3,0.0,F +coef_maintenance_calibration_constant_duration_4,0.25854243359403917,F +coef_maintenance_calibration_constant_duration_5,0.22211610318873198,F +coef_mode_choice_logsum,0.23911188143948614,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/configs/resident/tour_scheduling_nonmandatory_shopping.csv b/configs/resident/tour_scheduling_nonmandatory_shopping.csv new file mode 100644 index 0000000..fa7fbf1 --- /dev/null +++ b/configs/resident/tour_scheduling_nonmandatory_shopping.csv @@ -0,0 +1,59 @@ +Label,Description,Expression,Coefficient +#SHOPPING,,,#SHOPPING +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_shoppping_driving_age_student_duration_greater_than_reference,SHOPPING - Driving age student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 6)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_driving_age_student_duration_greater_than_reference +util_shoppping_full_time_worker_duration_greater_than_reference,SHOPPING - Full-time worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 1)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_full_time_worker_duration_greater_than_reference +util_shoppping_non_driving_student_duration_greater_than_reference,SHOPPING - Non-driving Student: Duration > Reference,"@np.where(((df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_non_driving_student_duration_greater_than_reference +util_shoppping_pre_school_child_duration_less_than_reference,SHOPPING - Pre-school Child: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 8)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_pre_school_child_duration_less_than_reference +util_shoppping_part_time_worker_duration_less_than_reference,SHOPPING - Part Time Worker: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_part_time_worker_duration_less_than_reference +util_shopping_part_time_worker_duration_greater_than_reference,SHOPPING - Part Time Worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_part_time_worker_duration_greater_than_reference +util_shopping_retired_duration_less_than_reference,SHOPPING - Retired: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_retired_duration_less_than_reference +util_shopping_retired_duration_greater_than_reference,SHOPPING - Retired: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_retired_duration_greater_than_reference +util_shopping_university_student_duration_greater_than_reference,SHOPPING - University Student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 3)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_university_student_duration_greater_than_reference +util_shopping_female_duration_less_than_reference,SHOPPING - Female: Duration < Reference,"@np.where(((df.duration<2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_female_duration_less_than_reference +util_shopping_female_duration_greater_than_reference,SHOPPING - Female: Duration > Reference,"@np.where(((df.duration>2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_female_duration_greater_than_reference +util_shopping_low_income_duration_greater_than_reference,"SHOPPING - Low Income (<=$25,000): Duration > Reference","@np.where(((df.is_income_less25K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_low_income_duration_greater_than_reference +util_shopping_medium_income_duration_less_than_reference,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < Reference","@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_medium_income_duration_less_than_reference +util_shopping_medium_high_income_duration_greater_than_reference,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > Reference","@np.where(((df.is_income_60K_to_120K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_medium_high_income_duration_greater_than_reference +util_shopping_distance_duration_less_than_reference,SHOPPING - Distance: Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_reference +util_shopping_distance_duration_greater_than_reference,SHOPPING - Distance: Duration > Reference,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_reference +util_shopping_time_pressure_duration_greater_than_reference,SHOPPING - Time Pressure - Duration > Reference,"@np.where(((df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_reference +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,SHOPPING - Number of additional individual shop and maint. tours - Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,SHOPPING - Number of additional individual shop and maint. tours - Duration > Reference,"@np.where(((df.tour_type == 'shopping') &(df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root +util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.start<13)),coef_shopping_departure_constant_before_9_am +util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am +util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am +util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am +util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am +util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.start>16)),coef_shopping_departure_constant_after_11_am +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), (((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.end<19)), ((19-df.end)*(df.end<=19) + (df.end-19)*(df.end>19)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear +util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.end<20)),coef_shopping_arrival_constant_before_12_30_pm +util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@(( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm +util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm +util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm +util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm +util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm +util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm +util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm +util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm +util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.end>37)),coef_shopping_arrival_constant_after_9_30_pm +util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.end>38)), ((38-df.end)*(df.end<=38) + (df.end-38)*(df.end>38)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear +util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.duration==0)),coef_shopping_duration_constant_0_hr +util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.duration==1)),coef_shopping_duration_constant_30_minutes +util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.duration==2)),coef_shopping_duration_constant_1_hr +util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes +util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.duration==4)),coef_shopping_duration_constant_2_hrs +util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.duration>5)), (((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root +util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.duration==0)),coef_shopping_calibration_constant_duration_1 +util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.duration==1)),coef_shopping_calibration_constant_duration_2 +util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.duration==2)),coef_shopping_calibration_constant_duration_3 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/configs/resident/tour_scheduling_nonmandatory_shopping_coefficients.csv b/configs/resident/tour_scheduling_nonmandatory_shopping_coefficients.csv new file mode 100644 index 0000000..d566754 --- /dev/null +++ b/configs/resident/tour_scheduling_nonmandatory_shopping_coefficients.csv @@ -0,0 +1,55 @@ +coefficient_name,value,constrain +coef_shoppping_driving_age_student_duration_greater_than_reference,0.0,F +coef_shoppping_full_time_worker_duration_greater_than_reference,0.051734822464608594,F +coef_shoppping_non_driving_student_duration_greater_than_reference,0.0,F +coef_shoppping_pre_school_child_duration_less_than_reference,0.0,F +coef_shoppping_part_time_worker_duration_less_than_reference,0.0,F +coef_shopping_part_time_worker_duration_greater_than_reference,0.0,F +coef_shopping_retired_duration_less_than_reference,-0.2659967828872843,F +coef_shopping_retired_duration_greater_than_reference,0.0,F +coef_shopping_university_student_duration_greater_than_reference,0.0,F +coef_shopping_female_duration_less_than_reference,-0.7103244823264783,F +coef_shopping_female_duration_greater_than_reference,0.061234392582043815,F +coef_shopping_low_income_duration_greater_than_reference,0.07853541383214849,F +coef_shopping_medium_income_duration_less_than_reference,0.0,F +coef_shopping_medium_high_income_duration_greater_than_reference,0.0,F +coef_shopping_distance_duration_less_than_reference,-0.26768192696442833,F +coef_shopping_distance_duration_greater_than_reference,0.011027213392240262,F +coef_shopping_time_pressure_duration_greater_than_reference,0.18521165094993614,F +coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,0.0,F +coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,-0.07338200940622887,F +coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-1.273440003246542,F +coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,0.5863247462472069,F +coef_shopping_departure_constant_before_9_am,-0.8347867320157243,F +coef_shopping_departure_constant_9_am_to_9_30_am,0.0,F +coef_shopping_departure_constant_9_30_am_to_10_am,0.4960610257653812,F +coef_shopping_departure_constant_10_am_to_10_30_am,0.0,T +coef_shopping_departure_constant_10_30_am_to_11_00_am,1.4667804167469976,F +coef_shopping_departure_constant_after_11_am,2.1721583581726733,F +coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.43973246404978805,F +coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.002212882762524496,F +coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,0.4117758420908247,F +coef_shopping_arrival_constant_before_12_30_pm,2.184513901207351,F +coef_shopping_arrival_constant_12_30_pm_to_3_pm,0.0,F +coef_shopping_arrival_constant_3_pm_to_3_30_pm,-0.9695940929413058,F +coef_shopping_arrival_constant_3_30_pm_to_4_pm,-1.3735993941519913,F +coef_shopping_arrival_constant_4_pm_to_4_30_pm,-1.8739500715809843,T +coef_shopping_arrival_constant_4_30_pm_to_5_pm,-2.5156987192481095,F +coef_shopping_arrival_constant_5_pm_to_5_30_pm,-2.953009790810334,F +coef_shopping_arrival_constant_5_30_pm_to_7_pm,-4.394261858507323,F +coef_shopping_arrival_constant_7_pm_to_9_30_pm,-6.569745153088775,F +coef_shopping_arrival_constant_after_9_30_pm,-9.11110014040306,F +coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,-0.8231833778531431,F +coef_shopping_duration_constant_0_hr,0.0,F +coef_shopping_duration_constant_30_minutes,1.1937104784952166,F +coef_shopping_duration_constant_1_hr,0.0,T +coef_shopping_duration_constant_1_hour_30_minutes,0.0,F +coef_shopping_duration_constant_2_hrs,-0.4668647914524515,F +coef_shopping_duration_constant_longer_than_2_hrs,-1.107254910068048,F +coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,-0.42751861595763324,F +coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,0.0,F +coef_shopping_calibration_constant_duration_1,0.0,F +coef_shopping_calibration_constant_duration_2,0.0,F +coef_shopping_calibration_constant_duration_3,0.0,F +coef_mode_choice_logsum,0.2,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/configs/resident/tour_scheduling_nonmandatory_social.csv b/configs/resident/tour_scheduling_nonmandatory_social.csv new file mode 100644 index 0000000..ceb875d --- /dev/null +++ b/configs/resident/tour_scheduling_nonmandatory_social.csv @@ -0,0 +1,59 @@ +Label,Description,Expression,Coefficient +#SOCIAL,#SOCIAL,,#SOCIAL +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_social_person_less_than_18_years_old_duration_less_than_reference_linear,SOCIAL - Person< 18 years old: Duration < Reference - Linear,"@np.where(((df.duration<6) & (df.age<18)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_reference_linear +util_social_person_less_than_18_years_old_duration_greater_than_reference_linear,SOCIAL - Person< 18 years old: Duration > Reference - Linear,"@np.where(((df.duration>6) & (df.age<18)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_reference_linear +util_social_non_working_senior_or_retiree_duration_less_than_reference_linear,SOCIAL - Non-working senior/ retiree: Duration < Reference - Linear,"@np.where(((df.duration<6) & (df.ptype == 5)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_reference_linear +util_social_retiree_or_non_working_senior_only_HH_duration_less_than_reference_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < Reference - Linear,"@np.where(((df.retired_adults_only_hh) & (df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_reference_linear +util_social_zero_auto_households_duration_less_than_reference_linear,SOCIAL - Zero auto households: Duration < Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_zero_auto_households_duration_less_than_reference_linear +util_social_zero_auto_households_duration_greater_than_reference_linear,SOCIAL - Zero auto households: Duration > Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_zero_auto_households_duration_greater_than_reference_linear +util_social_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,SOCIAL - Number of auto more than number of adults: Duration < Reference - Linear,"@np.where(((df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear +util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,SOCIAL - Number of auto more than number of adults: Duration > Reference - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear +"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, +util_social_auto_distance_duration_less_than_reference_linear,SOCIAL - Auto Distance: Duration < Reference - Linear,"@np.where(((df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_less_than_reference_linear +util_social_auto_distance_duration_greater_than_reference_linear,SOCIAL - Auto Distance: Duration > Reference - Linear,"@np.where(((df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_greater_than_reference_linear +util_social_time_pressure_duration_less_than_reference,SOCIAL - Time Pressure - Duration < Reference,"@np.where(((df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6))* (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_social_time_pressure_duration_less_than_reference +util_social_time_pressure_duration_greater_than_reference,SOCIAL - Time Pressure - Duration > Reference,"@np.where(((df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_social_time_pressure_duration_greater_than_reference +util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_reference,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_reference +util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,@((df.start<12)) * ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)),coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.start<13),coef_social_departure_constant_before_9_am +util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.start==13),coef_social_departure_constant_9_am_to_9_30_am +util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.start<29), ((29-df.start)*(df.start<=29) + (df.start-29)*(df.start>29)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear +util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.start<30)),coef_social_departure_constant_before_5_30_pm +util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm +util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm +util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm +util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm +util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.start>33)),coef_social_departure_constant_after_7_30_pm +util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.start>34), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm +util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm +util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm +util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm +util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.end<35)), ((35-df.end)*(df.end<=35) + (df.end-35)*(df.end>35)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear +util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.end<36)),coef_social_arrival_constant_before_8_30_pm +util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm +util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm +util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm +util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm +util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.end>39)),coef_social_arrival_constant_after_10_30_pm +util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.end>40)), ((40-df.end)*(df.end<=40) + (df.end-40)*(df.end>40)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear +util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.duration<5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear +util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.duration<6)),coef_social_duration_constant_less_than_3_hours +util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.duration==6)),coef_social_duration_constant_3_hours +util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes +util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.duration>7)),coef_social_duration_constant_4_hours_or_more +util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.duration>8)), ((8-df.duration)*(df.duration<=8) + (df.duration-8)*(df.duration>8)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear +util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.duration ==0)),coef_social_calibration_constant_duration_1 +util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.duration == 1)),coef_social_calibration_constant_duration_2 +util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.duration ==2)),coef_social_calibration_constant_duration_3 +util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.duration ==3)),coef_social_calibration_constant_duration_4 +util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.duration ==4)),coef_social_calibration_constant_duration_5 +util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.duration ==5)),coef_social_calibration_constant_duration_6 +util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.duration ==6)),coef_social_calibration_constant_duration_7 +util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.duration ==7)),coef_social_calibration_constant_duration_8 +util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.duration ==8)),coef_social_calibration_constant_duration_9 +# Including terms for school escorting,,, +util_outbound_school_escort_tour_start,Outbound school escort tours must match the start time of the escort tour,is_outbound_school_escort_tour & (start != school_escort_tour_start),coef_unavailable +util_outbound_school_escort_tour_next_start,Outbound school escort tours must end before next escort tour start,is_outbound_school_escort_tour & (end > school_escort_tour_next_start) & (school_escort_tour_next_start > 0),coef_unavailable +util_inbound_school_escort_tour_end,Inbound school escort tours must match the end time of the escort tour,is_inbound_school_escort_tour & (end != school_escort_tour_end),coef_unavailable \ No newline at end of file diff --git a/configs/resident/tour_scheduling_nonmandatory_social_coefficients.csv b/configs/resident/tour_scheduling_nonmandatory_social_coefficients.csv new file mode 100644 index 0000000..1cc972d --- /dev/null +++ b/configs/resident/tour_scheduling_nonmandatory_social_coefficients.csv @@ -0,0 +1,54 @@ +coefficient_name,value,constrain +coef_social_person_less_than_18_years_old_duration_less_than_reference_linear,0.0,F +coef_social_person_less_than_18_years_old_duration_greater_than_reference_linear,0.1848544664093724,F +coef_social_non_working_senior_or_retiree_duration_less_than_reference_linear,0.0,F +coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_reference_linear,0.0,F +coef_social_zero_auto_households_duration_less_than_reference_linear,0.0,F +coef_social_zero_auto_households_duration_greater_than_reference_linear,0.0,F +coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,0.0,F +coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,0.0,F +coef_social_auto_distance_duration_less_than_reference_linear,-0.037191211367614524,F +coef_social_auto_distance_duration_greater_than_reference_linear,0.004103999782121744,F +coef_social_time_pressure_duration_less_than_reference,0.0,F +coef_social_time_pressure_duration_greater_than_reference,0.2472747423164598,F +coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_reference,0.0,F +coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.35038347561648353,F +coef_social_departure_constant_before_9_am,0.0,F +coef_social_departure_constant_9_am_to_9_30_am,0.48320177182957835,F +coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,0.10579475979139634,F +coef_social_departure_constant_before_5_30_pm,2.9248450547864326,F +coef_social_departure_constant_5_30_pm_to_6_pm,3.396566813557204,F +coef_social_departure_constant_6_pm_to_6_30_pm,0.0,T +coef_social_departure_constant_6_30_pm_to_7_pm,3.2332806668467815,F +coef_social_departure_constant_7_pm_to_7_30_pm,2.9564723583835772,F +coef_social_departure_constant_after_7_30_pm,2.5033367748702537,F +coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.31575816708433413,F +coef_social_arrival_constant_3_pm_to_3_30_pm,0.6128608441643291,F +coef_social_arrival_constant_3_30_pm_to_4_pm,0.0,F +coef_social_arrival_constant_4_pm_to_4_30_pm,0.0,F +coef_social_arrival_constant_5_pm_to_6_pm,0.0,F +coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,-0.11543939695197131,F +coef_social_arrival_constant_before_8_30_pm,0.0,F +coef_social_arrival_constant_8_30_pm_to_9_pm,0.3725347628206525,F +coef_social_arrival_constant_9_pm_to_9_30_pm,0.34127159737778273,F +coef_social_arrival_constant_9_30_pm_to_10_pm,0.0,T +coef_social_arrival_constant_10_pm_to_10_30_pm,0.349324126243515,F +coef_social_arrival_constant_after_10_30_pm,0.0,F +coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,-0.22982597094534407,F +coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,0.0,F +coef_social_duration_constant_less_than_3_hours,0.0,F +coef_social_duration_constant_3_hours,0.0,T +coef_social_duration_constant_3_hrs_30_minutes,-0.9879910839979239,F +coef_social_duration_constant_4_hours_or_more,-1.9671125098564861,F +coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,-1.081195937845935,F +coef_social_calibration_constant_duration_1,0.0,F +coef_social_calibration_constant_duration_2,0.0,F +coef_social_calibration_constant_duration_3,0.0,F +coef_social_calibration_constant_duration_4,0.0,F +coef_social_calibration_constant_duration_5,0.0,F +coef_social_calibration_constant_duration_6,0.0,F +coef_social_calibration_constant_duration_7,0.0,F +coef_social_calibration_constant_duration_8,0.0,F +coef_social_calibration_constant_duration_9,0.0,F +coef_mode_choice_logsum,0.014255126759576003,F +coef_unavailable,-999.0,F \ No newline at end of file diff --git a/configs/resident/tour_scheduling_school.csv b/configs/resident/tour_scheduling_school.csv new file mode 100644 index 0000000..6470156 --- /dev/null +++ b/configs/resident/tour_scheduling_school.csv @@ -0,0 +1,62 @@ +Label,Description,Expression,Coefficient +# writing out varibles we may need for estimation,,, +round_trip_auto_time_to_work,round_trip_auto_time_to_work,@df.roundtrip_auto_time_to_work,zero_coef +age,age,@df.age,zero_coef +util_Mode_Choice_Logsum,SCHOOL - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Low_income_lt25000_Departure_before_730_am__Linear,SCHOOL - Low income (<25000) - Departure before 7:30 am - Linear,"@((df.is_income_less25K) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Low_income_lt25000_Departure_before_730_am__Linear +util_Low_income_lt25000_Departure_after_800_am_Linear,SCHOOL - Low income (<25000) - Departure after 8:00 am - Linear,"@((df.is_income_less25K) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_Low_income_lt25000_Departure_after_800_am_Linear +util_Low_income_lt25000_Duration_lt_8hrs,SCHOOL - Low income (<25000) - Duration < 8hrs,"@((df.is_income_less25K) & (df.start < df.durationRefBin_school)) * df.durationShift_school",coef_Low_income_lt25000_Duration_lt_8hrs +util_Low_income_lt25000_Duration_gt_8hrs,SCHOOL - Low income (<25000) - Duration > 8hrs,"@((df.is_income_less25K) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Low_income_lt25000_Duration_gt_8hrs +util_Med_income_25k_to_60k_Departure_before_730_am__Linear,SCHOOL - Med income (25k to 60k) - Departure before 7:30 am - Linear,"@((df.is_income_25K_to_60K) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Med_income_25k_to_60k_Departure_before_730_am__Linear +util_Age_0_to_5_yrs_Departure_Before_730_am,SCHOOL - Age 0 to 5 yrs - Departure Before 7:30 am,"@(((df.age>=0) & (df.age<=5)) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_0_to_5_yrs_Departure_Before_730_am +util_Age_13_to_15_yrs_Departure_Before_730_am,SCHOOL - Age 13 to 15 yrs - Departure Before 7:30 am,"@(((df.age>=13) & (df.age<=15)) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_13_to_15_yrs_Departure_Before_730_am +util_Age_13_to_15_yrs_Departure_After_800_am,SCHOOL - Age 13 to 15 yrs - Departure After 8:00 am,"@(((df.age>=13) & (df.age<=15)) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_13_to_15_yrs_Departure_After_800_am +util_Age_16_to_17_yrs_Departure_After_800_am,SCHOOL - Age 16 to 17 yrs - Departure After 8:00 am,"@(((df.age>=16) & (df.age<=17)) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_16_to_17_yrs_Departure_After_800_am +util_Age_0_to_5_yrs_Duration_lt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration < 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.start < df.durationRefBin_school)) * df.durationShift_school",coef_Age_0_to_5_yrs_Duration_lt_8hrs +util_Age_0_to_5_yrs_Duration_gt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration > 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Age_0_to_5_yrs_Duration_gt_8hrs +util_Age_13_to_15_yrs_Duration_lt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration < 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.start < df.durationRefBin_school)) * df.durationShift_school",coef_Age_13_to_15_yrs_Duration_lt_8hrs +util_Age_13_to_15_yrs_Duration_gt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration > 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Age_13_to_15_yrs_Duration_gt_8hrs +util_Age_16_to_17_yrs_Duration_gt_8hrs,SCHOOL - Age 16 to 17 yrs - Duration > 8hrs,"@(((df.age>=16) & (df.age<=17)) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Age_16_to_17_yrs_Duration_gt_8hrs +util_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,SCHOOL - Time (SOV freeflow) to destination - Departure before 7:30 am - Linear,"@(df.start < df.departureRefBin_school) *df.departureLinearShift1 * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear +util_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,SCHOOL - Time (SOV freeflow) to destination - Departure after 8:00 am - Linear,"@(df.start > df.departureRefBin_school) *df.departureLinearShift1 * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear +util_Time_SOV_freeflow_to_destination_arrival_before_ref,SCHOOL - Time (SOV freeflow) to destination - Arrival < Reference Bin,"@(df.end < df.arrivalRefBin_school) * df.arrivalLinearShift1_school * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_arrival_before_ref +util_Time_SOV_freeflow_to_destination_arrival_after_ref,SCHOOL - Time (SOV freeflow) to destination - Duration > 8hrs,"@(df.end > df.arrivalRefBin_school) * df.arrivalLinearShift1_school * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_arrival_after_ref +util_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,SCHOOL - All adults in the household are fulltime workers - Departure before 7:30 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear +util_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,SCHOOL - All adults in the household are fulltime workers - Departure after 8:00 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear +util_All_adults_in_the_household_are_fulltime_workers_arrival_before_ref,SCHOOL - All adults in the household are fulltime workers - Duration < 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end < df.arrivalRefBin_school)) * df.arrivalLinearShift1_school",coef_All_adults_in_the_household_are_fulltime_workers_arrival_before_ref +util_All_adults_in_the_household_are_fulltime_workers_arrival_after_ref,SCHOOL - All adults in the household are fulltime workers - Duration > 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end > df.arrivalRefBin_school)) * df.arrivalLinearShift1_school",coef_All_adults_in_the_household_are_fulltime_workers_arrival_after_ref +util_Subsequent_tour_is_work_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is work tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start < df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours +util_Subsequent_tour_is_work_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is work tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start > df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours +util_Subsequent_tour_is_school_tour_Departure_after_800_am,SCHOOL - Subsequent tour is school tour: Departure after 8:00 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start > df.departureRefBin_school))) *df.departureLinearShift1",coef_Subsequent_tour_is_school_tour_Departure_after_800_am +util_Subsequent_tour_is_school_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is school tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start < df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours +util_Subsequent_tour_is_school_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is school tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start > df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours +util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<7)) * (((7-df.duration)*(df.duration<=7)) + ((df.duration-7)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>7)) * (((7-df.duration)*(df.duration<=7)) + ((df.duration-7)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours +util_Departure_Constant_Before_0600_AM,SCHOOL - Departure Constant: Before 06:00 AM,@(df.start<7),coef_Departure_Constant_Before_0600_AM +util_Departure_Constant_0600_AM_to_0630_AM_7,SCHOOL - Departure Constant: 06:00 AM to 06:30 AM (7),@(df.start==7),coef_Departure_Constant_0600_AM_to_0630_AM_7 +util_Departure_Constant_0630_AM_to_0700_AM_8,SCHOOL - Departure Constant: 06:30 AM to 07:00 AM (8),@(df.start==8),coef_Departure_Constant_0630_AM_to_0700_AM_8 +util_Departure_Constant_0700_AM_to_0730_AM_9,SCHOOL - Departure Constant: 07:00 AM to 07:30 AM (9),@(df.start==9),coef_Departure_Constant_0700_AM_to_0730_AM_9 +util_Departure_Constant_0730_AM_to_0800_AM_10,SCHOOL - Departure Constant: 07:30 AM to 08:00 AM (10),@(df.start==10),coef_Departure_Constant_0730_AM_to_0800_AM_10 +util_Departure_Constant_After_0800_AM,SCHOOL - Departure Constant: After 08:00 AM,@(df.start > df.departureRefBin_school),coef_Departure_Constant_After_0800_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Linear,"@((df.start>11)) * (((11-df.start)*(df.start<11)) + ((df.start-11)*(df.start>11)))",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear +util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Square Root,"@((df.start>11) * (np.maximum(df.start - 11, 0) ** 0.5))",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root +util_Arrival_Constant_Before_0230_PM,SCHOOL - Arrival Constant: Before 02:30 PM,@(df.end<24),coef_Arrival_Constant_Before_0230_PM +util_Arrival_Constant_0230_PM_0300_PM_24_,SCHOOL - Arrival Constant: 02:30 PM - 03:00 PM (24) ,@(df.end==24),coef_Arrival_Constant_0230_PM_0300_PM_24_ +util_Arrival_Constant_0300_PM_0330_PM_25_,SCHOOL - Arrival Constant: 03:00 PM - 03:30 PM (25) ,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM_25_ +util_Arrival_Constant_0330_PM_0400_PM_26_,SCHOOL - Arrival Constant: 03:30 PM - 04:00 PM (26) ,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM_26_ +util_Arrival_Constant_0400_PM_0430_PM_27_,SCHOOL - Arrival Constant: 04:00 PM - 04:30 PM (27) ,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM_27_ +util_Arrival_Constant_0430_PM_0500_PM_28_,SCHOOL - Arrival Constant: 04:30 PM - 05:00 PM (28) ,@(df.end==28),coef_Arrival_Constant_0430_PM_0500_PM_28_ +util_Arrival_Constant_0500_PM_0530_PM_29,SCHOOL - Arrival Constant: 05:00 PM - 05:30 PM (29),@(df.end==29),coef_Arrival_Constant_0500_PM_0530_PM_29 +util_Arrival_Constant_0530_PM_0600_PM_30_,SCHOOL - Arrival Constant: 05:30 PM - 06:00 PM (30) ,@(df.end==30),coef_Arrival_Constant_0530_PM_0600_PM_30_ +util_Arrival_Constant_After_0600_PM,SCHOOL - Arrival Constant: After 06:00 PM,@(df.end>30),coef_Arrival_Constant_After_0600_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,SCHOOL - Arrival Constant: Shift for every 30 minutes after 6:30 pm - Linear,"@(df.end>31) * (((31-df.end)*(df.end<31)) + ((df.end-31)*(df.end>31)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear +util_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes less than 6.5 hrs - Linear,"@((df.duration<13)) * (((13-df.duration)*(df.duration<13)) + ((df.duration-13)*(df.duration>13)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear +util_Duration_Constant_Shorter_than_7_hrs,SCHOOL - Duration Constant: Shorter than 7 hrs,@(df.duration<14),coef_Duration_Constant_Shorter_than_7_hrs +util_Duration_Constant_7_hours,SCHOOL - Duration Constant: 7 hours,@(df.duration==14),coef_Duration_Constant_7_hours +util_Duration_Constant_7p5_hours,SCHOOL - Duration Constant: 7.5 hours,@(df.duration==15),coef_Duration_Constant_7p5_hours +util_Duration_Constant_8_hours,SCHOOL - Duration Constant: 8 hours,@(df.duration==16),coef_Duration_Constant_8_hours +util_Duration_Constant_8p5_hours,SCHOOL - Duration Constant: 8.5 hours,@(df.duration==17),coef_Duration_Constant_8p5_hours +util_Duration_Constant_9_hours,SCHOOL - Duration Constant: 9 hours,@(df.duration==18),coef_Duration_Constant_9_hours +util_Duration_Constant_Longer_than_9_hrs,SCHOOL - Duration Constant: Longer than 9 hrs,@(df.duration>18),coef_Duration_Constant_Longer_than_9_hrs +util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Linear,"@(df.duration>19) * (((19-df.duration)*(df.duration<19)) + ((df.duration-19)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear +util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Squared,"@(df.duration>19) * ((((19-df.duration)*(df.duration<19)) + ((df.duration-19)*(df.duration>19))) ** 2)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared diff --git a/configs/resident/tour_scheduling_school_coeffs.csv b/configs/resident/tour_scheduling_school_coeffs.csv new file mode 100644 index 0000000..1c11c6a --- /dev/null +++ b/configs/resident/tour_scheduling_school_coeffs.csv @@ -0,0 +1,60 @@ +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.0,F +coef_Low_income_lt25000_Departure_before_730_am__Linear,0.0,F +coef_Low_income_lt25000_Departure_after_800_am_Linear,0.0,F +coef_Low_income_lt25000_Duration_lt_8hrs,0.0,F +coef_Low_income_lt25000_Duration_gt_8hrs,0.0,F +coef_Med_income_25k_to_60k_Departure_before_730_am__Linear,0.0,F +coef_Age_0_to_5_yrs_Departure_Before_730_am,-0.8148835593502417,F +coef_Age_13_to_15_yrs_Departure_Before_730_am,0.27726279249507474,F +coef_Age_13_to_15_yrs_Departure_After_800_am,-0.7597642883716563,F +coef_Age_16_to_17_yrs_Departure_After_800_am,-0.8824101711801812,F +coef_Age_0_to_5_yrs_Duration_lt_8hrs,0.1741028592807906,F +coef_Age_0_to_5_yrs_Duration_gt_8hrs,0.241662039787307,F +coef_Age_13_to_15_yrs_Duration_lt_8hrs,0.0,F +coef_Age_13_to_15_yrs_Duration_gt_8hrs,0.5963387646724607,F +coef_Age_16_to_17_yrs_Duration_gt_8hrs,0.6956001833839539,F +coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,0.00454314976439104,F +coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,-0.008509123281468139,F +coef_Time_SOV_freeflow_to_destination_arrival_before_ref,-0.0026124484527274773,F +coef_Time_SOV_freeflow_to_destination_arrival_after_ref,0.00213627544235268,F +coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,0.0,F +coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,-0.14721190410201404,F +coef_All_adults_in_the_household_are_fulltime_workers_arrival_before_ref,-0.16604467456086497,F +coef_All_adults_in_the_household_are_fulltime_workers_arrival_after_ref,0.0,F +coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours,0.0,F +coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours,0.0,F +coef_Subsequent_tour_is_school_tour_Departure_after_800_am,0.0,F +coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours,0.0,F +coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours,0.0,F +coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,0.0,F +coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,0.0,F +coef_Departure_Constant_Before_0600_AM,-8.764796175382282,F +coef_Departure_Constant_0600_AM_to_0630_AM_7,-2.707793497506736,F +coef_Departure_Constant_0630_AM_to_0700_AM_8,-0.654253451781764,F +coef_Departure_Constant_0700_AM_to_0730_AM_9,0.0,F +coef_Departure_Constant_0730_AM_to_0800_AM_10,-0.48281221099875055,T +coef_Departure_Constant_After_0800_AM,1.0336602346049464,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,0.0,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,-0.4982940561474212,F +coef_Arrival_Constant_Before_0230_PM,0.0,F +coef_Arrival_Constant_0230_PM_0300_PM_24_,0.0,F +coef_Arrival_Constant_0300_PM_0330_PM_25_,0.22741669683682353,F +coef_Arrival_Constant_0330_PM_0400_PM_26_,0.0,F +coef_Arrival_Constant_0400_PM_0430_PM_27_,0.0,T +coef_Arrival_Constant_0430_PM_0500_PM_28_,-1.0249817399820995,F +coef_Arrival_Constant_0500_PM_0530_PM_29,-1.4403670450638697,F +coef_Arrival_Constant_0530_PM_0600_PM_30_,-1.8460834370368264,F +coef_Arrival_Constant_After_0600_PM,-2.3316983953964314,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,-0.6853763563949641,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,-0.19770146146375506,F +coef_Duration_Constant_Shorter_than_7_hrs,-3.0269316327052733,F +coef_Duration_Constant_7_hours,-1.8715765041190864,F +coef_Duration_Constant_7p5_hours,-0.3821662280789037,F +coef_Duration_Constant_8_hours,0.0,T +coef_Duration_Constant_8p5_hours,-0.2743767294042022,F +coef_Duration_Constant_9_hours,-0.8177669270743325,F +coef_Duration_Constant_Longer_than_9_hrs,-0.5422856490451977,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,0.21538163084141398,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,0.0,F +zero_coef,0.0,T diff --git a/configs/resident/tour_scheduling_university.csv b/configs/resident/tour_scheduling_university.csv new file mode 100644 index 0000000..9617383 --- /dev/null +++ b/configs/resident/tour_scheduling_university.csv @@ -0,0 +1,42 @@ +Label,Description,Expression,Coefficient +util_Mode_Choice_Logsum,UNIVERSITY - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Low_income_lt25000_Departure_before_800_am_Linear,UNIVERSITY - Low income (<25000) - Departure before 8:00 am - Linear,"@((df.is_income_less25K) & (df.start<11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Low_income_lt25000_Departure_before_800_am_Linear +util_Low_income_lt25000_Duration_lt_4hrs,UNIVERSITY - Low income (<25000) - Duration < 4hrs,"@((df.is_income_less25K) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Low_income_lt25000_Duration_lt_4hrs +util_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear,UNIVERSITY - Medium high income (60k to 120k) - Departure after 8:30 am - Linear,"@((df.is_income_60K_to_120K) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear +util_Medium_high_income_60k_to_120k_Duration_gt_4hrs,UNIVERSITY - Medium high income (60k to 120k) - Duration > 4hrs,"@((df.is_income_60K_to_120K) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Medium_high_income_60k_to_120k_Duration_gt_4hrs +util_High_income_120k_plus_Departure_after_830_am_Linear,UNIVERSITY - High income (120k+) - Departure after 8:30 am - Linear,"@((df.is_income_greater120K) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_High_income_120k_plus_Departure_after_830_am_Linear +util_Age_41_plus_Departure_after_830_am_Linear,UNIVERSITY - Age 41+ - Departure after 8:30 am - Linear,"@((df.age >= 41) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Age_41_plus_Departure_after_830_am_Linear +util_Age_41_plus_Durationlt_4_hrs_Linear,UNIVERSITY - Age 41+ - Duration< 4 hrs -Linear,"@((df.age >= 41) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Age_41_plus_Durationlt_4_hrs_Linear +util_Distance_to_destination_Departure_before_800_am_Linear,UNIVERSITY - Distance to destination - Departure before 8:00 am - Linear,"@((df.start<11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))*(df.distance_to_school)",coef_Distance_to_destination_Departure_before_800_am_Linear +util_Distance_to_destination_Departure_after_830_am_Linear,UNIVERSITY - Distance to destination - Departure after 8:30 am - Linear,"@((df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))*(df.distance_to_school)",coef_Distance_to_destination_Departure_after_830_am_Linear +util_Distance_to_destination_Durationlt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration< 4 hrs -Linear,"@((df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Linear +util_Distance_to_destination_Durationgt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration> 4 hrs- Linear,"@((df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationgt_4_hrs_Linear +util_Distance_to_destination_Durationlt_4_hrs_Square_Root,UNIVERSITY - Distance to destination - Duration< 4 hrs - Square Root,"@((df.duration<8))*(abs(((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))) ** 0.5)*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Square_Root +util_Subsequent_tour_is_work_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is work tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start>11)))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_work_tour_Departure_after_830_am +util_Subsequent_tour_is_work_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration<8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours +util_Subsequent_tour_is_work_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration>8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours +util_Subsequent_tour_is_school_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is school tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start>11)))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_school_tour_Departure_after_830_am +util_Subsequent_tour_is_school_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration<8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours +util_Subsequent_tour_is_school_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration>8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours +util_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,UNIVERSITY - Departure Constant: Shift for every 30 minutes before 07:00 am - Linear,"@((df.start<9))*((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13)))",coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear +util_Departure_Constant_Before_0730_AM,UNIVERSITY - Departure Constant: Before 07:30 AM,@(df.start<10),coef_Departure_Constant_Before_0730_AM +util_Departure_Constant_0730_AM_0800_AM,UNIVERSITY - Departure Constant: 07:30 AM - 08:00 AM,@(df.start==10),coef_Departure_Constant_0730_AM_0800_AM +util_Departure_Constant_0800_AM_0830_AM,UNIVERSITY - Departure Constant: 08:00 AM - 08:30 AM,@(df.start==11),coef_Departure_Constant_0800_AM_0830_AM +util_Departure_Constant_0830_AM_0900_AM,UNIVERSITY - Departure Constant: 08:30 AM - 09:00 AM,@(df.start==12),coef_Departure_Constant_0830_AM_0900_AM +util_Departure_Constant_After_0900_AM,UNIVERSITY - Departure Constant: After 09:00 AM,@(df.start>12),coef_Departure_Constant_After_0900_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root,UNIVERSITY - Departure Constant: Shift for every 30 minutes after 09:30 am - Square Root,"@((df.start>13))*(((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root +util_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear,UNIVERSITY - Arrival Constant: Shift for every 30 minutes before 02:30 pm - Linear,"@((df.end<24)) * ((np.minimum(24-df.end,12) * (df.end<24)) + (np.minimum(df.end-28,19) * (df.end>28)))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear +util_Arrival_Constant_Before_0300_PM,UNIVERSITY - Arrival Constant: Before 03:00 PM,@((df.end<25)),coef_Arrival_Constant_Before_0300_PM +util_Arrival_Constant_0300_PM_0330_PM,UNIVERSITY - Arrival Constant: 03:00 PM - 03:30 PM,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM +util_Arrival_Constant_0330_PM_0400_PM,UNIVERSITY - Arrival Constant: 03:30 PM - 04:00 PM,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM +util_Arrival_Constant_0400_PM_0430_PM,UNIVERSITY - Arrival Constant: 04:00 PM - 04:30 PM,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM +util_Arrival_Constant_After_0430_PM,UNIVERSITY - Arrival Constant: After 04:30 PM,@(df.end>27),coef_Arrival_Constant_After_0430_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear,UNIVERSITY - Arrival Constant: Shift for every 30 minutes after 05:00 pm - Linear,"@((df.end>28))*((np.minimum(24-df.end,12)*(df.end<24)) + (np.minimum(df.end-28,19)*(df.end>28)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear +util_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root,UNIVERSITY - Arrival Constant: Shift for every 30 minutes after 05:00 pm - Square Root,"@((df.end>28)) *(((np.minimum(24-df.end,12)*(df.end<24)) + (np.minimum(df.end-28,19)*(df.end>28))) ** 0.5)",coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root +util_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root,UNIVERSITY - Duration Constant: Shift for every 30 minutes less than 4.5 hrs - Square Root,"@((df.duration<9))*((np.minimum(9-df.duration,7)*(df.duration<9)) + (np.minimum(df.duration-11,25)*(df.duration>11)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root +util_Duration_Constant_4p5_hours_or_less,UNIVERSITY - Duration Constant: 4.5 hours or less,@(df.duration<10),coef_Duration_Constant_4p5_hours_or_less +util_Duration_Constant_5_hours,UNIVERSITY - Duration Constant: 5 hours,@(df.duration==10),coef_Duration_Constant_5_hours +util_Duration_Constant_5p5_hours_or_more,UNIVERSITY - Duration Constant: 5.5 hours or more,@(df.duration>10),coef_Duration_Constant_5p5_hours_or_more +util_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear,UNIVERSITY - Duration Constant: Shift for every 30 minutes more than 5.5 hrs - Linear,"@((df.duration>11))*(((np.minimum(9-df.duration,7)*(df.duration<9)) + (np.minimum(df.duration-11,25)*(df.duration>11))) ** 0.5)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear diff --git a/configs/resident/tour_scheduling_university_coeffs.csv b/configs/resident/tour_scheduling_university_coeffs.csv new file mode 100644 index 0000000..1472dfb --- /dev/null +++ b/configs/resident/tour_scheduling_university_coeffs.csv @@ -0,0 +1,42 @@ +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.384091138,F +coef_Low_income_lt25000_Departure_before_800_am_Linear,0.246389489,F +coef_Low_income_lt25000_Duration_lt_4hrs,-0.262288853,F +coef_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear,-0.039079271,F +coef_Medium_high_income_60k_to_120k_Duration_gt_4hrs,-0.041536976,F +coef_High_income_120k_plus_Departure_after_830_am_Linear,-0.039306518,F +coef_Age_41_plus_Departure_after_830_am_Linear,0.055344625,F +coef_Age_41_plus_Durationlt_4_hrs_Linear,-0.152498075,F +coef_Distance_to_destination_Departure_before_800_am_Linear,0.006869786,F +coef_Distance_to_destination_Departure_after_830_am_Linear,0.003686402,F +coef_Distance_to_destination_Durationlt_4_hrs_Linear,-0.04027172,F +coef_Distance_to_destination_Durationgt_4_hrs_Linear,0.003803244,F +coef_Distance_to_destination_Durationlt_4_hrs_Square_Root,0.041070113,F +coef_Subsequent_tour_is_work_tour_Departure_after_830_am,-0.29166292,F +coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours,-0.482292817,F +coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours,-0.364624965,F +coef_Subsequent_tour_is_school_tour_Departure_after_830_am,-0.286206955,F +coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours,0.30341795,F +coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours,-0.247436221,F +coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,-0.211059285,F +coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,-0.35316727,F +coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,-0.947594485,F +coef_Departure_Constant_Before_0730_AM,-0.296228472,F +coef_Departure_Constant_0730_AM_0800_AM,-0.650538708,F +coef_Departure_Constant_0800_AM_0830_AM,0,T +coef_Departure_Constant_0830_AM_0900_AM,-0.525569176,F +coef_Departure_Constant_After_0900_AM,-0.536008149,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root,-0.500045988,F +coef_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear,-0.209375282,F +coef_Arrival_Constant_Before_0300_PM,-0.962572172,F +coef_Arrival_Constant_0300_PM_0330_PM,-0.627901132,F +coef_Arrival_Constant_0330_PM_0400_PM,0,T +coef_Arrival_Constant_0400_PM_0430_PM,-0.190818088,F +coef_Arrival_Constant_After_0430_PM,-0.66545038,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear,-0.209562151,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root,0.503497689,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root,0.225706446,F +coef_Duration_Constant_4p5_hours_or_less,0.03106769,F +coef_Duration_Constant_5_hours,0,T +coef_Duration_Constant_5p5_hours_or_more,0.343447232,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear,-0.115312573,F diff --git a/configs/resident/tour_scheduling_work.csv b/configs/resident/tour_scheduling_work.csv new file mode 100644 index 0000000..ce63a8c --- /dev/null +++ b/configs/resident/tour_scheduling_work.csv @@ -0,0 +1,103 @@ +Label,Description,Expression,Coefficient +# writing out varibles we may need for estimation,,, +round_trip_auto_time_to_work,round_trip_auto_time_to_work,@df.roundtrip_auto_time_to_work,zero_coef +age,age,@df.age,zero_coef +util_Mode_Choice_Logsum,Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Female_Departure_before_7_am,Female - Departure before 7:00 am - Linear,@((df.female) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Female_Departure_before_7_am +util_Female_Arrival_after_6_pm,Female - Arrival after 6:00 pm - Linear,@((df.female) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Female_Arrival_after_6_pm +util_Female_with_preschool_child_Departure_before_7_am,Female with preschool child - Departure before 7:00 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_before_7_am +util_Female_with_preschool_child_Departure_after_7_am,Female with preschool child - Departure after 7:30 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_after_7_am +util_Female_with_preschool_child_Arrival_after_6_pm,Female with preschool child - Arrival after 6:00 pm - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Female_with_preschool_child_Arrival_after_6_pm +util_Low_income_lt_25000_Departure_before_7_am,Low income (<25000) - Departure before 7:00 am - Linear,@((df.is_income_less25K) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_before_7_am +util_Low_income_lt_25000_Departure_after_7_am,Low income (<25000) - Departure after 7:30 am - Linear,@((df.is_income_less25K) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_after_7_am +util_Low_income_lt_25000_Arrival_after_6_pm,Low income (<25000) - Arrival after 6:00 pm - Linear,@((df.is_income_less25K) & (df.start>30)) * df.arrivalLinearShift1,coef_Low_income_lt_25000_Arrival_after_6_pm +util_Med_income_25k_to_60k_Departure_before_7_am,Med income (25k to 60k) - Departure before 7:00 am - Linear,@((df.is_income_25K_to_60K) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Med_income_25k_to_60k_Departure_before_7_am +util_Med_income_25k_to_60k_Arrival_after_6_pm,Med income (25k to 60k) - Arrival after 6:00 pm - Linear,@((df.is_income_25K_to_60K) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Med_income_25k_to_60k_Arrival_after_6_pm +util_Medhigh_income_60k_to_120k_Departure_before_7_am,Med-high income (60k to 120k) - Departure before 7:00 am - Linear,@((df.is_income_60K_to_120K) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Medhigh_income_60k_to_120k_Departure_before_7_am +util_Age_16_to_18_yrs_Departure_Before_7_am,Age 16 to 18 yrs - Departure Before 7:00 am,@(((df.age>=16) & (df.age<=18)) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_Before_7_am +util_Age_16_to_18_yrs_Departure_After_7_am,Age 16 to 18 yrs - Departure After 7:30 am,@(((df.age>=16) & (df.age<=18)) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_After_7_am +util_Age_19_to_24_yrs_Departure_After_7_am,Age 19 to 24 yrs - Departure After 7:30 am,@(((df.age>=19) & (df.age<=24)) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Age_19_to_24_yrs_Departure_After_7_am +util_Age_25_to_40_yrs_Departure_Before_7_am,Age 25 to 40 yrs - Departure Before 7:00 am,@(((df.age>=25) & (df.age<=40)) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Age_25_to_40_yrs_Departure_Before_7_am +util_Age_65_plus_yrs_Departure_After_7_am,Age 65+ yrs - Departure After 7:30 am,@((df.age>=65) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Age_65_plus_yrs_Departure_After_7_am +util_Age_19_to_24_yrs_Arrival_after_6_pm,Age 19 to 24 yrs - Arrival after 6:00 pm ,@(((df.age>=19) & (df.age<=24)) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_19_to_24_yrs_Arrival_after_6_pm +util_Age_25_to_40_yrs_Arrival_before_5_pm,Age 25 to 40 yrs - Arrival before 5:30 pm ,@(((df.age>=25) & (df.age<=40)) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_25_to_40_yrs_Arrival_before_5_pm +util_Age_56_to_64_yrs_Arrival_after_6_pm,Age 56 to 64 yrs - Arrival after 6:00 pm ,@(((df.age>=56) & (df.age<65)) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_56_to_64_yrs_Arrival_after_6_pm +util_Age_65_plus_yrs_Arrival_before_5_pm,Age 65+ yrs - Arrival before 5:30 pm ,@((df.age>=65) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_before_5_pm +util_Age_65_plus_yrs_Arrival_after_6_pm,Age 65+ yrs - Arrival after 6:00 pm ,@((df.age>=65) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_after_6_pm +util_Zero_auto_HH_Departure_before_7_am,Zero auto HH - Departure before 7:00 am - Linear,@((df.auto_ownership == 0) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Zero_auto_HH_Departure_before_7_am +util_Zero_auto_HH_Arrival_after_6_pm,Zero auto HH - Arrival after 6:00 pm - Linear,@((df.auto_ownership == 0) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Zero_auto_HH_Arrival_after_6_pm +util_Parttime_worker_Departure_before_7_am,Part-time worker - Departure before 7:00 am - Linear,@((df.ptype==2) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Parttime_worker_Departure_before_7_am +util_Parttime_worker_Departure_after_7_am,Part-time worker - Departure after 7:30 am - Linear,@((df.ptype==2) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Parttime_worker_Departure_after_7_am +util_Parttime_worker_Arrival_before_5_pm,Part-time worker - Arrival before 5:30 pm - Linear,@((df.ptype==2) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_before_5_pm +util_Parttime_worker_Arrival_after_6_pm,Part-time worker - Arrival after 6:00 pm - Linear,@((df.ptype==2) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_after_6_pm +util_University_student_Departure_after_7_am,University student - Departure after 7:30 am - Linear,@((df.ptype==3) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_University_student_Departure_after_7_am +util_University_student_Arrival_before_5_pm,University student - Arrival before 5:30 pm - Linear,@((df.ptype==3) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_University_student_Arrival_before_5_pm +util_University_student_Arrival_after_6_pm,University student - Arrival after 6:00 pm - Linear,@((df.ptype==3) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_University_student_Arrival_after_6_pm +#util_Blue_collar_Departure_before_7_am,#Blue collar - Departure before 7:00 am - Linear,@((df.work_segment==5) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Blue_collar_Departure_before_7_am +#util_Blue_collar_Departure_after_7_am,#Blue collar - Departure after 7:30 am - Linear,@((df.work_segment==5)& (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Blue_collar_Departure_after_7_am +#util_Blue_collar_Arrival_before_5_pm,#Blue collar - Arrival before 5:30 pm - Linear,@((df.work_segment==5)& (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Blue_collar_Arrival_before_5_pm +#util_Service_Departure_before_7_am,#Service - Departure before 7:00 am - Linear,@((df.work_segment==2) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Service_Departure_before_7_am +#util_Service_Departure_after_7_am,#Service - Departure after 7:30 am - Linear,@((df.work_segment==2) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Service_Departure_after_7_am +#util_Service_Arrival_before_5_pm,#Service - Arrival before 5:30 pm - Linear,@((df.work_segment==2) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Service_Arrival_before_5_pm +#util_Health_Departure_before_7_am,#Health - Departure before 7:00 am - Linear,@((df.work_segment==3) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Health_Departure_before_7_am +#util_Health_Arrival_after_6_pm,#Health - Arrival after 6:00 pm - Linear,@((df.work_segment==3) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Health_Arrival_after_6_pm +#util_Retail_and_food_Departure_after_7_am,#Retail and food - Departure after 7:30 am - Linear,@((df.work_segment==4) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Retail_and_food_Departure_after_7_am +#util_Retail_and_food_Arrival_before_5_pm,#Retail and food - Arrival before 5:30 pm - Linear,@((df.work_segment==4) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_before_5_pm +#util_Retail_and_food_Arrival_after_6_pm,#Retail and food - Arrival after 6:00 pm - Linear,@((df.work_segment==4) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_after_6_pm +util_Time_SOV_freeflowto_destination_Departure_before_7_am,Time (SOV freeflow) to destination - Departure before 7:00 am - Linear,@(df.start < df.departureRefBin) * df.departureLinearShift1* (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_before_7_am +util_Time_SOV_freeflowto_destination_Departure_after_7_am,Time (SOV freeflow) to destination - Departure after 7:30 am - Linear,@(df.start > df.departureRefBin) * df.departureLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_after_7_am +util_Time_SOV_freeflowto_destination_Arrival_before_5_pm,Time (SOV freeflow) to destination - Arrival before 5:30 pm - Linear,@(df.end < df.arrivalRefBin) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm +util_Time_SOV_freeflowto_destination_Arrival_after_6_pm,Time (SOV freeflow) to destination - Arrival after 6:00 pm - Linear,@(df.end > df.arrivalRefBin) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm +util_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,Presence of Non-Working Adult in the HH - Departure before 7:00 am - Linear,@((df.is_non_worker_in_HH) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am +util_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,Presence of Non-Working Adult in the HH - Arrival before 5:30 pm - Linear,@((df.is_non_worker_in_HH) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm +util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,Presence of Pre-Driving Age Children in the HH - Departure before 7:30 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am +util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,Presence of Pre-Driving Age Children in the HH - Departure after 8 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am +util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,Presence of Pre-Driving Age Children in the HH - Arrival before 5:30 pm - Linear,@((df.is_pre_drive_child_in_HH) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm +util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,Presence of Pre-Driving Age Children in the HH - Arrival after 6:00 pm - Linear,@((df.is_pre_drive_child_in_HH)& (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm +util_First_of_2_plus_mandatory_tour_Departure_before_7_am,First of 2+ mandatory tour - Departure before 7:00 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_before_7_am +util_First_of_2_plus_mandatory_tour_Departure_after_7_am,First of 2+ mandatory tour - Departure after 7:30 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_after_7_am +util_First_of_2_plus_mandatory_tour_Duration_lt_dur_ref,First of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.end < df.durationRefBin)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_lt_dur_ref +util_First_of_2_plus_mandatory_tour_Duration_gt_dur_ref,First of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.end > df.durationRefBin)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_gt_dur_ref +util_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,2nd or later of 2+ mandatory tour - Departure before 1:30 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start<22)) * (((22-df.start)*(df.start<22)) + ((df.start-22)*(df.start>22))),coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm +util_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,2nd or later of 2+ mandatory tour - Departure after 2:00 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start>22)) * (((22-df.start)*(df.start<22)) + ((df.start-22)*(df.start>22))),coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm +util_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_dur_ref,2nd or later of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.end < df.durationRefBin)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_dur_ref +util_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_dur_ref,2nd or later of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.end > df.durationRefBin)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_dur_ref +#,#Departure Constants,,coef_Departure_Constants +util_Departure_Constant_Shift_for_every_30_minutes_before_6_am,Departure Constant: Shift for every 30 minutes before 6:00 am - Linear,"@((df.start<7) * (7-df.start))",coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am +util_Departure_Constant_Before_06_AM,Departure Constant: Before 06:00 AM,start<7,coef_Departure_Constant_Before_06_AM +util_Departure_Constant_06_AM_06_AM_7,Departure Constant: 06:00 AM - 06:30 AM (7) ,start==7,coef_Departure_Constant_06_AM_06_AM_7 +util_Departure_Constant_06_AM_07_AM_8,Departure Constant: 06:30 AM - 07:00 AM (8) ,start==8,coef_Departure_Constant_06_AM_07_AM_8 +util_Departure_Constant_07_AM_07_AM_9,Departure Constant: 07:00 AM - 07:30 AM (9) ,start==9,coef_Departure_Constant_07_AM_07_AM_9 +util_Departure_Constant_07_AM_08_AM_10,Departure Constant: 07:30 AM - 08:00 AM (10) ,start==10,coef_Departure_Constant_07_AM_08_AM_10 +util_Departure_Constant_08_AM_08_AM_11,Departure Constant: 08:00 AM - 08:30 AM (11) ,start==11,coef_Departure_Constant_08_AM_08_AM_11 +util_Departure_Constant_08_AM_09_AM_12,Departure Constant: 08:30 AM - 09:00 AM (12) ,start==12,coef_Departure_Constant_08_AM_09_AM_12 +util_Departure_Constant_After_09_AM,Departure Constant: After 09:00 AM,start>12,coef_Departure_Constant_After_09_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,Departure Constant: Shift for every 30 minutes after 9:30 am - Square Root,"@((df.start>13) * (np.maximum(df.start-13,0) ** 0.5))",coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root +#,#Arrival Constants,,coef_Arrival_Constants +util_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,Arrival Constant: Shift for every 30 minutes before 3:00 pm - Linear,"@((df.end<25) * (25-df.end))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm +util_Arrival_Constant_Before_03_PM,Arrival Constant: Before 03:30 PM,end<26,coef_Arrival_Constant_Before_03_PM +util_Arrival_Constant_03_PM_04_PM_26,Arrival Constant: 03:30 PM - 04:00 PM (26) ,end==26,coef_Arrival_Constant_03_PM_04_PM_26 +util_Arrival_Constant_04_PM_04_PM_27,Arrival Constant: 04:00 PM - 04:30 PM (27) ,end==27,coef_Arrival_Constant_04_PM_04_PM_27 +util_Arrival_Constant_04_PM_05_PM_28,Arrival Constant: 04:30 PM - 05:00 PM (28) ,end==28,coef_Arrival_Constant_04_PM_05_PM_28 +util_Arrival_Constant_05_PM_05_PM_29,Arrival Constant: 05:00 PM - 05:30 PM (29),end==29,coef_Arrival_Constant_05_PM_05_PM_29 +util_Arrival_Constant_05_PM_06_PM_30,Arrival Constant: 05:30 PM - 06:00 PM (30) ,end==30,coef_Arrival_Constant_05_PM_06_PM_30 +util_Arrival_Constant_06_PM_06_PM_31,Arrival Constant: 06:00 PM - 06:30 PM (31) ,end==31,coef_Arrival_Constant_06_PM_06_PM_31 +util_Arrival_Constant_06_PM_7_PM_32,Arrival Constant: 06:30 PM - 7:00 PM (32) ,end==32,coef_Arrival_Constant_06_PM_7_PM_32 +util_Arrival_Constant_7_PM_7_PM_33,Arrival Constant: 7:00 PM - 7:30 PM (33) ,end==33,coef_Arrival_Constant_7_PM_7_PM_33 +util_Arrival_Constant_7_PM_8_PM_34,Arrival Constant: 7:30 PM - 8:00 PM (34) ,end==34,coef_Arrival_Constant_7_PM_8_PM_34 +util_Arrival_Constant_After_08_PM,Arrival Constant: After 08:00 PM,end>34,coef_Arrival_Constant_After_08_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_830_pm_Square_root,Arrival Constant: Shift for every 30 minutes after 830 pm - Square root,"@((df.end>35) * (np.maximum(df.end-35,0) ** 0.5))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_830_pm_Square_root +#,#Duration Constants,,coef_Duration_Constants +util_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,Duration Constant: Shift for every 30 minutes less than 8.5 hrs - Linear,"@((df.duration<17) * (17-df.duration))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs +util_Duration_Constant_Shorter_than_8p5_hrs,Duration Constant: Shorter than 8.5 hrs,duration<17,coef_Duration_Constant_Shorter_than_8p5_hrs +util_Duration_Constant_8p5_hours,Duration Constant: 8.5 hours,duration==17,coef_Duration_Constant_8p5_hours +util_Duration_Constant_9_hours,Duration Constant: 9 hours,duration==18,coef_Duration_Constant_9_hours +util_Duration_Constant_9p5_hours,Duration Constant: 9.5 hours,duration==19,coef_Duration_Constant_9p5_hours +util_Duration_Constant_10_hours,Duration Constant: 10 hours,duration==20,coef_Duration_Constant_10_hours +util_Duration_Constant_10p5_hours,Duration Constant: 10.5 hours,duration==21,coef_Duration_Constant_10p5_hours +util_Duration_Constant_11_hours,Duration Constant: 11 hours,duration==22,coef_Duration_Constant_11_hours +util_Duration_Constant_11p5_hours,Duration Constant: 11.5 hours,duration==23,coef_Duration_Constant_11p5_hours +util_Duration_Constant_12_hours,Duration Constant: 12 hours,duration==24,coef_Duration_Constant_12_hours +util_Duration_Constant_Longer_than_12_hrs,Duration Constant: Longer than 12 hrs,duration>24,coef_Duration_Constant_Longer_than_12_hrs +util_Duration_Constant_Shift_for_every_30_minutes_more_than_12_hrs,Duration Constant: Shift for every 30 minutes more than 12 hrs - Linear,"@((df.duration>24) * (df.duration-24))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_12_hrs +util_Calibration_constant_Duration_0,Calibration constant: Duration = 0,duration == 0,coef_Calibration_constant_Duration_0 diff --git a/configs/resident/tour_scheduling_work_coeffs.csv b/configs/resident/tour_scheduling_work_coeffs.csv new file mode 100644 index 0000000..e8d9607 --- /dev/null +++ b/configs/resident/tour_scheduling_work_coeffs.csv @@ -0,0 +1,98 @@ +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.2,T +coef_Female_Departure_before_7_am,-0.21983073617010354,F +coef_Female_Arrival_after_6_pm,-0.028931718675078898,F +coef_Female_with_preschool_child_Departure_before_7_am,0.0,F +coef_Female_with_preschool_child_Departure_after_7_am,0.0,F +coef_Female_with_preschool_child_Arrival_after_6_pm,0.0,F +coef_Low_income_lt_25000_Departure_before_7_am,0.23142273676542863,F +coef_Low_income_lt_25000_Departure_after_7_am,0.06394624853227485,F +coef_Low_income_lt_25000_Arrival_after_6_pm,-0.2101481937242358,F +coef_Med_income_25k_to_60k_Departure_before_7_am,0.20582925253502693,F +coef_Med_income_25k_to_60k_Arrival_after_6_pm,0.030759330025131475,F +coef_Medhigh_income_60k_to_120k_Departure_before_7_am,0.12339331900370523,F +coef_Age_16_to_18_yrs_Departure_Before_7_am,0.0,F +coef_Age_16_to_18_yrs_Departure_After_7_am,0.1569218088558017,F +coef_Age_19_to_24_yrs_Departure_After_7_am,0.03280621940285348,F +coef_Age_25_to_40_yrs_Departure_Before_7_am,-0.14071003044101923,F +coef_Age_65_plus_yrs_Departure_After_7_am,0.0,F +coef_Age_19_to_24_yrs_Arrival_after_6_pm,0.03801692388513126,F +coef_Age_25_to_40_yrs_Arrival_before_5_pm,-0.028091109013424525,F +coef_Age_56_to_64_yrs_Arrival_after_6_pm,0.0,F +coef_Age_65_plus_yrs_Arrival_before_5_pm,0.0,F +coef_Age_65_plus_yrs_Arrival_after_6_pm,0.0,F +coef_Zero_auto_HH_Departure_before_7_am,0.0,F +coef_Zero_auto_HH_Arrival_after_6_pm,0.054809482440729494,F +coef_Parttime_worker_Departure_before_7_am,-0.465300506980724,F +coef_Parttime_worker_Departure_after_7_am,0.24035601017298427,F +coef_Parttime_worker_Arrival_before_5_pm,0.27263218681666973,F +coef_Parttime_worker_Arrival_after_6_pm,-0.2533496036708649,F +coef_University_student_Departure_after_7_am,0.10708190818422741,F +coef_University_student_Arrival_before_5_pm,0.0,F +coef_University_student_Arrival_after_6_pm,-0.0784130766004373,F +coef_Blue_collar_Departure_before_7_am,0.327242475,F +coef_Blue_collar_Departure_after_7_am,0.047214248,F +coef_Blue_collar_Arrival_before_5_pm,0.04197056,F +coef_Service_Departure_before_7_am,0.117783508,F +coef_Service_Departure_after_7_am,0.081611629,F +coef_Service_Arrival_before_5_pm,0.0,T +coef_Health_Departure_before_7_am,0.135275931,F +coef_Health_Arrival_after_6_pm,0.062010123,F +coef_Retail_and_food_Departure_after_7_am,0.076302969,F +coef_Retail_and_food_Arrival_before_5_pm,0.052905387,F +coef_Retail_and_food_Arrival_after_6_pm,0.0270691939999999,F +coef_Time_SOV_freeflowto_destination_Departure_before_7_am,0.006152588569993294,F +coef_Time_SOV_freeflowto_destination_Departure_after_7_am,-0.002638509520279399,F +coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm,-0.003924739241949497,F +coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm,0.0016423390591779922,F +coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,0.0,F +coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,0.0,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,-0.08289214126546349,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,0.0,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,0.0,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,-0.04292555821779037,F +coef_First_of_2_plus_mandatory_tour_Departure_before_7_am,-0.15631713590985458,F +coef_First_of_2_plus_mandatory_tour_Departure_after_7_am,-0.037321682863975456,F +coef_First_of_2_plus_mandatory_tour_Duration_lt_dur_ref,0.3054062365147087,F +coef_First_of_2_plus_mandatory_tour_Duration_gt_dur_ref,0.15794972916369113,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,0.0,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,-0.13397035652802908,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_dur_ref,0.45835848769473103,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_dur_ref,0.5272527491008135,F +coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am,-0.7231020452773588,F +coef_Departure_Constant_Before_06_AM,-0.1915960040171813,F +coef_Departure_Constant_06_AM_06_AM_7,-0.5420940244335356,F +coef_Departure_Constant_06_AM_07_AM_8,-0.0814829941363625,F +coef_Departure_Constant_07_AM_07_AM_9,0.0,T +coef_Departure_Constant_07_AM_08_AM_10,-0.12487604386728021,F +coef_Departure_Constant_08_AM_08_AM_11,-0.4486786777966309,F +coef_Departure_Constant_08_AM_09_AM_12,-0.9838612097965408,F +coef_Departure_Constant_After_09_AM,-1.5618817419634867,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,-0.5783447729979645,F +coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,-0.20009504631293196,F +coef_Arrival_Constant_Before_03_PM,-0.8629231486548913,F +coef_Arrival_Constant_03_PM_04_PM_26,-0.644880302920333,F +coef_Arrival_Constant_04_PM_04_PM_27,-0.3047263117628279,F +coef_Arrival_Constant_04_PM_05_PM_28,-0.1886361914686483,F +coef_Arrival_Constant_05_PM_05_PM_29,0.0,F +coef_Arrival_Constant_05_PM_06_PM_30,0.2500583541484266,T +coef_Arrival_Constant_06_PM_06_PM_31,0.23990107930944812,F +coef_Arrival_Constant_06_PM_7_PM_32,0.15526238473246975,F +coef_Arrival_Constant_7_PM_7_PM_33,0.0,F +coef_Arrival_Constant_7_PM_8_PM_34,-0.18208024865786762,F +coef_Arrival_Constant_After_08_PM,0.0,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_830_pm_Square_root,0.0,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,-0.10663128725700759,F +coef_Duration_Constant_Shorter_than_8p5_hrs,0.0,F +coef_Duration_Constant_8p5_hours,-0.2403003279562287,F +coef_Duration_Constant_9_hours,0.0,F +coef_Duration_Constant_9p5_hours,0.0,F +coef_Duration_Constant_10_hours,0.0,F +coef_Duration_Constant_10p5_hours,-0.3088419298533547,T +coef_Duration_Constant_11_hours,-0.6583531336885191,F +coef_Duration_Constant_11p5_hours,-0.8734017033609268,F +coef_Duration_Constant_12_hours,-1.3551244638084843,F +coef_Duration_Constant_Longer_than_12_hrs,-1.0959910217157498,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_12_hrs,-0.4918560543397093,F +coef_Calibration_constant_Duration_0,-1.2069106964427703,F +zero_coef,0.0,T diff --git a/configs/resident/transit_pass_ownership.csv b/configs/resident/transit_pass_ownership.csv new file mode 100644 index 0000000..2bebb9f --- /dev/null +++ b/configs/resident/transit_pass_ownership.csv @@ -0,0 +1,27 @@ +Label,Description,Expression,no_pass,pass +util_pass_asc,Constant,1,0,asc_pass +util_age_19_to_34,Age Group - 19 yrs to 34 yrs,@(df.age >= 19) & (df.age < 35),0,coef_age_19_to_34_pass +util_age_35_to_44,Age Group - 35 yrs to 44 yrs,@(df.age >= 35) & (df.age < 45),0,coef_age_35_to_44_pass +util_age_55_to_64,Age Group - 55 yrs to 64 yrs,@(df.age >= 55) & (df.age < 65),0,coef_age_55_to_64_pass +util_age_65_to_79,Age Group - 65 yrs to 79 yrs,@(df.age >= 65) & (df.age < 80),0,coef_age_65_to_79_pass +util_hhinc_less_15,"Income less than 15,000",@df.income<15000,0,coef_hhinc_less_15 +util_hhinc_15_25,"income between 15,000- 24,999","@df.income.between(15000,24999)",0,coef_hhinc_15_25 +util_hhinc_50_100,"income between 50,000 and 99,999","@df.income.between(50000,99999)",0,coef_hhinc_50_100 +util_hhinc_100_200,"income between 100,000 and 199,999","@df.income.between(100000,199999)",0,coef_hhinc_100_200 +util_hhinc_200_plus,"income greater than 250,000",@df.income>=200000,0,coef_hhinc_200_plus +util_persons_0_4_in_hhld,Persons age 0-4 in hhld,@df.num_young_children,0,coef_persons_0_4_in_hhld_pass +util_persons_5_15_in_hhld,Persons age 5-15 in hhld,@df.num_children_5_to_15,0,coef_persons_5_15_in_hhld_pass +util_zero_autos,zero auto household ownership,@df.auto_ownership==0,0,coef_zero_autos_pass +util_auto_deficient,auto deficient household,@((df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),0,coef_auto_deficient_pass +util_subs,Subsidy offered,@df.transit_pass_subsidy,0,coef_subs_pass +util_inc_adj_transit_pass_cost,Income adjusted transit pass cost,"@df.transit_pass_cost_per_trip / (np.maximum(df.income, 1000) ** df.income_exponent) / 60",0,coef_tripmc_cost_coef +util_parking_cost_at_work,Parking cost at work,@df.exp_daily_work,0,coef_parking_cost_at_work_pass +#util_total_acc_transit,Total accessibility (0.66*PK + 0.34*OP) by transit,@(0.66*df.trPkTotal+0.34*df.trOpTotal),0,coef_total_transit_acc_pass +util_is_univ,University student,@df.is_university,0,coef_is_univ +util_ft_ASC,ptype_ft calibration constant,@df.ptype == 1,0,coef_ft_asc +util_pt_ASC,ptype_pt calibration constant,@df.ptype == 2,0,coef_pt_asc +util_un_ASC,ptype_un calibration constant,@df.ptype == 3,0,coef_un_asc +util_na_ASC,ptype_na calibration constant,@df.ptype == 4,0,coef_na_asc +util_da_ASC,ptype_da calibration constant,@df.ptype == 5,0,coef_da_asc +util_nd_ASC,ptype_nd calibration constant,@df.ptype == 6,0,coef_nd_asc +util_ps_ASC,ptype_ps calibration constant,@df.ptype == 7,0,coef_ps_asc diff --git a/configs/resident/transit_pass_ownership.yaml b/configs/resident/transit_pass_ownership.yaml new file mode 100644 index 0000000..9dc3644 --- /dev/null +++ b/configs/resident/transit_pass_ownership.yaml @@ -0,0 +1,13 @@ + +SPEC: transit_pass_ownership.csv +COEFFICIENTS: transit_pass_ownership_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +preprocessor: + SPEC: transit_pass_ownership_preprocessor + DF: choosers + TABLES: + - land_use + - persons \ No newline at end of file diff --git a/configs/resident/transit_pass_ownership_coeffs.csv b/configs/resident/transit_pass_ownership_coeffs.csv new file mode 100644 index 0000000..1b29485 --- /dev/null +++ b/configs/resident/transit_pass_ownership_coeffs.csv @@ -0,0 +1,27 @@ +coefficient_name,value,constrain +asc_pass,-4.1,F +coef_age_19_to_34_pass,0.52,F +coef_age_35_to_44_pass,0.346,F +coef_age_55_to_64_pass,0.384,F +coef_age_65_to_79_pass,0.315,F +coef_auto_deficient_pass,1.21,F +coef_hhinc_100_200,1.17,F +coef_hhinc_15_25,0.336,F +coef_hhinc_200_plus,1.17,F +coef_hhinc_50_100,0.233,F +coef_hhinc_less_15,0.492,F +coef_is_univ,1.22,F +coef_parking_cost_at_work_pass,0.0484,F +coef_persons_0_4_in_hhld_pass,-0.551,F +coef_persons_5_15_in_hhld_pass,-0.578,F +coef_subs_pass,1.49,F +coef_total_transit_acc_pass,0.0692,F +coef_tripmc_cost_coef,-1.25,T +coef_zero_autos_pass,3.32,F +coef_ft_asc,1.310,F +coef_pt_asc,1.364,F +coef_un_asc,0.654,F +coef_na_asc,1.650,F +coef_da_asc,1.000,F +coef_nd_asc,-2.0,F +coef_ps_asc,-2.0,F diff --git a/configs/resident/transit_pass_ownership_preprocessor.csv b/configs/resident/transit_pass_ownership_preprocessor.csv new file mode 100644 index 0000000..b570bd3 --- /dev/null +++ b/configs/resident/transit_pass_ownership_preprocessor.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +# transit pass cost converted to cost per trip and multiplied by trip mode choice cost coefficient in model,, +# assumes 22 working days per month and 2 trips per work day. reduced fare for children and seniors half price.,, +,income_exponent,0.6 +,transit_pass_cost_per_trip,monthly_transit_pass_cost / (22 * 2) +,transit_pass_cost_per_trip,"np.where((df.age >= 65) | (df.age <= 18), transit_pass_cost_per_trip / 2, transit_pass_cost_per_trip)" \ No newline at end of file diff --git a/configs/resident/transit_pass_subsidy.csv b/configs/resident/transit_pass_subsidy.csv new file mode 100644 index 0000000..b9d36aa --- /dev/null +++ b/configs/resident/transit_pass_subsidy.csv @@ -0,0 +1,27 @@ +Label,Description,Expression,no_subsidy,subsidy +util_pt,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_pt +util_un,University/College,@df.ptype==PTYPE_UNIVERSITY,0,coef_un +util_tr_hh_emp,Household transit accessibility to Employment,@df.trPkTotal,0,coef_tr_hh_emp +util_tr_emp_hh,Employment transit access to HHs,@df.trn_wk_access_hh,0,coef_tr_emp_hh +util_pkcost,Peak parking cost (dollars),@df.exp_daily_work,0,coef_pkcost +util_hhinc_less_15,"Income less than 15,000",@df.income<15000,0,coef_hhinc_less_15 +util_hhinc_100_200,"income between 100,000 and 200,000","@df.income.between(100000,199999)",0,coef_hhinc_100_200 +util_ind_bus_srv,Industry type is business services,@df.naics_code==54,0,coef_ind_bus_srv +util_ind_construct,Industry type is construction,@df.naics_code==23,0,coef_ind_construct +util_ind_edu,Industry type is education,@df.naics_code==61,0,coef_ind_edu +util_ind_food_srv,Industry type is food services,@df.naics_code==722,0,coef_ind_food_srv +util_ind_gov,Industry type is government,@df.naics_code==92,0,coef_ind_gov +util_ind_health,Industry type is healthcare,@df.naics_code==62,0,coef_ind_health +util_ind_mgmt_srv,Industry type is management services,@df.naics_code==55,0,coef_ind_mgmt_srv +util_ind_mil,Industry type is military,@df.naics_code==9000,0,coef_ind_mil +util_ind_retail,Industry type is retail,@df.naics_code==721,0,coef_ind_retail +util_is_student,Student status,@df.is_student,0,coef_is_student +utils_sub_asc,Constant,1,0,coef_sub_asc +util_availability,Availability of transit pass,transit_subsidy_available,0,coef_unavailable +util_ft_ASC,ptype_ft calibration constant,@df.ptype == 1,0,coef_ft_asc +util_pt_ASC,ptype_pt calibration constant,@df.ptype == 2,0,coef_pt_asc +util_un_ASC,ptype_un calibration constant,@df.ptype == 3,0,coef_un_asc +util_na_ASC,ptype_na calibration constant,@df.ptype == 4,0,coef_na_asc +util_da_ASC,ptype_da calibration constant,@df.ptype == 5,0,coef_da_asc +util_nd_ASC,ptype_nd calibration constant,@df.ptype == 6,0,coef_nd_asc +util_ps_ASC,ptype_ps calibration constant,@df.ptype == 7,0,coef_ps_asc diff --git a/configs/resident/transit_pass_subsidy.yaml b/configs/resident/transit_pass_subsidy.yaml new file mode 100644 index 0000000..a717359 --- /dev/null +++ b/configs/resident/transit_pass_subsidy.yaml @@ -0,0 +1,14 @@ +SPEC: transit_pass_subsidy.csv +COEFFICIENTS: transit_pass_subsidy_coefficients.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +preprocessor: + SPEC: transit_pass_subsidy_preprocessor + DF: persons + TABLES: + - accessibility + - land_use + +CHOOSER_FILTER_COLUMN_NAME: transit_subsidy_available diff --git a/configs/resident/transit_pass_subsidy_coefficients.csv b/configs/resident/transit_pass_subsidy_coefficients.csv new file mode 100644 index 0000000..dee443e --- /dev/null +++ b/configs/resident/transit_pass_subsidy_coefficients.csv @@ -0,0 +1,27 @@ +coefficient_name,value,constrain +coef_hhinc_100_200,-0.34,F +coef_hhinc_less_15,-0.866,F +coef_ind_bus_srv,1.07,F +coef_ind_construct,0.904,F +coef_ind_edu,0.635,F +coef_ind_food_srv,-2.11,F +coef_ind_gov,1.46,F +coef_ind_health,0.895,F +coef_ind_mgmt_srv,0.8,F +coef_ind_mil,1.72,F +coef_ind_retail,-1.59,F +coef_is_student,-0.87,F +coef_pkcost,0.063,F +coef_pt,-0.685,F +coef_sub_asc,-4.19,F +coef_tr_emp_hh,0.0431,F +coef_tr_hh_emp,0.0434,F +coef_un,0.508,F +coef_unavailable,-999.0,F +coef_ft_asc,2.000,F +coef_pt_asc,2.900,F +coef_un_asc,-0.100,F +coef_na_asc,-2.0,F +coef_da_asc,-2.0,F +coef_nd_asc,-2.0,F +coef_ps_asc,-3.0,F diff --git a/configs/resident/transit_pass_subsidy_coeffs.csv b/configs/resident/transit_pass_subsidy_coeffs.csv new file mode 100644 index 0000000..8e4b511 --- /dev/null +++ b/configs/resident/transit_pass_subsidy_coeffs.csv @@ -0,0 +1,10 @@ +coefficient_name,coefficient_name,value,constrain +coef_ft,Full-time worker,1.882665664,F +coef_pt,Part-time worker,1.360746182,F +coef_un,University/College,2.026985147,F +coef_na70,NAICS 70 (Entertain/accom),-0.659568416,F +coef_na80,NAICS 80 (Other services),-0.352588858,F +coef_publ,NAICS 90 (Public admin),0.864991571,F +coef_tr_hh_emp,Transit accessibility to households,0.049608562,F +coef_pkcost,Daily parking cost (dollars),0.060145818,F +coef_sub_asc,Constant,-5.138015944,F diff --git a/configs/resident/transit_pass_subsidy_preprocessor.csv b/configs/resident/transit_pass_subsidy_preprocessor.csv new file mode 100644 index 0000000..9a31a77 --- /dev/null +++ b/configs/resident/transit_pass_subsidy_preprocessor.csv @@ -0,0 +1,12 @@ +Description,Target,Expression +,_transit_ivt_home_to_work_walk_loc_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WALK_LOC_TOTALIVTT', 'AM'))" +,_transit_ivt_home_to_work_walk_prm_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WALK_PRM_TOTALIVTT', 'AM'))" +,_transit_ivt_home_to_work_walk_mix_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WALK_MIX_TOTALIVTT', 'AM'))" +,_work_transit_walk_available,(_transit_ivt_home_to_work_walk_loc_peak > 0) | (_transit_ivt_home_to_work_walk_prm_peak > 0) | (_transit_ivt_home_to_work_walk_mix_peak > 0) +,_transit_ivt_home_to_work_drive_loc_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('PNROUT_LOC_TOTALIVTT', 'AM'))" +,_transit_ivt_home_to_work_drive_prm_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('PNROUT_PRM_TOTALIVTT', 'AM'))" +,_transit_ivt_home_to_work_drive_mix_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('PNROUT_MIX_TOTALIVTT', 'AM'))" +,_work_transit_drive_available,(_transit_ivt_home_to_work_drive_loc_peak > 0) | (_transit_ivt_home_to_work_drive_prm_peak > 0) | (_transit_ivt_home_to_work_drive_mix_peak > 0) +,_work_transit_available,(_work_transit_walk_available > 0) | (_work_transit_drive_available > 0) +,transit_subsidy_available,(_work_transit_available) & (persons.workplace_zone_id > 0) +,trn_wk_access_hh,"np.where(persons.workplace_zone_id > 0, reindex(accessibility.trPkHH, persons.workplace_zone_id), 0)" \ No newline at end of file diff --git a/configs/resident/transponder_ownership.csv b/configs/resident/transponder_ownership.csv new file mode 100644 index 0000000..9d6a2a6 --- /dev/null +++ b/configs/resident/transponder_ownership.csv @@ -0,0 +1,15 @@ +Label,Description,Expression,No Pass,Pass +util_auto_ownership,auto_ownership zero would result in unavailability,@(df.auto_ownership==0) * (df.everyone_owns==0) * (df.av_ownership==0),,-999 +util_ml_dist,Distance to nearest ML,@df.ml_dist,,coef_distance_to_ML +util_Constant,Constant,1,,coef_Constant +util_total_hh_toll_dist,Sum of toll distance for all workers in hh,total_hh_toll_dist,,coef_total_hh_toll_dist +util_hh_income_verylow,household income <15k,@(df.income<15000),,coef_income_vlow +util_hh_income_low,household income 15k-50k,@((df.income>=15000) & (df.income<50000)),,coef_income_low +util_hh_income_mid,household income 50-100k,@((df.income>=50000) & (df.income<100000)),,coef_income_med +util_hh_income_veryhigh,household income 150k+,@(df.income>=150000),,coef_income_vhigh +util_autos_2plus,Two or more household vehicles,@(df.auto_ownership>1),,coef_autos_2plus +util_workers_0,No workers in household,@(df.num_workers==0),,coef_workers_0 +util_2016,2016 survey,@PRE_COVID,,coef_2016 +#,,,, +util_av_ownership_TR, if AV owned then must have transponder,av_ownership,-999,0 +util_Vehyear_based_TR, if HH owns vehicle model after a certain year hh must have TR,hh_tr_veh_year,-999,0 \ No newline at end of file diff --git a/configs/resident/transponder_ownership.yaml b/configs/resident/transponder_ownership.yaml new file mode 100644 index 0000000..d6ef4b9 --- /dev/null +++ b/configs/resident/transponder_ownership.yaml @@ -0,0 +1,15 @@ + +SPEC: transponder_ownership.csv +COEFFICIENTS: transponder_ownership_coefficients.csv + +LOGIT_TYPE: MNL + +TRANSPONDER_OWNERSHIP_ALT: 1 + +preprocessor: + SPEC: transponder_ownership_preprocessor + DF: df + TABLES: + - land_use + - persons + - vehicles diff --git a/configs/resident/transponder_ownership_coefficients.csv b/configs/resident/transponder_ownership_coefficients.csv new file mode 100644 index 0000000..30b93a5 --- /dev/null +++ b/configs/resident/transponder_ownership_coefficients.csv @@ -0,0 +1,11 @@ +coefficient_name,value,constrain +coef_Constant,-1.620223679,F +coef_total_hh_toll_dist,0.137425869,F +coef_distance_to_ML,0.033171894,F +coef_income_vlow,-1.642831954,F +coef_income_low,-1.066171325,F +coef_income_med,-0.312499363,F +coef_income_vhigh,0.513926592,F +coef_autos_2plus,0.252353117,F +coef_workers_0,-0.290876682,F +coef_2016,-0.023948545,F diff --git a/configs/resident/transponder_ownership_preprocessor.csv b/configs/resident/transponder_ownership_preprocessor.csv new file mode 100644 index 0000000..309aa12 --- /dev/null +++ b/configs/resident/transponder_ownership_preprocessor.csv @@ -0,0 +1,23 @@ +Description,Target,Expression +#,avg_roundtrip_auto_time_to_work,persons.groupby('household_id').roundtrip_auto_time_to_work.mean() +,home_taz,"reindex(land_use.TAZ, df.home_zone_id)" +#,hhs_in_taz,df.groupby('home_taz')['household_id'].transform(len) +#,, +,multiple_auto_hh_in_zone,"reindex_i(df[df.auto_ownership > 1].groupby('home_zone_id').size(), df.index)" +,multiple_auto_hh_in_zone_share,multiple_auto_hh_in_zone/df.num_hh_in_zone +#,, +2016 scenario,everyone_owns,0 +Non-geocoded records expansion factor,geoExp,1.06 +Overall share of auto-owning households,qNonZero,0.837 +Observed share of transponder ownership,pObs,0.009 +,Correction_for_nongeocoded_records,np.log(geoExp)-np.log((1-geoExp*pObs)/(1-pObs)) +,Correction_for_zeroauto_households,np.log(1/qNonZero)-np.log((1-1/qNonZero*pObs)/(1-pObs)) +#,, +,ml_dist,"reindex(land_use.ML_DIST, df.home_zone_id)" +,avgtts,"reindex(land_use.AVGTTS, df.home_zone_id)" +,pctDetour,"reindex(land_use.PCTDETOUR, df.home_zone_id)" +,total_hh_toll_travel_time_savings,persons.groupby('household_id').toll_travel_time_savings_work.sum().reindex(df.index) +,total_hh_toll_dist,persons.groupby('household_id').toll_dist_work.sum().reindex(df.index) +,total_hh_toll_cost,persons.groupby('household_id').toll_cost_work.sum().reindex(df.index) +compute the hh newest vehicle model year,hh_veh_year_model," vehicles.groupby('household_id').vehicle_year.max().reindex(df.index)" +if hh owns a vehicle newer than a certain year hh must own TR,hh_tr_veh_year," np.where(hh_veh_year_model >= hhTR_Vehyear,1,0)" diff --git a/configs/resident/trip_destination.csv b/configs/resident/trip_destination.csv new file mode 100644 index 0000000..6029cae --- /dev/null +++ b/configs/resident/trip_destination.csv @@ -0,0 +1,50 @@ +Label,Description,Expression,work,univ,school,escort,shopping,othmaint,eatout,social,othdiscr,atwork +local_dist_od,,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +local_dist_dp,,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +loca_dist_op,,_op_DIST@op_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +local_dist_nd,,_nd_DIST@nd_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +distance_deviation,distance deviation,_dist_dev@(_od_DIST +_dp_DIST - _op_DIST),1,1,1,1,1,1,1,1,1,1 +dist_deviation_logged,logged deviation distance,"_dist_dev_logged@np.where((_dist_dev > 0), np.log(_dist_dev),0)",1,1,1,1,1,1,1,1,1,1 +#,,,,,,,,,,,, +util_sizeterm,size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +util_Sampleofalternativescorrectionfactor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +util_Modechoicelogsum,Mode choice logsum,od_logsum + dp_logsum,coef_mode_choice_logsum_mandatory,coef_mode_choice_logsum_mandatory,coef_mode_choice_logsum_mandatory,coef_mode_choice_logsum_maint,coef_mode_choice_logsum_maint,coef_mode_choice_logsum_maint,coef_mode_choice_logsum_discr,coef_mode_choice_logsum_discr,coef_mode_choice_logsum_discr,coef_mode_choice_logsum_discr +#,,,,,,,,,,,, +util_DistanceDeviationLinearRelative,Distance Deviation Linear - Relative,"@np.minimum((_dist_dev)/_op_DIST, 100)",,,,coef_DistanceDeviationLinearRelative_maint,coef_DistanceDeviationLinearRelative_maint,coef_DistanceDeviationLinearRelative_maint,coef_DistanceDeviationLinearRelative_discr,coef_DistanceDeviationLinearRelative_discr,coef_DistanceDeviationLinearRelative_discr,coef_DistanceDeviationLinearRelative_discr +util_DistanceDeviationLinearAbsolute,Distance Deviation Linear - Absolute,@(_dist_dev),coef_DistanceDeviationLinearAbsolute_mandatory,coef_DistanceDeviationLinearAbsolute_mandatory,coef_DistanceDeviationLinearAbsolute_mandatory,coef_DistanceDeviationLinearAbsolute_maint,coef_DistanceDeviationLinearAbsolute_maint,coef_DistanceDeviationLinearAbsolute_maint,coef_DistanceDeviationLinearAbsolute_discr,coef_DistanceDeviationLinearAbsolute_discr,coef_DistanceDeviationLinearAbsolute_discr,coef_DistanceDeviationLinearAbsolute_discr +util_LogofDistanceRelativeDeviation,Log of Distance Relative Deviation,"@np.where((_dist_dev > 0), np.log(np.minimum((_dist_dev)/_op_DIST, 100)), 0)",,,,coef_LogofDistanceRelativeDeviation_maint,coef_LogofDistanceRelativeDeviation_maint,coef_LogofDistanceRelativeDeviation_maint,coef_LogofDistanceRelativeDeviation_discr,coef_LogofDistanceRelativeDeviation_discr,coef_LogofDistanceRelativeDeviation_discr,coef_LogofDistanceRelativeDeviation_discr +util_LogofDistanceAbsoluteDeviation,Log of Distance Absolute Deviation,"@(_dist_dev_logged)",coef_LogofDistanceAbsoluteDeviation_mandatory,coef_LogofDistanceAbsoluteDeviation_mandatory,coef_LogofDistanceAbsoluteDeviation_mandatory,coef_LogofDistanceAbsoluteDeviation_maint,coef_LogofDistanceAbsoluteDeviation_maint,coef_LogofDistanceAbsoluteDeviation_maint,coef_LogofDistanceAbsoluteDeviation_discr,coef_LogofDistanceAbsoluteDeviation_discr,coef_LogofDistanceAbsoluteDeviation_discr,coef_LogofDistanceAbsoluteDeviation_discr +util_DistanceDeviationsquaredRelative,Distance Deviation squared - Relative,"@np.power(np.minimum((_dist_dev)/_op_DIST, 100),2)",,,,coef_DistanceDeviationsquaredRelative_maint,coef_DistanceDeviationsquaredRelative_maint,coef_DistanceDeviationsquaredRelative_maint,coef_DistanceDeviationsquaredRelative_discr,coef_DistanceDeviationsquaredRelative_discr,coef_DistanceDeviationsquaredRelative_discr,coef_DistanceDeviationsquaredRelative_discr +util_DistanceDeviationsquaredAbsolute,Distance Deviation squared - Absolute,"@np.power(_dist_dev,2)",coef_DistanceDeviationsquaredAbsolute_mandatory,coef_DistanceDeviationsquaredAbsolute_mandatory,coef_DistanceDeviationsquaredAbsolute_mandatory,coef_DistanceDeviationsquaredAbsolute_maint,coef_DistanceDeviationsquaredAbsolute_maint,coef_DistanceDeviationsquaredAbsolute_maint,coef_DistanceDeviationsquaredAbsolute_discr,coef_DistanceDeviationsquaredAbsolute_discr,coef_DistanceDeviationsquaredAbsolute_discr,coef_DistanceDeviationsquaredAbsolute_discr +util_DistanceDeviation2ndstopofhalftour,Distance Deviation - 2nd stop of half tour,@(df.trip_num==2) * (_dist_dev),coef_DistanceDeviation2ndstopofhalftour_mandatory,coef_DistanceDeviation2ndstopofhalftour_mandatory,coef_DistanceDeviation2ndstopofhalftour_mandatory,coef_DistanceDeviation2ndstopofhalftour_maint,coef_DistanceDeviation2ndstopofhalftour_maint,coef_DistanceDeviation2ndstopofhalftour_maint,coef_DistanceDeviation2ndstopofhalftour_discr,coef_DistanceDeviation2ndstopofhalftour_discr,coef_DistanceDeviation2ndstopofhalftour_discr,coef_DistanceDeviation2ndstopofhalftour_discr +util_DistanceDeviation3rdmorestoponhalftour,Distance Deviation - 3rd+ stop on half tour,@(df.trip_num>2) * (_dist_dev),coef_DistanceDeviation3rdmorestoponhalftour_mandatory,coef_DistanceDeviation3rdmorestoponhalftour_mandatory,coef_DistanceDeviation3rdmorestoponhalftour_mandatory,coef_DistanceDeviation3rdmorestoponhalftour_maint,coef_DistanceDeviation3rdmorestoponhalftour_maint,coef_DistanceDeviation3rdmorestoponhalftour_maint,coef_DistanceDeviation3rdmorestoponhalftour_discr,coef_DistanceDeviation3rdmorestoponhalftour_discr,coef_DistanceDeviation3rdmorestoponhalftour_discr,coef_DistanceDeviation3rdmorestoponhalftour_discr +util_DistanceDeviationNumberofStopsonthehalftour,Distance Deviation - Number of Stops on the half-tour,"@np.minimum(_dist_dev,7.0)*df.trip_count",coef_DistanceDeviationNumberofStopsonthehalftour_mandatory,coef_DistanceDeviationNumberofStopsonthehalftour_mandatory,coef_DistanceDeviationNumberofStopsonthehalftour_mandatory,coef_DistanceDeviationNumberofStopsonthehalftour_maint,coef_DistanceDeviationNumberofStopsonthehalftour_maint,coef_DistanceDeviationNumberofStopsonthehalftour_maint,coef_DistanceDeviationNumberofStopsonthehalftour_discr,coef_DistanceDeviationNumberofStopsonthehalftour_discr,coef_DistanceDeviationNumberofStopsonthehalftour_discr,coef_DistanceDeviationNumberofStopsonthehalftour_discr +util_DistanceDeviationwalkbiketour,Distance Deviation - walk/bike tour,nonmotorTour * (_dist_dev),coef_DistanceDeviationwalkbiketour_mandatory,coef_DistanceDeviationwalkbiketour_mandatory,coef_DistanceDeviationwalkbiketour_mandatory,coef_DistanceDeviationwalkbiketour_maint,coef_DistanceDeviationwalkbiketour_maint,coef_DistanceDeviationwalkbiketour_maint,coef_DistanceDeviationwalkbiketour_discr,coef_DistanceDeviationwalkbiketour_discr,coef_DistanceDeviationwalkbiketour_discr,coef_DistanceDeviationwalkbiketour_discr +util_DistanceDeviationworkstoppurpose,Distance Deviation - work stop purpose,workStop *_dist_dev,coef_DistanceDeviationworkstoppurpose_mandatory,coef_DistanceDeviationworkstoppurpose_mandatory,coef_DistanceDeviationworkstoppurpose_mandatory,coef_DistanceDeviationworkstoppurpose_maint,coef_DistanceDeviationworkstoppurpose_maint,coef_DistanceDeviationworkstoppurpose_maint,coef_DistanceDeviationworkstoppurpose_discr,coef_DistanceDeviationworkstoppurpose_discr,coef_DistanceDeviationworkstoppurpose_discr,coef_DistanceDeviationworkstoppurpose_discr +util_DistanceDeviationuniversitystoppurpose,Distance Deviation - university stop purpose,"@df.univStop * np.minimum(_dist_dev,5.0)",coef_DistanceDeviationuniversitystoppurpose_mandatory,coef_DistanceDeviationuniversitystoppurpose_mandatory,coef_DistanceDeviationuniversitystoppurpose_mandatory,coef_DistanceDeviationuniversitystoppurpose_maint,coef_DistanceDeviationuniversitystoppurpose_maint,coef_DistanceDeviationuniversitystoppurpose_maint,coef_DistanceDeviationuniversitystoppurpose_discr,coef_DistanceDeviationuniversitystoppurpose_discr,coef_DistanceDeviationuniversitystoppurpose_discr,coef_DistanceDeviationuniversitystoppurpose_discr +util_DistanceDeviationmaintenancestoppurpose,Distance Deviation - maintenance stop purpose,othmainStop * _dist_dev,coef_DistanceDeviationmaintenancestoppurpose_mandatory,coef_DistanceDeviationmaintenancestoppurpose_mandatory,coef_DistanceDeviationmaintenancestoppurpose_mandatory,coef_DistanceDeviationmaintenancestoppurpose_maint,coef_DistanceDeviationmaintenancestoppurpose_maint,coef_DistanceDeviationmaintenancestoppurpose_maint,coef_DistanceDeviationmaintenancestoppurpose_discr,coef_DistanceDeviationmaintenancestoppurpose_discr,coef_DistanceDeviationmaintenancestoppurpose_discr,coef_DistanceDeviationmaintenancestoppurpose_discr +util_DistanceDeviationdiscretionarystoppurpose,Distance Deviation - discretionary stop purpose,"@df.discStop * np.minimum(_dist_dev,8.0)",coef_DistanceDeviationdiscretionarystoppurpose_mandatory,coef_DistanceDeviationdiscretionarystoppurpose_mandatory,coef_DistanceDeviationdiscretionarystoppurpose_mandatory,coef_DistanceDeviationdiscretionarystoppurpose_maint,coef_DistanceDeviationdiscretionarystoppurpose_maint,coef_DistanceDeviationdiscretionarystoppurpose_maint,coef_DistanceDeviationdiscretionarystoppurpose_discr,coef_DistanceDeviationdiscretionarystoppurpose_discr,coef_DistanceDeviationdiscretionarystoppurpose_discr,coef_DistanceDeviationdiscretionarystoppurpose_discr +util_DistanceDeviationshoppingstoppurpose,Distance Deviation - shopping stop purpose,shopStop *_dist_dev,,,,coef_DistanceDeviationshoppingstoppurpose_maint,coef_DistanceDeviationshoppingstoppurpose_maint,coef_DistanceDeviationshoppingstoppurpose_maint,coef_DistanceDeviationshoppingstoppurpose_discr,coef_DistanceDeviationshoppingstoppurpose_discr,coef_DistanceDeviationshoppingstoppurpose_discr,coef_DistanceDeviationshoppingstoppurpose_discr +util_DistanceDeviationeatoutstoppurpose,Distance Deviation - eatout stop purpose,eatStop *_dist_dev,,,,coef_DistanceDeviationeatoutstoppurpose_maint,coef_DistanceDeviationeatoutstoppurpose_maint,coef_DistanceDeviationeatoutstoppurpose_maint,coef_DistanceDeviationeatoutstoppurpose_discr,coef_DistanceDeviationeatoutstoppurpose_discr,coef_DistanceDeviationeatoutstoppurpose_discr,coef_DistanceDeviationeatoutstoppurpose_discr +util_DistanceDeviationsocialstoppurpose,Distance Deviation - social stop purpose,socStop * _dist_dev,,,,coef_DistanceDeviationsocialstoppurpose_maint,coef_DistanceDeviationsocialstoppurpose_maint,coef_DistanceDeviationsocialstoppurpose_maint,coef_DistanceDeviationsocialstoppurpose_discr,coef_DistanceDeviationsocialstoppurpose_discr,coef_DistanceDeviationsocialstoppurpose_discr,coef_DistanceDeviationsocialstoppurpose_discr +util_DistanceDeviationworkstoppurpose_log,Distance Deviation - work stop purpose,workStop * _dist_dev_logged,coef_DistanceDeviationworkstoppurpose,coef_DistanceDeviationworkstoppurpose,coef_DistanceDeviationworkstoppurpose,,,,,,, +util_DistanceDeviationshoppingstoppurpose_log,Distance Deviation - shopping stop purpose,shopStop * _dist_dev_logged,coef_DistanceDeviationshoppingstoppurpose_mandatory,coef_DistanceDeviationshoppingstoppurpose_mandatory,coef_DistanceDeviationshoppingstoppurpose_mandatory,,,,,,, +util_DistanceDeviationsocialvisitingstoppurpose_log,Distance Deviation - socialvisiting stop purpose,socStop * _dist_dev_logged,coef_DistanceDeviationsocialvisitingstoppurpose_mandatory,coef_DistanceDeviationsocialvisitingstoppurpose_mandatory,coef_DistanceDeviationsocialvisitingstoppurpose_mandatory,,,,,,, +util_DistanceDeviationSchool,Distance Deviation - School Tour purpose,schoolTour * _dist_dev,coef_DistanceDeviationSchool_mandatory,coef_DistanceDeviationSchool_mandatory,coef_DistanceDeviationSchool_mandatory,,,,,,, +util_DistanceDeviationUniversity,Distance Deviation - University Tour purpose,univTour * _dist_dev,coef_DistanceDeviationUniversity_mandatory,coef_DistanceDeviationUniversity_mandatory,coef_DistanceDeviationUniversity_mandatory,,,,,,, +util_DistanceDeviationShopping,Distance Deviation - Shopping Tour purpose,shopTour * _dist_dev,,,,coef_DistanceDeviationothmaint_maint,coef_DistanceDeviationothmaint_maint,coef_DistanceDeviationothmaint_maint,,,, +util_DistanceDeviationOthMaint,Distance Deviation - Other maintenanceTour purpose,othmainTour * _dist_dev,,,,coef_DistanceDeviationshopping_maint,coef_DistanceDeviationshopping_maint,coef_DistanceDeviationshopping_maint,,,, +util_DistanceDeviationDiscr,Distance Deviation - DiscTour purpose,discTour * _dist_dev,,,,,,,coef_DistanceDeviationdiscr_maint,coef_DistanceDeviationdiscr_maint,coef_DistanceDeviationdiscr_maint,coef_DistanceDeviationdiscr_maint +util_DistanceDeviationAtwork,Distance Deviation - AtworkTour purpose,atworkTour * _dist_dev,,,,,,,coef_DistanceDeviationatwork_maint,coef_DistanceDeviationatwork_maint,coef_DistanceDeviationatwork_maint,coef_DistanceDeviationatwork_maint +util_DistanceDeviationIncomeLessthan60k,"Distance Deviation - Income Less than $60,000","@(df.income_trips<60000) * np.minimum(_dist_dev,13.0)",coef_DistanceDeviationIncomeLessthan60k_mandatory,coef_DistanceDeviationIncomeLessthan60k_mandatory,coef_DistanceDeviationIncomeLessthan60k_mandatory,coef_DistanceDeviationIncomeLessthan60k_maint,coef_DistanceDeviationIncomeLessthan60k_maint,coef_DistanceDeviationIncomeLessthan60k_maint,coef_DistanceDeviationIncomeLessthan60k_discr,coef_DistanceDeviationIncomeLessthan60k_discr,coef_DistanceDeviationIncomeLessthan60k_discr,coef_DistanceDeviationIncomeLessthan60k_discr +util_DistanceDeviationFemale,Distance Deviation - Female,(female & (is_joint==0)) * (_dist_dev),coef_DistanceDeviationFemale_mandatory,coef_DistanceDeviationFemale_mandatory,coef_DistanceDeviationFemale_mandatory,coef_DistanceDeviationFemale_maint,coef_DistanceDeviationFemale_maint,coef_DistanceDeviationFemale_maint,coef_DistanceDeviationFemale_discr,coef_DistanceDeviationFemale_discr,coef_DistanceDeviationFemale_discr,coef_DistanceDeviationFemale_discr +util_DistanceDeviationAgebetween35and54years,Distance Deviation - Age between 35 and 54 years,"@(df.age_trips.between(35,54)*df.is_joint==0) * (_dist_dev)",coef_DistanceDeviationAgebetween35and54years_mandatory,coef_DistanceDeviationAgebetween35and54years_mandatory,coef_DistanceDeviationAgebetween35and54years_mandatory,coef_DistanceDeviationAgebetween35and54years_maint,coef_DistanceDeviationAgebetween35and54years_maint,coef_DistanceDeviationAgebetween35and54years_maint,coef_DistanceDeviationAgebetween35and54years_discr,coef_DistanceDeviationAgebetween35and54years_discr,coef_DistanceDeviationAgebetween35and54years_discr,coef_DistanceDeviationAgebetween35and54years_discr +util_DistanceDeviationAgeover54years,Distance Deviation - Age over 54 years,@(df.age_trips>54) * (df.is_joint==0) * (_dist_dev),coef_DistanceDeviationAgeover54years_mandatory,coef_DistanceDeviationAgeover54years_mandatory,coef_DistanceDeviationAgeover54years_mandatory,coef_DistanceDeviationAgeover54years_maint,coef_DistanceDeviationAgeover54years_maint,coef_DistanceDeviationAgeover54years_maint,coef_DistanceDeviationAgeover54years_discr,coef_DistanceDeviationAgeover54years_discr,coef_DistanceDeviationAgeover54years_discr,coef_DistanceDeviationAgeover54years_discr +util_DistanceRatio,Distance Ratio,(_nd_DIST)/ (_nd_DIST + _dp_DIST),coef_DistanceRatio_mandatory,coef_DistanceRatio_mandatory,coef_DistanceRatio_mandatory,coef_DistanceRatio_maint,coef_DistanceRatio_maint,coef_DistanceRatio_maint,coef_DistanceRatio_discr,coef_DistanceRatio_discr,coef_DistanceRatio_discr,coef_DistanceRatio_discr +util_DistanceRatioFirstOutboundStop,Distance Ratio - First Outbound Stop,@(df.trip_num==1)*(df.outbound) * ((_nd_DIST)/ (_nd_DIST + _dp_DIST)),coef_DistanceRatioFirstOutboundStop_mandatory,coef_DistanceRatioFirstOutboundStop_mandatory,coef_DistanceRatioFirstOutboundStop_mandatory,coef_DistanceRatioFirstOutboundStop_maint,coef_DistanceRatioFirstOutboundStop_maint,coef_DistanceRatioFirstOutboundStop_maint,coef_DistanceRatioFirstOutboundStop_discr,coef_DistanceRatioFirstOutboundStop_discr,coef_DistanceRatioFirstOutboundStop_discr,coef_DistanceRatioFirstOutboundStop_discr +util_DistanceRatioFirstInboundStop,Distance Ratio - First Inbound Stop,@(df.trip_num==1)*(df.outbound==0) * ((_nd_DIST)/ (_nd_DIST + _dp_DIST)),coef_DistanceRatioFirstInboundStop_mandatory,coef_DistanceRatioFirstInboundStop_mandatory,coef_DistanceRatioFirstInboundStop_mandatory,coef_DistanceRatioFirstInboundStop_maint,coef_DistanceRatioFirstInboundStop_maint,coef_DistanceRatioFirstInboundStop_maint,coef_DistanceRatioFirstInboundStop_discr,coef_DistanceRatioFirstInboundStop_discr,coef_DistanceRatioFirstInboundStop_discr,coef_DistanceRatioFirstInboundStop_discr +util_DistanceRatioMandatoryOutboundTour,Distance Ratio - Mandatory Outbound Tour,(outbound)* mandTour * ((_nd_DIST)/ (_nd_DIST + _dp_DIST)),coef_DistanceRatioMandatoryOutboundTour_mandatory,coef_DistanceRatioMandatoryOutboundTour_mandatory,coef_DistanceRatioMandatoryOutboundTour_mandatory,,,,,,, +util_DistanceRatioMandatoryInboundTour,Distance Ratio - Mandatory Inbound Tour,(outbound==0) * mandTour * ((_nd_DIST)/ (_nd_DIST + _dp_DIST)),coef_DistanceRatioMandatoryInboundTour_mandatory,coef_DistanceRatioMandatoryInboundTour_mandatory,coef_DistanceRatioMandatoryInboundTour_mandatory,,,,,,, +util_DistanceDeviationSquareWork,Distance Deviation Square - Work Tour purpose,"@df.workTour * np.power(_dist_dev,2)",coef_DistanceDeviationSquareWork_mandatory,coef_DistanceDeviationSquareWork_mandatory,coef_DistanceDeviationSquareWork_mandatory,,,,,,, +util_DistanceDeviationSocial,Distance Deviation SocialTour purpose,socTour * _dist_dev,,,,,,,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr +util_DistanceDeviationDiscr,Distance Deviation DiscrTour purpose,discTour * _dist_dev,,,,,,,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr,coef_DistanceDeviationDiscr diff --git a/configs/resident/trip_destination.yaml b/configs/resident/trip_destination.yaml new file mode 100644 index 0000000..ee6c9fc --- /dev/null +++ b/configs/resident/trip_destination.yaml @@ -0,0 +1,46 @@ +SAMPLE_SPEC: trip_destination_sample.csv +SPEC: trip_destination.csv +COEFFICIENTS: trip_destination_coefficients.csv + +SAMPLE_SIZE: 30 + +DESTINATION_SAMPLE_SPEC: trip_destination_sample.csv +DESTINATION_SPEC: trip_destination.csv + +LOGSUM_SETTINGS: trip_mode_choice.yaml + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: trip_destination_sample + +# model-specific logsum-related settings +TRIP_ORIGIN: origin +ALT_DEST_COL_NAME: dest_taz +PRIMARY_ORIGIN: tour_leg_origin +PRIMARY_DEST: tour_leg_dest # must be created in preprocessor + +# tour_mode is already in trips table, so we don't need it from tours +# (it is assigned in trip_destination_annotate_trips_preprocessor ) +REDUNDANT_TOURS_MERGED_CHOOSER_COLUMNS: + - tour_mode + + + +preprocessor: + SPEC: trip_destination_annotate_trips_preprocessor + DF: trips + TABLES: + - tours + - persons + - households + - land_use + + +# drop failed trips and cleanup failed trip leg_mates for consistency +# (i.e. adjust trip_count, trip_num, first for missing failed trips) +CLEANUP: False + +# this setting is used by testing code to force failed trip_destination +# fail_some_trips_for_testing: False diff --git a/configs/resident/trip_destination_annotate_trips_preprocessor.csv b/configs/resident/trip_destination_annotate_trips_preprocessor.csv new file mode 100644 index 0000000..b71ee58 --- /dev/null +++ b/configs/resident/trip_destination_annotate_trips_preprocessor.csv @@ -0,0 +1,39 @@ +Description,Target,Expression +#,, +,tour_mode,"reindex(tours.tour_mode, df.tour_id)" +,is_joint,"reindex(tours.tour_category, df.tour_id) == 'joint'" +,tour_leg_origin,"np.where(df.outbound,reindex(tours.origin, df.tour_id), reindex(tours.destination, df.tour_id))" +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" +#,, +,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" +,trip_period,network_los.skim_time_period_label(_tod) +#,, +adding _trips to avoid conflict with the variables in the tours_merged,income_trips,"reindex(households.income, df.person_id)" +adding _trips to avoid conflict with the variables in the tours_merged,age_trips,"reindex(persons.age, df.person_id)" +,female,"reindex(persons.female, df.person_id)" +#,age_55p,"reindex(persons.age_55_p, df.person_id)" +#,age_35_54,"reindex(persons.age_35_to_54, df.person_id)" +,workTour,"np.where(df.primary_purpose == 'work', 1, 0)" +,schoolTour,"np.where(df.primary_purpose == 'school', 1, 0)" +,univTour,"np.where(df.primary_purpose == 'univ', 1, 0)" +,shopTour,"np.where(df.primary_purpose == 'shopping', 1, 0)" +,othmainTour,"np.where(df.primary_purpose == 'othmaint', 1, 0)" +,discTour,"np.where(df.primary_purpose == 'discr', 1, 0)" +,socTour,"np.where(df.primary_purpose == 'social', 1, 0)" +,atworkTour,"np.where(df.primary_purpose == 'atwork', 1, 0)" +,mandTour,"np.where(df.primary_purpose.isin(['work', 'school', 'univ']), 1, 0)" +,workStop,"np.where(df.purpose == 'work', 1, 0)" +,univStop,"np.where(df.purpose == 'univ', 1, 0)" +,othmainStop,"np.where(df.purpose == 'othmaint', 1, 0)" +,discStop,"np.where(df.purpose == 'othdiscr', 1, 0)" +,shopStop,"np.where(df.purpose == 'shopping', 1, 0)" +,eatStop,"np.where(df.purpose == 'eatout', 1, 0)" +,socStop,"np.where(df.purpose == 'social', 1, 0)" +,nonmotorTour,"np.where(tour_mode.isin(['WALK','BIKE']), 1, 0)" +,walkTour,"np.where(tour_mode == 'WALK', 1, 0)" +,bikeTour,"np.where(tour_mode == 'BIKE', 1, 0)" +,microTour,"np.where((tour_mode == 'ESCOOTER') | (tour_mode == 'EBIKE'), 1, 0)" +Micromobility access Time,o_MicroAccessTime,"reindex(land_use.MicroAccessTime,df.origin)" +,max_walk_distance,max_walk_distance +,max_bike_distance,max_bike_distance +,microAccessThreshold,microAccessThreshold \ No newline at end of file diff --git a/configs/resident/trip_destination_coefficients.csv b/configs/resident/trip_destination_coefficients.csv new file mode 100644 index 0000000..feeca71 --- /dev/null +++ b/configs/resident/trip_destination_coefficients.csv @@ -0,0 +1,88 @@ +coefficient_name,value,constrain +coef_UNAVAILABLE,-999,T +coef_one,1,T +#,, +coef_mode_choice_logsum_mandatory,1.31417,F +coef_DistanceDeviationLinearAbsolute_mandatory,0,F +coef_LogofDistanceAbsoluteDeviation_mandatory,-0.83965,F +coef_DistanceDeviationsquaredAbsolute_mandatory,0,F +coef_DistanceDeviation2ndstopofhalftour_mandatory,-0.06176,F +coef_DistanceDeviation3rdmorestoponhalftour_mandatory,-0.07764,F +coef_DistanceDeviationNumberofStopsonthehalftour_mandatory,0,F +coef_DistanceDeviationwalkbiketour_mandatory,0,F +coef_DistanceDeviationworkstoppurpose_mandatory,-0.10657,F +coef_DistanceDeviationuniversitystoppurpose_mandatory,0.08433,F +coef_DistanceDeviationmaintenancestoppurpose_mandatory,0.02921,F +coef_DistanceDeviationdiscretionarystoppurpose_mandatory,0.07266,F +coef_DistanceDeviationworkstoppurpose,1.02985,F +coef_DistanceDeviationshoppingstoppurpose_mandatory,-0.19942,F +coef_DistanceDeviationsocialvisitingstoppurpose_mandatory,0.65223,F +coef_DistanceDeviationSchool_mandatory,0.11216,F +coef_DistanceDeviationUniversity_mandatory,0.02587,F +coef_DistanceDeviationIncomeLessthan60k_mandatory,0.0227,F +coef_DistanceDeviationFemale_mandatory,-0.07066,F +coef_DistanceDeviationAgebetween35and54years_mandatory,-0.08953,F +coef_DistanceDeviationAgeover54years_mandatory,-0.0987,F +coef_DistanceRatio_mandatory,-1.99261,F +coef_DistanceRatioFirstOutboundStop_mandatory,-0.6487,F +coef_DistanceRatioFirstInboundStop_mandatory,1.49723,F +coef_DistanceRatioMandatoryOutboundTour_mandatory,0,F +coef_DistanceRatioMandatoryInboundTour_mandatory,0.91734,F +coef_DistanceDeviationSquareWork_mandatory,0,F +#,, +coef_mode_choice_logsum_maint,1.00663,F +coef_DistanceDeviationLinearRelative_maint,0,F +coef_DistanceDeviationLinearAbsolute_maint,-0.0615,F +coef_LogofDistanceRelativeDeviation_maint,0,F +coef_LogofDistanceAbsoluteDeviation_maint,-0.94056,F +coef_DistanceDeviationsquaredRelative_maint,0,F +coef_DistanceDeviationsquaredAbsolute_maint,-0.00022,F +coef_DistanceDeviation2ndstopofhalftour_maint,-0.03146,F +coef_DistanceDeviation3rdmorestoponhalftour_maint,-0.0631,F +coef_DistanceDeviationNumberofStopsonthehalftour_maint,0.02045,F +coef_DistanceDeviationwalkbiketour_maint,-1.00489,F +coef_DistanceDeviationworkstoppurpose_maint,0.06367,F +coef_DistanceDeviationuniversitystoppurpose_maint,0.09372,F +coef_DistanceDeviationmaintenancestoppurpose_maint,0.02109,F +coef_DistanceDeviationdiscretionarystoppurpose_maint,0.05744,F +coef_DistanceDeviationshoppingstoppurpose_maint,-0.02269,F +coef_DistanceDeviationeatoutstoppurpose_maint,0.02249,F +coef_DistanceDeviationsocialstoppurpose_maint,0.06855,F +coef_DistanceDeviationothmaint_maint,0.02685,F +coef_DistanceDeviationshopping_maint,0.03415,F +coef_DistanceDeviationIncomeLessthan60k_maint,0.03634,F +coef_DistanceDeviationFemale_maint,-0.02723,F +coef_DistanceDeviationAgebetween35and54years_maint,-0.01022,F +coef_DistanceDeviationAgeover54years_maint,-0.01353,F +coef_DistanceRatio_maint,-0.4485,F +coef_DistanceRatioFirstOutboundStop_maint,-1.51595,F +coef_DistanceRatioFirstInboundStop_maint,1.80508,F +#,, +coef_mode_choice_logsum_discr,1.00663, +coef_DistanceDeviationLinearRelative_discr,0, +coef_DistanceDeviationLinearAbsolute_discr,-0.0615, +coef_LogofDistanceRelativeDeviation_discr,0, +coef_LogofDistanceAbsoluteDeviation_discr,-0.94056, +coef_DistanceDeviationsquaredRelative_discr,0, +coef_DistanceDeviationsquaredAbsolute_discr,-0.00022, +coef_DistanceDeviation2ndstopofhalftour_discr,-0.03146, +coef_DistanceDeviation3rdmorestoponhalftour_discr,-0.0631, +coef_DistanceDeviationNumberofStopsonthehalftour_discr,0.02045, +coef_DistanceDeviationwalkbiketour_discr,-1.00489, +coef_DistanceDeviationworkstoppurpose_discr,0.06367, +coef_DistanceDeviationuniversitystoppurpose_discr,0.09372, +coef_DistanceDeviationmaintenancestoppurpose_discr,0.02109, +coef_DistanceDeviationdiscretionarystoppurpose_discr,0.05744, +coef_DistanceDeviationshoppingstoppurpose_discr,-0.02269, +coef_DistanceDeviationeatoutstoppurpose_discr,0.02249, +coef_DistanceDeviationsocialstoppurpose_discr,0.06855, +coef_DistanceDeviationdiscr_maint,-0.045, +coef_DistanceDeviationatwork_maint,0.08779, +coef_DistanceDeviationIncomeLessthan60k_discr,0.03634, +coef_DistanceDeviationFemale_discr,-0.02723, +coef_DistanceDeviationAgebetween35and54years_discr,-0.01022, +coef_DistanceDeviationAgeover54years_discr,-0.01353, +coef_DistanceRatio_discr,-0.4485, +coef_DistanceRatioFirstOutboundStop_discr,-1.51595, +coef_DistanceRatioFirstInboundStop_discr,1.80508, +coef_DistanceDeviationDiscr,0.02, diff --git a/configs/resident/trip_destination_sample.csv b/configs/resident/trip_destination_sample.csv new file mode 100644 index 0000000..4572aad --- /dev/null +++ b/configs/resident/trip_destination_sample.csv @@ -0,0 +1,22 @@ +Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork +,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +,_op_DIST@op_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +,_od_bikeL@od_skims['BIKE_LOGSUM'],1,1,1,1,1,1,1,1,1,1 +,_dp_bikeL@dp_skims['BIKE_LOGSUM'],1,1,1,1,1,1,1,1,1,1 +# next line gets max MAZ micromobility access time in destination TAZ +,"_d_microAccTime@land_use.sort_values(by='MicroAccessTime',ascending=False).drop_duplicates('TAZ',keep='first').set_index('TAZ')['MicroAccessTime'].reindex(df.dest_taz)",1,1,1,1,1,1,1,1,1,1 +#,,,,,,,,,,, +size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +#,,,,,,,,,,, +,"@df.walkTour * (np.where(_od_DIST > df.max_walk_distance,1,0) + np.where(_dp_DIST > df.max_walk_distance,1,0))",-10,-10,-10,-10,-10,-10,-10,-10,-10,-10 +,@df.walkTour * (_od_DIST + _dp_DIST),-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5 +#,,,,,,,,,,, +,"@df.bikeTour * (np.where(_od_DIST > df.max_bike_distance,1,0) + np.where(_dp_DIST > df.max_bike_distance,1,0))",-10,-10,-10,-10,-10,-10,-10,-10,-10,-10 +,@df.bikeTour * (_od_DIST + _dp_DIST - _op_DIST),-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05 +,@df.bikeTour * (_od_bikeL < -300),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +,@df.bikeTour * (_dp_bikeL < -300),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +#,,,,,,,,,,, +,@(df.nonmotorTour==0) * (_od_DIST + _dp_DIST - _op_DIST),-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05 +,"@(df.microTour * (np.where(_d_microAccTime > df.microAccessThreshold,1,0) + np.where(df.o_MicroAccessTime > df.microAccessThreshold,1,0)))",-10,-10,-10,-10,-10,-10,-10,-10,-10,-10 diff --git a/configs/resident/trip_mode_choice.csv b/configs/resident/trip_mode_choice.csv new file mode 100644 index 0000000..31ff4dd --- /dev/null +++ b/configs/resident/trip_mode_choice.csv @@ -0,0 +1,462 @@ +Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_LOC,WALK_PRM,WALK_MIX,PNR_LOC,PNR_PRM,PNR_MIX,KNR_LOC,KNR_PRM,KNR_MIX,TNC_LOC,TNC_PRM,TNC_MIX,TAXI,TNC_SINGLE,TNC_SHARED,SCH_BUS,EBIKE,ESCOOTER +#,Drive alone,,,,,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable,Drive alone - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - In-vehicle time,Drive alone - In-vehicle time,(da_time_skims) * autoIVTFactor * time_factor,coef_ivt,,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - reliability ,Drive alone - reliability,"@df.da_rel_skims/np.maximum(df.da_dist_skims,0.1) * df.time_factor",coef_rel,,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - cost,Drive alone - cost,"@(df.auto_op_cost * df.autoCPMFactor * df.da_dist_skims + df.da_cost_skims)/(np.maximum(df.income,1000)**df.income_exponent)",coef_income,,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - Parking cost ,Drive alone - Parking cost ,"@(df.parkingCost * df.autoParkingCostFactor)/(np.maximum(df.income,1000)**df.income_exponent)",coef_income,,,,,,,,,,,,,,,,,,,,,, +util_Drive alone - Terminal Time,Drive alone - Terminal Time,(oTermTime + dTermTime) * autoTermTimeFactor * time_factor,coef_acctime,,,,,,,,,,,,,,,,,,,,,, +#,Shared ride2,,,,,,,,,,,,,,,,,,,,,,,, +util_sr2_available,sr2 available,sr2_available == False,,-999,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 - In -vehicle time,Shared ride 2 - In -vehicle time,(s2_time_skims) * autoIVTFactor * time_factor,,coef_ivt,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 - reliability,Shared ride 2 - reliability,"@df.s2_rel_skims/np.maximum(df.s2_dist_skims,0.1) * df.time_factor",,coef_rel,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 - cost,Shared ride 2 - cost,"@((df.auto_op_cost * df.autoCPMFactor * df.s2_dist_skims) + df.s2_cost_skims)/(np.maximum(df.income,1000)**df.income_exponent)",,coef_income,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 - Parking cost ,Shared ride 2 - Parking cost ,"@(df.parkingCost * df.costFactorS2 * df.autoParkingCostFactor)/(np.maximum(df.income,1000)**df.income_exponent)",,coef_income,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 Terminal Time - acc,Shared ride 2 - Terminal Time ,(oTermTime + dTermTime) * autoTermTimeFactor * time_factor,,coef_acctime,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 _Two_person_household,Shared ride 2 - Two person household,@(df.hhsize == 2),,coef_size2_sr2,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 _Three_person_household,Shared ride 2 - Three person household,@(df.hhsize == 3),,coef_size3_sr2,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 _Four+_person_household,Shared ride 2 - Four plus person household,@(df.hhsize > 3),,coef_size4p_sr2,,,,,,,,,,,,,,,,,,,,, +util_Shared ride 2 - Female,Shared ride 2 - female,@(df.female == 1),,coef_female_sr2,,,,,,,,,,,,,,,,,,,,, +#,Shared ride 3,,,,,,,,,,,,,,,,,,,,,,,, +util_sr3_available,sr3 available,sr3_available == False,,,-999,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - In -vehicle time,Shared ride 3 - In -vehicle time,(s3_time_skims) * autoIVTFactor * time_factor,,,coef_ivt,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - reliability,Shared ride 3 - reliability,"@df.s3_rel_skims/np.maximum(df.s3_dist_skims, 0.1) * df.time_factor",,,coef_rel,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - cost ,Shared ride 3 - cost,"@(df.auto_op_cost * df.s3_dist_skims * df.autoCPMFactor + df.s3_cost_skims)/(np.maximum(df.income,1000)**df.income_exponent)",,,coef_income,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - Parking cost ,Shared ride 3 - Parking cost ,"@(df.parkingCost * df.costFactorS3 * df.autoParkingCostFactor)/(np.maximum(df.income,1000)**df.income_exponent)",,,coef_income,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - Terminal Time,Shared ride 3 - Terminal Time,(oTermTime + dTermTime) * autoTermTimeFactor * time_factor,,,coef_acctime,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - Two_person_household,Shared ride 3 - Two person household,@(df.hhsize == 2),,,coef_size2_sr3p,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - Three_person_household,Shared ride 3 - Three person household,@(df.hhsize == 3),,,coef_size3_sr3p,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - Four+_person_household,Shared ride 3 - Four plus person household,@(df.hhsize > 3),,,coef_size4p_sr3p,,,,,,,,,,,,,,,,,,,, +util_Shared ride 3 - Female,Shared ride 3 - female,@(df.female == 1),,,coef_female_sr3p,,,,,,,,,,,,,,,,,,,, +#,Walk,,,,,,,,,,,,,,,,,,,,,,,, +#,school escorting can force longer walk trips so not turning off completely if walk tour,,,,,,,,,,,,,,,,,,,,,,,, +util_Walk - Unavailable,Walk - Unavailable,(walkAvailable == 0) & (tourWalk == 0),,,,-999,,,,,,,,,,,,,,,,,,, +util_Walk - Unavailable - walkTour,Walk - Unavailable - Walk Tour,(walkAvailable == 0) & (tourWalk == 1),,,,-20,,,,,,,,,,,,,,,,,,, +util_Walk - Female,Walk - Female,@df.female,,,,coef_female_nmot,,,,,,,,,,,,,,,,,,, +util_Walk - time,Walk - time - clipped for really long walk trips to avoid zero probs,@(df.walk_time_skims * df.time_factor).clip(upper=1000),,,,coef_walkTime,,,,,,,,,,,,,,,,,,, +util_Walk - Origin Mix,Walk - Origin Mix,oMGRAMix,,,,coef_oMix_nmot,,,,,,,,,,,,,,,,,,, +util_Walk - Origin Intersection Density,Walk - Origin Intersection Density,oMGRATotInt,,,,coef_oIntDen_nmot,,,,,,,,,,,,,,,,,,, +util_Walk - Destination Employment Density,Walk - Destination Employment Density,dMGRAEmpDen,,,,coef_dEmpDen_nmot,,,,,,,,,,,,,,,,,,, +util_Walk - Age 1-5 (school only),Walk - Age 1-6,"@df.age.between(1,5)",,,,coef_age1to5_nmot,,,,,,,,,,,,,,,,,,, +util_Walk - Age 6-12 (school only),Walk - Age 6-13,"@df.age.between(6,12)",,,,coef_age6to12_nmot,,,,,,,,,,,,,,,,,,, +util_Walk - Age 13-15 (school only),Walk - Age 13-16,"@df.age.between(13,15)",,,,coef_age13to15_nmot,,,,,,,,,,,,,,,,,,, +#,Bike,,,,,,,,,,,,,,,,,,,,,,,, +util_BIKE_Mode unavailable,Bike - Mode unavailable unless on bike tour,tourBike == 0,,,,,-999,,,,,,,,,,,,,,,,,, +util_BIKE_Female - bike,Bike - Female,@df.female,,,,,coef_female_nmot,,,,,,,,,,,,,,,,,, +util_BIKE_Bike - logsum,Bike - logsum clipped to allow for long bike trips on school escort tours,@df.bikeLS.clip(lower=-100),,,,,coef_bikeLogsum,,,,,,,,,,,,,,,,,, +#,WalktoTransit,,,,,,,,,,,,,,,,,,,,,,,, +util_WalkTransit_Unavailable,WalkTransit_Available,walk_local_available == False,,,,,,-999,,,,,,,,,,,,,,,,, +util_WALKLOC__In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WALK_LOC_TOTALIVTT']) * df.time_factor,,,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_WALK_LOC_iwait_time,WALK_LOC - wait time,@(odt_skims['WALK_LOC_FIRSTWAIT']) * df.time_factor,,,,,,coef_wait,,,,,,,,,,,,,,,,, +util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@(odt_skims['WALK_LOC_XFERWAIT'])* df.time_factor,,,,,,coef_xwait,,,,,,,,,,,,,,,,, +util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@(df.origin_local_time)* df.time_factor,,,,,,coef_acctime,,,,,,,,,,,,,,,,, +util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@(df.dest_local_time)* df.time_factor,,,,,,coef_acctime,,,,,,,,,,,,,,,,, +util_WALK_LOC_transfer_walk_time,WALK_LOC - transfer walk time,@(odt_skims['WALK_LOC_XFERWALK'])* df.time_factor,,,,,,coef_xwalk,,,,,,,,,,,,,,,,, +util_WALK_LOC_transfers_penalty,WALK_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WALK_LOC_XFERS'], a_min=None,a_max=4))) * df.time_factor",,,,,,coef_xfer,,,,,,,,,,,,,,,,, +util_WALK_LOC_Fare,WALK_LOC - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['WALK_LOC_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,coef_income,,,,,,,,,,,,,,,,, +util_WALK_LOC - Female,WALK_LOC - Female,@(df.female),,,,,,coef_female_tran,,,,,,,,,,,,,,,,, +util_WALK_LOC - Origin Mix,WALK_LOC - Origin Mix,oMGRAMix,,,,,,coef_oMix_wTran,,,,,,,,,,,,,,,,, +util_WALK_LOC - Origin Intersection Density,WALK_LOC - Origin Intersection Density,oMGRATotInt,,,,,,coef_oIntDen_wTran,,,,,,,,,,,,,,,,, +util_WALK_LOC - Destination Employment Density,WALK_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,coef_dEmpDen_wTran,,,,,,,,,,,,,,,,, +#,WALK_PRM,,,,,,,,,,,,,,,,,,,,,,,, +util_WalkTransit_Unavailable,WalkTransit_Available,walk_premium_available == False,,,,,,,-999,,,,,,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time,WALK_PRM - In-vehicle time,@(odt_skims['WALK_PRM_TOTALIVTT']) * df.time_factor,,,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_LRT,WALK_PRM - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['WALK_PRM_LRTIVTT']) * df.time_factor,,,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_CMR,WALK_PRM - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['WALK_PRM_CMRIVTT']) * df.time_factor,,,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_LTD_EXP,WALK_PRM - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['WALK_PRM_LTDEXPIVTT'] + odt_skims['WALK_PRM_EXPIVTT']) * df.time_factor,,,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_URB,WALK_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['WALK_PRM_BRTIVTT']) * df.time_factor,,,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_WALK_PRM_wait_time,WALK_PRM - wait time,@(odt_skims['WALK_PRM_FIRSTWAIT']) * df.time_factor,,,,,,,coef_wait,,,,,,,,,,,,,,,, +util_WALK_PRM_transfer_wait_time,WALK_PRM - transfer wait time,@(odt_skims['WALK_PRM_XFERWAIT'])* df.time_factor,,,,,,,coef_xwait,,,,,,,,,,,,,,,, +util_WALK_PRM_transfer_waLK_time,WALK_PRM - transfer walk time,@(odt_skims['WALK_PRM_XFERWALK'])* df.time_factor,,,,,,,coef_xwalk,,,,,,,,,,,,,,,, +util_WALK_PRM_transfers_penalty,WALK_PRM - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WALK_PRM_XFERS'] + df.outbound*df.mtnev_egr_xfer_out + ~df.outbound*df.mtnev_acc_xfer_in, a_min=None,a_max=4))) * df.time_factor",,,,,,,coef_xfer,,,,,,,,,,,,,,,, +util_WALK_PRM_Walk_access_time,WALK_PRM - Walk access time,"@np.where(df.nev_access_available_in & ~df.outbound, df.nev_access_time_in, np.where(df.microtransit_access_available_in & ~df.outbound, df.microtransit_access_time_in, df.origin_prm_time))* df.time_factor",,,,,,,coef_acctime,,,,,,,,,,,,,,,, +util_WALK_PRM_Walk_egress_time,WALK_PRM - Walk egress time,"@np.where(df.nev_egress_available_out & df.outbound, df.nev_egress_time_out, np.where(df.microtransit_egress_available_out & df.outbound, df.microtransit_egress_time_out, df.dest_prm_time))* df.time_factor",,,,,,,coef_acctime,,,,,,,,,,,,,,,, +util_WALK_PRM_Fare,WALK_PRM - Fare,"@df.transitSubsidyPassDiscount*odt_skims['WALK_PRM_FARE']*100/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,coef_income,,,,,,,,,,,,,,,, +util_WALK_PRM_ASC,WALK_PRM - Alternative-specific constant,@df.wlk_prm_asc * df.time_factor,,,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_WALK_PRM - Female,WALK_PRM - Female,@(df.female),,,,,,,coef_female_tran,,,,,,,,,,,,,,,, +util_WALK_PRM - Origin Mix,WALK_PRM - Origin Mix,oMGRAMix,,,,,,,coef_oMix_wTran,,,,,,,,,,,,,,,, +util_WALK_PRM - Origin Intersection Density,WALK_PRM - Origin Intersection Density,oMGRATotInt,,,,,,,coef_oIntDen_wTran,,,,,,,,,,,,,,,, +util_WALK_PRM - Destination Employment Density,WALK_PRM - Destination Employment Density,dMGRAEmpDen,,,,,,,coef_dEmpDen_wTran,,,,,,,,,,,,,,,, +#,WALK_MIX,,,,,,,,,,,,,,,,,,,,,,,, +util_WALK_MIX_Unavailable,WALK_MIX - Unavailable,walk_mix_available == False,,,,,,,,-999,,,,,,,,,,,,,,, +util_WALK_MIX_In_vehicle_time,WALK_MIX - In-vehicle time,@(odt_skims['WALK_MIX_TOTALIVTT'])* df.time_factor,,,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_LRT,WALK_MIX - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['WALK_MIX_LRTIVTT'])* df.time_factor,,,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_CMR,WALK_MIX - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['WALK_MIX_CMRIVTT'])* df.time_factor,,,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_LTD_EXP,WALK_MIX - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['WALK_MIX_LTDEXPIVTT'] + odt_skims['WALK_MIX_EXPIVTT'])* df.time_factor,,,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_URB,WALK_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['WALK_MIX_BRTIVTT'])* df.time_factor,,,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_WALK_MIX_FIRST_wait_time,WALK_MIX - First wait time,@(odt_skims['WALK_MIX_FIRSTWAIT'])* df.time_factor,,,,,,,,coef_wait,,,,,,,,,,,,,,, +util_WALK_MIX_transfer_wait_time,WALK_MIX - transfer wait time,@(odt_skims['WALK_MIX_XFERWAIT'])* df.time_factor,,,,,,,,coef_xwait,,,,,,,,,,,,,,, +util_WALK_MIX_number_of_transfers,WALK_MIX - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['WALK_MIX_XFERS'], a_min=None,a_max=4))) * df.time_factor",,,,,,,,coef_xfer,,,,,,,,,,,,,,, +util_WALK_MIX_Walk_access_time,WALK_MIX - Walk access time,@(df.origin_mix_time)* df.time_factor,,,,,,,,coef_acctime,,,,,,,,,,,,,,, +util_WALK_MIX_Walk_egress_time,WALK_MIX - Walk egress time,@(df.dest_mix_time)* df.time_factor,,,,,,,,coef_acctime,,,,,,,,,,,,,,, +util_WALK_MIX_Walk_other_time,WALK_MIX - Walk other time,@(odt_skims['WALK_MIX_XFERWALK'])* df.time_factor,,,,,,,,coef_xwalk,,,,,,,,,,,,,,, +util_WALK_MIX_Fare,WALK_MIX - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['WALK_MIX_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,coef_income,,,,,,,,,,,,,,, +util_WALK_MIX_ASC,WALK_MIX - Alternative-specific constant,@df.wlk_mix_asc * df.time_factor,,,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_WALK_MIX - Female,WALK_MIX - Female,@(df.female),,,,,,,,coef_female_tran,,,,,,,,,,,,,,, +util_WALK_MIX - Origin Mix,WALK_MIX - Origin Mix,oMGRAMix,,,,,,,,coef_oMix_wTran,,,,,,,,,,,,,,, +util_WALK_MIX - Origin Intersection Density,WALK_MIX - Origin Intersection Density,oMGRATotInt,,,,,,,,coef_oIntDen_wTran,,,,,,,,,,,,,,, +util_WALK_MIX - Destination Employment Density,WALK_MIX - Destination Employment Density,dMGRAEmpDen,,,,,,,,coef_dEmpDen_wTran,,,,,,,,,,,,,,, +#,PNR_LOC,,,,,,,,,,,,,,,,,,,,,,,, +util_PNR_LOC_Unavailable,PNR_LOC - Unavailable,(pnr_local_available == False)|(PNR_available==0),,,,,,,,,-999,,,,,,,,,,,,,, +util_PNR_LOC_In_vehicle_time,PNR_LOC - In-vehicle time,@(odt_skims['PNROUT_LOC_TOTALIVTT'])* df.time_factor * df.outbound,,,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_PNR_LOC_iwait_time,PNR_LOC - First iwait time,@(odt_skims['PNROUT_LOC_FIRSTWAIT'])* df.time_factor * df.outbound,,,,,,,,,coef_wait,,,,,,,,,,,,,, +util_PNR_LOC_transfer_wait_time,PNR_LOC - transfer wait time,@(odt_skims['PNROUT_LOC_XFERWAIT'])* df.time_factor * df.outbound,,,,,,,,,coef_xwait,,,,,,,,,,,,,, +util_PNR_LOC_number_of_transfers,PNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNROUT_LOC_XFERS'], a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,,,coef_xferdrive,,,,,,,,,,,,,, +util_PNR_LOC_PNR_time,PNR_LOC - PNR time,@odt_skims['PNROUT_LOC_ACC']* df.time_factor * df.outbound,,,,,,,,,coef_acctime,,,,,,,,,,,,,, +util_PNR_LOC_PNR_cost,PNR_LOC - PNR cost,"@(df.auto_op_cost * (odt_skims['PNROUT_LOC_ACC']) *driveSpeed)* df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,coef_income,,,,,,,,,,,,,, +util_PNRIN_LOC_PNR_cost,PNR_LOC - PNR cost,"@(df.auto_op_cost * (dot_skims['PNRIN_LOC_ACC']) *driveSpeed) * ~df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,coef_income,,,,,,,,,,,,,, +util_PNR_LOC_Walk_egress_time_(at_attraction_end),PNR_LOC - Walk egress time (at attraction end),@(df.dest_local_time)* df.time_factor * df.outbound,,,,,,,,,coef_acctime,,,,,,,,,,,,,, +util_PNR_LOC_Walk_other_time,PNR_LOC - Walk other time,@odt_skims['PNROUT_LOC_XFERWALK']* df.time_factor * df.outbound,,,,,,,,,coef_xwalk,,,,,,,,,,,,,, +util_PNR_LOC_Fare_and_operating_cost,PNR_LOC - Fare ,"@df.transitSubsidyPassDiscount*(odt_skims['PNROUT_LOC_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,,,coef_income,,,,,,,,,,,,,, +util_PNRIN_LOC_In_vehicle_time,PNRIN_LOC - In-vehicle time,@(odt_skims['PNRIN_LOC_TOTALIVTT'])* df.time_factor * ~df.outbound,,,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_PNRIN_LOC_iwait_time,PNRIN_LOC - First iwait time,@(odt_skims['PNRIN_LOC_FIRSTWAIT']) * df.time_factor* ~df.outbound,,,,,,,,,coef_wait,,,,,,,,,,,,,, +util_PNRIN_LOC_transfer_wait_time,PNRIN_LOC - transfer wait time,@(odt_skims['PNRIN_LOC_XFERWAIT'])* ~df.outbound,,,,,,,,,coef_xwait,,,,,,,,,,,,,, +util_PNRIN_LOC_number_of_transfers,PNRIN_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNRIN_LOC_XFERS'], a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,,,coef_xferdrive,,,,,,,,,,,,,, +util_PNRIN_LOC_PNRIN_time,PNRIN_LOC - PNR time,@odt_skims['PNRIN_LOC_EGR'] * df.time_factor * ~df.outbound,,,,,,,,,coef_acctime,,,,,,,,,,,,,, +util_PNRIN_LOC_Walk_access_time,PNRIN_LOC - Walk access time,@(df.origin_local_time)* df.time_factor * ~df.outbound,,,,,,,,,coef_acctime,,,,,,,,,,,,,, +util_PNRIN_LOC_Walk_other_time,PNRIN_LOC - Walk other time,@odt_skims['PNRIN_LOC_XFERWALK']* df.time_factor * ~df.outbound,,,,,,,,,coef_xwalk,,,,,,,,,,,,,, +util_PNRIN_LOC_Fare_and_operating_cost,PNRIN_LOC - Fare ,"@df.transitSubsidyPassDiscount*(odt_skims['PNRIN_LOC_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,,,coef_income,,,,,,,,,,,,,, +util_PNRIN_LOC_Destination_zone_densityIndex,PNRIN_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_PNR_LOC - Female,PNR_LOC - Female,@(df.female),,,,,,,,,coef_female_tran,,,,,,,,,,,,,, +util_PNR_LOC - Destination Employment Density,PNR_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,,,coef_dEmpDen_dTran,,,,,,,,,,,,,, +#,PNR_PRM,,,,,,,,,,,,,,,,,,,,,,,, +util_PNR_PRM_Unavailable,PNR_PRM - Unavailable,(pnr_premium_available == False)|(PNR_available==0),,,,,,,,,,-999,,,,,,,,,,,,, +util_PNR_PRM_In_vehicle_time,PNR_PRM - In-vehicle time,@(odt_skims['PNROUT_PRM_TOTALIVTT']) * df.time_factor * df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_LRT,PNR_PRM - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['PNROUT_PRM_LRTIVTT']) * df.time_factor * df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_CMR,PNR_PRM - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['PNROUT_PRM_CMRIVTT']) * df.time_factor * df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_LTD_EXP,PNR_PRM - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['PNROUT_PRM_LTDEXPIVTT'] + odt_skims['PNROUT_PRM_EXPIVTT']) * df.time_factor * df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_URB,PNR_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['PNROUT_PRM_BRTIVTT']) * df.time_factor * df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM_FIRST_iwait_time,PNR_PRM - First iwait time,@(odt_skims['PNROUT_PRM_FIRSTWAIT']) * df.time_factor * df.outbound,,,,,,,,,,coef_wait,,,,,,,,,,,,, +util_PNR_PRM_transfer_wait_time,PNR_PRM - transfer wait time,@(odt_skims['PNROUT_PRM_XFERWAIT']) * df.time_factor * df.outbound,,,,,,,,,,coef_xwait,,,,,,,,,,,,, +util_PNR_PRM_number_of_transfers,PNR_PRM - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNROUT_PRM_XFERS'] + df.mtnev_egr_xfer_out, a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,,,,coef_xferdrive,,,,,,,,,,,,, +util_PNR_PRM_PNR_time,PNR_PRM - PNR time,@(odt_skims['PNROUT_PRM_ACC']) * df.time_factor * df.outbound,,,,,,,,,,coef_acctime,,,,,,,,,,,,, +util_PNR_PRM_Walk_egress_time_(at_attraction_end),PNR_PRM - Walk egress time (at attraction end),"@np.where(df.nev_egress_available_out, df.nev_egress_time_out, np.where(df.microtransit_egress_available_out, df.microtransit_egress_time_out, df.dest_prm_time))* df.time_factor * df.outbound",,,,,,,,,,coef_acctime,,,,,,,,,,,,, +util_PNR_PRM_Walk_other_time,PNR_PRM - Walk other time,@(odt_skims['PNROUT_PRM_XFERWALK']) * df.time_factor * df.outbound,,,,,,,,,,coef_xwalk,,,,,,,,,,,,, +util_PNR_PRM_Fare,PNR_PRM - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['PNROUT_PRM_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,,,,coef_income,,,,,,,,,,,,, +util_PNR_PRM_ASC,PNR_PRM - Alternative-specific constant,@df.pnr_prm_out_asc * df.time_factor * df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM_PNR_cost,PNR_PRM - PNR cost,"@(df.auto_op_cost * (odt_skims['PNROUT_PRM_ACC']) *driveSpeed) * df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,coef_income,,,,,,,,,,,,, +util_PNRIN_PRM_PNR_cost,PNR_PRM - PNR cost,"@(df.auto_op_cost * (dot_skims['PNRIN_PRM_ACC']) *driveSpeed)*~df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,coef_income,,,,,,,,,,,,, +util_PNRIN_PRM_In_vehicle_time,PNRIN_PRM - In-vehicle time,@(odt_skims['PNRIN_PRM_TOTALIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNRIN_PRM_In_vehicle_time_on_LRT,PNRIN_PRM - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['PNRIN_PRM_LRTIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNRIN_PRM_In_vehicle_time_on_CMR,PNRIN_PRM - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['PNRIN_PRM_CMRIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNRIN_PRM_In_vehicle_time_on_LTD_EXP,PNRIN_PRM - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['PNRIN_PRM_LTDEXPIVTT'] + odt_skims['PNRIN_PRM_EXPIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNRIN_PRM_In_vehicle_time_on_URB,PNRIN_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['PNRIN_PRM_BRTIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNRIN_PRM_FIRST_iwait_time,PNRIN_PRM - First iwait time,@(odt_skims['PNRIN_PRM_FIRSTWAIT'])* df.time_factor * ~df.outbound,,,,,,,,,,coef_wait,,,,,,,,,,,,, +util_PNRIN_PRM_transfer_wait_time,PNRIN_PRM - transfer wait time,@(odt_skims['PNRIN_PRM_XFERWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,coef_xwait,,,,,,,,,,,,, +util_PNRIN_PRM_number_of_transfers,PNRIN_PRM - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNRIN_PRM_XFERS'] + df.mtnev_acc_xfer_in, a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,,,,coef_xferdrive,,,,,,,,,,,,, +util_PNRIN_PRM_PNRIN_time,PNRIN_PRM - PNR time,@(odt_skims['PNRIN_PRM_EGR']) * df.time_factor * ~df.outbound,,,,,,,,,,coef_acctime,,,,,,,,,,,,, +util_PNRIN_PRM_Walk_access_time_(at_attraction_end),PNRIN_PRM - Walk access time (at attraction end),"@np.where(df.nev_access_available_in, df.nev_access_time_in, np.where(df.microtransit_access_available_in, df.microtransit_access_time_in, df.origin_prm_time)) * df.time_factor * ~df.outbound",,,,,,,,,,coef_acctime,,,,,,,,,,,,, +util_PNRIN_PRM_Walk_other_time,PNRIN_PRM - Walk other time,@(odt_skims['PNRIN_PRM_XFERWALK']) * df.time_factor * ~df.outbound,,,,,,,,,,coef_xwalk,,,,,,,,,,,,, +util_PNRIN_PRM_Fare,PNRIN_PRM - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['PNRIN_PRM_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,,,,coef_income,,,,,,,,,,,,, +util_PNRIN_PRM_ASC,PNRIN_PRM - Alternative-specific constant,@df.pnr_prm_inb_asc * df.time_factor * ~df.outbound,,,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_PNR_PRM - Female,PNR_PRM - Female,@(df.female),,,,,,,,,,coef_female_tran,,,,,,,,,,,,, +util_PNR_PRM - Destination Employment Density,PNR_PRM - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,coef_dEmpDen_dTran,,,,,,,,,,,,, +#,PNR_MIX,,,,,,,,,,,,,,,,,,,,,,,, +util_PNR_MIX_Unavailable,PNR_mix - Unavailable,(pnr_mix_available == False)|(PNR_available==0),,,,,,,,,,,-999,,,,,,,,,,,, +util_PNR_MIX_In_vehicle_time,PNR_MIX - In-vehicle time,@(odt_skims['PNROUT_MIX_TOTALIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX_In_vehicle_time_on_LRT,PNR_MIX - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['PNROUT_MIX_LRTIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX_In_vehicle_time_on_CMR,PNR_MIX - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['PNROUT_MIX_CMRIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX_In_vehicle_time_on_LTD_EXP,PNR_MIX - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['PNROUT_MIX_LTDEXPIVTT'] + odt_skims['PNROUT_MIX_EXPIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX_In_vehicle_time_on_URB,PNR_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['PNROUT_MIX_BRTIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX_FIRST_iwait_time,PNR_MIX - First iwait time,@(odt_skims['PNROUT_MIX_FIRSTWAIT']) * df.time_factor * df.outbound,,,,,,,,,,,coef_wait,,,,,,,,,,,, +util_PNR_MIX_transfer_wait_time,PNR_MIX - transfer wait time,@(odt_skims['PNROUT_MIX_XFERWAIT']) * df.time_factor * df.outbound,,,,,,,,,,,coef_xwait,,,,,,,,,,,, +util_PNR_MIX_number_of_transfers,PNR_MIX - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNROUT_MIX_XFERS'], a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,,,,,coef_xferdrive,,,,,,,,,,,, +util_PNR_MIX_PNR_time,PNR_MIX - PNR time,@odt_skims['PNROUT_MIX_ACC'] * df.time_factor * df.outbound,,,,,,,,,,,coef_acctime,,,,,,,,,,,, +util_PNR_MIX_Walk_egress_time_(at_attraction_end),PNR_MIX - Walk egress time (at attraction end),@(df.dest_mix_time) * df.time_factor * df.outbound,,,,,,,,,,,coef_acctime,,,,,,,,,,,, +util_PNR_MIX_Walk_other_time,PNR_MIX - Walk other time,@(odt_skims['PNROUT_MIX_XFERWALK']) * df.time_factor * df.outbound,,,,,,,,,,,coef_xwalk,,,,,,,,,,,, +util_PNR_MIX_Fare,PNR_MIX - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['PNROUT_MIX_FARE'])*100 /(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,,,,,coef_income,,,,,,,,,,,, +util_PNR_MIX_ASC,PNR_MIX - Alternative-specific constant,@df.pnr_mix_out_asc * df.time_factor * df.outbound,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX_PNR_cost,PNR_MIX - PNR cost,"@(df.auto_op_cost * (odt_skims['PNROUT_MIX_ACC']) *driveSpeed) * df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,coef_income,,,,,,,,,,,, +util_PNRIN_MIX_PNR_cost,PNR_MIX - PNR cost,"@(df.auto_op_cost * (dot_skims['PNRIN_MIX_ACC']) *driveSpeed)*~df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,coef_income,,,,,,,,,,,, +util_PNRIN_MIX_In_vehicle_time,PNRIN_MIX - In-vehicle time,@(odt_skims['PNRIN_MIX_TOTALIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNRIN_MIX_In_vehicle_time_on_LRT,PNRIN_MIX - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['PNRIN_MIX_LRTIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNRIN_MIX_In_vehicle_time_on_CMR,PNRIN_MIX - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['PNRIN_MIX_CMRIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNRIN_MIX_In_vehicle_time_on_LTD_EXP,PNRIN_MIX - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['PNRIN_MIX_LTDEXPIVTT'] + odt_skims['PNRIN_MIX_EXPIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNRIN_MIX_In_vehicle_time_on_URB,PNRIN_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['PNRIN_MIX_BRTIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNRIN_MIX_FIRST_iwait_time,PNRIN_MIX - First iwait time,@(odt_skims['PNRIN_MIX_FIRSTWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,,coef_wait,,,,,,,,,,,, +util_PNRIN_MIX_transfer_wait_time,PNRIN_MIX - transfer wait time,@(odt_skims['PNRIN_MIX_XFERWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,,coef_xwait,,,,,,,,,,,, +util_PNRIN_MIX_number_of_transfers,PNRIN_MIX - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNRIN_MIX_XFERS'], a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,,,,,coef_xferdrive,,,,,,,,,,,, +util_PNRIN_MIX_PNRIN_time,PNRIN_MIX - PNR time,@odt_skims['PNRIN_MIX_EGR'] * df.time_factor * ~df.outbound,,,,,,,,,,,coef_acctime,,,,,,,,,,,, +util_PNRIN_MIX_Walk_access_time_(at_attraction_end),PNRIN_MIX - Walk access time (at attraction end),@(df.origin_mix_time) * df.time_factor * ~df.outbound,,,,,,,,,,,coef_acctime,,,,,,,,,,,, +util_PNRIN_MIX_Walk_other_time,PNRIN_MIX - Walk other time,@(odt_skims['PNRIN_MIX_XFERWALK']) * df.time_factor * ~df.outbound,,,,,,,,,,,coef_xwalk,,,,,,,,,,,, +util_PNRIN_MIX_Fare,PNRIN_MIX - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['PNRIN_MIX_FARE'])*100 /(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,,,,,coef_income,,,,,,,,,,,, +util_PNRIN_MIX_ASC,PNRIN_MIX - Alternative-specific constant,@df.pnr_mix_inb_asc * df.time_factor * ~df.outbound,,,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_PNR_MIX - Female,PNR_MIX - Female,@(df.female),,,,,,,,,,,coef_female_tran,,,,,,,,,,,, +util_PNR_MIX - Destination Employment Density,PNR_MIX - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,coef_dEmpDen_dTran,,,,,,,,,,,, +#,,,,,,,,,,,,,,,,,,,,,,,,, +util_KNR_LOC_Unavailable,KNR_LOC - Unavailable,(knr_local_available == False)|(KNR_available==0),,,,,,,,,,,,-999,,,,,,,,,,, +util_KNR_LOC_In_vehicle_time,KNR_LOC - In-vehicle time,@(odt_skims['KNROUT_LOC_TOTALIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_KNR_LOC_iwait_time,KNR_LOC - First iwait time,@(odt_skims['KNROUT_LOC_FIRSTWAIT']) * df.time_factor * df.outbound,,,,,,,,,,,,coef_wait,,,,,,,,,,, +util_KNR_LOC_transfer_wait_time,KNR_LOC - transfer wait time,@(odt_skims['KNROUT_LOC_XFERWAIT']) * df.time_factor * df.outbound,,,,,,,,,,,,coef_xwait,,,,,,,,,,, +util_KNR_LOC_number_of_transfers,KNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KNROUT_LOC_XFERS'], a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,,,,,,coef_xferdrive,,,,,,,,,,, +util_KNR_LOC_KNR_time,KNR_LOC - KNR time,@(odt_skims['KNROUT_LOC_ACC']) * df.time_factor * df.outbound,,,,,,,,,,,,coef_acctime,,,,,,,,,,, +util_KNR_LOC_Walk_egress_time_(at_attraction_end),KNR_LOC - Walk egress time (at attraction end),@(df.dest_local_time) * df.time_factor * df.outbound,,,,,,,,,,,,coef_acctime,,,,,,,,,,, +util_KNR_LOC_Walk_other_time,KNR_LOC - Walk other time,@(odt_skims['KNROUT_LOC_XFERWALK']) * df.time_factor * df.outbound,,,,,,,,,,,,coef_xwalk,,,,,,,,,,, +util_KNR_LOC_Fare,KNR_LOC - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['KNROUT_LOC_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,,,,,,coef_income,,,,,,,,,,, +util_KNR_LOC_KNR_cost,KNR_LOC - KNR cost,"@(df.auto_op_cost * df.autoCPMFactor * (odt_skims['KNROUT_LOC_ACC']) *driveSpeed )*df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,,coef_income,,,,,,,,,,, +util_KNRIN_LOC_KNR_cost,KNR_LOC - KNR cost,"@(df.auto_op_cost * df.autoCPMFactor * (dot_skims['KNRIN_LOC_ACC']) *driveSpeed)*~df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,,coef_income,,,,,,,,,,, +util_KNRIN_LOC_In_vehicle_time,KNRIN_LOC - In-vehicle time,@(odt_skims['KNRIN_LOC_TOTALIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_KNRIN_LOC_iwait_time,KNRIN_LOC - First iwait time,@(odt_skims['KNRIN_LOC_FIRSTWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,coef_wait,,,,,,,,,,, +util_KNRIN_LOC_transfer_wait_time,KNRIN_LOC - transfer wait time,@(odt_skims['KNRIN_LOC_XFERWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,coef_xwait,,,,,,,,,,, +util_KNRIN_LOC_number_of_transfers,KNRIN_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KNRIN_LOC_XFERS'], a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,,,,,,coef_xferdrive,,,,,,,,,,, +util_KNRIN_LOC_KNRIN_time,KNRIN_LOC - KNR time,@odt_skims['KNRIN_LOC_EGR'] * df.time_factor * ~df.outbound,,,,,,,,,,,,coef_acctime,,,,,,,,,,, +util_KNRIN_LOC_Walk_access_time,KNRIN_LOC - Walk access time,@df.origin_local_time * df.time_factor * ~df.outbound,,,,,,,,,,,,coef_acctime,,,,,,,,,,, +util_KNRIN_LOC_Walk_other_time,KNRIN_LOC - Walk other time,@(odt_skims['KNRIN_LOC_XFERWALK']) * df.time_factor * ~df.outbound,,,,,,,,,,,,coef_xwalk,,,,,,,,,,, +util_KNRIN_LOC_Fare_and_operating_cost,KNRIN_LOC - Fare ,"@df.transitSubsidyPassDiscount*(odt_skims['KNRIN_LOC_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,,,,,,coef_income,,,,,,,,,,, +util_KNR_LOC - Female,KNR_LOC - Female,@(df.female),,,,,,,,,,,,coef_female_tran,,,,,,,,,,, +util_KNR_LOC - Destination Employment Density,KNR_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,,coef_dEmpDen_dTran,,,,,,,,,,, +#,KNR_PRM,,,,,,,,,,,,,,,,,,,,,,,, +util_KNR_PRM_Unavailable,KNR_PRM - Unavailable,(knr_premium_available == False)|(KNR_available==0),,,,,,,,,,,,,-999,,,,,,,,,, +util_KNR_PRM_In_vehicle_time,KNR_PRM - In-vehicle time,@(odt_skims['KNROUT_PRM_TOTALIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM_In_vehicle_time_on_LRT,KNR_PRM - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['KNROUT_PRM_LRTIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM_In_vehicle_time_on_CMR,KNR_PRM - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['KNROUT_PRM_CMRIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM_In_vehicle_time_on_LTD_EXP,KNR_PRM - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['KNROUT_PRM_LTDEXPIVTT'] + odt_skims['KNROUT_PRM_EXPIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM_In_vehicle_time_on_URB,KNR_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['KNROUT_PRM_BRTIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM_FIRST_iwait_time,KNR_PRM - First iwait time,@(odt_skims['KNROUT_PRM_FIRSTWAIT']) * df.time_factor * df.outbound,,,,,,,,,,,,,coef_wait,,,,,,,,,, +util_KNR_PRM_transfer_wait_time,KNR_PRM - transfer wait time,@(odt_skims['KNROUT_PRM_XFERWAIT']) * df.time_factor * df.outbound,,,,,,,,,,,,,coef_xwait,,,,,,,,,, +util_KNR_PRM_number_of_transfers,KNR_PRM - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KNROUT_PRM_XFERS'] + df.mtnev_egr_xfer_out, a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,,,,,,,coef_xferdrive,,,,,,,,,, +util_KNR_PRM_KNR_time,KNR_PRM - KNR time,@(odt_skims['KNROUT_PRM_ACC']) * df.time_factor * df.outbound,,,,,,,,,,,,,coef_acctime,,,,,,,,,, +util_KNR_PRM_Walk_egress_time_(at_attraction_end),KNR_PRM - Walk egress time (at attraction end),"@np.where(df.nev_egress_available_out, df.nev_egress_time_out, np.where(df.microtransit_egress_available_out, df.microtransit_egress_time_out, df.dest_prm_time)) * df.time_factor * df.outbound",,,,,,,,,,,,,coef_acctime,,,,,,,,,, +util_KNR_PRM_Walk_other_time,KNR_PRM - Walk other time,@(odt_skims['KNROUT_PRM_XFERWALK']) * df.time_factor * df.outbound,,,,,,,,,,,,,coef_xwalk,,,,,,,,,, +util_KNR_PRM_Fare,KNR_PRM - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['KNROUT_PRM_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,,,,,,,coef_income,,,,,,,,,, +util_KNR_PRM_ASC,PNR_PRM - Alternative-specific constant,@df.knr_prm_out_asc * df.time_factor * df.outbound,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM_KNR_cost,KNR_PRM - KNR cost,"@(df.auto_op_cost * df.autoCPMFactor * (odt_skims['KNROUT_PRM_ACC']) *driveSpeed)*df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,,,coef_income,,,,,,,,,, +util_KNRIN_PRM_KNR_cost,KNR_PRM - KNR cost,"@(df.auto_op_cost * df.autoCPMFactor * (dot_skims['KNRIN_PRM_ACC']) *driveSpeed)*~df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,,,coef_income,,,,,,,,,, +util_KNRIN_PRM_In_vehicle_time,KNRIN_PRM - In-vehicle time,@(odt_skims['KNRIN_PRM_TOTALIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNRIN_PRM_In_vehicle_time_on_LRT,KNRIN_PRM - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['KNRIN_PRM_LRTIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNRIN_PRM_In_vehicle_time_on_CMR,KNRIN_PRM - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['KNRIN_PRM_CMRIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNRIN_PRM_In_vehicle_time_on_LTD_EXP,KNRIN_PRM - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['KNRIN_PRM_LTDEXPIVTT'] + odt_skims['KNRIN_PRM_EXPIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNRIN_PRM_In_vehicle_time_on_URB,KNRIN_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['KNRIN_PRM_BRTIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNRIN_PRM_FIRST_iwait_time,KNRIN_PRM - First iwait time,@(odt_skims['KNRIN_PRM_FIRSTWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,coef_wait,,,,,,,,,, +util_KNRIN_PRM_transfer_wait_time,KNRIN_PRM - transfer wait time,@(odt_skims['KNRIN_PRM_XFERWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,coef_xwait,,,,,,,,,, +util_KNRIN_PRM_number_of_transfers,KNRIN_PRM - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KNRIN_PRM_XFERS'] + df.mtnev_acc_xfer_in, a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,,,,,,,coef_xferdrive,,,,,,,,,, +util_KNRIN_PRM_KNRIN_time,KNRIN_PRM - KNR time,@(odt_skims['KNRIN_PRM_EGR']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,coef_acctime,,,,,,,,,, +util_KNRIN_PRM_Walk_access_time_(at_attraction_end),KNRIN_PRM - Walk access time (at attraction end),"@np.where(df.nev_access_available_in, df.nev_access_time_in, np.where(df.microtransit_access_available_in, df.microtransit_access_time_in, df.origin_prm_time)) * df.time_factor * ~df.outbound",,,,,,,,,,,,,coef_acctime,,,,,,,,,, +util_KNRIN_PRM_Walk_other_time,KNRIN_PRM - Walk other time,@(odt_skims['KNRIN_PRM_XFERWALK']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,coef_xwalk,,,,,,,,,, +util_KNRIN_PRM_Fare,KNRIN_PRM - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['KNRIN_PRM_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,,,,,,,coef_income,,,,,,,,,, +util_KNRIN_PRM_ASC,KNRIN_PRM - Alternative-specific constant,@df.knr_prm_inb_asc * df.time_factor * ~df.outbound,,,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_KNR_PRM - Female,KNR_PRM - Female,@(df.female),,,,,,,,,,,,,coef_female_tran,,,,,,,,,, +util_KNR_PRM - Destination Employment Density,KNR_PRM - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,,,coef_dEmpDen_dTran,,,,,,,,,, +#,KNR_MIX,,,,,,,,,,,,,,,,,,,,,,,, +util_KNR_MIX_Unavailable,KNR_mix - Unavailable,(knr_mix_available == False)|(KNR_available==0),,,,,,,,,,,,,,-999,,,,,,,,, +util_KNR_MIX_In_vehicle_time,KNR_MIX - In-vehicle time,@(odt_skims['KNROUT_MIX_TOTALIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX_In_vehicle_time_on_LRT,KNR_MIX - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['KNROUT_MIX_LRTIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX_In_vehicle_time_on_CMR,KNR_MIX - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['KNROUT_MIX_CMRIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX_In_vehicle_time_on_LTD_EXP,KNR_MIX - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['KNROUT_MIX_LTDEXPIVTT'] + odt_skims['KNROUT_MIX_EXPIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX_In_vehicle_time_on_URB,KNR_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['KNROUT_MIX_BRTIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX_FIRST_iwait_time,KNR_MIX - First iwait time,@(odt_skims['KNROUT_MIX_FIRSTWAIT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,coef_wait,,,,,,,,, +util_KNR_MIX_transfer_wait_time,KNR_MIX - transfer wait time,@(odt_skims['KNROUT_MIX_XFERWAIT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,coef_xwait,,,,,,,,, +util_KNR_MIX_number_of_transfers,KNR_MIX - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KNROUT_PRM_XFERS'], a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,,,,,,,,coef_xferdrive,,,,,,,,, +util_KNR_MIX_KNR_time,KNR_MIX - KNR time,@(odt_skims['KNROUT_MIX_ACC']) * df.time_factor * df.outbound,,,,,,,,,,,,,,coef_acctime,,,,,,,,, +util_KNR_MIX_Walk_egress_time_(at_attraction_end),KNR_MIX - Walk egress time (at attraction end),@(df.dest_mix_time) * df.time_factor * df.outbound,,,,,,,,,,,,,,coef_acctime,,,,,,,,, +util_KNR_MIX_Walk_other_time,KNR_MIX - Walk other time,@(odt_skims['KNROUT_MIX_XFERWALK']) * df.time_factor * df.outbound,,,,,,,,,,,,,,coef_xwalk,,,,,,,,, +util_KNR_MIX_Fare,KNR_MIX - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['KNROUT_MIX_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,,,,,,,,coef_income,,,,,,,,, +util_KNR_MIX_ASC,PNR_MIX - Alternative-specific constant,@df.knr_mix_out_asc * df.time_factor * df.outbound,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX_KNR_cost,KNR_MIX - KNR cost,"@(df.auto_op_cost * df.autoCPMFactor * (odt_skims['KNROUT_MIX_ACC']) *driveSpeed)*df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,,,,coef_income,,,,,,,,, +util_KNRIN_MIX_KNR_cost,KNR_MIX - KNR cost,"@(df.auto_op_cost * df.autoCPMFactor * (dot_skims['KNRIN_MIX_ACC']) *driveSpeed)*~df.outbound/(np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,,,,coef_income,,,,,,,,, +util_KNRIN_MIX_In_vehicle_time,KNRIN_MIX - In-vehicle time,@(odt_skims['KNRIN_MIX_TOTALIVTT']) * df.time_factor,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNRIN_MIX_In_vehicle_time_on_LRT,KNRIN_MIX - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['KNRIN_MIX_LRTIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNRIN_MIX_In_vehicle_time_on_CMR,KNRIN_MIX - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['KNRIN_MIX_CMRIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNRIN_MIX_In_vehicle_time_on_LTD_EXP,KNRIN_MIX - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['KNRIN_MIX_LTDEXPIVTT'] + odt_skims['KNRIN_MIX_EXPIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNRIN_MIX_In_vehicle_time_on_URB,KNRIN_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['KNRIN_MIX_BRTIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNRIN_MIX_FIRST_iwait_time,KNRIN_MIX - First iwait time,@(odt_skims['KNRIN_MIX_FIRSTWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,coef_wait,,,,,,,,, +util_KNRIN_MIX_transfer_wait_time,KNRIN_MIX - transfer wait time,@(odt_skims['KNRIN_MIX_XFERWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,coef_xwait,,,,,,,,, +util_KNRIN_MIX_number_of_transfers,KNRIN_MIX - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['KNRIN_MIX_XFERS'], a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,,,,,,,,coef_xferdrive,,,,,,,,, +util_KNRIN_MIX_KNRIN_time,KNRIN_MIX - KNR time,@(odt_skims['KNRIN_MIX_EGR']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,coef_acctime,,,,,,,,, +util_KNRIN_MIX_Walk_access_time_(at_attraction_end),KNRIN_MIX - Walk access time (at attraction end),@(df.origin_mix_time) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,coef_acctime,,,,,,,,, +util_KNRIN_MIX_Walk_other_time,KNRIN_MIX - Walk other time,@(odt_skims['KNRIN_MIX_XFERWALK']) * ~df.outbound,,,,,,,,,,,,,,coef_xwalk,,,,,,,,, +util_KNRIN_MIX_Fare,KNRIN_MIX - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['KNRIN_MIX_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,,,,,,,,coef_income,,,,,,,,, +util_KNRIN_MIX_ASC,KNRIN_MIX - Alternative-specific constant,@df.knr_mix_inb_asc * df.time_factor * ~df.outbound,,,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_KNR_MIX - Female,KNR_MIX - Female,@(df.female),,,,,,,,,,,,,,coef_female_tran,,,,,,,,, +util_KNR_MIX - Destination Employment Density,KNR_MIX - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,,,,coef_dEmpDen_dTran,,,,,,,,, +#,,,,,,,,,,,,,,,,,,,,,,,,, +util_TNC_LOC_Unavailable,TNC_LOC - Unavailable,(tnc_local_available == False)|(TNR_available==0),,,,,,,,,,,,,,,-999,,,,,,,, +util_TNC_LOC_Unavailable_for_persons_less_than_16,TNC_LOC - Unavailable for persons less than 16,age < 12,,,,,,,,,,,,,,,-999,,,,,,,, +util_TNC_LOC_In_vehicle_time,TNC_LOC - In-vehicle time,@(odt_skims['TNCOUT_LOC_TOTALIVTT'])* df.time_factor * df.outbound,,,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_TNC_LOC_iwait_time,TNC_LOC - First iwait time,@(odt_skims['TNCOUT_LOC_FIRSTWAIT'])* df.time_factor * df.outbound,,,,,,,,,,,,,,,coef_wait,,,,,,,, +util_TNC_LOC_transfer_wait_time,TNC_LOC - transfer wait time,@odt_skims['TNCOUT_LOC_XFERWAIT']* df.time_factor * df.outbound,,,,,,,,,,,,,,,coef_xwait,,,,,,,, +util_TNC_LOC_number_of_transfers,TNC_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['TNCOUT_LOC_XFERS'], a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,,,,,,,,,coef_xferdrive,,,,,,,, +util_TNC_LOC_TNC_time,TNC_LOC - TNC time,@odt_skims['TNCOUT_LOC_ACC']* df.time_factor * df.outbound,,,,,,,,,,,,,,,coef_acctime,,,,,,,, +util_TNC_LOC_Walk_egress_time_(at_attraction_end),TNC_LOC - Walk egress time (at attraction end),@df.dest_local_time* df.time_factor * df.outbound,,,,,,,,,,,,,,,coef_acctime,,,,,,,, +util_TNC_LOC_Walk_other_time,TNC_LOC - Walk other time,@odt_skims['TNCOUT_LOC_XFERWALK']* df.time_factor * df.outbound,,,,,,,,,,,,,,,coef_xwalk,,,,,,,, +util_TNC_LOC_Fare_and_operating_cost,TNC_LOC - Fare ,"@odt_skims['TNCOUT_LOC_FARE']*100/(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,,,,,,,,,coef_income,,,,,,,, +util_TNC_LOC_In_vehicle_time,TNC_LOC - In-vehicle time,@(odt_skims['TNCIN_LOC_TOTALIVTT'])* df.time_factor * ~df.outbound,,,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_TNC_LOC_iwait_time,TNC_LOC - First iwait time,@(odt_skims['TNCIN_LOC_FIRSTWAIT'])* df.time_factor * ~df.outbound,,,,,,,,,,,,,,,coef_wait,,,,,,,, +util_TNC_LOC_transfer_wait_time,TNC_LOC - transfer wait time,@odt_skims['TNCIN_LOC_XFERWAIT']* df.time_factor * ~df.outbound,,,,,,,,,,,,,,,coef_xwait,,,,,,,, +util_TNC_LOC_number_of_transfers,TNC_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['TNCIN_LOC_XFERS'], a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,,,,,,,,,coef_xferdrive,,,,,,,, +util_TNC_LOC_TNC_time,TNC_LOC - TNC time,@odt_skims['TNCIN_LOC_EGR']* df.time_factor * ~df.outbound,,,,,,,,,,,,,,,coef_acctime,,,,,,,, +util_TNC_LOC_Walk_egress_time_(at_attraction_end),TNC_LOC - Walk egress time (at attraction end),@df.origin_local_time* df.time_factor * ~df.outbound,,,,,,,,,,,,,,,coef_acctime,,,,,,,, +util_TNC_LOC_Walk_other_time,TNC_LOC - Walk other time,@odt_skims['TNCIN_LOC_XFERWALK']* df.time_factor * ~df.outbound,,,,,,,,,,,,,,,coef_xwalk,,,,,,,, +util_TNC_LOC_Fare,TNC_LOC - Fare ,"@odt_skims['TNCIN_LOC_FARE']*100/(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,,,,,,,,,coef_income,,,,,,,, +util_TNC_LOC - Female,TNC_LOC - Female,@(df.female),,,,,,,,,,,,,,,coef_female_tran,,,,,,,, +util_TNC_LOC - Destination Employment Density,TNC_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,,,,,coef_dEmpDen_dTran,,,,,,,, +#,TNC_PRM,,,,,,,,,,,,,,,,,,,,,,,, +util_TNC_PRM_Unavailable,TNC_PRM - Unavailable,(tnc_premium_available == False)|(TNR_available==0),,,,,,,,,,,,,,,,-999,,,,,,, +util_TNC_PRM_Unavailable_for_persons_less_than_16,TNC_PRM - Unavailable for persons less than 16,@df.age < 12,,,,,,,,,,,,,,,,-999,,,,,,, +util_TNC_PRM_In_vehicle_time,TNC_PRM - In-vehicle time,@(odt_skims['TNCOUT_PRM_TOTALIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNC_PRM_In_vehicle_time_on_LRT,TNC_PRM - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['TNCOUT_PRM_LRTIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNC_PRM_In_vehicle_time_on_CMR,TNC_PRM - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['TNCOUT_PRM_CMRIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNC_PRM_In_vehicle_time_on_LTD_EXP,TNC_PRM - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['TNCOUT_PRM_LTDEXPIVTT'] + odt_skims['TNCOUT_PRM_EXPIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNC_PRM_In_vehicle_time_on_URB,TNC_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['TNCOUT_PRM_BRTIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNC_PRM_FIRST_iwait_time,TNC_PRM - First iwait time,@(odt_skims['TNCOUT_PRM_FIRSTWAIT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,coef_wait,,,,,,, +util_TNC_PRM_transfer_wait_time,TNC_PRM - transfer wait time,@(odt_skims['TNCOUT_PRM_XFERWAIT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,coef_xwait,,,,,,, +util_TNC_PRM_number_of_transfers,TNC_PRM - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['TNCOUT_PRM_XFERS'] + df.mtnev_acc_xfer_out + df.mtnev_egr_xfer_out, a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,,,,,,,,,,coef_xferdrive,,,,,,, +util_TNC_PRM_TNC_time,TNC_PRM - TNC time,"@np.where(df.nev_access_available_out, df.nev_access_time_out, np.where(df.microtransit_access_available_out, df.microtransit_access_time_out, odt_skims['TNCOUT_PRM_ACC'])) * df.time_factor * df.outbound",,,,,,,,,,,,,,,,coef_acctime,,,,,,, +util_TNC_PRM_Walk_egress_time_(at_attraction_end),TNC_PRM - Walk egress time (at attraction end),"@np.where(df.nev_egress_available_out, df.nev_egress_time_out, np.where(df.microtransit_egress_available_out, df.microtransit_egress_time_out, df.dest_prm_time)) * df.time_factor * df.outbound",,,,,,,,,,,,,,,,coef_acctime,,,,,,, +util_TNC_PRM_Walk_other_time,TNC_PRM - Walk other time,@(odt_skims['TNCOUT_PRM_XFERWALK']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,coef_xwalk,,,,,,, +util_TNC_PRM_Fare,TNC_PRM - Fare,"@np.where(df.microtransit_access_available_out | df.nev_access_available_out, df.transitSubsidyPassDiscount*odt_skims['KNROUT_PRM_FARE'], df.transitSubsidyPassDiscount*odt_skims['TNCOUT_PRM_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,,,,,,,,,,coef_income,,,,,,, +util_TNC_PRM_ASC,TNC_PRM - Alternative-specific constant,@df.tnc_prm_out_asc * df.time_factor * df.outbound,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNCIN_PRM_In_vehicle_time,TNCIN_PRM - In-vehicle time,@(odt_skims['TNCIN_PRM_TOTALIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNCIN_PRM_In_vehicle_time_on_LRT,TNCIN_PRM - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['TNCIN_PRM_LRTIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNCIN_PRM_In_vehicle_time_on_CMR,TNCIN_PRM - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['TNCIN_PRM_CMRIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNCIN_PRM_In_vehicle_time_on_LTD_EXP,TNCIN_PRM - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['TNCIN_PRM_LTDEXPIVTT'] + odt_skims['TNCIN_PRM_EXPIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNCIN_PRM_In_vehicle_time_on_URB,TNCIN_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['TNCIN_PRM_BRTIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNCIN_PRM_FIRST_iwait_time,TNCIN_PRM - First iwait time,@(odt_skims['TNCIN_PRM_FIRSTWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,coef_wait,,,,,,, +util_TNCIN_PRM_transfer_wait_time,TNCIN_PRM - transfer wait time,@(odt_skims['TNCIN_PRM_XFERWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,coef_xwait,,,,,,, +util_TNCIN_PRM_number_of_transfers,TNCIN_PRM - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['TNCIN_PRM_XFERS'] + df.mtnev_acc_xfer_in + df.mtnev_egr_xfer_in, a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,,,,,,,,,,coef_xferdrive,,,,,,, +util_TNCIN_PRM_TNCIN_time,TNCIN_PRM - TNC time,"@np.where(df.nev_egress_available_in, df.nev_egress_time_in, np.where(df.microtransit_egress_available_in, df.microtransit_egress_time_in, odt_skims['TNCIN_PRM_EGR'])) * df.time_factor * ~df.outbound",,,,,,,,,,,,,,,,coef_acctime,,,,,,, +util_TNCIN_PRM_Walk_access_time_(at_attraction_end),TNCIN_PRM - Walk access time (at attraction end),"@np.where(df.nev_access_available_in, df.nev_access_time_in, np.where(df.microtransit_access_available_in, df.microtransit_access_time_in, df.origin_prm_time)) * df.time_factor * ~df.outbound",,,,,,,,,,,,,,,,coef_acctime,,,,,,, +util_TNCIN_PRM_Walk_other_time,TNCIN_PRM - Walk other time,@(odt_skims['TNCOUT_PRM_XFERWALK']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,coef_xwalk,,,,,,, +util_TNCIN_PRM_Fare,TNCIN_PRM - Fare,"@np.where(df.microtransit_egress_available_in | df.nev_egress_available_in, df.transitSubsidyPassDiscount*odt_skims['KNRIN_PRM_FARE'], df.transitSubsidyPassDiscount*odt_skims['TNCIN_PRM_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,,,,,,,,,,coef_income,,,,,,, +util_TNCIN_PRM_ASC,TNCIN_PRM - Alternative-specific constant,@df.tnc_prm_inb_asc * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_TNC_PRM - Female,TNC_PRM - Female,@(df.female),,,,,,,,,,,,,,,,coef_female_tran,,,,,,, +util_TNC_PRM - Destination Employment Density,TNC_PRM - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,,,,,,coef_dEmpDen_dTran,,,,,,, +#,TNC_MIX,,,,,,,,,,,,,,,,,,,,,,,, +util_TNC_MIX_Unavailable,TNC_mix - Unavailable,(tnc_mix_available == False)|(TNR_available==0),,,,,,,,,,,,,,,,,-999,,,,,, +util_TNC_PRM_Unavailable_for_persons_less_than_16,TNC_mix - Unavailable for persons less than 16,@df.age < 12,,,,,,,,,,,,,,,,,-999,,,,,, +util_TNC_MIX_In_vehicle_time,TNC_MIX - In-vehicle time,@(odt_skims['TNCOUT_MIX_TOTALIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNC_MIX_In_vehicle_time_on_LRT,TNC_MIX - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['TNCOUT_MIX_LRTIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNC_MIX_In_vehicle_time_on_CMR,TNC_MIX - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['TNCOUT_MIX_CMRIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNC_MIX_In_vehicle_time_on_LTD_EXP,TNC_MIX - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['TNCOUT_MIX_LTDEXPIVTT']+ odt_skims['TNCOUT_MIX_EXPIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNC_MIX_In_vehicle_time_on_URB,TNC_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['TNCOUT_MIX_BRTIVTT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNC_MIX_FIRST_iwait_time,TNC_MIX - First iwait time,@(odt_skims['TNCOUT_MIX_FIRSTWAIT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,,coef_wait,,,,,, +util_TNC_MIX_transfer_wait_time,TNC_MIX - transfer wait time,@ (odt_skims['TNCOUT_MIX_XFERWAIT']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,,coef_xwait,,,,,, +util_TNC_MIX_number_of_transfers,TNC_MIX - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['TNCOUT_MIX_XFERS'], a_min=None,a_max=4))) * df.time_factor * df.outbound",,,,,,,,,,,,,,,,,coef_xferdrive,,,,,, +util_TNC_MIX_TNC_time,TNC_MIX - TNC time,@(odt_skims['TNCOUT_MIX_ACC']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,,coef_acctime,,,,,, +util_TNC_MIX_Walk_egress_time_(at_attraction_end),TNC_MIX - Walk egress time (at attraction end),@(df.dest_mix_time) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,,coef_acctime,,,,,, +util_TNC_MIX_Walk_other_time,TNC_MIX - Walk other time,@(odt_skims['TNCOUT_MIX_XFERWALK']) * df.time_factor * df.outbound,,,,,,,,,,,,,,,,,coef_xwalk,,,,,, +util_TNC_MIX_Fare,TNC_MIX - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['TNCOUT_MIX_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * df.outbound",,,,,,,,,,,,,,,,,coef_income,,,,,, +util_TNC_MIX_ASC,TNC_MIX - Alternative-specific constant,@df.tnc_mix_out_asc * df.time_factor * df.outbound,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNCIN_MIX_In_vehicle_time,TNCIN_MIX - In-vehicle time,@(odt_skims['TNCIN_MIX_TOTALIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNCIN_MIX_In_vehicle_time_on_LRT,TNCIN_MIX - In-vehicle time on LRT,@(ivt_lrt_multiplier - 1) * (odt_skims['TNCIN_MIX_LRTIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNCIN_MIX_In_vehicle_time_on_CMR,TNCIN_MIX - In-vehicle time on CMR,@(ivt_cmr_multiplier - 1) * (odt_skims['TNCIN_MIX_CMRIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNCIN_MIX_In_vehicle_time_on_LTD_EXP,TNCIN_MIX - In-vehicle time on LTD and EXP,@(ivt_ltd_multiplier - 1) * (odt_skims['TNCIN_MIX_LTDEXPIVTT'] + odt_skims['TNCIN_MIX_EXPIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNCIN_MIX_In_vehicle_time_on_URB,TNCIN_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['TNCIN_MIX_BRTIVTT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNCIN_MIX_FIRST_iwait_time,TNCIN_MIX - First iwait time,@(odt_skims['TNCIN_MIX_FIRSTWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,,coef_wait,,,,,, +util_TNCIN_MIX_transfer_wait_time,TNCIN_MIX - transfer wait time,@(odt_skims['TNCIN_MIX_XFERWAIT']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,,coef_xwait,,,,,, +util_TNCIN_MIX_number_of_transfers,TNCIN_MIX - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['TNCIN_MIX_XFERS'], a_min=None,a_max=4))) * df.time_factor * ~df.outbound",,,,,,,,,,,,,,,,,coef_xferdrive,,,,,, +util_TNCIN_MIX_TNCIN_time,TNCIN_MIX - TNC time,@(odt_skims['TNCIN_MIX_EGR']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,,coef_acctime,,,,,, +util_TNCIN_MIX_Walk_access_time_(at_attraction_end),TNCIN_MIX - Walk access time (at attraction end),@(df.origin_mix_time) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,,coef_acctime,,,,,, +util_TNCIN_MIX_Walk_other_time,TNCIN_MIX - Walk other time,@(odt_skims['TNCIN_MIX_XFERWALK']) * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,,coef_xwalk,,,,,, +util_TNCIN_MIX_Fare,TNCIN_MIX - Fare,"@df.transitSubsidyPassDiscount*(odt_skims['TNCIN_MIX_FARE'])*100/(np.maximum(df.income,1000)**df.income_exponent) * ~df.outbound",,,,,,,,,,,,,,,,,coef_income,,,,,, +util_TNCIN_MIX_ASC,TNCIN_MIX - Alternative-specific constant,@df.tnc_mix_inb_asc * df.time_factor * ~df.outbound,,,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_TNC_MIX - Female,TNC_MIX - Female,@(df.female),,,,,,,,,,,,,,,,,coef_female_tran,,,,,, +util_TNC_MIX - Destination Employment Density,TNC_MIX - Destination Employment Density,dMGRAEmpDen,,,,,,,,,,,,,,,,,coef_dEmpDen_dTran,,,,,, +#,Taxi,,,,,,,,,,,,,,,,,,,,,,,, +util_Taxi_Unavailable,Taxi - Unavailable,RideHail_available==0,,,,,,,,,,,,,,,,,,-999,,,,, +util_Taxi - In-vehicle time,Taxi - In-vehicle time,(s2_time_skims) * time_factor,,,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_Taxi - Wait time,Taxi - Wait time,totalWaitTaxi * time_factor,,,,,,,,,,,,,,,,,,coef_wait,,,,, +util_Taxi - Fare,Taxi - Fare,"@((Taxi_baseFare + df.s2_dist_skims * Taxi_costPerMile + df.s2_time_skims * Taxi_costPerMinute) * 100 + df.s2_cost_skims) / (np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,,,,,,,,coef_income,,,,, +#,TNC Single,,,,,,,,,,,,,,,,,,,,,,,, +util_TNC Single_Unavailable,TNC Single - Unavailable,RideHail_available==0,,,,,,,,,,,,,,,,,,,-999,,,, +util_TNC Single - In-vehicle time,TNC Single - In-vehicle time,(s2_time_skims) * time_factor,,,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_TNC Single - Wait time,TNC Single - Wait time,totalWaitSingleTNC * time_factor,,,,,,,,,,,,,,,,,,,coef_wait,,,, +util_TNC Single - Cost,TNC Single - Cost,"@(np.maximum(TNC_single_baseFare + df.s2_dist_skims * TNC_single_costPerMile + df.s2_time_skims * TNC_single_costPerMinute, TNC_single_baseFare) * 100 + df.s2_cost_skims) / (np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,,,,,,,,,coef_income,,,, +#,TNC Shared,,,,,,,,,,,,,,,,,,,,,,,, +util_TNC Shared_switch,TNC Shared - switch turn-off (depends on data availability),False,,,,,,,,,,,,,,,,,,,,-999,,, +util_TNC Shared - In-vehicle time,TNC Shared - In-vehicle time,"@(np.where(df.nev_available, df.nev_time, np.where(df.microtransit_available, df.microtransit_time, df.s2_time_skims))) * TNC_shared_IVTFactor * df.time_factor",,,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_TNC Shared - Wait time,TNC Shared - Wait time,"@np.where(df.nev_available, nevWaitTime, np.where(df.microtransit_available, microtransitWaitTime, df.totalWaitSharedTNC)) * df.time_factor",,,,,,,,,,,,,,,,,,,,coef_wait,,, +util_TNC Shared - Cost,TNC Shared - Cost,"@np.where(df.nev_available, nevCost, np.where(df.microtransit_available, microtransitCost, (np.maximum(TNC_shared_baseFare + df.s2_dist_skims * TNC_shared_costPerMile + df.s2_time_skims * TNC_shared_costPerMinute, TNC_shared_baseFare) * 100 + df.s2_cost_skims))) / (np.maximum(df.income,1000)**df.income_exponent)",,,,,,,,,,,,,,,,,,,,coef_income,,, +#,School bus,,,,,,,,,,,,,,,,,,,,,,,, +util_School Bus - Unavailable,School Bus - Unavailable,SCHBUS_available==0,,,,,,,,,,,,,,,,,,,,,-999,, +util_School Bus - In-vehicle Time at 20 miles per hour,School Bus - In-vehicle Time at 20 miles per hour,(da_dist_skims)* 3 * time_factor,,,,,,,,,,,,,,,,,,,,,coef_ivt,, +util_School Bus - Wait Time (asserted),School Bus - Wait Time (asserted),10 * time_factor,,,,,,,,,,,,,,,,,,,,,coef_wait,, +util_School Bus - Walk Time (asserted),School Bus - Walk Time (asserted),10 * time_factor,,,,,,,,,,,,,,,,,,,,,coef_acctime,, +util_School Bus - Age 6 to 12,School Bus - Age 6 to 12,@(df.age > 5) * (df.age < 13),,,,,,,,,,,,,,,,,,,,,coef_age6to12_schb,, +#,Escooter,,,,,,,,,,,,,,,,,,,,,,,, +util_Escooter - Unavailable,Escooter - Unavailable,Escooter_available==0,,,,,,,,,,,,,,,,,,,,,,,-999 +#,Ebike,,,,,,,,,,,,,,,,,,,,,,,, +util_Ebike - Unavailable,Ebike - Unavailable,Ebike_available==0,,,,,,,,,,,,,,,,,,,,,,-999, +#,indiv tour ASCs,,,,,,,,,,,,,,,,,,,,,,,, +util_calib_numberofparticipan,abm 2+ calibration,(numberOfParticipantsInJointTour<=2)&(jointTour==1),,,coef_calib_numberofparticipants_SHARED3,,,,,,,,,,,,,,,,,,,, +util_calib_KNR Transit - Distance Parameter,abm 2+ calibration,"@df.c_ivt*5*np.maximum((10-df.da_dist_skims),0)*0.5",,,,,,,,,,,,coef_calib_civt5max10sovdistski_KNR_TRANSIT,coef_calib_civt5max10sovdistski_KNR_TRANSIT,coef_calib_civt5max10sovdistski_KNR_TRANSIT,coef_calib_civt5max10sovdistski_TNC_TRANSIT,coef_calib_civt5max10sovdistski_TNC_TRANSIT,coef_calib_civt5max10sovdistski_TNC_TRANSIT,,,,,, +util_calib_Walk-Transit - Distance Parameter,abm 2+ calibration,"@df.c_ivt*np.maximum((200+(-133*df.da_dist_skims)),0)*0.5",,,,,,coef_calib_civtmax200133sovdist_WALK_TRANSIT,coef_calib_civtmax200133sovdist_WALK_TRANSIT,coef_calib_civtmax200133sovdist_WALK_TRANSIT,,,,,,,,,,,,,,, +util_calib_PNR-Transit - Distance Parameter,abm 2+ calibration,"@df.c_ivt*np.maximum((45+(-2.5*df.da_dist_skims)),0)*0.5",,,,,,,,,coef_calib_civtmax4525sovdistsk_PNR_TRANSIT,coef_calib_civtmax4525sovdistsk_PNR_TRANSIT,coef_calib_civtmax4525sovdistsk_PNR_TRANSIT,,,,,,,,,,,, +util_calib_esctour1,abm 2+ calibration,tour_type=='escort',,,,coef_calib_esctour1_WALK,,,,,,,,,,,,,,,,,,, +#,,,,,,,,,,,,,,,,,,,,,,,,, +util_calib_tourda,abm 2+ calibration,tourDA,,coef_calib_tourda_SHARED2,coef_calib_tourda_SHARED3,coef_calib_tourda_WALK,coef_calib_tourda_BIKE,coef_calib_tourda_WALK_TRANSIT,coef_calib_tourda_WALK_TRANSIT,coef_calib_tourda_WALK_TRANSIT,coef_calib_tourda_PNR_TRANSIT,coef_calib_tourda_PNR_TRANSIT,coef_calib_tourda_PNR_TRANSIT,coef_calib_tourda_KNR_TRANSIT,coef_calib_tourda_KNR_TRANSIT,coef_calib_tourda_KNR_TRANSIT,coef_calib_tourda_TNC_TRANSIT,coef_calib_tourda_TNC_TRANSIT,coef_calib_tourda_TNC_TRANSIT,coef_calib_tourda_TAXI,coef_calib_tourda_TNC_SINGLE,coef_calib_tourda_TNC_SHARED,coef_calib_tourda_SCH_BUS,-999,-999 +util_calib_tours2,abm 2+ calibration,tourS2,coef_calib_tours2_DRIVEALONE,coef_calib_tours2_SHARED2,coef_calib_tours2_SHARED3,coef_calib_tours2_WALK,coef_calib_tours2_BIKE,coef_calib_tours2_WALK_TRANSIT,coef_calib_tours2_WALK_TRANSIT,coef_calib_tours2_WALK_TRANSIT,coef_calib_tours2_PNR_TRANSIT,coef_calib_tours2_PNR_TRANSIT,coef_calib_tours2_PNR_TRANSIT,coef_calib_tours2_KNR_TRANSIT,coef_calib_tours2_KNR_TRANSIT,coef_calib_tours2_KNR_TRANSIT,coef_calib_tours2_TNC_TRANSIT,coef_calib_tours2_TNC_TRANSIT,coef_calib_tours2_TNC_TRANSIT,coef_calib_tours2_TAXI,coef_calib_tours2_TNC_SINGLE,coef_calib_tours2_TNC_SHARED,coef_calib_tours2_SCH_BUS,0,0 +util_calib_tours3,abm 2+ calibration,tourS3,coef_calib_tours3_DRIVEALONE,coef_calib_tours3_SHARED2,coef_calib_tours3_SHARED3,coef_calib_tours3_WALK,coef_calib_tours3_BIKE,coef_calib_tours3_WALK_TRANSIT,coef_calib_tours3_WALK_TRANSIT,coef_calib_tours3_WALK_TRANSIT,coef_calib_tours3_PNR_TRANSIT,coef_calib_tours3_PNR_TRANSIT,coef_calib_tours3_PNR_TRANSIT,coef_calib_tours3_KNR_TRANSIT,coef_calib_tours3_KNR_TRANSIT,coef_calib_tours3_KNR_TRANSIT,coef_calib_tours3_TNC_TRANSIT,coef_calib_tours3_TNC_TRANSIT,coef_calib_tours3_TNC_TRANSIT,coef_calib_tours3_TAXI,coef_calib_tours3_TNC_SINGLE,coef_calib_tours3_TNC_SHARED,coef_calib_tours3_SCH_BUS,0,0 +util_calib_tourwalk,abm 2+ calibration,tourWalk,coef_calib_tourwalk_DRIVEALONE,coef_calib_tourwalk_SHARED2,coef_calib_tourwalk_SHARED3,0,coef_calib_tourwalk_BIKE,coef_calib_tourwalk_WALK_TRANSIT,coef_calib_tourwalk_WALK_TRANSIT,coef_calib_tourwalk_WALK_TRANSIT,coef_calib_tourwalk_PNR_TRANSIT,coef_calib_tourwalk_PNR_TRANSIT,coef_calib_tourwalk_PNR_TRANSIT,coef_calib_tourwalk_KNR_TRANSIT,coef_calib_tourwalk_KNR_TRANSIT,coef_calib_tourwalk_KNR_TRANSIT,coef_calib_tourwalk_TNC_TRANSIT,coef_calib_tourwalk_TNC_TRANSIT,coef_calib_tourwalk_TNC_TRANSIT,coef_calib_tourwalk_TAXI,coef_calib_tourwalk_TNC_SINGLE,coef_calib_tourwalk_TNC_SHARED,coef_calib_tourwalk_SCH_BUS,-999,-999 +util_calib_tourbike,abm 2+ calibration,tourBike,coef_calib_tourbike_DRIVEALONE,coef_calib_tourbike_SHARED2,coef_calib_tourbike_SHARED3,coef_calib_tourbike_WALK,coef_calib_tourbike_BIKE,coef_calib_tourbike_WALK_TRANSIT,coef_calib_tourbike_WALK_TRANSIT,coef_calib_tourbike_WALK_TRANSIT,coef_calib_tourbike_PNR_TRANSIT,coef_calib_tourbike_PNR_TRANSIT,coef_calib_tourbike_PNR_TRANSIT,coef_calib_tourbike_KNR_TRANSIT,coef_calib_tourbike_KNR_TRANSIT,coef_calib_tourbike_KNR_TRANSIT,coef_calib_tourbike_TNC_TRANSIT,coef_calib_tourbike_TNC_TRANSIT,coef_calib_tourbike_TNC_TRANSIT,coef_calib_tourbike_TAXI,coef_calib_tourbike_TNC_SINGLE,coef_calib_tourbike_TNC_SHARED,coef_calib_tourbike_SCH_BUS,-999,-999 +util_calib_tourwtran,abm 2+ calibration,tourWTran,coef_calib_tourwtran_DRIVEALONE,coef_calib_tourwtran_SHARED2,coef_calib_tourwtran_SHARED3,coef_calib_tourwtran_WALK,coef_calib_tourwtran_BIKE,coef_calib_tourwtran_WALK_TRANSIT,coef_calib_tourwtran_WALK_TRANSIT,coef_calib_tourwtran_WALK_TRANSIT,coef_calib_tourwtran_PNR_TRANSIT,coef_calib_tourwtran_PNR_TRANSIT,coef_calib_tourwtran_PNR_TRANSIT,coef_calib_tourwtran_KNR_TRANSIT,coef_calib_tourwtran_KNR_TRANSIT,coef_calib_tourwtran_KNR_TRANSIT,coef_calib_tourwtran_TNC_TRANSIT,coef_calib_tourwtran_TNC_TRANSIT,coef_calib_tourwtran_TNC_TRANSIT,coef_calib_tourwtran_TAXI,coef_calib_tourwtran_TNC_SINGLE,coef_calib_tourwtran_TNC_SHARED,coef_calib_tourwtran_SCH_BUS,0,0 +util_calib_tourpnr,abm 2+ calibration,tourPNR,coef_calib_tourpnr_DRIVEALONE,coef_calib_tourpnr_SHARED2,coef_calib_tourpnr_SHARED3,coef_calib_tourpnr_WALK,coef_calib_tourpnr_BIKE,coef_calib_tourpnr_WALK_TRANSIT,coef_calib_tourpnr_WALK_TRANSIT,coef_calib_tourpnr_WALK_TRANSIT,coef_calib_tourpnr_PNR_TRANSIT,coef_calib_tourpnr_PNR_TRANSIT,coef_calib_tourpnr_PNR_TRANSIT,coef_calib_tourpnr_KNR_TRANSIT,coef_calib_tourpnr_KNR_TRANSIT,coef_calib_tourpnr_KNR_TRANSIT,coef_calib_tourpnr_TNC_TRANSIT,coef_calib_tourpnr_TNC_TRANSIT,coef_calib_tourpnr_TNC_TRANSIT,coef_calib_tourpnr_TAXI,coef_calib_tourpnr_TNC_SINGLE,coef_calib_tourpnr_TNC_SHARED,coef_calib_tourpnr_SCH_BUS,-999,-999 +util_calib_tourknr,abm 2+ calibration,tourKNR,coef_calib_tourknr_DRIVEALONE,coef_calib_tourknr_SHARED2,coef_calib_tourknr_SHARED3,coef_calib_tourknr_WALK,coef_calib_tourknr_BIKE,coef_calib_tourknr_WALK_TRANSIT,coef_calib_tourknr_WALK_TRANSIT,coef_calib_tourknr_WALK_TRANSIT,coef_calib_tourknr_PNR_TRANSIT,coef_calib_tourknr_PNR_TRANSIT,coef_calib_tourknr_PNR_TRANSIT,coef_calib_tourknr_KNR_TRANSIT,coef_calib_tourknr_KNR_TRANSIT,coef_calib_tourknr_KNR_TRANSIT,coef_calib_tourknr_TNC_TRANSIT,coef_calib_tourknr_TNC_TRANSIT,coef_calib_tourknr_TNC_TRANSIT,coef_calib_tourknr_TAXI,coef_calib_tourknr_TNC_SINGLE,coef_calib_tourknr_TNC_SHARED,coef_calib_tourknr_SCH_BUS,-999,-999 +util_calib_tourtnr,abm 2+ calibration,tourTNR,coef_calib_tourtnr_DRIVEALONE,0,0,0,coef_calib_tourtnr_BIKE,0,0,0,coef_calib_tourtnr_PNR_TRANSIT,coef_calib_tourtnr_PNR_TRANSIT,coef_calib_tourtnr_PNR_TRANSIT,0,0,0,0,0,0,0,0,0,coef_calib_tourtnr_SCH_BUS,-999,-999 +util_calib_tourmaas,abm 2+ calibration,tourMaaS,coef_calib_tourmaas_DRIVEALONE,coef_calib_tourmaas_SHARED2,coef_calib_tourmaas_SHARED3,coef_calib_tourmaas_WALK,coef_calib_tourmaas_BIKE,coef_calib_tourmaas_WALK_TRANSIT,coef_calib_tourmaas_WALK_TRANSIT,coef_calib_tourmaas_WALK_TRANSIT,coef_calib_tourmaas_PNR_TRANSIT,coef_calib_tourmaas_PNR_TRANSIT,coef_calib_tourmaas_PNR_TRANSIT,coef_calib_tourmaas_KNR_TRANSIT,coef_calib_tourmaas_KNR_TRANSIT,coef_calib_tourmaas_KNR_TRANSIT,coef_calib_tourmaas_TNC_TRANSIT,coef_calib_tourmaas_TNC_TRANSIT,coef_calib_tourmaas_TNC_TRANSIT,coef_calib_tourmaas_TAXI,coef_calib_tourmaas_TNC_SINGLE,coef_calib_tourmaas_TNC_SHARED,coef_calib_tourmaas_TNC_SCH_BUS,0,0 +util_calib_tourschbus,abm 2+ calibration,tourSchBus,coef_calib_tourschbus_DRIVEALONE,coef_calib_tourschbus_SHARED2,coef_calib_tourschbus_SHARED3,coef_calib_tourschbus_WALK,coef_calib_tourschbus_BIKE,coef_calib_tourschbus_WALK_TRANSIT,coef_calib_tourschbus_WALK_TRANSIT,coef_calib_tourschbus_WALK_TRANSIT,coef_calib_tourschbus_PNR_TRANSIT,coef_calib_tourschbus_PNR_TRANSIT,coef_calib_tourschbus_PNR_TRANSIT,coef_calib_tourschbus_KNR_TRANSIT,coef_calib_tourschbus_KNR_TRANSIT,coef_calib_tourschbus_KNR_TRANSIT,coef_calib_tourschbus_TNC_TRANSIT,coef_calib_tourschbus_TNC_TRANSIT,coef_calib_tourschbus_TNC_TRANSIT,coef_calib_tourschbus_TAXI,coef_calib_tourschbus_TNC_SINGLE,coef_calib_tourschbus_TNC_SHARED,0,-999,-999 +util_availability_tourebike,,tourEbike,-999,-999,-999,0,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,0,-999 +util_availability_tourescooter,,tourEscooter,-999,-999,-999,0,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,0 +,,,,,,,,,,,,,,,,,,,,,,,,, +#,,,,,,,,,,,,,,,,,,,,,,,,, +util_calib_jointtour1,abm 2+ calibration,jointTour==1,0,0,0,coef_calib_jointtour1_WALK,0,coef_calib_jointtour1_WALK_TRANSIT,coef_calib_jointtour1_WALK_TRANSIT,coef_calib_jointtour1_WALK_TRANSIT,coef_calib_jointtour1_PNR_TRANSIT,coef_calib_jointtour1_PNR_TRANSIT,coef_calib_jointtour1_PNR_TRANSIT,coef_calib_jointtour1_KNR_TRANSIT,coef_calib_jointtour1_KNR_TRANSIT,coef_calib_jointtour1_KNR_TRANSIT,coef_calib_jointtour1_TNC_TRANSIT,coef_calib_jointtour1_TNC_TRANSIT,coef_calib_jointtour1_TNC_TRANSIT,0,0,0,0,, +util_calib_tourbikejointtour0,abm 2+ calibration,tourBike*(jointTour==0),coef_calib_tourbikejointtour0_DRIVEALONE,coef_calib_tourbikejointtour0_SHARED2,coef_calib_tourbikejointtour0_SHARED3,coef_calib_tourbikejointtour0_WALK,coef_calib_tourbikejointtour0_BIKE,coef_calib_tourbikejointtour0_WALK_TRANSIT,coef_calib_tourbikejointtour0_WALK_TRANSIT,coef_calib_tourbikejointtour0_WALK_TRANSIT,coef_calib_tourbikejointtour0_PNR_TRANSIT,coef_calib_tourbikejointtour0_PNR_TRANSIT,coef_calib_tourbikejointtour0_PNR_TRANSIT,coef_calib_tourbikejointtour0_KNR_TRANSIT,coef_calib_tourbikejointtour0_KNR_TRANSIT,coef_calib_tourbikejointtour0_KNR_TRANSIT,coef_calib_tourbikejointtour0_TNC_TRANSIT,coef_calib_tourbikejointtour0_TNC_TRANSIT,coef_calib_tourbikejointtour0_TNC_TRANSIT,coef_calib_tourbikejointtour0_TAXI,coef_calib_tourbikejointtour0_TNC_SINGLE,coef_calib_tourbikejointtour0_TNC_SHARED,0,, +util_calib_tourbikejointtour1,abm 2+ calibration,tourBike*(jointTour==1),0,0,0,0,coef_calib_tourbikejointtour1_BIKE,0,0,0,0,0,0,0,0,0,0,0,0,coef_calib_tourbikejointtour1_TAXI,coef_calib_tourbikejointtour1_TNC_SINGLE,coef_calib_tourbikejointtour1_TNC_SHARED,0,, +util_calib_tourdajointtour0,abm 2+ calibration,tourDA*(jointTour==0),coef_calib_tourdajointtour0_DRIVEALONE,coef_calib_tourdajointtour0_SHARED2,coef_calib_tourdajointtour0_SHARED3,coef_calib_tourdajointtour0_WALK,coef_calib_tourdajointtour0_BIKE,coef_calib_tourdajointtour0_WALK_TRANSIT,coef_calib_tourdajointtour0_WALK_TRANSIT,coef_calib_tourdajointtour0_WALK_TRANSIT,coef_calib_tourdajointtour0_PNR_TRANSIT,coef_calib_tourdajointtour0_PNR_TRANSIT,coef_calib_tourdajointtour0_PNR_TRANSIT,coef_calib_tourdajointtour0_KNR_TRANSIT,coef_calib_tourdajointtour0_KNR_TRANSIT,coef_calib_tourdajointtour0_KNR_TRANSIT,coef_calib_tourdajointtour0_TNC_TRANSIT,coef_calib_tourdajointtour0_TNC_TRANSIT,coef_calib_tourdajointtour0_TNC_TRANSIT,coef_calib_tourdajointtour0_TAXI,coef_calib_tourdajointtour0_TNC_SINGLE,coef_calib_tourdajointtour0_TNC_SHARED,0,, +util_calib_tourdajointtour1,abm 2+ calibration,tourDA*(jointTour==1),0,coef_calib_tourdajointtour1_SHARED2,coef_calib_tourdajointtour1_SHARED3,coef_calib_tourdajointtour1_WALK,coef_calib_tourdajointtour1_BIKE,coef_calib_tourdajointtour1_WALK_TRANSIT,coef_calib_tourdajointtour1_WALK_TRANSIT,coef_calib_tourdajointtour1_WALK_TRANSIT,coef_calib_tourdajointtour1_PNR_TRANSIT,coef_calib_tourdajointtour1_PNR_TRANSIT,coef_calib_tourdajointtour1_PNR_TRANSIT,coef_calib_tourdajointtour1_KNR_TRANSIT,coef_calib_tourdajointtour1_KNR_TRANSIT,coef_calib_tourdajointtour1_KNR_TRANSIT,coef_calib_tourdajointtour1_TNC_TRANSIT,coef_calib_tourdajointtour1_TNC_TRANSIT,coef_calib_tourdajointtour1_TNC_TRANSIT,coef_calib_tourdajointtour1_TAXI,coef_calib_tourdajointtour1_TNC_SINGLE,coef_calib_tourdajointtour1_TNC_SHARED,0,, +util_calib_tourknrjointtour0,abm 2+ calibration,tourKNR*(jointTour==0),coef_calib_tourknrjointtour0_DRIVEALONE,coef_calib_tourknrjointtour0_SHARED2,coef_calib_tourknrjointtour0_SHARED3,coef_calib_tourknrjointtour0_WALK,coef_calib_tourknrjointtour0_BIKE,coef_calib_tourknrjointtour0_WALK_TRANSIT,coef_calib_tourknrjointtour0_WALK_TRANSIT,coef_calib_tourknrjointtour0_WALK_TRANSIT,coef_calib_tourknrjointtour0_PNR_TRANSIT,coef_calib_tourknrjointtour0_PNR_TRANSIT,coef_calib_tourknrjointtour0_PNR_TRANSIT,coef_calib_tourknrjointtour0_KNR_TRANSIT,coef_calib_tourknrjointtour0_KNR_TRANSIT,coef_calib_tourknrjointtour0_KNR_TRANSIT,coef_calib_tourknrjointtour0_TNC_TRANSIT,coef_calib_tourknrjointtour0_TNC_TRANSIT,coef_calib_tourknrjointtour0_TNC_TRANSIT,coef_calib_tourknrjointtour0_TAXI,coef_calib_tourknrjointtour0_TNC_SINGLE,coef_calib_tourknrjointtour0_TNC_SHARED,0,, +util_calib_tourknrjointtour1,abm 2+ calibration,tourKNR*(jointTour==1),coef_calib_tourknrjointtour1_DRIVEALONE,0,0,0,coef_calib_tourknrjointtour1_BIKE,0,0,0,coef_calib_tourknrjointtour1_PNR_TRANSIT,coef_calib_tourknrjointtour1_PNR_TRANSIT,coef_calib_tourknrjointtour1_PNR_TRANSIT,0,0,0,coef_calib_tourknrjointtour1_TNC_TRANSIT,coef_calib_tourknrjointtour1_TNC_TRANSIT,coef_calib_tourknrjointtour1_TNC_TRANSIT,coef_calib_tourknrjointtour1_TAXI,coef_calib_tourknrjointtour1_TNC_SINGLE,coef_calib_tourknrjointtour1_TNC_SHARED,0,, +util_calib_tourknrtotstops0,abm 2+ calibration,tourKNR*(totStops==0),coef_calib_tourknrtotstops0_DRIVEALONE,coef_calib_tourknrtotstops0_SHARED2,coef_calib_tourknrtotstops0_SHARED3,coef_calib_tourknrtotstops0_WALK,coef_calib_tourknrtotstops0_BIKE,coef_calib_tourknrtotstops0_WALK_TRANSIT,coef_calib_tourknrtotstops0_WALK_TRANSIT,coef_calib_tourknrtotstops0_WALK_TRANSIT,coef_calib_tourknrtotstops0_PNR_TRANSIT,coef_calib_tourknrtotstops0_PNR_TRANSIT,coef_calib_tourknrtotstops0_PNR_TRANSIT,0,0,0,0,0,0,0,0,0,0,, +util_calib_tourknrtotstops>0,abm 2+ calibration,tourKNR*(totStops>0),coef_calib_tourknrtotstopsm0_DRIVEALONE,0,0,0,coef_calib_tourknrtotstopsm0_BIKE,0,0,0,coef_calib_tourknrtotstopsm0_PNR_TRANSIT,coef_calib_tourknrtotstopsm0_PNR_TRANSIT,coef_calib_tourknrtotstopsm0_PNR_TRANSIT,0,0,0,0,0,0,0,0,0,0,, +util_calib_tourknrfirstofmult,abm 2+ calibration,tourKNR*firstOfMultipleTrips,coef_calib_tourknrfirstofmultip_DRIVEALONE,0,0,0,coef_calib_tourknrfirstofmultip_BIKE,0,0,0,coef_calib_tourknrfirstofmultip_PNR_TRANSIT,coef_calib_tourknrfirstofmultip_PNR_TRANSIT,coef_calib_tourknrfirstofmultip_PNR_TRANSIT,0,0,0,0,0,0,0,0,0,0,, +util_calib_tourknrlastofmulti,abm 2+ calibration,tourKNR*lastofMultipleTrips,coef_calib_tourknrlastofmultipl_DRIVEALONE,0,0,0,coef_calib_tourknrlastofmultipl_BIKE,0,0,0,coef_calib_tourknrlastofmultipl_PNR_TRANSIT,coef_calib_tourknrlastofmultipl_PNR_TRANSIT,coef_calib_tourknrlastofmultipl_PNR_TRANSIT,0,0,0,0,0,0,0,0,0,0,, +util_calib_tourebikejointtour0,,tourEbike*(jointTour==0),,,,coef_calib_tourebikejointtour0_WALK,,,,,,,,,,,,,,,,,,coef_calib_tourebikejointtour0_EBIKE, +util_calib_tourebikejointtour1,,tourEbike*(jointTour==1),,,,coef_calib_tourebikejointtour1_WALK,,,,,,,,,,,,,,,,,,coef_calib_tourebikejointtour1_EBIKE, +util_calib_tourescooterjointtour0,,tourEscooter*(jointTour==0),,,,coef_calib_tourescooterjointtour0_WALK,,,,,,,,,,,,,,,,,,,coef_calib_tourescooterjointtour0_ESCOOTER +util_calib_tourescooterjointtour1,,tourEscooter*(jointTour==1),,,,coef_calib_tourescooterjointtour1_WALK,,,,,,,,,,,,,,,,,,,coef_calib_tourescooterjointtour1_ESCOOTER +,,,,,,,,,,,,,,,,,,,,,,,,, +#,,,,,,,,,,,,,,,,,,,,,,,,, +util_calib_tourmaasjointtour0,abm 2+ calibration,tourMaaS*(jointTour==0),0,coef_calib_tourmaasjointtour0_SHARED2,coef_calib_tourmaasjointtour0_SHARED3,coef_calib_tourmaasjointtour0_WALK,0,0,0,0,0,0,0,0,0,0,0,0,0,coef_calib_tourmaasjointtour0_TAXI,coef_calib_tourmaasjointtour0_TNC_SINGLE,coef_calib_tourmaasjointtour0_TNC_SHARED,0,, +util_calib_tourmaasjointtour1,abm 2+ calibration,tourMaaS*(jointTour==1),0,coef_calib_tourmaasjointtour1_SHARED2,coef_calib_tourmaasjointtour1_SHARED3,coef_calib_tourmaasjointtour1_WALK,0,0,0,0,0,0,0,0,0,0,0,0,0,0,coef_calib_tourmaasjointtour1_TNC_SINGLE,coef_calib_tourmaasjointtour1_TNC_SHARED,0,, +util_calib_tourpnrjointtour0,abm 2+ calibration,tourPNR*(jointTour==0),coef_calib_tourpnrjointtour0_DRIVEALONE,coef_calib_tourpnrjointtour0_SHARED2,coef_calib_tourpnrjointtour0_SHARED3,coef_calib_tourpnrjointtour0_WALK,coef_calib_tourpnrjointtour0_BIKE,coef_calib_tourpnrjointtour0_WALK_TRANSIT,coef_calib_tourpnrjointtour0_WALK_TRANSIT,coef_calib_tourpnrjointtour0_WALK_TRANSIT,coef_calib_tourpnrjointtour0_PNR_TRANSIT,coef_calib_tourpnrjointtour0_PNR_TRANSIT,coef_calib_tourpnrjointtour0_PNR_TRANSIT,coef_calib_tourpnrjointtour0_KNR_TRANSIT,coef_calib_tourpnrjointtour0_KNR_TRANSIT,coef_calib_tourpnrjointtour0_KNR_TRANSIT,coef_calib_tourpnrjointtour0_TNC_TRANSIT,coef_calib_tourpnrjointtour0_TNC_TRANSIT,coef_calib_tourpnrjointtour0_TNC_TRANSIT,coef_calib_tourpnrjointtour0_TAXI,coef_calib_tourpnrjointtour0_TNC_SINGLE,coef_calib_tourpnrjointtour0_TNC_SHARED,0,, +util_calib_tourpnrjointtour1,abm 2+ calibration,tourPNR*(jointTour==1),0,0,0,0,coef_calib_tourpnrjointtour1_BIKE,0,0,0,0,0,0,coef_calib_tourpnrjointtour1_KNR_TRANSIT,coef_calib_tourpnrjointtour1_KNR_TRANSIT,coef_calib_tourpnrjointtour1_KNR_TRANSIT,coef_calib_tourpnrjointtour1_TNC_TRANSIT,coef_calib_tourpnrjointtour1_TNC_TRANSIT,coef_calib_tourpnrjointtour1_TNC_TRANSIT,coef_calib_tourpnrjointtour1_TAXI,coef_calib_tourpnrjointtour1_TNC_SINGLE,coef_calib_tourpnrjointtour1_TNC_SHARED,0,, +util_calib_tourpnrtotstops0,abm 2+ calibration,tourPNR*(totStops==0),coef_calib_tourpnrtotstops0_DRIVEALONE,coef_calib_tourpnrtotstops0_SHARED2,coef_calib_tourpnrtotstops0_SHARED3,coef_calib_tourpnrtotstops0_WALK,coef_calib_tourpnrtotstops0_BIKE,coef_calib_tourpnrtotstops0_WALK_TRANSIT,coef_calib_tourpnrtotstops0_WALK_TRANSIT,coef_calib_tourpnrtotstops0_WALK_TRANSIT,0,0,0,coef_calib_tourpnrtotstops0_KNR_TRANSIT,coef_calib_tourpnrtotstops0_KNR_TRANSIT,coef_calib_tourpnrtotstops0_KNR_TRANSIT,coef_calib_tourpnrtotstops0_TNC_TRANSIT,coef_calib_tourpnrtotstops0_TNC_TRANSIT,coef_calib_tourpnrtotstops0_TNC_TRANSIT,0,0,0,0,, +util_calib_tourpnrtotstops>0,abm 2+ calibration,tourPNR*(totStops>0),0,0,0,0,coef_calib_tourpnrtotstopsm0_BIKE,coef_calib_tourpnrtotstopsm0_WALK_TRANSIT,coef_calib_tourpnrtotstopsm0_WALK_TRANSIT,coef_calib_tourpnrtotstopsm0_WALK_TRANSIT,0,0,0,coef_calib_tourpnrtotstopsm0_KNR_TRANSIT,coef_calib_tourpnrtotstopsm0_KNR_TRANSIT,coef_calib_tourpnrtotstopsm0_KNR_TRANSIT,coef_calib_tourpnrtotstopsm0_TNC_TRANSIT,coef_calib_tourpnrtotstopsm0_TNC_TRANSIT,coef_calib_tourpnrtotstopsm0_TNC_TRANSIT,0,0,0,0,, +util_calib_tourpnrfirstofmult,abm 2+ calibration,tourPNR*firstOfMultipleTrips,0,0,0,0,coef_calib_tourpnrfirstofmultip_BIKE,0,0,0,0,0,0,coef_calib_tourpnrfirstofmultip_KNR_TRANSIT,coef_calib_tourpnrfirstofmultip_KNR_TRANSIT,coef_calib_tourpnrfirstofmultip_KNR_TRANSIT,coef_calib_tourpnrfirstofmultip_TNC_TRANSIT,coef_calib_tourpnrfirstofmultip_TNC_TRANSIT,coef_calib_tourpnrfirstofmultip_TNC_TRANSIT,0,0,0,0,, +util_calib_tourpnrlastofmulti,abm 2+ calibration,tourPNR*lastofMultipleTrips,0,0,0,0,coef_calib_tourpnrlastofmultipl_BIKE,0,0,0,0,0,0,coef_calib_tourpnrlastofmultipl_KNR_TRANSIT,coef_calib_tourpnrlastofmultipl_KNR_TRANSIT,coef_calib_tourpnrlastofmultipl_KNR_TRANSIT,coef_calib_tourpnrlastofmultipl_TNC_TRANSIT,coef_calib_tourpnrlastofmultipl_TNC_TRANSIT,coef_calib_tourpnrlastofmultipl_TNC_TRANSIT,0,0,0,0,, +util_calib_tours2jointtour0,abm 2+ calibration,tourS2*(jointTour==0),coef_calib_tours2jointtour0_DRIVEALONE,coef_calib_tours2jointtour0_SHARED2,coef_calib_tours2jointtour0_SHARED3,coef_calib_tours2jointtour0_WALK,coef_calib_tours2jointtour0_BIKE,coef_calib_tours2jointtour0_WALK_TRANSIT,coef_calib_tours2jointtour0_WALK_TRANSIT,coef_calib_tours2jointtour0_WALK_TRANSIT,coef_calib_tours2jointtour0_PNR_TRANSIT,coef_calib_tours2jointtour0_PNR_TRANSIT,coef_calib_tours2jointtour0_PNR_TRANSIT,coef_calib_tours2jointtour0_KNR_TRANSIT,coef_calib_tours2jointtour0_KNR_TRANSIT,coef_calib_tours2jointtour0_KNR_TRANSIT,coef_calib_tours2jointtour0_TNC_TRANSIT,coef_calib_tours2jointtour0_TNC_TRANSIT,coef_calib_tours2jointtour0_TNC_TRANSIT,coef_calib_tours2jointtour0_TAXI,coef_calib_tours2jointtour0_TNC_SINGLE,coef_calib_tours2jointtour0_TNC_SHARED,0,, +util_calib_tours2jointtour1,abm 2+ calibration,tourS2*(jointTour==1),coef_calib_tours2jointtour1_DRIVEALONE,coef_calib_tours2jointtour1_SHARED2,coef_calib_tours2jointtour1_SHARED3,coef_calib_tours2jointtour1_WALK,coef_calib_tours2jointtour1_BIKE,coef_calib_tours2jointtour1_WALK_TRANSIT,coef_calib_tours2jointtour1_WALK_TRANSIT,coef_calib_tours2jointtour1_WALK_TRANSIT,coef_calib_tours2jointtour1_PNR_TRANSIT,coef_calib_tours2jointtour1_PNR_TRANSIT,coef_calib_tours2jointtour1_PNR_TRANSIT,coef_calib_tours2jointtour1_KNR_TRANSIT,coef_calib_tours2jointtour1_KNR_TRANSIT,coef_calib_tours2jointtour1_KNR_TRANSIT,coef_calib_tours2jointtour1_TNC_TRANSIT,coef_calib_tours2jointtour1_TNC_TRANSIT,coef_calib_tours2jointtour1_TNC_TRANSIT,coef_calib_tours2jointtour1_TAXI,coef_calib_tours2jointtour1_TNC_SINGLE,coef_calib_tours2jointtour1_TNC_SHARED,0,, +#,,,,,,,,,,,,,,,,,,,,,,,,, +util_calib_tours2totstops0,abm 2+ calibration,tourS2*(totStops==0),coef_calib_tours2totstops0_DRIVEALONE,0,coef_calib_tours2totstops0_SHARED3,coef_calib_tours2totstops0_WALK,coef_calib_tours2totstops0_BIKE,coef_calib_tours2totstops0_WALK_TRANSIT,coef_calib_tours2totstops0_WALK_TRANSIT,coef_calib_tours2totstops0_WALK_TRANSIT,coef_calib_tours2totstops0_PNR_TRANSIT,coef_calib_tours2totstops0_PNR_TRANSIT,coef_calib_tours2totstops0_PNR_TRANSIT,coef_calib_tours2totstops0_KNR_TRANSIT,coef_calib_tours2totstops0_KNR_TRANSIT,coef_calib_tours2totstops0_KNR_TRANSIT,coef_calib_tours2totstops0_TNC_TRANSIT,coef_calib_tours2totstops0_TNC_TRANSIT,coef_calib_tours2totstops0_TNC_TRANSIT,0,0,0,0,, +util_calib_tours2firstofmulti,abm 2+ calibration,tourS2*firstOfMultipleTrips,coef_calib_tours2firstofmultipl_DRIVEALONE,0,coef_calib_tours2firstofmultipl_SHARED3,coef_calib_tours2firstofmultipl_WALK,coef_calib_tours2firstofmultipl_BIKE,coef_calib_tours2firstofmultipl_WALK_TRANSIT,coef_calib_tours2firstofmultipl_WALK_TRANSIT,coef_calib_tours2firstofmultipl_WALK_TRANSIT,coef_calib_tours2firstofmultipl_PNR_TRANSIT,coef_calib_tours2firstofmultipl_PNR_TRANSIT,coef_calib_tours2firstofmultipl_PNR_TRANSIT,coef_calib_tours2firstofmultipl_KNR_TRANSIT,coef_calib_tours2firstofmultipl_KNR_TRANSIT,coef_calib_tours2firstofmultipl_KNR_TRANSIT,coef_calib_tours2firstofmultipl_TNC_TRANSIT,coef_calib_tours2firstofmultipl_TNC_TRANSIT,coef_calib_tours2firstofmultipl_TNC_TRANSIT,0,0,0,0,, +util_calib_tours2lastofmultip,abm 2+ calibration,tourS2*lastofMultipleTrips,coef_calib_tours2lastofmultiple_DRIVEALONE,0,coef_calib_tours2lastofmultiple_SHARED3,coef_calib_tours2lastofmultiple_WALK,coef_calib_tours2lastofmultiple_BIKE,coef_calib_tours2lastofmultiple_WALK_TRANSIT,coef_calib_tours2lastofmultiple_WALK_TRANSIT,coef_calib_tours2lastofmultiple_WALK_TRANSIT,coef_calib_tours2lastofmultiple_PNR_TRANSIT,coef_calib_tours2lastofmultiple_PNR_TRANSIT,coef_calib_tours2lastofmultiple_PNR_TRANSIT,coef_calib_tours2lastofmultiple_KNR_TRANSIT,coef_calib_tours2lastofmultiple_KNR_TRANSIT,coef_calib_tours2lastofmultiple_KNR_TRANSIT,coef_calib_tours2lastofmultiple_TNC_TRANSIT,coef_calib_tours2lastofmultiple_TNC_TRANSIT,coef_calib_tours2lastofmultiple_TNC_TRANSIT,0,0,0,0,, +util_calib_tours3jointtour0,abm 2+ calibration,tourS3*(jointTour==0),coef_calib_tours3jointtour0_DRIVEALONE,coef_calib_tours3jointtour0_SHARED2,coef_calib_tours3jointtour0_SHARED3,coef_calib_tours3jointtour0_WALK,coef_calib_tours3jointtour0_BIKE,coef_calib_tours3jointtour0_WALK_TRANSIT,coef_calib_tours3jointtour0_WALK_TRANSIT,coef_calib_tours3jointtour0_WALK_TRANSIT,coef_calib_tours3jointtour0_PNR_TRANSIT,coef_calib_tours3jointtour0_PNR_TRANSIT,coef_calib_tours3jointtour0_PNR_TRANSIT,coef_calib_tours3jointtour0_KNR_TRANSIT,coef_calib_tours3jointtour0_KNR_TRANSIT,coef_calib_tours3jointtour0_KNR_TRANSIT,coef_calib_tours3jointtour0_TNC_TRANSIT,coef_calib_tours3jointtour0_TNC_TRANSIT,coef_calib_tours3jointtour0_TNC_TRANSIT,coef_calib_tours3jointtour0_TAXI,coef_calib_tours3jointtour0_TNC_SINGLE,coef_calib_tours3jointtour0_TNC_SHARED,0,, +util_calib_tours3jointtour1,abm 2+ calibration,tourS3*(jointTour==1),coef_calib_tours3jointtour1_DRIVEALONE,coef_calib_tours3jointtour1_SHARED2,coef_calib_tours3jointtour1_SHARED3,coef_calib_tours3jointtour1_WALK,coef_calib_tours3jointtour1_BIKE,coef_calib_tours3jointtour1_WALK_TRANSIT,coef_calib_tours3jointtour1_WALK_TRANSIT,coef_calib_tours3jointtour1_WALK_TRANSIT,coef_calib_tours3jointtour1_PNR_TRANSIT,coef_calib_tours3jointtour1_PNR_TRANSIT,coef_calib_tours3jointtour1_PNR_TRANSIT,coef_calib_tours3jointtour1_KNR_TRANSIT,coef_calib_tours3jointtour1_KNR_TRANSIT,coef_calib_tours3jointtour1_KNR_TRANSIT,coef_calib_tours3jointtour1_TNC_TRANSIT,coef_calib_tours3jointtour1_TNC_TRANSIT,coef_calib_tours3jointtour1_TNC_TRANSIT,coef_calib_tours3jointtour1_TAXI,coef_calib_tours3jointtour1_TNC_SINGLE,coef_calib_tours3jointtour1_TNC_SHARED,0,, +util_calib_tours3totstops0,abm 2+ calibration,tourS3*(totStops==0),coef_calib_tours3totstops0_DRIVEALONE,coef_calib_tours3totstops0_SHARED2,0,coef_calib_tours3totstops0_WALK,coef_calib_tours3totstops0_BIKE,coef_calib_tours3totstops0_WALK_TRANSIT,coef_calib_tours3totstops0_WALK_TRANSIT,coef_calib_tours3totstops0_WALK_TRANSIT,coef_calib_tours3totstops0_PNR_TRANSIT,coef_calib_tours3totstops0_PNR_TRANSIT,coef_calib_tours3totstops0_PNR_TRANSIT,coef_calib_tours3totstops0_KNR_TRANSIT,coef_calib_tours3totstops0_KNR_TRANSIT,coef_calib_tours3totstops0_KNR_TRANSIT,coef_calib_tours3totstops0_TNC_TRANSIT,coef_calib_tours3totstops0_TNC_TRANSIT,coef_calib_tours3totstops0_TNC_TRANSIT,0,0,0,0,, +util_calib_tours3autodeficien,abm 2+ calibration,tourS3*autoDeficientHH,0,coef_calib_tours3autodeficienth_SHARED2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,, +util_calib_tours3firstofmulti,abm 2+ calibration,tourS3*firstOfMultipleTrips,coef_calib_tours3firstofmultipl_DRIVEALONE,coef_calib_tours3firstofmultipl_SHARED2,0,coef_calib_tours3firstofmultipl_WALK,coef_calib_tours3firstofmultipl_BIKE,coef_calib_tours3firstofmultipl_WALK_TRANSIT,coef_calib_tours3firstofmultipl_WALK_TRANSIT,coef_calib_tours3firstofmultipl_WALK_TRANSIT,coef_calib_tours3firstofmultipl_PNR_TRANSIT,coef_calib_tours3firstofmultipl_PNR_TRANSIT,coef_calib_tours3firstofmultipl_PNR_TRANSIT,coef_calib_tours3firstofmultipl_KNR_TRANSIT,coef_calib_tours3firstofmultipl_KNR_TRANSIT,coef_calib_tours3firstofmultipl_KNR_TRANSIT,coef_calib_tours3firstofmultipl_TNC_TRANSIT,coef_calib_tours3firstofmultipl_TNC_TRANSIT,coef_calib_tours3firstofmultipl_TNC_TRANSIT,0,0,0,0,, +util_calib_tours3lastofmultip,abm 2+ calibration,tourS3*lastofMultipleTrips,coef_calib_tours3lastofmultiple_DRIVEALONE,coef_calib_tours3lastofmultiple_SHARED2,0,coef_calib_tours3lastofmultiple_WALK,coef_calib_tours3lastofmultiple_BIKE,coef_calib_tours3lastofmultiple_WALK_TRANSIT,coef_calib_tours3lastofmultiple_WALK_TRANSIT,coef_calib_tours3lastofmultiple_WALK_TRANSIT,coef_calib_tours3lastofmultiple_PNR_TRANSIT,coef_calib_tours3lastofmultiple_PNR_TRANSIT,coef_calib_tours3lastofmultiple_PNR_TRANSIT,coef_calib_tours3lastofmultiple_KNR_TRANSIT,coef_calib_tours3lastofmultiple_KNR_TRANSIT,coef_calib_tours3lastofmultiple_KNR_TRANSIT,coef_calib_tours3lastofmultiple_TNC_TRANSIT,coef_calib_tours3lastofmultiple_TNC_TRANSIT,coef_calib_tours3lastofmultiple_TNC_TRANSIT,0,0,0,0,, +util_calib_tours3zeroautohh,abm 2+ calibration,tourS3*zeroAutoHH,0,coef_calib_tours3zeroautohh_SHARED2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,, +#,,,,,,,,,,,,,,,,,,,,,,,,, +util_calib_tourtnrjointtour0,abm 2+ calibration,tourTNR*(jointTour==0),0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,, +util_calib_tourtnrjointtour1,abm 2+ calibration,tourTNR*(jointTour==1),0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,, +util_calib_tourwtranjointtour0,abm 2+ calibration,tourWTran*(jointTour==0),coef_calib_tourwtranjointtour0_DRIVEALONE,coef_calib_tourwtranjointtour0_SHARED2,coef_calib_tourwtranjointtour0_SHARED3,coef_calib_tourwtranjointtour0_WALK,coef_calib_tourwtranjointtour0_BIKE,coef_calib_tourwtranjointtour0_WALK_TRANSIT,coef_calib_tourwtranjointtour0_WALK_TRANSIT,coef_calib_tourwtranjointtour0_WALK_TRANSIT,coef_calib_tourwtranjointtour0_PNR_TRANSIT,coef_calib_tourwtranjointtour0_PNR_TRANSIT,coef_calib_tourwtranjointtour0_PNR_TRANSIT,coef_calib_tourwtranjointtour0_KNR_TRANSIT,coef_calib_tourwtranjointtour0_KNR_TRANSIT,coef_calib_tourwtranjointtour0_KNR_TRANSIT,coef_calib_tourwtranjointtour0_TNC_TRANSIT,coef_calib_tourwtranjointtour0_TNC_TRANSIT,coef_calib_tourwtranjointtour0_TNC_TRANSIT,0,coef_calib_tourwtranjointtour0_TNC_SINGLE,coef_calib_tourwtranjointtour0_TNC_SHARED,0,, +util_calib_tourwtranjointtour1,abm 2+ calibration,tourWTran*(jointTour==1),coef_calib_tourwtranjointtour1_DRIVEALONE,coef_calib_tourwtranjointtour1_SHARED2,coef_calib_tourwtranjointtour1_SHARED3,coef_calib_tourwtranjointtour1_WALK,coef_calib_tourwtranjointtour1_BIKE,coef_calib_tourwtranjointtour1_WALK_TRANSIT,coef_calib_tourwtranjointtour1_WALK_TRANSIT,coef_calib_tourwtranjointtour1_WALK_TRANSIT,coef_calib_tourwtranjointtour1_PNR_TRANSIT,coef_calib_tourwtranjointtour1_PNR_TRANSIT,coef_calib_tourwtranjointtour1_PNR_TRANSIT,coef_calib_tourwtranjointtour1_KNR_TRANSIT,coef_calib_tourwtranjointtour1_KNR_TRANSIT,coef_calib_tourwtranjointtour1_KNR_TRANSIT,coef_calib_tourwtranjointtour1_TNC_TRANSIT,coef_calib_tourwtranjointtour1_TNC_TRANSIT,coef_calib_tourwtranjointtour1_TNC_TRANSIT,coef_calib_tourwtranjointtour1_TAXI,coef_calib_tourwtranjointtour1_TNC_SINGLE,coef_calib_tourwtranjointtour1_TNC_SHARED,0,, +util_calib_tourwtrantotstops0,abm 2+ calibration,tourWTran*(totStops==0),coef_calib_tourwtrantotstops0_DRIVEALONE,coef_calib_tourwtrantotstops0_SHARED2,coef_calib_tourwtrantotstops0_SHARED3,coef_calib_tourwtrantotstops0_WALK,coef_calib_tourwtrantotstops0_BIKE,0,0,0,coef_calib_tourwtrantotstops0_PNR_TRANSIT,coef_calib_tourwtrantotstops0_PNR_TRANSIT,coef_calib_tourwtrantotstops0_PNR_TRANSIT,coef_calib_tourwtrantotstops0_KNR_TRANSIT,coef_calib_tourwtrantotstops0_KNR_TRANSIT,coef_calib_tourwtrantotstops0_KNR_TRANSIT,coef_calib_tourwtrantotstops0_TNC_TRANSIT,coef_calib_tourwtrantotstops0_TNC_TRANSIT,coef_calib_tourwtrantotstops0_TNC_TRANSIT,0,0,0,coef_calib_tourwtrantotstops0_SCH_BUS,, +#,,,,,,,,,,,,,,,,,,,,,,,,, +util_calib_tourwtranfirstofmu,abm 2+ calibration,tourWTran*firstOfMultipleTrips,coef_calib_tourwtranfirstofmult_DRIVEALONE,coef_calib_tourwtranfirstofmult_SHARED2,coef_calib_tourwtranfirstofmult_SHARED3,coef_calib_tourwtranfirstofmult_WALK,coef_calib_tourwtranfirstofmult_BIKE,0,0,0,coef_calib_tourwtranfirstofmult_PNR_TRANSIT,coef_calib_tourwtranfirstofmult_PNR_TRANSIT,coef_calib_tourwtranfirstofmult_PNR_TRANSIT,coef_calib_tourwtranfirstofmult_KNR_TRANSIT,coef_calib_tourwtranfirstofmult_KNR_TRANSIT,coef_calib_tourwtranfirstofmult_KNR_TRANSIT,coef_calib_tourwtranfirstofmult_TNC_TRANSIT,coef_calib_tourwtranfirstofmult_TNC_TRANSIT,coef_calib_tourwtranfirstofmult_TNC_TRANSIT,0,0,0,coef_calib_tourwtranfirstofmult_SCH_BUS,, +util_calib_tourwtranlastofmul,abm 2+ calibration,tourWTran*lastofMultipleTrips,coef_calib_tourwtranlastofmulti_DRIVEALONE,coef_calib_tourwtranlastofmulti_SHARED2,coef_calib_tourwtranlastofmulti_SHARED3,coef_calib_tourwtranlastofmulti_WALK,coef_calib_tourwtranlastofmulti_BIKE,0,0,0,coef_calib_tourwtranlastofmulti_PNR_TRANSIT,coef_calib_tourwtranlastofmulti_PNR_TRANSIT,coef_calib_tourwtranlastofmulti_PNR_TRANSIT,coef_calib_tourwtranlastofmulti_KNR_TRANSIT,coef_calib_tourwtranlastofmulti_KNR_TRANSIT,coef_calib_tourwtranlastofmulti_KNR_TRANSIT,coef_calib_tourwtranlastofmulti_TNC_TRANSIT,coef_calib_tourwtranlastofmulti_TNC_TRANSIT,coef_calib_tourwtranlastofmulti_TNC_TRANSIT,0,0,0,coef_calib_tourwtranlastofmulti_SCH_BUS,, +util_calib_tourwtranzeroautoh,abm 2+ calibration,tourWTran*zeroAutoHH,0,0,0,coef_calib_tourwtranzeroautohh_WALK,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,, +util_calib_tourwalkjointtour0,abm 2+ calibration,tourWalk*(jointTour==0),coef_calib_tourwalkjointtour0_DRIVEALONE,coef_calib_tourwalkjointtour0_SHARED2,coef_calib_tourwalkjointtour0_SHARED3,coef_calib_tourwalkjointtour0_WALK,coef_calib_tourwalkjointtour0_BIKE,coef_calib_tourwalkjointtour0_WALK_TRANSIT,coef_calib_tourwalkjointtour0_WALK_TRANSIT,coef_calib_tourwalkjointtour0_WALK_TRANSIT,coef_calib_tourwalkjointtour0_PNR_TRANSIT,coef_calib_tourwalkjointtour0_PNR_TRANSIT,coef_calib_tourwalkjointtour0_PNR_TRANSIT,coef_calib_tourwalkjointtour0_KNR_TRANSIT,coef_calib_tourwalkjointtour0_KNR_TRANSIT,coef_calib_tourwalkjointtour0_KNR_TRANSIT,coef_calib_tourwalkjointtour0_TNC_TRANSIT,coef_calib_tourwalkjointtour0_TNC_TRANSIT,coef_calib_tourwalkjointtour0_TNC_TRANSIT,coef_calib_tourwalkjointtour0_TAXI,coef_calib_tourwalkjointtour0_TNC_SINGLE,coef_calib_tourwalkjointtour0_TNC_SHARED,0,, +util_calib_tourwalkjointtour1,abm 2+ calibration,tourWalk*(jointTour==1),coef_calib_tourwalkjointtour1_DRIVEALONE,coef_calib_tourwalkjointtour1_SHARED2,coef_calib_tourwalkjointtour1_SHARED3,0,coef_calib_tourwalkjointtour1_BIKE,coef_calib_tourwalkjointtour1_WALK_TRANSIT,coef_calib_tourwalkjointtour1_WALK_TRANSIT,coef_calib_tourwalkjointtour1_WALK_TRANSIT,coef_calib_tourwalkjointtour1_PNR_TRANSIT,coef_calib_tourwalkjointtour1_PNR_TRANSIT,coef_calib_tourwalkjointtour1_PNR_TRANSIT,coef_calib_tourwalkjointtour1_KNR_TRANSIT,coef_calib_tourwalkjointtour1_KNR_TRANSIT,coef_calib_tourwalkjointtour1_KNR_TRANSIT,coef_calib_tourwalkjointtour1_TNC_TRANSIT,coef_calib_tourwalkjointtour1_TNC_TRANSIT,coef_calib_tourwalkjointtour1_TNC_TRANSIT,coef_calib_tourwalkjointtour1_TAXI,coef_calib_tourwalkjointtour1_TNC_SINGLE,coef_calib_tourwalkjointtour1_TNC_SHARED,0,, +#,Micromobility,,,,,,,,,,,,,,,,,,,,,,,, +util_micromobility_long_access,Shut off micromobility if access time > threshold and not micromobility tour,@((df.MicroAccessTime > microAccessThreshold)& ~(df.tourEbike | df.tourEscooter)),,,,,,,,,,,,,,,,,,,,,,-999,-999 +util_micromobility_long_access_microTour,Decrease micromobility if access time > threshold but tour is micromobility,@((df.MicroAccessTime > microAccessThreshold) & (df.tourEbike | df.tourEscooter)),,,,,,,,,,,,,,,,,,,,,,-20,-20 +#util_micromobility_long_trip,Shut off ebike if distance > threshold,ebikeMaxDistance,,,,,,,,,,,,,,,,,,,,,,-999, +#util_micromobility_long_trip,Shut off escooter if distance > threshold,escooterMaxDistance,,,,,,,,,,,,,,,,,,,,,,,-999 +util_ebike_ivt,Ebike utility for in-vehicle time,@(df.ebike_time * df.time_factor),,,,,,,,,,,,,,,,,,,,,,coef_ivt, +util_ebike_access,Ebike utility for access time time,@(microConstant + ((~df.ebike_owner)&(microRentTime + df.MicroAccessTime)))*df.time_factor,,,,,,,,,,,,,,,,,,,,,,coef_acctime, +util_ebike_cost_inb,Ebike utility for inbound cost,@((~df.ebike_owner) & ((microFixedCost + microVarCost*df.ebike_time)/df.cost_sensitivity)),,,,,,,,,,,,,,,,,,,,,,coef_income, +util_escooter_ivt,Escooter utility for in-vehicle time,@(df.escooter_time)*df.time_factor,,,,,,,,,,,,,,,,,,,,,,,coef_ivt +util_escooter_access,Escooter utility for access time,@(microRentTime + microConstant + df.MicroAccessTime) *df.time_factor,,,,,,,,,,,,,,,,,,,,,,,coef_acctime +util_escooter_cost_inb,Escooter utility for inbound cost,@(microFixedCost + microVarCost*df.escooter_time)/df.cost_sensitivity,,,,,,,,,,,,,,,,,,,,,,,coef_income diff --git a/configs/resident/trip_mode_choice.yaml b/configs/resident/trip_mode_choice.yaml new file mode 100644 index 0000000..5bf0436 --- /dev/null +++ b/configs/resident/trip_mode_choice.yaml @@ -0,0 +1,126 @@ +SPEC: trip_mode_choice.csv +COEFFICIENTS: trip_mode_choice_coefficients.csv +COEFFICIENT_TEMPLATE: trip_mode_choice_coefficients_template.csv + +LOGIT_TYPE: NL + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - DRIVEALONE + - SHARED2 + - SHARED3 + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: MICROMOBILITY + coefficient: coef_nest_MICROMOBILITY + alternatives: + - EBIKE + - ESCOOTER + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - name: WALKACCESS + coefficient: coef_nest_TRANSIT_WALKACCESS + alternatives: + - WALK_LOC + - WALK_PRM + - WALK_MIX + - name: PNRACCESS + coefficient: coef_nest_TRANSIT_PNRACCESS + alternatives: + - PNR_LOC + - PNR_PRM + - PNR_MIX + - name: KNRACCESS + coefficient: coef_nest_TRANSIT_KNRACCESS + alternatives: + - KNR_LOC + - KNR_PRM + - KNR_MIX + - name: TNCACCESS + coefficient: coef_nest_TRANSIT_TNCACCESS + alternatives: + - TNC_LOC + - TNC_PRM + - TNC_MIX + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + - name: SCHOOL_BUS + coefficient: coef_nest_SCHOOL_BUS + alternatives: + - SCH_BUS + +CONSTANTS: + orig_col_name: origin + dest_col_name: destination + + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: trip_mode_choice_annotate_trips_preprocessor + DF: df + TABLES: + - land_use + - tours + - vehicles + - households + - persons + +annotate_trips: + SPEC: trip_mode_choice_annotate_trips + DF: trips_merged + TABLES: + - tours + - land_use + +# to reduce memory needs filter chooser table to these fields +TOURS_MERGED_CHOOSER_COLUMNS: + - hhsize + - age + - num_adults + - auto_ownership + - number_of_participants + - tour_category + - parent_tour_id + - tour_mode + - duration + - tour_type + - free_parking_at_work + - income_segment + - income + - sex + - time_factor_work + - time_factor_nonwork + - transponder_ownership + - ptype + - ebike_owner + - start + - end + - transit_pass_subsidy + - transit_pass_ownership + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum + +CHOOSER_COLS_TO_KEEP: + - vot_da + - vot_s2 + - vot_s3 + - ownsTransponder + - parkingCost + - totalWaitSingleTNC + - totalWaitSharedTNC + - s2_time_skims + - s2_dist_skims + - s2_cost_skims \ No newline at end of file diff --git a/configs/resident/trip_mode_choice_annotate_trips.csv b/configs/resident/trip_mode_choice_annotate_trips.csv new file mode 100644 index 0000000..ce3e85e --- /dev/null +++ b/configs/resident/trip_mode_choice_annotate_trips.csv @@ -0,0 +1,54 @@ +Description,Target,Expression +,_origin_local_dist,"reindex(land_use.walk_dist_local_bus, df.origin)" +,_origin_prm_dist,"reindex(land_use.walk_dist_premium_transit, df.origin)" +,_origin_mix_dist,"np.minimum(_origin_local_dist, _origin_prm_dist)" +,_dest_local_dist,"reindex(land_use.walk_dist_local_bus, df.destination)" +,_dest_prm_dist,"reindex(land_use.walk_dist_premium_transit, df.destination)" +,_dest_mix_dist,"np.minimum(_dest_local_dist, _dest_prm_dist)" +# +,cost_parking,"np.where(df.trip_mode.isin(['DRIVEALONE','SHARED2','SHARED3']),df.parkingCost,0)" +#,, +,_cost_fare_drive,0 +,_cost_fare_drive,"_cost_fare_drive + np.where(df.trip_mode == 'TAXI',1,0) * ((Taxi_baseFare + df.s2_dist_skims * Taxi_costPerMile + df.s2_time_skims * Taxi_costPerMinute) * 100 + df.s2_cost_skims)" +,_cost_fare_drive,"_cost_fare_drive + np.where(df.trip_mode=='TNC_SINGLE',1,0)*((TNC_single_baseFare + df.s2_dist_skims * TNC_single_costPerMile + df.s2_time_skims * TNC_single_costPerMinute) * 100 + df.s2_cost_skims)" +,_cost_fare_drive,"_cost_fare_drive + np.where(df.trip_mode=='TNC_SHARED',1,0)*((TNC_shared_baseFare + df.s2_dist_skims * TNC_shared_costPerMile + df.s2_time_skims * TNC_shared_costPerMinute) * 100 + df.s2_cost_skims)" +,_cost_fare_drive,"_cost_fare_drive + df.trip_mode.isin(['TNC_LOC','TNC_PRM','TNC_MIX'])*df.outbound*(TNC_single_baseFare + odt_skims['TNCOUT_LOC_ACC']*25/60 * TNC_single_costPerMile + odt_skims['TNCOUT_LOC_ACC'] * TNC_single_costPerMinute) * 100" +,cost_fare_drive,"_cost_fare_drive + df.trip_mode.isin(['TNC_LOC','TNC_PRM','TNC_MIX'])*~df.outbound*(TNC_single_baseFare + odt_skims['TNCIN_LOC_ACC']*25/60 * TNC_single_costPerMile + odt_skims['TNCIN_LOC_ACC'] * TNC_single_costPerMinute) * 100" +#,, +,_distance_walk,0 +,_distance_walk,"_distance_walk + (df.trip_mode=='WALK') * od_skims['walkTime']/walkSpeed*60" +,_distance_walk,"(_origin_local_dist+_dest_local_dist) * np.where(df.trip_mode == 'WALK_LOC',1,0)" +,_distance_walk,"(_origin_prm_dist+_dest_prm_dist) * np.where(df.trip_mode == 'WALK_PRM',1,0)" +,_distance_walk,"(_origin_mix_dist+_dest_mix_dist) * np.where(df.trip_mode == 'WALK_MIX',1,0)" +,_distance_walk,"_distance_walk + _dest_local_dist * np.where(df.trip_mode == 'PNR_LOC',1,0) * np.where(df.outbound,1,0)" +,_distance_walk,"_distance_walk + _dest_prm_dist * np.where(df.trip_mode == 'PNR_PRM',1,0) * np.where(df.outbound,1,0)" +,_distance_walk,"_distance_walk + _dest_mix_dist * np.where(df.trip_mode == 'PNR_MIX',1,0) * np.where(df.outbound,1,0)" +,_distance_walk,"_distance_walk + _dest_local_dist * np.where(df.trip_mode == 'KNR_LOC',1,0) * np.where(df.outbound,1,0)" +,_distance_walk,"_distance_walk + _dest_prm_dist * np.where(df.trip_mode == 'KNR_PRM',1,0) * np.where(df.outbound,1,0)" +,_distance_walk,"_distance_walk + _dest_mix_dist * np.where(df.trip_mode == 'KNR_MIX',1,0) * np.where(df.outbound,1,0)" +,_distance_walk,"_distance_walk + _dest_local_dist * np.where(df.trip_mode == 'TNC_LOC',1,0) * np.where(df.outbound,1,0)" +,_distance_walk,"_distance_walk + _dest_prm_dist * np.where(df.trip_mode == 'TNC_PRM',1,0) * np.where(df.outbound,1,0)" +,_distance_walk,"_distance_walk + _dest_mix_dist * np.where(df.trip_mode == 'TNC_MIX',1,0) * np.where(df.outbound,1,0)" +,_distance_walk,"_distance_walk + _origin_local_dist * np.where(df.trip_mode == 'PNR_LOC',1,0) * np.where(~df.outbound,1,0)" +,_distance_walk,"_distance_walk + _origin_prm_dist * np.where(df.trip_mode == 'PNR_PRM',1,0) * np.where(~df.outbound,1,0)" +,_distance_walk,"_distance_walk + _origin_mix_dist * np.where(df.trip_mode == 'PNR_MIX',1,0) * np.where(~df.outbound,1,0)" +,_distance_walk,"_distance_walk + _dest_local_dist * np.where(df.trip_mode == 'KNR_LOC',1,0) * np.where(~df.outbound,1,0)" +,_distance_walk,"_distance_walk + _origin_prm_dist * np.where(df.trip_mode == 'KNR_PRM',1,0) * np.where(~df.outbound,1,0)" +,_distance_walk,"_distance_walk + _origin_mix_dist * np.where(df.trip_mode == 'KNR_MIX',1,0) * np.where(~df.outbound,1,0)" +,_distance_walk,"_distance_walk + _origin_local_dist * np.where(df.trip_mode == 'TNC_LOC',1,0) * np.where(~df.outbound,1,0)" +,_distance_walk,"_distance_walk + _origin_prm_dist * np.where(df.trip_mode == 'TNC_PRM',1,0) * np.where(~df.outbound,1,0)" +,distance_walk,"_distance_walk + _origin_mix_dist * np.where(df.trip_mode == 'TNC_MIX',1,0) * np.where(~df.outbound,1,0)" +#,, +,_time_mm,0 +,_time_mm,"_time_mm + df.trip_mode.isin(['EBIKE'])* od_skims['BIKE_TIME'] * bikeSpeed / ebikeSpeed" +,time_mm,"_time_mm + df.trip_mode.isin(['ESCOOTER'])*od_skims['BIKE_TIME'] * bikeSpeed / escooterSpeed" +,_distance_mm,0 +,_distance_mm,"_distance_mm + df.trip_mode.isin(['EBIKE'])*od_skims['BIKE_TIME'] * (bikeSpeed/ebikeSpeed)/60 * ebikeSpeed" +,distance_mm,"_distance_mm + df.trip_mode.isin(['ESCOOTER'])*od_skims['BIKE_TIME'] * (bikeSpeed/escooterSpeed)/60 * escooterSpeed" +,_cost_fare_mm,0 +,_cost_fare_mm,"_cost_fare_mm + df.trip_mode.isin(['EBIKE'])*(microFixedCost + microVarCost*time_mm)" +,cost_fare_mm,"_cost_fare_mm + df.trip_mode.isin(['ESCOOTER'])*(microFixedCost + microVarCost*time_mm)" +,_distance_bike,0 +,distance_bike,"_distance_bike + df.trip_mode.isin(['BIKE'])*od_skims['BIKE_TIME']/60 * bikeSpeed" +#,, +,time_wait_drive,df.totalWaitSingleTNC+df.totalWaitSharedTNC diff --git a/configs/resident/trip_mode_choice_annotate_trips_preprocessor.csv b/configs/resident/trip_mode_choice_annotate_trips_preprocessor.csv new file mode 100644 index 0000000..54a5bb2 --- /dev/null +++ b/configs/resident/trip_mode_choice_annotate_trips_preprocessor.csv @@ -0,0 +1,331 @@ +Description,Target,Expression +,is_joint,(df.number_of_participants > 1) +,is_indiv,(df.number_of_participants == 1) +#,, +,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])" +#,, +,tour_type,"df.get('tour_type', default='work')" +# cost coef,, +,income_exponent,"np.where(df.tour_type == 'work', 0.6, 0.5)" +,c_cost,"(coef_income) /(df.income.clip(0,1000).pow(income_exponent))" +,cost_sensitivity,"np.maximum(df.income,1000).pow(income_exponent)" +# ivt coef,, +,time_factor,"np.where(df.tour_type=='work', df.time_factor_work, df.time_factor_nonwork)" +#time_factor placeholder,time_factor,1 +,c_ivt,coef_ivt * time_factor +,origin,df.origin if 'origin' in df.columns else df.home_zone_id +,destination,df.destination if 'destination' in df.columns else df.alt_dest +#,, +# cost coef,, +,income_exponent,"np.where(tour_type == 'work', 0.6, 0.5)" +,c_cost,"(coef_income) /(np.maximum(df.income,1000).pow(income_exponent))" +#,, +,vot_da,c_ivt / c_cost * 0.6 +,vot_s2,vot_da / cost_share_s2 +,vot_s3,vot_da / cost_share_s3 +,_vot_bin_da,"np.where(vot_da < vot_threshold_low, 1, np.where(vot_da < vot_threshold_med, 2, 3))" +,_vot_bin_s2,"np.where(vot_s2 < vot_threshold_low, 1, np.where(vot_s2 < vot_threshold_med, 2, 3))" +,_vot_bin_s3,"np.where(vot_s3 < vot_threshold_low, 1, np.where(vot_s3 < vot_threshold_med, 2, 3))" +#,, +#,is_atwork_subtour,(df.tour_category == 'atwork') if _DF_IS_TOUR else False +#,parent_tour_veh,"reindex(tours['selected_vehicle'], df['parent_tour_id']) if 'selected_vehicle' in df.columns else np.nan" +#,sov_veh_option,"np.where(is_atwork_subtour, parent_tour_veh, df.get('vehicle_occup_1', np.nan))" +#,sr2_veh_option,"np.where(is_atwork_subtour, parent_tour_veh, df.get('vehicle_occup_2', np.nan))" +#,sr3p_veh_option,"np.where(is_atwork_subtour, parent_tour_veh, df.get('vehicle_occup_3.5', np.nan))" +#,sov_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sov_veh_option, df.index)) if 'vehicle_occup_1' in df.columns else np.nan" +#,sov_auto_op_cost,"np.where(sov_auto_op_cost.isna() | (pd.Series(sov_veh_option, df.index) == 'non_hh_veh'), costPerMile, sov_auto_op_cost)" +#,sr2_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sr2_veh_option, df.index)) if 'vehicle_occup_2' in df.columns else np.nan" +#,sr2_auto_op_cost,"np.where(sr2_auto_op_cost.isna() | (pd.Series(sr2_veh_option, df.index) == 'non_hh_veh'), costPerMile, sr2_auto_op_cost)" +#,sr3p_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(sr3p_veh_option, df.index)) if 'vehicle_occup_3.5' in df.columns else np.nan" +#,sr3p_auto_op_cost,"np.where(sr3p_auto_op_cost.isna() | (pd.Series(sr3p_veh_option, df.index) == 'non_hh_veh'), costPerMile, sr3p_auto_op_cost)" +#Result of transponder ownership model (1=owns," 0=not)""",ownsTransponder +#household Variable transponder_ownership,, +Transponder ownership model,ownsTransponder,df.transponder_ownership +#vot-indexed skims,, +DA skim based on Transponder ownership,da_dist_skims,"(np.where(ownsTransponder==1, odt_skims['SOV_TR_L_DIST'], odt_skims['SOV_NT_L_DIST']) * (_vot_bin_da == 1)) + (np.where(ownsTransponder==1, odt_skims['SOV_TR_M_DIST'], odt_skims['SOV_NT_M_DIST']) * (_vot_bin_da == 2)) + (np.where(ownsTransponder==1, odt_skims['SOV_TR_H_DIST'], odt_skims['SOV_NT_H_DIST']) * (_vot_bin_da == 3))" +,da_cost_skims,"(np.where(ownsTransponder==1, odt_skims['SOV_TR_L_TOLLCOST'], odt_skims['SOV_NT_L_TOLLCOST']) * (_vot_bin_da == 1)) + (np.where(ownsTransponder==1, odt_skims['SOV_TR_M_TOLLCOST'], odt_skims['SOV_NT_M_TOLLCOST']) * (_vot_bin_da == 2)) + (np.where(ownsTransponder==1, odt_skims['SOV_TR_H_TOLLCOST'], odt_skims['SOV_NT_H_TOLLCOST']) * (_vot_bin_da == 3))" +,da_time_skims,"(np.where(ownsTransponder==1, odt_skims['SOV_TR_L_TIME'], odt_skims['SOV_NT_L_TIME']) * (_vot_bin_da == 1)) + (np.where(ownsTransponder==1, odt_skims['SOV_TR_M_TIME'], odt_skims['SOV_NT_M_TIME']) * (_vot_bin_da == 2)) + (np.where(ownsTransponder==1, odt_skims['SOV_TR_H_TIME'], odt_skims['SOV_NT_H_TIME']) * (_vot_bin_da == 3))" +,da_rel_skims,(((odt_skims['SOV_NT_L_REL']) * (_vot_bin_da == 1)) + ((odt_skims['SOV_NT_M_REL']) * (_vot_bin_da == 2)) + ((odt_skims['SOV_NT_H_REL']) * (_vot_bin_da == 3))) +,s2_dist_skims,(((odt_skims['HOV2_L_DIST']) * (_vot_bin_s2 == 1)) + ((odt_skims['HOV2_M_DIST']) * (_vot_bin_s2 == 2)) + ((odt_skims['HOV2_H_DIST']) * (_vot_bin_s2 == 3))) +,s2_cost_skims,(((odt_skims['HOV2_L_TOLLCOST']) * (_vot_bin_s2 == 1)) + ((odt_skims['HOV2_M_TOLLCOST']) * (_vot_bin_s2 == 2)) + ((odt_skims['HOV2_H_TOLLCOST']) * (_vot_bin_s2 == 3))) +,s2_time_skims,(((odt_skims['HOV2_L_TIME']) * (_vot_bin_s2 == 1)) + ((odt_skims['HOV2_M_TIME']) * (_vot_bin_s2 == 2)) + ((odt_skims['HOV2_H_TIME']) * (_vot_bin_s2 == 3))) +,s2_rel_skims,(((odt_skims['HOV2_L_REL']) * (_vot_bin_da == 1)) + ((odt_skims['HOV2_M_REL']) * (_vot_bin_da == 2)) + ((odt_skims['HOV2_H_REL']) * (_vot_bin_da == 3))) +,s3_dist_skims,(((odt_skims['HOV3_L_DIST']) * (_vot_bin_s2 == 1)) + ((odt_skims['HOV3_M_DIST']) * (_vot_bin_s2 == 2)) + ((odt_skims['HOV3_H_DIST']) * (_vot_bin_s2 == 3))) +,s3_cost_skims,(((odt_skims['HOV3_L_TOLLCOST']) * (_vot_bin_s2 == 1)) + ((odt_skims['HOV3_M_TOLLCOST']) * (_vot_bin_s2 == 2)) + ((odt_skims['HOV3_H_TOLLCOST']) * (_vot_bin_s2 == 3))) +,s3_time_skims,(((odt_skims['HOV3_L_TIME']) * (_vot_bin_s2 == 1)) + ((odt_skims['HOV3_M_TIME']) * (_vot_bin_s2 == 2)) + ((odt_skims['HOV3_H_TIME']) * (_vot_bin_s2 == 3))) +,s3_rel_skims,(((odt_skims['HOV3_L_REL']) * (_vot_bin_da == 1)) + ((odt_skims['HOV3_M_REL']) * (_vot_bin_da == 2)) + ((odt_skims['HOV3_H_REL']) * (_vot_bin_da == 3))) +,walk_time_skims,od_skims['walkTime'] +#,, +,inbound,~df.outbound +,first_trip,df.trip_num == 1 +,last_trip,df.trip_num == df.trip_count +#,, +# FIXME no transit subzones so all zones short walk to transit,, +,_walk_transit_origin,True +,_walk_transit_destination,True +,walk_transit_available,_walk_transit_origin & _walk_transit_destination +,drive_transit_available,"np.where(df.outbound, _walk_transit_destination, _walk_transit_origin) & (df.auto_ownership > 0)" +# RIDEHAIL,, +household_density calculated in annotate_landuse in acres and is converted to sq miles here,_origin_density_measure,"reindex(land_use.PopEmpDenPerMi, df[orig_col_name])" +employment_density calculated in annotate_landuse in acres and is converted to sq miles here,_dest_density_measure,"reindex(land_use.PopEmpDenPerMi, df[dest_col_name])" +,origin_density,"pd.cut(_origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,dest_density,"pd.cut(_dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +Origin MGRA Dwelling Unit Density,oMGRADUDen,"reindex(land_use.duden,df.origin)" +Origin MGRA Employment Density,oMGRAEmpDen,"reindex(land_use.empden,df.origin)" +Origin MGRA Total Intersections,oMGRATotInt,"reindex(land_use.totint,df.origin)" +Destination MGRA Dwelling Unit Density,dMGRADUDen,"reindex(land_use.duden,df.destination)" +Destination MGRA Employment Density,dMGRAEmpDen,"reindex(land_use.empden,df.destination)" +Destination MGRA Total Intersections,dMGRATotInt,"reindex(land_use.totint,df.destination)" +Origin MGRA Mix,oMGRAMix,"np.where(oMGRADUDen+oMGRAEmpDen > 0,(oMGRADUDen*oMGRAEmpDen)/(oMGRADUDen+oMGRAEmpDen),0)" +Destination MGRA Mix,dMGRAMix,"np.where(dMGRADUDen+dMGRAEmpDen > 0,(dMGRADUDen*dMGRAEmpDen)/(dMGRADUDen+dMGRAEmpDen),0)" +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +# ,, Note that the mean and standard deviation are not the values for the distribution itself +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +# wrong? producing bad probs error,dest_zone_sharedTNC_wait_time_mean,"np.maximum((200+(-133 * da_dist_skims)),0) * time_factor" +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime +#,, +Household autos,autos,df.auto_ownership +Number of adults (age 18+) in household,adults,df.num_adults +Household size (number of persons),hhSize,df.hhsize +Person age,age,df.age +Person is female,female,(df.sex == 2) +# Female in party,femaleInParty,df.femaleInParty +# Male in party,maleInParty,df.maleInParty +# Auto operating costs,, +,selected_tour_vehicle,"reindex(tours.selected_vehicle, df.tour_id)" +,auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(selected_tour_vehicle, df.index))" +,auto_op_cost,"np.where(pd.isna(auto_op_cost), costPerMile, auto_op_cost)" +#,, +Does tour use an AV,useAV,selected_tour_vehicle.str.contains('AV') +Auto IVT Factor,autoIVTFactor,"np.where(useAV,1.0,autoIVTFactorAV)" +Auto Parking Cost Factor,autoParkingCostFactor,"np.where(useAV,1.0,autoParkingCostFactorAV)" +Auto CPM Factor,autoCPMFactor,"np.where(useAV,1.0,autoCostPerMileFactorAV)" +Auto Terminal Time Factor,autoTermTimeFactor,"np.where(useAV,1.0,autoTerminalTimeFactorAV)" +MinimumAgeDriveAlone,minimumAgeDA,"np.where(useAV,16,minAgeDriveAloneAV)" +#,, +Zero auto households,zeroAutoHH,"np.where(autos==0,1,0)" +Auto deficient household (more adults than autos),autoDeficientHH,"np.where(autos=adults,1,0) * (np.where(zeroAutoHH,0,1))" +Joint tour,jointTour,(df.tour_category == 'joint') +Number of participants in joint tour,numberOfParticipantsInJointTour,df.number_of_participants * jointTour +Individual tour,indivTour,1 * (jointTour==0) +Number of stops - outbound direction,outStops,df.trip_count * df.outbound +Number of stops - return direction,retStops,df.trip_count * ~df.outbound +Total stops on tour,totStops,outStops + retStops +First trip of tour,firstTrip,df.trip_num == 1 +Last trip of tour,lastTrip,df.trip_num == df.trip_count +First leg of multi-stop outbound,firstOfMultipleTrips,firstTrip *(outStops>1) +Last leg of multi-stop return,lastofMultipleTrips,lastTrip *( retStops>1) +# Flag for setting availability of auto modes for drive-transit access/egress trip segments,autoAllowedForDriveTransit,df.autoModeAllowedForTripSegment +# Flag for setting availability of walk mode for drive-transit access/egress trip segments,walkAllowedForDriveTransit,df.walkModeAllowedForTripSegment +Tour mode is drive-alone,tourDA,(df.tour_mode == 'DRIVEALONE').astype(int) +Tour mode is shared-2,tourS2,(df.tour_mode == 'SHARED2').astype(int) +Tour mode is shared-3+,tourS3,(df.tour_mode == 'SHARED3').astype(int) +Tour mode is walk,tourWalk,(df.tour_mode == 'WALK').astype(int) +Tour mode is bike,tourBike,(df.tour_mode == 'BIKE').astype(int) +Tour mode is walk-transit,tourWTran,df.tour_mode.str.contains('WALK_').astype(int) +Tour mode is PNR-transit,tourPNR,df.tour_mode.str.contains('PNR_').astype(int) +Tour mode is KNR-transit,tourKNR,df.tour_mode.str.contains('KNR_').astype(int) +Tour mode is TNR-transit,tourTNR,"df.tour_mode.isin(['TNC_LOC','TNC_PRM','TNC_MIX']).astype(int)" +Tour mode is MaaS,tourMaaS,"(df.tour_mode.isin(['TAXI', 'TNC_SINGLE', 'TNC_SHARED'])).astype(int)" +Tour mode is school bus,tourSchBus,(df.tour_mode == 'SCH_BUS').astype(int) +Tour mode is Ebike,tourEbike,(df.tour_mode == 'EBIKE').astype(int) +Tour mode is Escooter,tourEscooter,(df.tour_mode == 'ESCOOTER').astype(int) +#,#, +,free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work +person has free on-site parking at workplace,freeOnsite,"(free_parking_available)*np.where(is_indiv,1,0)" +new reimbursement amount,reimburseProportion,0 +tour primary destination,tour_dest,"reindex(tours.destination, df.tour_id)" +half tour duration,tourDuration,"reindex(tours.duration, df.tour_id)/2" +new daily parking cost with reimbursement,parkingCostDayDollars,"reindex(land_use.exp_daily, tour_dest)" +new hourly parking cost with reimbursement,parkingCostHourDollars,"reindex(land_use.exp_hourly, tour_dest)" +new monthly parking cost with reimbursement,parkingCostMonthDollars,"reindex(land_use.exp_monthly, tour_dest)" +Parking area,parkingConstrainedArea,"np.where(reindex(land_use.parking_type, tour_dest) == 1, 1, 0)" +daily cost converted to cents,parkingCostDay,parkingCostDayDollars*100 +hourly cost converted to cents,parkingCostHour,parkingCostHourDollars*100 +monthly cost converted to cents,parkingCostMonth,parkingCostMonthDollars*100 +Tour parking cost for full-time workers and university students,_parkingCostBeforeReimb,"df.ptype.isin([1,3]) * is_indiv * np.minimum(parkingCostMonth/22, parkingCostDay)" +Tour parking cost for full-time workers and university students,_parkingCostBeforeReimb,"df.ptype.isin([1,3]) * is_indiv * np.minimum(_parkingCostBeforeReimb, parkingCostHour * tourDuration)" +Tour parking cost for other person types,parkingCostBeforeReimb,"np.where((~df.ptype.isin([1,3]) * is_indiv) | (is_joint), np.minimum(parkingCostDay, parkingCostHour * tourDuration), _parkingCostBeforeReimb)" +Reimbursement applies to this tour purpose,reimbursePurpose,"reindex(tours.tour_type, df.tour_id)=='work'" +Effective parking cost for free parkers,_parkingCost,"0 * np.where(reimbursePurpose*freeOnsite,1,0)" +Effective parking cost for reimbursed parkers,_parkingCost,"np.where(is_indiv*reimbursePurpose*(1-freeOnsite), np.maximum((1-reimburseProportion) * parkingCostBeforeReimb, 0),_parkingCost)" +Effective parking cost applied to tour purpose,parkingCostPrimDest,"np.where(is_joint+is_indiv*(1-reimbursePurpose), parkingCostBeforeReimb,_parkingCost)" +#,, +Total trips on tour,totalTrips,totStops+2 +Indicator for trip origin is tour prim. Destination,tripOrigIsTourDest,~df.outbound*df.trip_num==1 +Indicator for trip destination is tour prim. Destination,tripDestIsTourDest,df.outbound*df.trip_num==df.trip_count +Contribution to trip parking cost from primary destination,parkCostTourDestContrib,parkingCostPrimDest/(totalTrips+2) +Hourly parking cost at trip origin,parkCostTripOrig,"reindex(land_use.exp_hourly, origin)*100" +Hourly parking cost at trip destination,parkCostTripDest,"reindex(land_use.exp_hourly, destination)*100" +Contribution to trip parking cost from trip origin,parkCostTripOrigContrib,parkCostTripOrig*(1-firstTrip)*(1-tripOrigIsTourDest) +Contribution to trip parking cost from trip origin,parkCostTripOrigContrib,parkingCostPrimDest/(totalTrips+2) * tripOrigIsTourDest +Contribution to trip parking cost from trip destination,parkCostTripDestContrib,parkCostTripDest * (1-lastTrip)*(1-tripDestIsTourDest) +Contribution to trip parking cost from trip destination,parkCostTripDestContrib,parkingCostPrimDest/(totalTrips+2) * tripDestIsTourDest +Final parking cost,parkingCost,parkCostTourDestContrib + parkCostTripOrigContrib + parkCostTripDestContrib +#,, +Origin Terminal Time,oTermTime,"reindex(land_use.terminal_time,origin)" +Destination Terminal Time,dTermTime,"reindex(land_use.terminal_time,destination)" +#,, +bike logsum,bikeLS,od_skims['BIKE_LOGSUM'] +bike time inbound,bike_time,od_skims['BIKE_TIME'] +#,, +"Cost factor for shared 2 tours, 1/(2^0.8)",costFactorS2,0.57 +"Cost factor for shared 3+ tours, 1/(3.5^0.8)",costFactorS3,0.37 +# no sov for 0 autos or age< min drving age,sov_available,(autos>0) * (age>=minimumAgeDA) +no sov for age < min drving age,sov_available,"(age>=minimumAgeDA) * is_indiv * np.where((tourPNR)|(tourKNR)|(tourTNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,sr2_available,"np.where((tourPNR)|(tourKNR)|(tourTNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter)|(df.number_of_participants>2),0,1)" +,sr3_available,"np.where((tourPNR)|(tourKNR)|(tourTNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter)|(df.number_of_participants==2),0,1)" +no long walks,walkAvailable,"np.where((walk_time_skims < max_walk_time),1,0) * np.where((tourPNR)|(tourKNR)|(tourTNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,Escooter_available,"np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourTNR)|(tourMaaS)|(tourSchBus)|(tourEbike),0,1)" +,Ebike_available,"np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourTNR)|(tourMaaS)|(tourSchBus)|(tourEscooter),0,1)" +,PNR_available,"(autos>0) * (age>15) * np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourKNR)|(tourTNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,KNR_available,"np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourTNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,TNR_available,"np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourMaaS)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,RideHail_available,"np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourTNR)|(tourSchBus)|(tourEbike)|(tourEscooter),0,1)" +,SCHBUS_available,"(df.tour_type =='school') & (df.ptype!=3)*np.where((tourDA)|(tourS2)|(tourS3)|(tourWalk)|(tourBike)|(tourWTran)|(tourPNR)|(tourKNR)|(tourTNR)|(tourMaaS)|(tourEbike)|(tourEscooter),0,1)" +#,, +,walk_local_available,(odt_skims['WALK_LOC_TOTALIVTT']>0)&(tourWTran) +,walk_premium_available,(odt_skims['WALK_PRM_TOTALIVTT']>0)&(tourWTran) +,walk_mix_available,(odt_skims['WALK_MIX_TOTALIVTT']>0)&(odt_skims['WALK_MIX_XFERS']>0)&(tourWTran) +,pnr_local_available,((df.outbound & (odt_skims['PNROUT_LOC_TOTALIVTT']>0)) | (~df.outbound & (odt_skims['PNRIN_LOC_TOTALIVTT']>0)))&(tourPNR) +,pnr_premium_available,((df.outbound & (odt_skims['PNROUT_PRM_TOTALIVTT']>0)) | (~df.outbound & (odt_skims['PNRIN_PRM_TOTALIVTT']>0)))&(tourPNR) +,pnr_mix_available,((df.outbound & (odt_skims['PNROUT_MIX_TOTALIVTT']>0)&(odt_skims['PNROUT_MIX_XFERS']>0)) | (~df.outbound & (odt_skims['PNRIN_MIX_TOTALIVTT']>0)&(odt_skims['PNRIN_MIX_XFERS']>0)))&(tourPNR) +,knr_local_available,((df.outbound & (odt_skims['KNROUT_LOC_TOTALIVTT']>0)) | (~df.outbound & (odt_skims['KNRIN_LOC_TOTALIVTT']>0)))&(tourKNR) +,knr_premium_available,((df.outbound & (odt_skims['KNROUT_PRM_TOTALIVTT']>0)) | (~df.outbound & (odt_skims['KNRIN_PRM_TOTALIVTT']>0)))&(tourKNR) +,knr_mix_available,((df.outbound & (odt_skims['KNROUT_MIX_TOTALIVTT']>0)&(odt_skims['KNROUT_MIX_XFERS']>0)) | (~df.outbound & (odt_skims['KNRIN_MIX_TOTALIVTT']>0)&(odt_skims['KNRIN_MIX_XFERS']>0)))&(tourKNR) +,tnc_local_available,((df.outbound & (odt_skims['TNCOUT_LOC_TOTALIVTT']>0)) | (~df.outbound & (odt_skims['TNCIN_LOC_TOTALIVTT']>0)))&(tourTNR) +,tnc_premium_available,((df.outbound & (odt_skims['TNCOUT_PRM_TOTALIVTT']>0)) | (~df.outbound & (odt_skims['TNCIN_PRM_TOTALIVTT']>0)))&(tourTNR) +,tnc_mix_available,((df.outbound & (odt_skims['TNCOUT_MIX_TOTALIVTT']>0)&(odt_skims['TNCOUT_MIX_XFERS']>0)) | (~df.outbound & (odt_skims['TNCIN_MIX_TOTALIVTT']>0)&(odt_skims['TNCIN_MIX_XFERS']>0)))&(tourTNR) +#access egress distances,, +,origin_local_dist,"reindex(land_use.walk_dist_local_bus, origin)" +,origin_prm_dist,"reindex(land_use.walk_dist_premium_transit, origin)" +,origin_mix_dist,"np.minimum(origin_local_dist, origin_prm_dist)" +,origin_micro_prm_dist,"reindex(land_use.micro_dist_premium_transit, origin)" +,dest_local_dist,"reindex(land_use.walk_dist_local_bus, destination)" +,dest_prm_dist,"reindex(land_use.walk_dist_premium_transit, destination)" +,dest_mix_dist,"np.minimum(dest_local_dist, dest_prm_dist)" +,dest_micro_prm_dist,"reindex(land_use.micro_dist_premium_transit, destination)" +#access egress times,, +,origin_local_time,origin_local_dist * 60/walk_speed +,origin_prm_time,origin_prm_dist * 60/walk_speed +,origin_mix_time,origin_mix_dist * 60/walk_speed +,dest_local_time,dest_local_dist * 60/walk_speed +,dest_prm_time,dest_prm_dist * 60/walk_speed +,dest_mix_time,dest_mix_dist * 60/walk_speed +# added for school escorting model,, +Number of school children in vehicle on trip,num_escortees,df.escort_participants.fillna('').apply(lambda x: len(x.split('_')) if len(x)>0 else 0) +# commuter rail constants based on commuter rail IVTT,, +,wlk_prm_cr_asc,"(-1.0) * (np.minimum(odt_skims['WALK_PRM_CMRIVTT'],60) + np.where(odt_skims['WALK_PRM_CMRIVTT']>40,(np.minimum(odt_skims['WALK_PRM_CMRIVTT'],60)-40)*0.6,0))" +,wlk_mix_cr_asc,"(-1.0) * (np.minimum(odt_skims['WALK_MIX_CMRIVTT'],60) + np.where(odt_skims['WALK_MIX_CMRIVTT']>40,(np.minimum(odt_skims['WALK_MIX_CMRIVTT'],60)-40)*0.6,0))" +,pnr_prm_out_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['PNROUT_PRM_CMRIVTT']>20,(odt_skims['PNROUT_PRM_CMRIVTT']-20),0)+np.where(odt_skims['PNROUT_PRM_CMRIVTT']>40,(odt_skims['PNROUT_PRM_CMRIVTT']-40)*1.5,0)),65)" +,pnr_prm_inb_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['PNRIN_PRM_CMRIVTT']>20,(odt_skims['PNRIN_PRM_CMRIVTT']-20),0)+np.where(odt_skims['PNRIN_PRM_CMRIVTT']>40,(odt_skims['PNRIN_PRM_CMRIVTT']-40)*1.5,0)),65)" +,pnr_mix_out_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['PNROUT_MIX_CMRIVTT']>20,(odt_skims['PNROUT_MIX_CMRIVTT']-20),0)+np.where(odt_skims['PNROUT_MIX_CMRIVTT']>40,(odt_skims['PNROUT_MIX_CMRIVTT']-40)*1.5,0)),65)" +,pnr_mix_inb_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['PNRIN_MIX_CMRIVTT']>20,(odt_skims['PNRIN_MIX_CMRIVTT']-20),0)+np.where(odt_skims['PNRIN_MIX_CMRIVTT']>40,(odt_skims['PNRIN_MIX_CMRIVTT']-40)*1.5,0)),65)" +,knr_prm_out_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['KNROUT_PRM_CMRIVTT']>20,(odt_skims['KNROUT_PRM_CMRIVTT']-20),0)+np.where(odt_skims['KNROUT_PRM_CMRIVTT']>40,(odt_skims['KNROUT_PRM_CMRIVTT']-40)*1.5,0)),65)" +,knr_prm_inb_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['KNRIN_PRM_CMRIVTT']>20,(odt_skims['KNRIN_PRM_CMRIVTT']-20),0)+np.where(odt_skims['KNRIN_PRM_CMRIVTT']>40,(odt_skims['KNRIN_PRM_CMRIVTT']-40)*1.5,0)),65)" +,knr_mix_out_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['KNROUT_MIX_CMRIVTT']>20,(odt_skims['KNROUT_MIX_CMRIVTT']-20),0)+np.where(odt_skims['KNROUT_MIX_CMRIVTT']>40,(odt_skims['KNROUT_MIX_CMRIVTT']-40)*1.5,0)),65)" +,knr_mix_inb_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['KNRIN_MIX_CMRIVTT']>20,(odt_skims['KNRIN_MIX_CMRIVTT']-20),0)+np.where(odt_skims['KNRIN_MIX_CMRIVTT']>40,(odt_skims['KNRIN_MIX_CMRIVTT']-40)*1.5,0)),65)" +,tnc_prm_out_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['TNCOUT_PRM_CMRIVTT']>20,(odt_skims['TNCOUT_PRM_CMRIVTT']-20),0)+np.where(odt_skims['TNCOUT_PRM_CMRIVTT']>40,(odt_skims['TNCOUT_PRM_CMRIVTT']-40)*1.5,0)),65)" +,tnc_prm_inb_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['TNCIN_PRM_CMRIVTT']>20,(odt_skims['TNCIN_PRM_CMRIVTT']-20),0)+np.where(odt_skims['TNCIN_PRM_CMRIVTT']>40,(odt_skims['TNCIN_PRM_CMRIVTT']-40)*1.5,0)),65)" +,tnc_mix_out_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['TNCOUT_MIX_CMRIVTT']>20,(odt_skims['TNCOUT_MIX_CMRIVTT']-20),0)+np.where(odt_skims['TNCOUT_MIX_CMRIVTT']>40,(odt_skims['TNCOUT_MIX_CMRIVTT']-40)*1.5,0)),65)" +,tnc_mix_inb_cr_asc,"(-1.0) * np.minimum((15 + np.where(odt_skims['TNCIN_MIX_CMRIVTT']>20,(odt_skims['TNCIN_MIX_CMRIVTT']-20),0)+np.where(odt_skims['TNCIN_MIX_CMRIVTT']>40,(odt_skims['TNCIN_MIX_CMRIVTT']-40)*1.5,0)),65)" +# Note: Hyperloop not implemented in ASC calculations since we arent skimming hyperloop IVTT,, +,wlk_prm_asc,"np.where(odt_skims['WALK_PRM_CMRIVTT'] > 10, wlk_prm_cr_asc,np.where(odt_skims['WALK_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['WALK_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['WALK_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))" +,pnr_prm_out_asc,"np.where(odt_skims['PNROUT_PRM_CMRIVTT'] > 10, pnr_prm_out_cr_asc,np.where(odt_skims['PNROUT_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['PNROUT_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['PNROUT_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))" +,pnr_prm_inb_asc,"np.where(odt_skims['PNRIN_PRM_CMRIVTT'] > 10, pnr_prm_inb_cr_asc,np.where(odt_skims['PNRIN_PRM_LRTIVTT']>0,pnr_lrt_equiv_asc,np.where(odt_skims['PNRIN_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['PNRIN_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))" +,knr_prm_out_asc,"np.where(odt_skims['KNROUT_PRM_CMRIVTT'] > 10, knr_prm_out_cr_asc,np.where(odt_skims['KNROUT_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['KNROUT_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['KNROUT_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))" +,knr_prm_inb_asc,"np.where(odt_skims['KNRIN_PRM_CMRIVTT'] > 10, knr_prm_inb_cr_asc,np.where(odt_skims['KNRIN_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['KNRIN_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['KNRIN_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))" +,tnc_prm_out_asc,"np.where(odt_skims['TNCOUT_PRM_CMRIVTT'] > 10, tnc_prm_out_cr_asc,np.where(odt_skims['TNCOUT_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['TNCOUT_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['TNCOUT_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))" +,tnc_prm_inb_asc,"np.where(odt_skims['TNCIN_PRM_CMRIVTT'] > 10, tnc_prm_inb_cr_asc,np.where(odt_skims['TNCIN_PRM_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['TNCIN_PRM_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['TNCIN_PRM_LTDEXPIVTT'] > 0, eb_equiv_asc,0))))" +,wlk_mix_max_asc,"np.where(odt_skims['WALK_MIX_CMRIVTT'] > 10, wlk_mix_cr_asc,np.where(odt_skims['WALK_MIX_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['WALK_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['WALK_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))" +,pnr_mix_out_max_asc,"np.where(odt_skims['PNROUT_MIX_CMRIVTT'] > 10, pnr_mix_out_cr_asc,np.where(odt_skims['PNROUT_MIX_LRTIVTT']>0,pnr_lrt_equiv_asc,np.where(odt_skims['PNROUT_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['PNROUT_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))" +,pnr_mix_inb_max_asc,"np.where(odt_skims['PNRIN_MIX_CMRIVTT'] > 10, pnr_mix_inb_cr_asc,np.where(odt_skims['PNRIN_MIX_LRTIVTT']>0,pnr_lrt_equiv_asc,np.where(odt_skims['PNRIN_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['PNRIN_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))" +,knr_mix_out_max_asc,"np.where(odt_skims['KNROUT_MIX_CMRIVTT'] > 10,knr_mix_out_cr_asc,np.where(odt_skims['KNROUT_MIX_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['KNROUT_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['KNROUT_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))" +,knr_mix_inb_max_asc,"np.where(odt_skims['KNRIN_MIX_CMRIVTT'] > 10, knr_mix_inb_cr_asc,np.where(odt_skims['KNRIN_MIX_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['KNRIN_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['KNRIN_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))" +,tnc_mix_out_max_asc,"np.where(odt_skims['TNCOUT_MIX_CMRIVTT'] > 10, tnc_mix_out_cr_asc,np.where(odt_skims['TNCOUT_MIX_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['TNCOUT_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['TNCOUT_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))" +,tnc_mix_inb_max_asc,"np.where(odt_skims['TNCIN_MIX_CMRIVTT'] > 10, tnc_mix_inb_cr_asc,np.where(odt_skims['TNCIN_MIX_LRTIVTT']>0,lrt_equiv_asc,np.where(odt_skims['TNCIN_MIX_BRTIVTT']>0,brt_equiv_asc,np.where(odt_skims['WALK_PRM_EXPIVTT']+odt_skims['TNCIN_MIX_LTDEXPIVTT']> 0, eb_equiv_asc,0))))" +# reduce by 50% if bus transfer and constant is positive,, +,wlk_mix_asc,"np.where((wlk_mix_max_asc > 0) & (odt_skims['WALK_MIX_BUSIVTT'] > 0), 0.5 * wlk_mix_max_asc, wlk_mix_max_asc)" +,pnr_mix_out_asc,"np.where((pnr_mix_out_max_asc > 0) & (odt_skims['PNROUT_MIX_BUSIVTT'] > 0), 0.5 * pnr_mix_out_max_asc, pnr_mix_out_max_asc)" +,pnr_mix_inb_asc,"np.where((pnr_mix_inb_max_asc > 0) & (odt_skims['PNRIN_MIX_BUSIVTT'] > 0), 0.5 * pnr_mix_inb_max_asc, pnr_mix_inb_max_asc)" +,knr_mix_out_asc,"np.where((knr_mix_out_max_asc > 0) & (odt_skims['KNROUT_MIX_BUSIVTT'] > 0), 0.5 * knr_mix_out_max_asc, knr_mix_out_max_asc)" +,knr_mix_inb_asc,"np.where((knr_mix_inb_max_asc > 0) & (odt_skims['KNRIN_MIX_BUSIVTT'] > 0), 0.5 * knr_mix_inb_max_asc, knr_mix_inb_max_asc)" +,tnc_mix_out_asc,"np.where((tnc_mix_out_max_asc > 0) & (odt_skims['TNCOUT_MIX_BUSIVTT'] > 0), 0.5 * tnc_mix_out_max_asc, tnc_mix_out_max_asc)" +,tnc_mix_inb_asc,"np.where((tnc_mix_inb_max_asc > 0) & (odt_skims['TNCIN_MIX_BUSIVTT'] > 0), 0.5 * tnc_mix_inb_max_asc, tnc_mix_inb_max_asc)" +# reduce by 50% if transfers happen between cmr and other prm lines,,, +,wlk_prm_asc,"np.where((wlk_prm_asc > 0) & (odt_skims['WALK_PRM_XFERS'] > 0), 0.5 * wlk_prm_asc, wlk_prm_asc)" +,pnr_prm_out_asc,"np.where((pnr_prm_out_asc > 0) & (odt_skims['PNROUT_PRM_XFERS'] > 0), 0.5 * pnr_prm_out_asc, pnr_prm_out_asc)" +,pnr_prm_inb_asc,"np.where((pnr_prm_inb_asc > 0) & (dot_skims['PNRIN_PRM_XFERS'] > 0), 0.5 * pnr_prm_inb_asc, pnr_prm_inb_asc)" +,knr_prm_out_asc,"np.where((knr_prm_out_asc > 0) & (odt_skims['KNROUT_PRM_XFERS'] > 0), 0.5 * knr_prm_out_asc, knr_prm_out_asc)" +,knr_prm_inb_asc,"np.where((knr_prm_inb_asc > 0) & (dot_skims['KNRIN_PRM_XFERS'] > 0), 0.5 * knr_prm_inb_asc, knr_prm_inb_asc)" +,tnc_prm_out_asc,"np.where((tnc_prm_out_asc > 0) & (odt_skims['TNCOUT_PRM_XFERS'] > 0), 0.5 * tnc_prm_out_asc, tnc_prm_out_asc)" +,tnc_prm_inb_asc,"np.where((tnc_prm_inb_asc > 0) & (dot_skims['TNCIN_PRM_XFERS'] > 0), 0.5 * tnc_prm_inb_asc, tnc_prm_inb_asc)" +# Micromobility times,, +ebike time,ebike_time,bike_time * bikeSpeed / ebikeSpeed +escooter time,escooter_time,bike_time * bikeSpeed / escooterSpeed +Micromobility access Time,MicroAccessTime,"reindex(land_use.MicroAccessTime,origin)" +ebike max distance availability,ebikeMaxDistance,(od_skims['DIST'] > ebikeMaxDist) +escooter max distance availability,escooterMaxDistance,(od_skims['DIST'] > escooterMaxDist) +# Microtransit and NEV,, +microtransit available at origin,microtransit_orig,"reindex(land_use.microtransit, df[orig_col_name])" +microtransit available at destination,microtransit_dest,"reindex(land_use.microtransit, df[dest_col_name])" +microtransit available,microtransit_available,(microtransit_orig > 0) & (microtransit_orig == microtransit_dest) & (od_skims['DIST'] < microtransitMaxDist) +microtransit direct time,microtransit_direct_time,"np.maximum(od_skims['DIST']/microtransitSpeed*60, s2_time_skims)" +microtransit total time,microtransit_time,"np.maximum(microtransit_direct_time + microtransitDiversionConstant, microtransitDiversionFactor*microtransit_direct_time)" +nev available at origin,nev_orig,"reindex(land_use.nev, df[orig_col_name])" +nev available at destination,nev_dest,"reindex(land_use.nev, df[dest_col_name])" +nev available,nev_available,(nev_orig > 0) & (nev_orig == nev_dest) & (od_skims['DIST'] < nevMaxDist) +nev direct time,nev_direct_time,"np.maximum(od_skims['DIST']/nevSpeed*60, s2_time_skims)" +nev total time,nev_time,"np.maximum(nev_direct_time + nevDiversionConstant, nevDiversionFactor*nev_direct_time)" +# Microtransit and NEV access to transit,, +outbound microtransit access available,microtransit_access_available_out,df.outbound & (microtransit_orig>0) & (origin_micro_prm_dist0) & (dest_micro_prm_dist0) & (origin_micro_prm_dist0) & (dest_micro_prm_dist0) & (dest_micro_prm_dist>maxWalkIfMTAccessAvailable) & (dest_micro_prm_dist0) & (origin_micro_prm_dist>maxWalkIfMTAccessAvailable) & (origin_micro_prm_dist0) & (dest_micro_prm_dist>maxWalkIfMTAccessAvailable) & (dest_micro_prm_dist0) & (origin_micro_prm_dist>maxWalkIfMTAccessAvailable) & (origin_micro_prm_dist= 25) & (df['outbound'])) | ((periods_left >= 34) & (~df['outbound'])), 47, periods_left)" +,tour_purpose,"reindex(tours.tour_type, df.tour_id)" +,tour_purpose_grouped,"np.where(tour_purpose.isin(['work','school','univ']), 'mand', 'non_mand')" +,half_tour_stops_remaining_grouped,(df.trip_count - df.trip_num).clip(upper=1) \ No newline at end of file diff --git a/configs/resident/trip_scheduling_probs_purpose_stops.csv b/configs/resident/trip_scheduling_probs_purpose_stops.csv new file mode 100644 index 0000000..411be00 --- /dev/null +++ b/configs/resident/trip_scheduling_probs_purpose_stops.csv @@ -0,0 +1,245 @@ +periods_left_min,periods_left_max,outbound,tour_purpose_grouped,half_tour_stops_remaining_grouped,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,All +0,0,True,non_mand,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +0,0,True,non_mand,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +0,0,True,mand,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +0,0,True,mand,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +1,1,True,non_mand,0,0.8189802736716861,0.18101972632831478,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +1,1,True,non_mand,1,0.7721889137040513,0.22781108629594915,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +1,1,True,mand,0,0.7471719634686398,0.2528280365313602,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +1,1,True,mand,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +2,2,True,non_mand,0,0.5313044094657583,0.46665243420937097,0.00204315632485774,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +2,2,True,non_mand,1,0.7175829817374099,0.2807545194400709,0.0016624988225180234,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +2,2,True,mand,0,0.5035022647690804,0.49649773523092017,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +2,2,True,mand,1,0.8745153839084898,0.12548461609151035,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +3,3,True,non_mand,0,0.3871957673026593,0.5842636573575687,0.027848779906802835,0.0006917954329635533,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +3,3,True,non_mand,1,0.5010068584030869,0.4944859928509608,0.004507148745955219,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +3,3,True,mand,0,0.47374691698873894,0.48185051672462087,0.04440256628664039,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +3,3,True,mand,1,0.4975171975856041,0.4834274927388484,0.019055309675547517,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +4,4,True,non_mand,0,0.3000152058882846,0.5523183073306172,0.13664303181097184,0.011023454970117077,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +4,4,True,non_mand,1,0.3743770992053098,0.5727827563748306,0.05252770967230497,0.00031243474755708465,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +4,4,True,mand,0,0.4391344468565407,0.4730858153439597,0.08579467818492309,0.001985059614577168,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +4,4,True,mand,1,0.17374342144703583,0.705501446138433,0.12075513241453113,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +5,5,True,non_mand,0,0.24443687276143128,0.48727017005994244,0.24105582258752195,0.027237134591092966,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +5,5,True,non_mand,1,0.3498737043600855,0.5468850124616544,0.0957015471401984,0.002142098611144654,0.0053976374269204585,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +5,5,True,mand,0,0.4140440879091029,0.4627566659226456,0.10589932963919767,0.014346985277468863,0.0029529312515844176,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +5,5,True,mand,1,0.5359817354115067,0.40328213098074,0.037043756388711026,0.023692377219042413,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +6,6,True,non_mand,0,0.2243718573226493,0.4740049987478467,0.2185942840913799,0.07387248869388857,0.008608771317053272,0.0005475998271755717,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +6,6,True,non_mand,1,0.2947249323070468,0.520484919863231,0.16267063104650237,0.021821311374649363,0.00029820540857341003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +6,6,True,mand,0,0.34571265959006825,0.45183505047762074,0.11547038429330879,0.08043856620520728,0.006543339433795003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +6,6,True,mand,1,0.46036107799104353,0.3701917482866324,0.14122662957933466,0.028220544142989194,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +7,7,True,non_mand,0,0.227621158038117,0.417953229518577,0.19141787402389457,0.12197351063472531,0.024548137961874116,0.016486089822811145,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +7,7,True,non_mand,1,0.2832486215408319,0.5166096440702325,0.14826728166817885,0.03482546263765433,0.017048990083102438,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +7,7,True,mand,0,0.361803588610528,0.418900938560931,0.14154525076540292,0.06743958274104898,0.006099978058599388,0.004210661263489495,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +7,7,True,mand,1,0.4127559556392927,0.4462328382983683,0.1195063008397756,0.012231152152406215,0.009273753070157131,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +8,8,True,non_mand,0,0.19426921480484255,0.40592917871708584,0.19011533368438208,0.15982429426585337,0.040643179164081826,0.006644362842185375,0.002011822208828069,0.0005626143127424159,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +8,8,True,non_mand,1,0.23006768855966434,0.4951325777522361,0.16375321837246617,0.09014636059161886,0.012948244048729797,0.007951910675286304,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +8,8,True,mand,0,0.3404793706490589,0.4564428277054562,0.14909890279509022,0.024535359848871188,0.007981254754395728,0.020513594855995246,0.0,0.0009486893911321145,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +8,8,True,mand,1,0.399341249303726,0.41517942529414553,0.1028491817940402,0.04745891472898081,0.03517122887910787,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +9,9,True,non_mand,0,0.19874760126421334,0.3630228482412267,0.1444748881448116,0.16435014395285896,0.10036289384754772,0.017397511428007644,0.008202316900872985,0.0031073806650114963,0.0003344155554514234,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +9,9,True,non_mand,1,0.22748125907091696,0.4891138773218324,0.21855171544454474,0.04724707111068227,0.016939736746825574,0.00045246712463884055,0.000213873180559771,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 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+31,31,False,non_mand,1,0.0,0.0,0.0,0.01833810824001649,0.0,0.0,0.0,0.0,0.011516290886021865,0.16308529577680958,0.0,0.0,0.0,0.02547074507105579,0.01652281484738158,0.0,0.04452351185239044,0.009317122261056536,0.19971424002149518,0.013045312623538401,0.0075661778015869464,0.3102328065307183,0.0,0.025003003703219125,0.035055083246462164,0.030069783720618536,0.018526881151892546,0.034928530599363024,0.037084291666373344,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +31,31,False,mand,0,0.0,0.0,0.0,0.0,0.0,0.011660697134411879,0.08359356906747217,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0074351633681512585,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.053515133972031996,0.0,0.046642788537647514,0.06362339255281085,0.004389112388089102,0.04017428022808142,0.4817135990383709,0.20725226371293273,0.0,0.0,0.0,1.0 +31,31,False,mand,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.014153420408759243,0.0,0.0,0.0,0.0,0.023248141345199345,0.008890938931074744,0.05285349575062624,0.04990553163058654,0.16677175839150424,0.11331994669851629,0.1181638409115989,0.07387812106291494,0.15865917983104275,0.031235937800509726,0.06596532918651592,0.035468217376292764,0.013865230438545558,0.033059807910658684,0.03141248412869757,0.006106628878031803,0.003041989318924492,0.0,0.0,0.0,0.0,1.0 +32,32,False,non_mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09030713454422928,0.10225330344102772,0.0,0.0,0.0,0.0,0.34201165614137685,0.10656854103943249,0.35885936483393366,0.0,0.0,1.0 +32,32,False,non_mand,1,0.0,0.03615936143527503,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11870838866384083,0.0,0.0,0.030664587449557346,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.026266199236874226,0.0,0.0,0.08761524377109123,0.0,0.0,0.7005862194433614,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +32,32,False,mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05789616370086815,0.1344598373883522,0.3375637056076247,0.2230108052807423,0.24706948802241244,0.0,0.0,1.0 +32,32,False,mand,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.009238420497671889,0.0,0.0,0.0,0.0,0.0,0.0,0.027952902624459142,0.004745183696099721,0.041750935844626774,0.03560104623729895,0.019208273165269348,0.039862390831722126,0.2445054537893148,0.1472889280930601,0.10677793164025022,0.07872994398102684,0.04261569901118799,0.005675051098870241,0.0815413323203732,0.019486477408908912,0.0,0.09502002975985951,0.0,0.0,0.0,0.0,0.0,1.0 +33,33,False,non_mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3937868145193479,0.4486503063939636,0.15756287908668853,0.0,1.0 +33,33,False,non_mand,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.020230772694571117,0.0,0.13595146003291136,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09279893744389224,0.0,0.0,0.07918749737876385,0.0,0.0,0.12358922634696451,0.07179448760198139,0.0,0.0,0.1394804713644242,0.0,0.2868037587900601,0.0,0.0,0.05016338834643131,0.0,0.0,1.0 +33,33,False,mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.21087359210326426,0.35509399657354956,0.28936975269124166,0.14466265863194447,0.0,1.0 +33,33,False,mand,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.023247760476100464,0.0,0.024677767802522798,0.0,0.009363181643807422,0.029519156456030052,0.11193010731182607,0.02285317247116739,0.051385963088573655,0.04473753625556975,0.14095762264166245,0.055024985843556244,0.17384148255260748,0.029384651649520036,0.16325933704399054,0.03425760908549876,0.013563487427945331,0.015926159431522906,0.007948502073252565,0.0,0.022039827009254335,0.005005115351559031,0.013479183790602863,0.0,0.007597390593429946,0.0,0.0,1.0 +34,47,False,non_mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.018384368573649112,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5577089454113409,0.0,0.4239066860150099,1.0 +34,47,False,non_mand,1,0.016657687671303212,0.02183836474581605,0.0,0.021670440655999233,0.0,0.10173930098123364,0.02498724707017388,0.0,0.14226492691657533,0.004760495563960537,0.0,0.0,0.0,0.0,0.0,0.0,0.02638875495711764,0.0,0.0,0.05929170885647927,0.0,0.0,0.0,0.0,0.0,0.008986741490552959,0.0,0.0,0.22252478651398835,0.033523970436925156,0.0,0.010929820731753599,0.017516567897537586,0.28691918551058354,0.0,1.0 +34,47,False,mand,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0778976250021037,0.030793465479170962,0.0,0.0,0.012379085610171855,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07427570742104288,0.0,0.0,0.0,0.0,0.10023495355353525,0.26524960444707757,0.1397015192237292,0.29946803926316845,1.0 +34,47,False,mand,1,0.0,0.0,0.0,0.0,0.1018436706346972,0.0,0.0,0.0,0.0,0.002263878168597051,0.0,0.000983293615706626,0.0,0.008218649939220985,0.023737052572553673,0.021264126033222547,0.05518389314660721,0.010993620292826722,0.20415745418381434,0.07475058698068834,0.07116274568401747,0.07424603443354091,0.06495972196633373,0.03217245961981454,0.08352494702875252,0.03514324066332105,0.026534322261271913,0.04879548794140681,0.004334841005982029,0.02812637911830777,0.0,0.0,0.015797438844245334,0.0,0.011806155865071414,1.0 diff --git a/configs/resident/vehicle_allocation.csv b/configs/resident/vehicle_allocation.csv new file mode 100644 index 0000000..7a4bd77 --- /dev/null +++ b/configs/resident/vehicle_allocation.csv @@ -0,0 +1,174 @@ +Label,Description,Expression,veh_num1,veh_num2,veh_num3,veh_num4,non_hh_veh +#,Availability Conditions,,,, +util_alt1_unavail,Household does not own vehicle,"veh_num1.isna() | (veh_num1 == '')",coef_unavail,0,0,0,0 +util_alt2_unavail,Household does not own vehicle,"veh_num2.isna() | (veh_num2 == '')",0,coef_unavail,0,0,0 +util_alt3_unavail,Household does not own vehicle,"veh_num3.isna() | (veh_num3 == '')",0,0,coef_unavail,0,0 +util_alt4_unavail,Household does not own vehicle,"veh_num4.isna() | (veh_num4 == '')",0,0,0,coef_unavail,0 +#,BEV Range,,,, +util_dstgtrng1,Round trip tour distance > BEV range,"@np.where((df.tot_tour_dist > df.Range_1) & (df.fuel_type_1 == 'BEV'), 1, 0)",coef_dstgtrng,0,0,0,0 +util_dstgtrng2,Round trip tour distance > BEV range,"@np.where((df.tot_tour_dist > df.Range_2) & (df.fuel_type_2 == 'BEV'), 1, 0)",0,coef_dstgtrng,0,0,0 +util_dstgtrng3,Round trip tour distance > BEV range,"@np.where((df.tot_tour_dist > df.Range_3) & (df.fuel_type_3 == 'BEV'), 1, 0)",0,0,coef_dstgtrng,0,0 +util_dstgtrng4,Round trip tour distance > BEV range,"@np.where((df.tot_tour_dist > df.Range_4) & (df.fuel_type_4 == 'BEV'), 1, 0)",0,0,0,coef_dstgtrng,0 +#,Vehicles & Driver interactions,,,, +util_vehltdr_nh,Vehicles < Drivers -- Non-Household Vehicle,"hh_veh_lt_drivers",0,0,0,0,coef_vehltdr_nh +util_vehltdr_nh,Vehicles > Drivers -- Non-Household Vehicle,"hh_veh_gt_drivers",0,0,0,0,coef_vehgtdr_nh +util_vehltdr_van1,Vehicles > Drivers -- Van alt 1,"hh_veh_gt_drivers * (body_type_1 == 'Van')",coef_vehltdr_van,0,0,0,0 +util_vehltdr_van2,Vehicles > Drivers -- Van alt 2,"hh_veh_gt_drivers * (body_type_2 == 'Van')",0,coef_vehltdr_van,0,0,0 +util_vehltdr_van3,Vehicles > Drivers -- Van alt 3,"hh_veh_gt_drivers * (body_type_3 == 'Van')",0,0,coef_vehltdr_van,0,0 +util_vehltdr_van4,Vehicles > Drivers -- Van alt 4,"hh_veh_gt_drivers * (body_type_4 == 'Van')",0,0,0,coef_vehltdr_van,0 +util_vehltdr_suv1,Vehicles > Drivers -- SUV alt 1,"hh_veh_gt_drivers * (body_type_1 == 'SUV')",coef_vehltdr_suv,0,0,0,0 +util_vehltdr_suv2,Vehicles > Drivers -- SUV alt 2,"hh_veh_gt_drivers * (body_type_2 == 'SUV')",0,coef_vehltdr_suv,0,0,0 +util_vehltdr_suv3,Vehicles > Drivers -- SUV alt 3,"hh_veh_gt_drivers * (body_type_3 == 'SUV')",0,0,coef_vehltdr_suv,0,0 +util_vehltdr_suv4,Vehicles > Drivers -- SUV alt 4,"hh_veh_gt_drivers * (body_type_4 == 'SUV')",0,0,0,coef_vehltdr_suv,0 +util_vehltdr_pu1,Vehicles > Drivers -- Pickup alt 1,"hh_veh_gt_drivers * (body_type_1 == 'Pickup')",coef_vehltdr_pu,0,0,0,0 +util_vehltdr_pu2,Vehicles > Drivers -- Pickup alt 2,"hh_veh_gt_drivers * (body_type_2 == 'Pickup')",0,coef_vehltdr_pu,0,0,0 +util_vehltdr_pu3,Vehicles > Drivers -- Pickup alt 3,"hh_veh_gt_drivers * (body_type_3 == 'Pickup')",0,0,coef_vehltdr_pu,0,0 +util_vehltdr_pu4,Vehicles > Drivers -- Pickup alt 4,"hh_veh_gt_drivers * (body_type_4 == 'Pickup')",0,0,0,coef_vehltdr_pu,0 +util_vehltdr_mc1,Vehicles > Drivers -- Motorcycle alt 1,"hh_veh_gt_drivers * (body_type_1 == 'Motorcycle')",coef_vehltdr_mc,0,0,0,0 +util_vehltdr_mc2,Vehicles > Drivers -- Motorcycle alt 2,"hh_veh_gt_drivers * (body_type_2 == 'Motorcycle')",0,coef_vehltdr_mc,0,0,0 +util_vehltdr_mc3,Vehicles > Drivers -- Motorcycle alt 3,"hh_veh_gt_drivers * (body_type_3 == 'Motorcycle')",0,0,coef_vehltdr_mc,0,0 +util_vehltdr_mc4,Vehicles > Drivers -- Motorcycle alt 4,"hh_veh_gt_drivers * (body_type_4 == 'Motorcycle')",0,0,0,coef_vehltdr_mc,0 +util_vehltdr_hyb1,Vehicles > Drivers -- Hybrid alt 1,"hh_veh_gt_drivers * (fuel_type_1 == 'Hybrid')",coef_vehltdr_hyb,0,0,0,0 +util_vehltdr_hyb2,Vehicles > Drivers -- Hybrid alt 2,"hh_veh_gt_drivers * (fuel_type_2 == 'Hybrid')",0,coef_vehltdr_hyb,0,0,0 +util_vehltdr_hyb3,Vehicles > Drivers -- Hybrid alt 3,"hh_veh_gt_drivers * (fuel_type_3 == 'Hybrid')",0,0,coef_vehltdr_hyb,0,0 +util_vehltdr_hyb4,Vehicles > Drivers -- Hybrid alt 4,"hh_veh_gt_drivers * (fuel_type_4 == 'Hybrid')",0,0,0,coef_vehltdr_hyb,0 +util_vehltdr_ev1,Vehicles > Drivers -- Hybrid alt 1,"hh_veh_gt_drivers * ((fuel_type_1=='PEV') | (fuel_type_1=='BEV'))",coef_vehltdr_ev,0,0,0,0 +util_vehltdr_ev2,Vehicles > Drivers -- Hybrid alt 2,"hh_veh_gt_drivers * ((fuel_type_2=='PEV') | (fuel_type_2=='BEV'))",0,coef_vehltdr_ev,0,0,0 +util_vehltdr_ev3,Vehicles > Drivers -- Hybrid alt 3,"hh_veh_gt_drivers * ((fuel_type_3=='PEV') | (fuel_type_3=='BEV'))",0,0,coef_vehltdr_ev,0,0 +util_vehltdr_ev4,Vehicles > Drivers -- Hybrid alt 4,"hh_veh_gt_drivers * ((fuel_type_4=='PEV') | (fuel_type_4=='BEV'))",0,0,0,coef_vehltdr_ev,0 +util_vehltdr_age1,Vehicles > Drivers -- Age alt 1,"hh_veh_gt_drivers * age_1",coef_vehltdr_age,0,0,0,0 +util_vehltdr_age2,Vehicles > Drivers -- Age alt 2,"hh_veh_gt_drivers * age_2",0,coef_vehltdr_age,0,0,0 +util_vehltdr_age3,Vehicles > Drivers -- Age alt 3,"hh_veh_gt_drivers * age_3",0,0,coef_vehltdr_age,0,0 +util_vehltdr_age4,Vehicles > Drivers -- Age alt 4,"hh_veh_gt_drivers * age_4",0,0,0,coef_vehltdr_age,0 +#,Occupancy interactions,,,, +util_maxocc_van1,Maximum Occupancy -- Van alt 1,"@occup * (df.body_type_1 == 'Van')",coef_maxocc_van,0,0,0,0 +util_maxocc_van2,Maximum Occupancy -- Van alt 2,"@occup * (df.body_type_2 == 'Van')",0,coef_maxocc_van,0,0,0 +util_maxocc_van3,Maximum Occupancy -- Van alt 3,"@occup * (df.body_type_3 == 'Van')",0,0,coef_maxocc_van,0,0 +util_maxocc_van4,Maximum Occupancy -- Van alt 4,"@occup * (df.body_type_4 == 'Van')",0,0,0,coef_maxocc_van,0 +util_maxocc_suv1,Maximum Occupancy -- SUV alt 1,"@occup * (df.body_type_1 == 'SUV')",coef_maxocc_suv,0,0,0,0 +util_maxocc_suv2,Maximum Occupancy -- SUV alt 2,"@occup * (df.body_type_2 == 'SUV')",0,coef_maxocc_suv,0,0,0 +util_maxocc_suv3,Maximum Occupancy -- SUV alt 3,"@occup * (df.body_type_3 == 'SUV')",0,0,coef_maxocc_suv,0,0 +util_maxocc_suv4,Maximum Occupancy -- SUV alt 4,"@occup * (df.body_type_4 == 'SUV')",0,0,0,coef_maxocc_suv,0 +util_maxocc_pu1,Maximum Occupancy -- Pickup alt 1,"@occup * (df.body_type_1 == 'Pickup')",coef_maxocc_pu,0,0,0,0 +util_maxocc_pu2,Maximum Occupancy -- Pickup alt 2,"@occup * (df.body_type_2 == 'Pickup')",0,coef_maxocc_pu,0,0,0 +util_maxocc_pu3,Maximum Occupancy -- Pickup alt 3,"@occup * (df.body_type_3 == 'Pickup')",0,0,coef_maxocc_pu,0,0 +util_maxocc_pu4,Maximum Occupancy -- Pickup alt 4,"@occup * (df.body_type_4 == 'Pickup')",0,0,0,coef_maxocc_pu,0 +util_maxocc_mc1,Maximum Occupancy -- Motorcycle alt 1,"@occup * (df.body_type_1 == 'Motorcycle')",coef_maxocc_mc,0,0,0,0 +util_maxocc_mc2,Maximum Occupancy -- Motorcycle alt 2,"@occup * (df.body_type_2 == 'Motorcycle')",0,coef_maxocc_mc,0,0,0 +util_maxocc_mc3,Maximum Occupancy -- Motorcycle alt 3,"@occup * (df.body_type_3 == 'Motorcycle')",0,0,coef_maxocc_mc,0,0 +util_maxocc_mc4,Maximum Occupancy -- Motorcycle alt 4,"@occup * (df.body_type_4 == 'Motorcycle')",0,0,0,coef_maxocc_mc,0 +util_maxocc_age1,Maximum Occupancy -- Age alt 1,"@occup * df.age_1",coef_maxocc_age,0,0,0,0 +util_maxocc_age2,Maximum Occupancy -- Age alt 2,"@occup * df.age_2",0,coef_maxocc_age,0,0,0 +util_maxocc_age3,Maximum Occupancy -- Age alt 3,"@occup * df.age_3",0,0,coef_maxocc_age,0,0 +util_maxocc_age4,Maximum Occupancy -- Age alt 4,"@occup * df.age_4",0,0,0,coef_maxocc_age,0 +util_maxocc_nh,Maximum Occupancy -- Age alt 1,"@occup",0,0,0,0,util_maxocc_nh +#,Alternative Specific Constants,,,, +util_non_hh,Non-Household Vehicle Constant,"1",0,0,0,0,coef_non_hh +util_van1,Van ASC alt 1,"(body_type_1 == 'Van')",coef_van,0,0,0,0 +util_van2,Van ASC alt 2,"(body_type_2 == 'Van')",0,coef_van,0,0,0 +util_van3,Van ASC alt 3,"(body_type_3 == 'Van')",0,0,coef_van,0,0 +util_van4,Van ASC alt 4,"(body_type_4 == 'Van')",0,0,0,coef_van,0 +util_suv1,SUV ASC alt 1,"(body_type_1 == 'SUV')",coef_suv,0,0,0,0 +util_suv2,SUV ASC alt 2,"(body_type_2 == 'SUV')",0,coef_suv,0,0,0 +util_suv3,SUV ASC alt 3,"(body_type_3 == 'SUV')",0,0,coef_suv,0,0 +util_suv4,SUV ASC alt 4,"(body_type_4 == 'SUV')",0,0,0,coef_suv,0 +util_pu1,Pickup ASC alt 1,"(body_type_1 == 'Pickup')",coef_pu,0,0,0,0 +util_pu2,Pickup ASC alt 2,"(body_type_2 == 'Pickup')",0,coef_pu,0,0,0 +util_pu3,Pickup ASC alt 3,"(body_type_3 == 'Pickup')",0,0,coef_pu,0,0 +util_pu4,Pickup ASC alt 4,"(body_type_4 == 'Pickup')",0,0,0,coef_pu,0 +util_mc1,Motorcycle ASC alt 1,"(body_type_1 == 'Motorcycle')",coef_mc,0,0,0,0 +util_mc2,Motorcycle ASC alt 2,"(body_type_2 == 'Motorcycle')",0,coef_mc,0,0,0 +util_mc3,Motorcycle ASC alt 3,"(body_type_3 == 'Motorcycle')",0,0,coef_mc,0,0 +util_mc4,Motorcycle ASC alt 4,"(body_type_4 == 'Motorcycle')",0,0,0,coef_mc,0 +util_dsl1,Diesel ASC alt 1,"(fuel_type_1 == 'Diesel')",coef_dsl,0,0,0,0 +util_dsl2,Diesel ASC alt 2,"(fuel_type_2 == 'Diesel')",0,coef_dsl,0,0,0 +util_dsl3,Diesel ASC alt 3,"(fuel_type_3 == 'Diesel')",0,0,coef_dsl,0,0 +util_dsl4,Diesel ASC alt 4,"(fuel_type_4 == 'Diesel')",0,0,0,coef_dsl,0 +util_hyb1,Hybrid ASC alt 1,"(fuel_type_1 == 'Hybrid')",coef_hyb,0,0,0,0 +util_hyb2,Hybrid ASC alt 2,"(fuel_type_2 == 'Hybrid')",0,coef_hyb,0,0,0 +util_hyb3,Hybrid ASC alt 3,"(fuel_type_3 == 'Hybrid')",0,0,coef_hyb,0,0 +util_hyb4,Hybrid ASC alt 4,"(fuel_type_4 == 'Hybrid')",0,0,0,coef_hyb,0 +util_pev1,PEV ASC alt 1,"(fuel_type_1 == 'PEV')",coef_pev,0,0,0,0 +util_pev2,PEV ASC alt 2,"(fuel_type_2 == 'PEV')",0,coef_pev,0,0,0 +util_pev3,PEV ASC alt 3,"(fuel_type_3 == 'PEV')",0,0,coef_pev,0,0 +util_pev4,PEV ASC alt 4,"(fuel_type_4 == 'PEV')",0,0,0,coef_pev,0 +util_bev1,BEV ASC alt 1,"(fuel_type_1 == 'BEV')",coef_bev,0,0,0,0 +util_bev2,BEV ASC alt 2,"(fuel_type_2 == 'BEV')",0,coef_bev,0,0,0 +util_bev3,BEV ASC alt 3,"(fuel_type_3 == 'BEV')",0,0,coef_bev,0,0 +util_bev4,BEV ASC alt 4,"(fuel_type_4 == 'BEV')",0,0,0,coef_bev,0 +util_age2_1,Age 1 alt 1,"(age_1 == 2)",coef_age2,0,0,0,0 +util_age2_2,Age 1 alt 2,"(age_2 == 2)",0,coef_age2,0,0,0 +util_age2_3,Age 1 alt 3,"(age_3 == 2)",0,0,coef_age2,0,0 +util_age2_4,Age 1 alt 4,"(age_4 == 2)",0,0,0,coef_age2,0 +util_age3_1,Age 3 alt 1,"(age_1 == 3)",coef_age3,0,0,0,0 +util_age3_2,Age 3 alt 2,"(age_2 == 3)",0,coef_age3,0,0,0 +util_age3_3,Age 3 alt 3,"(age_3 == 3)",0,0,coef_age3,0,0 +util_age3_4,Age 4 alt 4,"(age_4 == 3)",0,0,0,coef_age3,0 +util_age4_1,Age 4 alt 1,"(age_1 == 4)",coef_age4,0,0,0,0 +util_age4_2,Age 4 alt 2,"(age_2 == 4)",0,coef_age4,0,0,0 +util_age4_3,Age 4 alt 3,"(age_3 == 4)",0,0,coef_age4,0,0 +util_age4_4,Age 3 alt 4,"(age_4 == 4)",0,0,0,coef_age4,0 +util_age5_1,Age 5 alt 1,"(age_1 == 5)",coef_age5,0,0,0,0 +util_age5_2,Age 5 alt 2,"(age_2 == 5)",0,coef_age5,0,0,0 +util_age5_3,Age 5 alt 3,"(age_3 == 5)",0,0,coef_age5,0,0 +util_age5_4,Age 5 alt 4,"(age_4 == 5)",0,0,0,coef_age5,0 +util_age6_1,Age 6 alt 1,"(age_1 == 6)",coef_age6,0,0,0,0 +util_age6_2,Age 6 alt 2,"(age_2 == 6)",0,coef_age6,0,0,0 +util_age6_3,Age 6 alt 3,"(age_3 == 6)",0,0,coef_age6,0,0 +util_age6_4,Age 6 alt 4,"(age_4 == 6)",0,0,0,coef_age6,0 +util_age7_1,Age 7 alt 1,"(age_1 == 7)",coef_age7,0,0,0,0 +util_age7_2,Age 7 alt 2,"(age_2 == 7)",0,coef_age7,0,0,0 +util_age7_3,Age 7 alt 3,"(age_3 == 7)",0,0,coef_age7,0,0 +util_age7_4,Age 7 alt 4,"(age_4 == 7)",0,0,0,coef_age7,0 +util_age8_1,Age 8 alt 1,"(age_1 == 8)",coef_age8,0,0,0,0 +util_age8_2,Age 8 alt 2,"(age_2 == 8)",0,coef_age8,0,0,0 +util_age8_3,Age 8 alt 3,"(age_3 == 8)",0,0,coef_age8,0,0 +util_age8_4,Age 8 alt 4,"(age_4 == 8)",0,0,0,coef_age8,0 +util_age9_1,Age 9 alt 1,"(age_1 == 9)",coef_age9,0,0,0,0 +util_age9_2,Age 9 alt 2,"(age_2 == 9)",0,coef_age9,0,0,0 +util_age9_3,Age 9 alt 3,"(age_3 == 9)",0,0,coef_age9,0,0 +util_age9_4,Age 9 alt 4,"(age_4 == 9)",0,0,0,coef_age9,0 +util_age10_1,Age 10 alt 1,"(age_1 == 10)",coef_age10,0,0,0,0 +util_age10_2,Age 10 alt 2,"(age_2 == 10)",0,coef_age10,0,0,0 +util_age10_3,Age 10 alt 3,"(age_3 == 10)",0,0,coef_age10,0,0 +util_age10_4,Age 10 alt 4,"(age_4 == 10)",0,0,0,coef_age10,0 +util_age11_1,Age 11 alt 1,"(age_1 == 11)",coef_age11,0,0,0,0 +util_age11_2,Age 11 alt 2,"(age_2 == 11)",0,coef_age11,0,0,0 +util_age11_3,Age 11 alt 3,"(age_3 == 11)",0,0,coef_age11,0,0 +util_age11_4,Age 11 alt 4,"(age_4 == 11)",0,0,0,coef_age11,0 +util_age12_1,Age 12 alt 1,"(age_1 == 12)",coef_age12,0,0,0,0 +util_age12_2,Age 12 alt 2,"(age_2 == 12)",0,coef_age12,0,0,0 +util_age12_3,Age 12 alt 3,"(age_3 == 12)",0,0,coef_age12,0,0 +util_age12_4,Age 12 alt 4,"(age_4 == 12)",0,0,0,coef_age12,0 +util_age13_1,Age 13 alt 1,"(age_1 == 13)",coef_age13,0,0,0,0 +util_age13_2,Age 13 alt 2,"(age_2 == 13)",0,coef_age13,0,0,0 +util_age13_3,Age 13 alt 3,"(age_3 == 13)",0,0,coef_age13,0,0 +util_age13_4,Age 13 alt 4,"(age_4 == 13)",0,0,0,coef_age13,0 +util_age14_1,Age 14 alt 1,"(age_1 == 14)",coef_age14,0,0,0,0 +util_age14_2,Age 14 alt 2,"(age_2 == 14)",0,coef_age14,0,0,0 +util_age14_3,Age 14 alt 3,"(age_3 == 14)",0,0,coef_age14,0,0 +util_age14_4,Age 14 alt 4,"(age_4 == 14)",0,0,0,coef_age14,0 +util_age15_1,Age 15 alt 1,"(age_1 == 15)",coef_age15,0,0,0,0 +util_age15_2,Age 15 alt 2,"(age_2 == 15)",0,coef_age15,0,0,0 +util_age15_3,Age 15 alt 3,"(age_3 == 15)",0,0,coef_age15,0,0 +util_age15_4,Age 15 alt 4,"(age_4 == 15)",0,0,0,coef_age15,0 +util_age16_1,Age 16 alt 1,"(age_1 == 16)",coef_age16,0,0,0,0 +util_age16_2,Age 16 alt 2,"(age_2 == 16)",0,coef_age16,0,0,0 +util_age16_3,Age 16 alt 3,"(age_3 == 16)",0,0,coef_age16,0,0 +util_age16_4,Age 16 alt 4,"(age_4 == 16)",0,0,0,coef_age16,0 +util_age17_1,Age 17 alt 1,"(age_1 == 17)",coef_age17,0,0,0,0 +util_age17_2,Age 17 alt 2,"(age_2 == 17)",0,coef_age17,0,0,0 +util_age17_3,Age 17 alt 3,"(age_3 == 17)",0,0,coef_age17,0,0 +util_age17_4,Age 17 alt 4,"(age_4 == 17)",0,0,0,coef_age17,0 +util_age18_1,Age 18 alt 1,"(age_1 == 18)",coef_age18,0,0,0,0 +util_age18_2,Age 18 alt 2,"(age_2 == 18)",0,coef_age18,0,0,0 +util_age18_3,Age 18 alt 3,"(age_3 == 18)",0,0,coef_age18,0,0 +util_age18_4,Age 18 alt 4,"(age_4 == 18)",0,0,0,coef_age18,0 +util_age19_1,Age 19 alt 1,"(age_1 == 19)",coef_age19,0,0,0,0 +util_age19_2,Age 19 alt 2,"(age_2 == 19)",0,coef_age19,0,0,0 +util_age19_3,Age 19 alt 3,"(age_3 == 19)",0,0,coef_age19,0,0 +util_age19_4,Age 19 alt 4,"(age_4 == 19)",0,0,0,coef_age19,0 +util_age20_1,Age 20 alt 1,"(age_1 == 20)",coef_age20,0,0,0,0 +util_age20_2,Age 20 alt 2,"(age_2 == 20)",0,coef_age20,0,0,0 +util_age20_3,Age 20 alt 3,"(age_3 == 20)",0,0,coef_age20,0,0 +util_age20_4,Age 20 alt 4,"(age_4 == 20)",0,0,0,coef_age20,0 diff --git a/configs/resident/vehicle_allocation.yaml b/configs/resident/vehicle_allocation.yaml new file mode 100644 index 0000000..23222fd --- /dev/null +++ b/configs/resident/vehicle_allocation.yaml @@ -0,0 +1,28 @@ +# vehicle_choice.yaml + +# last column in spec needs to be non_hh_veh +SPEC: vehicle_allocation.csv +COEFFICIENTS: vehicle_allocation_coefficients.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: vehicle_allocation_annotate_choosers_preprocessor + DF: choosers + TABLES: + - vehicles + +# will create columns in the tour table selecting a vehicle for each of the following +# occupancy levels. They are named vehicle_occup_1, vehicle_occup_2, ... +# if not supplied, will default to only one occupancy level of 1 +OCCUPANCY_LEVELS: + - 1 + - 2 + - 3.5 + +# optional annotate tours +# annotate_tours: +# SPEC: annotate_tours_vehicle_allocation +# DF: tours +# TABLES: +# - vehicles diff --git a/configs/resident/vehicle_allocation_annotate_choosers_preprocessor.csv b/configs/resident/vehicle_allocation_annotate_choosers_preprocessor.csv new file mode 100644 index 0000000..a970a77 --- /dev/null +++ b/configs/resident/vehicle_allocation_annotate_choosers_preprocessor.csv @@ -0,0 +1,20 @@ +Description,Target,Expression +Round Trip Tour Distance,tot_tour_dist,"(odt_skims['SOV_TR_M_DIST'] + dot_skims['SOV_TR_M_DIST'])" +number of vehicles is greater than the number of drivers,hh_veh_gt_drivers,"np.where(df.auto_ownership > df.num_drivers, 1, 0)" +number of vehicles is less than the number of drivers,hh_veh_lt_drivers,"np.where(df.auto_ownership < df.num_drivers, 1, 0)" +Age of vehicle alternative 1,age_1,"df.veh_num1.astype(str).str.split('_').str[1].fillna(0).astype(int)" +Age of vehicle alternative 2,age_2,"df.veh_num2.astype(str).str.split('_').str[1].fillna(0).astype(int)" +Age of vehicle alternative 3,age_3,"df.veh_num3.astype(str).str.split('_').str[1].fillna(0).astype(int)" +Age of vehicle alternative 4,age_4,"df.veh_num4.astype(str).str.split('_').str[1].fillna(0).astype(int)" +body type of vehicle alternative 1,body_type_1,"df.veh_num1.astype(str).str.split('_').str[0]" +body type of vehicle alternative 2,body_type_2,"df.veh_num2.astype(str).str.split('_').str[0]" +body type of vehicle alternative 3,body_type_3,"df.veh_num3.astype(str).str.split('_').str[0]" +body type of vehicle alternative 4,body_type_4,"df.veh_num4.astype(str).str.split('_').str[0]" +fuel type of vehicle alternative 1,fuel_type_1,"df.veh_num1.astype(str).str.split('_').str[2]" +fuel type of vehicle alternative 2,fuel_type_2,"df.veh_num2.astype(str).str.split('_').str[2]" +fuel type of vehicle alternative 3,fuel_type_3,"df.veh_num3.astype(str).str.split('_').str[2]" +fuel type of vehicle alternative 4,fuel_type_4,"df.veh_num4.astype(str).str.split('_').str[2]" +Range of vehicle alternative 1,Range_1,"reindex(vehicles.groupby('vehicle_type')['Range'].mean(), df.veh_num1)" +Range of vehicle alternative 2,Range_2,"reindex(vehicles.groupby('vehicle_type')['Range'].mean(), df.veh_num2)" +Range of vehicle alternative 3,Range_3,"reindex(vehicles.groupby('vehicle_type')['Range'].mean(), df.veh_num3)" +Range of vehicle alternative 4,Range_4,"reindex(vehicles.groupby('vehicle_type')['Range'].mean(), df.veh_num4)" diff --git a/configs/resident/vehicle_allocation_coefficients.csv b/configs/resident/vehicle_allocation_coefficients.csv new file mode 100644 index 0000000..3a1a8fa --- /dev/null +++ b/configs/resident/vehicle_allocation_coefficients.csv @@ -0,0 +1,46 @@ +coefficient_name,value,constrain +coef_unavail,-999,F +coef_dstgtrng,-3.454,F +coef_vehltdr_nh,0.172,F +coef_vehgtdr_nh,-0.212,F +coef_vehltdr_van,0.048,F +coef_vehltdr_suv,0.104,F +coef_vehltdr_pu,-0.011,F +coef_vehltdr_mc,-0.749,F +coef_vehltdr_hyb,0.156,F +coef_vehltdr_ev,0.207,F +coef_vehltdr_age,-0.048,F +coef_maxocc_van,0.570,F +coef_maxocc_suv,0.316,F +coef_maxocc_pu,-0.077,F +coef_maxocc_mc,-0.938,F +coef_maxocc_age,-0.026,F +util_maxocc_nh,-0.212,F +coef_non_hh,-4.549,F +coef_van,-0.942,F +coef_suv,-0.429,F +coef_pu,-0.048,F +coef_mc,-0.470,F +coef_dsl,-0.227,F +coef_hyb,0.024,F +coef_pev,-0.168,F +coef_bev,-0.105,F +coef_age2,0.070,F +coef_age3,0.104,F +coef_age4,0.136,F +coef_age5,0.149,F +coef_age6,0.161,F +coef_age7,0.223,F +coef_age8,0.272,F +coef_age9,0.291,F +coef_age10,0.256,F +coef_age11,0.318,F +coef_age12,0.334,F +coef_age13,0.338,F +coef_age14,0.348,F +coef_age15,0.307,F +coef_age16,0.377,F +coef_age17,0.324,F +coef_age18,0.237,F +coef_age19,0.292,F +coef_age20,-0.181,F diff --git a/configs/resident/vehicle_type_choice.yaml b/configs/resident/vehicle_type_choice.yaml new file mode 100644 index 0000000..6661b72 --- /dev/null +++ b/configs/resident/vehicle_type_choice.yaml @@ -0,0 +1,123 @@ +# vehicle_type_choice.yaml + +SPEC: vehicle_type_choice_op4.csv +COEFFICIENTS: vehicle_type_choice_op4_coefficients.csv +ALTS: vehicle_type_choice_op4_alternatives.csv + +# SPEC: vehicle_type_choice_op2.csv +# COEFFICIENTS: vehicle_type_choice_op2_coefficients.csv +# ALTS: vehicle_type_choice_op2_alternatives.csv +# # probs must have body_type and vehicle_year columns +# # probs spec has vehicle age calculated from FLEET_YEAR +# PROBS_SPEC: vehicle_type_choice_op2_fuel_type_probs.csv + +LOGIT_TYPE: MNL + +# options: simple_simulate or interaction_simulate +# if interaction_simulate, will build alteratives from combinatorial_alts below +# if simple_simulate, alternatives need to be specified in the columns of the model spec +SIMULATION_TYPE: interaction_simulate + +# additional vehicle_type_data merged to the alternatives for use in utility expressions +# need to have columns body_type, fuel_type, and vehicle_year +# entries in the data need to match the combinations in combinatorial_alts below +# VEHICLE_TYPE_DATA_FILE: vehicle_type_data.csv +VEHICLE_TYPE_DATA_FILE: vehicle_type_data_extended.csv + +REQUIRE_DATA_FOR_ALL_ALTS: False + +# age is computed as (1 + FLEET_YEAR - vehicle_year) +# FLEET_YEAR: 2017 +FLEET_YEAR: 2022 + +# if PROBS_SPEC is supplied, auto operating cost will not be +# merged until after a fuel type is selected +COLS_TO_INCLUDE_IN_VEHICLE_TABLE: + - auto_operating_cost + - Range + - MPG + - vehicle_year + +# If PROBS_SPEC is supplied, fuel_type will be ignored +combinatorial_alts: + body_type: + - Car + - Car-AV + - Van + - Van-AV + - SUV + - SUV-AV + - Pickup + - Pickup-AV + - Motorcycle + age: + - 1 + - 2 + - 3 + - 4 + - 5 + - 6 + - 7 + - 8 + - 9 + - 10 + - 11 + - 12 + - 13 + - 14 + - 15 + - 16 + - 17 + - 18 + - 19 + - 20 + fuel_type: + - Gas + - Diesel + - Hybrid + - PEV + - BEV + +# will write out the created vehicle_type_model_alternatives.csv file +# to the base configs directory. Helpful for debugging alternaties. +WRITE_OUT_ALTS_FILE: False + +preprocessor: + SPEC: vehicle_type_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - vehicles + - persons + - households + +# annotate_persons: +# SPEC: annotate_persons_vehicle_type +# DF: persons +# TABLES: +# - vehicles +# +annotate_households: + SPEC: annotate_households_vehicle_type + DF: households + TABLES: + - vehicles +# +# annotate_vehicles: +# SPEC: annotate_vehicles_vehicle_type +# DF: vehicles +# TABLES: + +CONSTANTS: + # Select region for model and input the MSA population + # Expressions for CBSA values are implemented for: + # San Francisco (SFO), San Diego (SAN), Atlanta (ATL), + # Seattle (SEA), Detroit (DET), Minneapolis (MSP), + # Washington DC (DCA), Oregon (OREG), Ohio (OHIO) + CBSA: SAN + MSA_POP: 3266698 + # calculated using 1130 chargers / MSA_POP + # just an initial estimate from https://www.plugshare.com/directory/us/california/san-diego + CHARGERS_PER_CAP: 0.00034592 + # chargers per cap used in vehicle type model estimation + # CHARGERS_PER_CAP: 0.000721205 diff --git a/configs/resident/vehicle_type_choice_annotate_choosers_preprocessor.csv b/configs/resident/vehicle_type_choice_annotate_choosers_preprocessor.csv new file mode 100644 index 0000000..57e1af0 --- /dev/null +++ b/configs/resident/vehicle_type_choice_annotate_choosers_preprocessor.csv @@ -0,0 +1,14 @@ +Description,Target,Expression +total household distance to work,total_hh_dist_to_work,persons.groupby('household_id')['distance_to_work'].sum().reindex(df.household_id).fillna(0) +total household distance to work capped at 100 mi per worker,total_hh_dist_to_work_cap,"np.where(total_hh_dist_to_work > (100*df.num_workers.values), 100*df.num_workers.values, total_hh_dist_to_work)" +average household distance to work,avg_hh_dist_to_work,persons.groupby('household_id')['distance_to_work'].mean().reindex(df.household_id).fillna(0) +number of vehicles is greater than the number of drivers,hh_veh_gt_drivers,"np.where(df.auto_ownership > df.num_drivers, 1, 0)" +number of household vehicles owned,num_hh_veh_owned,df[df['already_owned_veh'].str.len() > 0].groupby('household_id').size().reindex(df.household_id).fillna(0) +number of household Vans,num_hh_Van,"df[df['already_owned_veh'].str.contains('Van', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household SUVs,num_hh_SUV,"df[df['already_owned_veh'].str.contains('SUV', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household Pickups,num_hh_Pickup,"df[df['already_owned_veh'].str.contains('Pickup', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household Motorcycles,num_hh_Motorcycle,"df[df['already_owned_veh'].str.contains('Motorcycle', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household Hybrid,num_hh_Hybrid,"df[df['already_owned_veh'].str.contains('Hybrid', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household BEV,num_hh_BEV,"df[df['already_owned_veh'].str.contains('BEV', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household PEV,num_hh_PEV,"df[df['already_owned_veh'].str.contains('PEV', na=False)].groupby('household_id').size().reindex(df.household_id).fillna(0)" +number of household EVs,num_hh_EV,"num_hh_BEV + num_hh_PEV" diff --git a/configs/resident/vehicle_type_choice_op4.csv b/configs/resident/vehicle_type_choice_op4.csv new file mode 100644 index 0000000..529f1e1 --- /dev/null +++ b/configs/resident/vehicle_type_choice_op4.csv @@ -0,0 +1,208 @@ +Label,Description,Expression,Coefficient +util_ln_nmods,number of models available,"@np.log(1 + df.NumModels)",coef_ln_nmods +util_ln_nmakes,number of makes available,"@np.log(1 + df.NumMakes)",coef_ln_nmakes +util_mpg,miles per gallon (or equivalent),"@df.MPG",coef_mpg +util_crange,Range for BEV (mi),"@df.Range",coef_crange +util_crangeltwk,range less than average round trip distance to work,"@np.where((df.Range < df.avg_hh_dist_to_work * 2) & (df.fuel_type == 'BEV'), 1, 0)",coef_crangeltwk +util_ln_chpc_ev,ln(1+number of chargers per capita in MSA/state),"@np.log(1+CHARGERS_PER_CAP) * ((df.fuel_type == 'PEV') | (df.fuel_type == 'BEV'))",coef_ln_chpc_ev +#,autonomous vehicle related variables,, +util_must_select_av,Must select autonomous vehicle if hh owns one,av_ownership & ~body_type.str.contains('-AV') & (num_hh_veh_owned == 0),coef_unavail +util_must_select_av,Cannot select AV if hh does not own one,~av_ownership & body_type.str.contains('-AV'),coef_unavail +#,income related variables,, +util_cprice0,New Purchase Price (2017$) Segmented by Income,"((income < 25000) & (income > -1)) * NewPrice",coef_cprice0 +util_cprice25,New Purchase Price (2017$) Segmented by Income,"((income < 50000) & (income >= 25000)) * NewPrice",coef_cprice25 +util_cprice50,New Purchase Price (2017$) Segmented by Income,"((income < 100000) & (income >=50000)) * NewPrice",coef_cprice50 +util_cprice100,New Purchase Price (2017$) Segmented by Income,"((income < 150000) & (income >= 100000)) * NewPrice",coef_cprice100 +util_cprice150,New Purchase Price (2017$) Segmented by Income,"(income > 150000) * NewPrice",coef_cprice150 +util_cpricem,New Purchase Price (2017$) Missing Income,"((income < 0) | income.isna()) * NewPrice",coef_cpricem +util_i025_age,Vehicle Age Segmented by Income,"((income < 25000) & (income > -1)) * age",coef_i025_age +util_i2550_age,Vehicle Age Segmented by Income,"((income < 50000) & (income >= 25000)) * age",coef_i2550_age +util_i100_age,Vehicle Age Segmented by Income,"((income < 150000) & (income >= 100000)) * age",coef_i100_age +util_i150p_age,Vehicle Age Segmented by Income,"((income < 150000) & (income >= 100000)) * age",coef_i150p_age +util_imiss_age,Vehicle Age Missing Income,"((income < 0) | income.isna()) * age",coef_imiss_age +#,household density variables,, +util_den3_van,500-999 HH / sq mi Van,"((household_density < 1000) & (household_density >= 500)) & (body_type.str.contains('Van'))",coef_den3_van +util_den4_van,1000-1999 HH / sq mi Van,"((household_density < 2000) & (household_density >= 1000)) & (body_type.str.contains('Van'))",coef_den4_van +util_den5_van,2000-3999 HH / sq mi Van,"((household_density < 4000) & (household_density >= 2000)) & (body_type.str.contains('Van'))",coef_den5_van +util_den6_van,4000-9999 HH / sq mi Van,"((household_density < 10000) & (household_density >= 4000)) & (body_type.str.contains('Van'))",coef_den6_van +util_den7_van,10000-24999 HH / sq mi Van,"((household_density < 25000) & (household_density >= 10000)) & (body_type.str.contains('Van'))",coef_den7_van +util_den8_van,25000+ HH / sq mi Van,"(household_density >= 25000) & (body_type.str.contains('Van'))",coef_den8_van +util_den3_suv,500-999 HH / sq mi SUV,"((household_density < 1000) & (household_density >= 500)) & (body_type.str.contains('SUV'))",coef_den3_suv +util_den4_suv,1000-1999 HH / sq mi SUV,"((household_density < 2000) & (household_density >= 1000)) & (body_type.str.contains('SUV'))",coef_den4_suv +util_den5_suv,2000-3999 HH / sq mi SUV,"((household_density < 4000) & (household_density >= 2000)) & (body_type.str.contains('SUV'))",coef_den5_suv +util_den6_suv,4000-9999 HH / sq mi SUV,"((household_density < 10000) & (household_density >= 4000)) & (body_type.str.contains('SUV'))",coef_den6_suv +util_den7_suv,10000-24999 HH / sq mi SUV,"((household_density < 25000) & (household_density >= 10000)) & (body_type.str.contains('SUV'))",coef_den7_suv +util_den8_suv,25000+ HH / sq mi SUV,"(household_density >= 25000) & (body_type.str.contains('SUV'))",coef_den8_suv +util_den3_pu,500-999 HH / sq mi Pickup,"((household_density < 1000) & (household_density >= 500)) & (body_type.str.contains('Pickup'))",coef_den3_pu +util_den4_pu,1000-1999 HH / sq mi Pickup,"((household_density < 2000) & (household_density >= 1000)) & (body_type.str.contains('Pickup'))",coef_den4_pu +util_den5_pu,2000-3999 HH / sq mi Pickup,"((household_density < 4000) & (household_density >= 2000)) & (body_type.str.contains('Pickup'))",coef_den5_pu +util_den6_pu,4000-9999 HH / sq mi Pickup,"((household_density < 10000) & (household_density >= 4000)) & (body_type.str.contains('Pickup'))",coef_den6_pu +util_den7_pu,10000-24999 HH / sq mi Pickup,"((household_density < 25000) & (household_density >= 10000)) & (body_type.str.contains('Pickup'))",coef_den7_pu +util_den8_mc,25000+ HH / sq mi Pickup,"(household_density >= 25000) & (body_type.str.contains('Pickup'))",coef_den8_pu +util_den3_mc,500-999 HH / sq mi Motorcycle,"((household_density < 1000) & (household_density >= 500)) & (body_type.str.contains('Motorcycle'))",coef_den3_mc +util_den4_mc,1000-1999 HH / sq mi Motorcycle,"((household_density < 2000) & (household_density >= 1000)) & (body_type.str.contains('Motorcycle'))",coef_den4_mc +util_den5_mc,2000-3999 HH / sq mi Motorcycle,"((household_density < 4000) & (household_density >= 2000)) & (body_type.str.contains('Motorcycle'))",coef_den5_mc +util_den6_mc,4000-9999 HH / sq mi Motorcycle,"((household_density < 10000) & (household_density >= 4000)) & (body_type.str.contains('Motorcycle'))",coef_den6_mc +util_den7_mc,10000-24999 HH / sq mi Motorcycle,"((household_density < 25000) & (household_density >= 10000)) & (body_type.str.contains('Motorcycle'))",coef_den7_mc +util_den8_mc,25000+ HH / sq mi Motorcycle,"(household_density >= 25000) & (body_type.str.contains('Motorcycle'))",coef_den8_mc +util_den3_hyb,500-999 HH / sq mi Hybrid,"((household_density < 1000) & (household_density >= 500)) & (fuel_type == 'Hybrid')",coef_den3_hyb +util_den4_hyb,1000-1999 HH / sq mi Hybrid,"((household_density < 2000) & (household_density >= 1000)) & (fuel_type == 'Hybrid')",coef_den4_hyb +util_den5_hyb,2000-3999 HH / sq mi Hybrid,"((household_density < 4000) & (household_density >= 2000)) & (fuel_type == 'Hybrid')",coef_den5_hyb +util_den6_hyb,4000-9999 HH / sq mi Hybrid,"((household_density < 10000) & (household_density >= 4000)) & (fuel_type == 'Hybrid')",coef_den6_hyb +util_den7_hyb,10000-24999 HH / sq mi Hybrid,"((household_density < 25000) & (household_density >= 10000)) & (fuel_type == 'Hybrid')",coef_den7_hyb +util_den8_hyb,25000+ HH / sq mi Hybrid,"(household_density >= 25000) & (fuel_type == 'Hybrid')",coef_den8_hyb +util_den34_ev,500-1999 HH / sq mi Electric,"((household_density < 2000) & (household_density >= 500)) & ((fuel_type == 'PEV') | (fuel_type == 'BEV'))",coef_den34_ev +util_den5_ev,2000-3999 HH / sq mi Electric,"((household_density < 4000) & (household_density >= 2000)) & ((fuel_type == 'PEV') | (fuel_type == 'BEV'))",coef_den5_ev +util_den6_ev,4000-9999 HH / sq mi Electric,"((household_density < 10000) & (household_density >= 4000)) & ((fuel_type == 'PEV') | (fuel_type == 'BEV'))",coef_den6_ev +util_den78_ev,10000+ HH / sq mi Electric,"(household_density >= 10000) & ((fuel_type == 'PEV') | (fuel_type == 'BEV'))",coef_den78_ev +#,household drivers,, +util_oneveh_age,Household owns only one vehicle * vehicle age,"(auto_ownership == 1) * age",coef_oneveh_age +util_vhgtdr_van,Household vehicles gt drivers for Van,hh_veh_gt_drivers * (body_type.str.contains('Van')),coef_vhgtdr_van +util_vhgtdr_suv,Household vehicles gt drivers for SUV,hh_veh_gt_drivers * (body_type.str.contains('SUV')),coef_vhgtdr_suv +util_vhgtdr_pu,Household vehicles gt drivers for Pickup,hh_veh_gt_drivers * (body_type.str.contains('Pickup')),coef_vhgtdr_pu +util_vhgtdr_mc,Household vehicles gt drivers for Motorcycle,hh_veh_gt_drivers * (body_type.str.contains('Motorcycle')),coef_vhgtdr_mc +util_vhgtdr_hy,Household vehicles gt drivers for Hybrid,hh_veh_gt_drivers * (fuel_type == 'Hybrid'),coef_vhgtdr_hy +util_vhgtdr_ev,Household vehicles gt drivers for EVs,hh_veh_gt_drivers * ((fuel_type == 'PEV') | (fuel_type == 'BEV')),coef_vhgtdr_ev +util_vhgtdr_age,Household vehicles gt drivers for age,hh_veh_gt_drivers * age,coef_vhgtdr_age +#,interacting with number of children,, +util_nchld_van,Number of children max 3 * Van,num_children.clip(upper=3) * (body_type.str.contains('Van')),coef_nchld_van +util_nchld_suv,Number of children max 3 * SUV,num_children.clip(upper=3) * (body_type.str.contains('SUV')),coef_nchld_suv +util_nchld_pu,Number of children max 3 * Pickup,num_children.clip(upper=3) * (body_type.str.contains('Pickup')),coef_nchld_pu +util_nchld_mc,Number of children max 3 * Motorcycle,num_children.clip(upper=3) * (body_type.str.contains('Motorcycle')),coef_nchld_mc +util_nchld_hy,Number of children max 3 * Hybrid,num_children.clip(upper=3) * (fuel_type == 'Hybrid'),coef_nchld_hy +util_nchld_ev,Number of children max 3 * EVs,num_children.clip(upper=3) * ((fuel_type == 'PEV') | (fuel_type == 'BEV')),coef_nchld_ev +util_nchld_age,Number of children max 3 * age,num_children.clip(upper=3) * age,coef_nchld_age +util_dstwkt_age,Total distance to work * age,total_hh_dist_to_work_cap * age,coef_dstwkt_age +#,household already owning vehicles,, +util_van_van,Household already owns a Van -- Van,"(num_hh_Van > 0) & (body_type.str.contains('Van'))",coef_van_van +util_van_suv,Household already owns a Van -- SUV,"(num_hh_Van > 0) & (body_type.str.contains('SUV'))",coef_van_suv +util_van_pu,Household already owns a Van -- Pickup,"(num_hh_Van > 0) & (body_type.str.contains('Pickup'))",coef_van_pu +util_van_mc,Household already owns a Van -- Motorcycle,"(num_hh_Van > 0) & (body_type.str.contains('Motorcycle'))",coef_van_mc +util_van_suv,Household already owns an SUV -- Van (symmetrical with above),"(num_hh_SUV > 0) & (body_type.str.contains('Van'))",coef_van_suv +util_suv_suv,Household already owns an SUV -- SUV,"(num_hh_SUV > 0) & (body_type.str.contains('SUV'))",coef_suv_suv +util_suv_pu,Household already owns an SUV -- Pickup,"(num_hh_SUV > 0) & (body_type.str.contains('Pickup'))",coef_suv_pu +util_suv_mc,Household already owns an SUV -- Motorcycle,"(num_hh_SUV > 0) & (body_type.str.contains('Motorcycle'))",coef_suv_mc +util_van_pu,Household already owns a Pickup -- Van (symmetrical with above),"(num_hh_Pickup > 0) & (body_type.str.contains('Van'))",coef_van_pu +util_suv_pu,Household already owns a Pickup -- SUV (symmetrical with above),"(num_hh_Pickup > 0) & (body_type.str.contains('SUV'))",coef_suv_pu +util_pu_pu,Household already owns a Pickup -- Pickup,"(num_hh_Pickup > 0) & (body_type.str.contains('Pickup'))",coef_pu_pu +util_pu_mc,Household already owns a Pickup -- Motorcycle,"(num_hh_Pickup > 0) & (body_type.str.contains('Motorcycle'))",coef_pu_mc +util_van_mc,Household already owns a Motorcycle -- Van (symmetrical with above),"(num_hh_Motorcycle > 0) & (body_type.str.contains('Van'))",coef_van_mc +util_suv_mc,Household already owns a Motorcycle -- SUV (symmetrical with above),"(num_hh_Motorcycle > 0) & (body_type.str.contains('SUV'))",coef_suv_mc +util_pu_mc,Household already owns a Motorcycle -- Pickup (symmetrical with above),"(num_hh_Motorcycle > 0) & (body_type.str.contains('Pickup'))",coef_pu_mc +util_mc_mc,Household already owns a Motorcycle -- Motorcycle,"(num_hh_Motorcycle > 0) & (body_type.str.contains('Motorcycle'))",coef_mc_mc +util_hyb_hyb,Houeshold already owns a Hybrid -- Hybrid,"(num_hh_Hybrid > 0) & (fuel_type == 'Hybrid')",coef_hyb_hyb +util_hyb_ev,Houeshold already owns a Hybrid -- EV,"(num_hh_Hybrid > 0) & ((fuel_type == 'PEV') | (fuel_type == 'BEV'))",coef_hyb_ev +util_hyb_ev,Houeshold already owns an EV -- Hybrid (symmetrical with above),"(num_hh_EV> 0) & (fuel_type == 'Hybrid')",coef_hyb_ev +util_only_van,Household only owns Vans,"(num_hh_Van > 0) & (num_hh_Van == num_hh_veh_owned) & (body_type.str.contains('Van'))",coef_only_van +util_only_suv,Household only owns SUVs,"(num_hh_SUV > 0) & (num_hh_SUV == num_hh_veh_owned) & (body_type.str.contains('SUV'))",coef_only_suv +util_only_pu,Household only owns Pickups,"(num_hh_Pickup > 0) & (num_hh_Pickup == num_hh_veh_owned) & (body_type.str.contains('Pickup'))",coef_only_pu +util_only_mc,Household only owns Motorcycles,"(num_hh_Motorcycle > 0) & (num_hh_Motorcycle == num_hh_veh_owned) & (body_type.str.contains('Motorcycle'))",coef_only_mc +util_only_bev,Household only owns BEVs,"(num_hh_BEV > 0) & (num_hh_BEV == num_hh_veh_owned) & (fuel_type == 'BEV')",coef_only_bev +#,constants,, +util_van,Van ASC,(body_type.str.contains('Van')),coef_van +util_suv,SUV ASC,(body_type.str.contains('SUV')),coef_suv +util_pu,Pickup ASC,(body_type.str.contains('Pickup')),coef_pu +util_mc,Motorcycle ASC,(body_type.str.contains('Motorcycle')),coef_mc +util_dsl,Diesel ASC,(fuel_type == 'Diesel'),coef_dsl +util_hyb,Hybrid ASC,(fuel_type == 'Hybrid'),coef_hyb +util_pev,PEV ASC,(fuel_type == 'PEV'),coef_pev +util_bev,BEV ASC,(fuel_type == 'BEV'),coef_bev +util_age2,Age 2 ASC,(age==2),coef_age2 +util_age3,Age 3 ASC,(age==3),coef_age3 +util_age4,Age 4 ASC,(age==4),coef_age4 +util_age5,Age 5 ASC,(age==5),coef_age5 +util_age6,Age 6 ASC,(age==6),coef_age6 +util_age7,Age 7 ASC,(age==7),coef_age7 +util_age8,Age 8 ASC,(age==8),coef_age8 +util_age9,Age 9 ASC,(age==9),coef_age9 +util_age10,Age 10 ASC,(age==10),coef_age10 +util_age11,Age 11 ASC,(age==11),coef_age11 +util_age12,Age 12 ASC,(age==12),coef_age12 +util_age13,Age 13 ASC,(age==13),coef_age13 +util_age14,Age 14 ASC,(age==14),coef_age14 +util_age15,Age 15 ASC,(age==15),coef_age15 +util_age16,Age 16 ASC,(age==16),coef_age16 +util_age17,Age 17 ASC,(age==17),coef_age17 +util_age18,Age 18 ASC,(age==18),coef_age18 +util_age19,Age 19 ASC,(age==19),coef_age19 +util_age20,Age 20 ASC,(age==20),coef_age20 +#,rural household coefficients,, +util_rural_van,Household is in Rural Area - Van,"home_is_rural & (body_type.str.contains('Van'))",coef_rural_van +util_rural_suv,Household is in Rural Area - SUV,"home_is_rural & (body_type.str.contains('SUV'))",coef_rural_suv +util_rural_pu,Household is in Rural Area - Pickup,"home_is_rural & (body_type.str.contains('Pickup'))",coef_rural_pu +util_rural_mc,Household is in Rural Area - Motorcycle,"home_is_rural & (body_type.str.contains('Motorcycle'))",coef_rural_mc +util_rural_hyb,Household is in Rural Area - Hybrid,"home_is_rural & (fuel_type == 'Hybrid')",coef_rural_hyb +util_rural_ev,Household is in Rural Area - Electric,"home_is_rural & ((fuel_type == 'PEV') | (fuel_type == 'BEV'))",coef_rural_ev +util_rural_age,Household is in Rural Area - Age,"home_is_rural * age",coef_rural_age +#,MSA population coefficients,, +util_smsa_van,MSA population less than 1 million - Van,"@(MSA_POP < 1000000) & (df.body_type.str.contains('Van'))",coef_smsa_van +util_smsa_suv,MSA population less than 1 million - SUV,"@(MSA_POP < 1000000) & (df.body_type.str.contains('SUV'))",coef_smsa_suv +util_smsa_pu,MSA population less than 1 million - Pickup,"@(MSA_POP < 1000000) & (df.body_type.str.contains('Pickup'))",coef_smsa_pu +util_smsa_mc,MSA population less than 1 million - Motorcycle,"@(MSA_POP < 1000000) & (df.body_type.str.contains('Motorcycle'))",coef_smsa_mc +util_smsa_hyb,MSA population less than 1 million - Hybrid,"@(MSA_POP < 1000000) & (df.fuel_type == 'Hybrid')",coef_smsa_hyb +util_smsa_ev,MSA population less than 1 million - Electric,"@(MSA_POP < 1000000) & ((df.fuel_type == 'PEV') | (df.fuel_type == 'BEV'))",coef_smsa_ev +util_smsa_age,MSA population less than 1 million - Age,"@(MSA_POP < 1000000) * df.age",coef_smsa_age +#,Region Specific Constants,, +util_sfo_van,SF and San Jose - Van,"@(CBSA == 'SFO') & (df.body_type.str.contains('Van'))",coef_sfo_van +util_sfo_suv,SF and San Jose - SUV,"@(CBSA == 'SFO') & (df.body_type.str.contains('SUV'))",coef_sfo_suv +util_sfo_pu,SF and San Jose - Pickup,"@(CBSA == 'SFO') & (df.body_type.str.contains('Pickup'))",coef_sfo_pu +util_sfo_mc,SF and San Jose - Motorcycle,"@(CBSA == 'SFO') & (df.body_type.str.contains('Motorcycle'))",coef_sfo_mc +util_sfo_hyb,SF and San Jose - Hybrid,"@(CBSA == 'SFO') & (df.fuel_type == 'Hybrid')",coef_sfo_hyb +util_sfo_ev,SF and San Jose - Electric,"@(CBSA == 'SFO') & ((df.fuel_type == 'PEV') | (df.fuel_type == 'BEV'))",coef_sfo_ev +util_sfo_age,SF and San Jose - Age,"@(CBSA == 'SFO') * df.age",coef_sfo_age +util_san_van,San Diego - Van,"@(CBSA == 'SAN') & (df.body_type.str.contains('Van'))",coef_san_van +util_san_suv,San Diego - SUV,"@(CBSA == 'SAN') & (df.body_type.str.contains('SUV'))",coef_san_suv +util_san_pu,San Diego - Pickup,"@(CBSA == 'SAN') & (df.body_type.str.contains('Pickup'))",coef_san_pu +util_san_mc,San Diego - Motorcycle,"@(CBSA == 'SAN') & (df.body_type.str.contains('Motorcycle'))",coef_san_mc +util_san_hyb,San Diego - Hybrid,"@(CBSA == 'SAN') & (df.fuel_type == 'Hybrid')",coef_san_hyb +util_san_ev,San Diego - Electric,"@(CBSA == 'SAN') & ((df.fuel_type == 'PEV') | (df.fuel_type == 'BEV'))",coef_san_ev +util_san_age,San Diego - Age,"@(CBSA == 'SAN') * df.age",coef_san_age +util_atl_van,Atlanta - Van,"@(CBSA == 'ATL') & (df.body_type.str.contains('Van'))",coef_atl_van +util_atl_suv,Atlanta - SUV,"@(CBSA == 'ATL') & (df.body_type.str.contains('SUV'))",coef_atl_suv +util_atl_pu,Atlanta - Pickup,"@(CBSA == 'ATL') & (df.body_type.str.contains('Pickup'))",coef_atl_pu +util_atl_mc,Atlanta - Motorcycle,"@(CBSA == 'ATL') & (df.body_type.str.contains('Motorcycle'))",coef_atl_mc +util_atl_hyb,Atlanta - Hybrid,"@(CBSA == 'ATL') & (df.fuel_type == 'Hybrid')",coef_atl_hyb +util_atl_ev,Atlanta - Electric,"@(CBSA == 'ATL') & ((df.fuel_type == 'PEV') | (df.fuel_type == 'BEV'))",coef_atl_ev +util_atl_age,Atlanta - Age,"@(CBSA == 'ATL') * df.age",coef_atl_age +util_sea_van,Seattle - Van,"@(CBSA == 'SEA') & (df.body_type.str.contains('Van'))",coef_sea_van +util_sea_suv,Seattle - SUV,"@(CBSA == 'SEA') & (df.body_type.str.contains('SUV'))",coef_sea_suv +util_sea_pu,Seattle - Pickup,"@(CBSA == 'SEA') & (df.body_type.str.contains('Pickup'))",coef_sea_pu +util_sea_mc,Seattle - Motorcycle,"@(CBSA == 'SEA') & (df.body_type.str.contains('Motorcycle'))",coef_sea_mc +util_sea_hyb,Seattle - Hybrid,"@(CBSA == 'SEA') & (df.fuel_type == 'Hybrid')",coef_sea_hyb +util_sea_ev,Seattle - Electric,"@(CBSA == 'SEA') & ((df.fuel_type == 'PEV') | (df.fuel_type == 'BEV'))",coef_sea_ev +util_sea_age,Seattle - Age,"@(CBSA == 'SEA') * df.age",coef_sea_age +util_det_van,Detroit - Van,"@(CBSA == 'DET') & (df.body_type.str.contains('Van'))",coef_det_van +util_det_suv,Detroit - SUV,"@(CBSA == 'DET') & (df.body_type.str.contains('SUV'))",coef_det_suv +util_det_pu,Detroit - Pickup,"@(CBSA == 'DET') & (df.body_type.str.contains('Pickup'))",coef_det_pu +util_det_mc,Detroit - Motorcycle,"@(CBSA == 'DET') & (df.body_type.str.contains('Motorcycle'))",coef_det_mc +util_det_hyb,Detroit - Hybrid,"@(CBSA == 'DET') & (df.fuel_type == 'Hybrid')",coef_det_hyb +util_det_ev,Detroit - Electric,"@(CBSA == 'DET') & ((df.fuel_type == 'PEV') | (df.fuel_type == 'BEV'))",coef_det_ev +util_det_age,Detroit - Age,"@(CBSA == 'DET') * df.age",coef_det_age +util_msp_van,Minneapolis - Van,"@(CBSA == 'MSP') & (df.body_type.str.contains('Van'))",coef_msp_van +util_msp_suv,Minneapolis - SUV,"@(CBSA == 'MSP') & (df.body_type.str.contains('SUV'))",coef_msp_suv +util_msp_pu,Minneapolis - Pickup,"@(CBSA == 'MSP') & (df.body_type.str.contains('Pickup'))",coef_msp_pu +util_msp_mc,Minneapolis - Motorcycle,"@(CBSA == 'MSP') & (df.body_type.str.contains('Motorcycle'))",coef_msp_mc +util_msp_hyb,Minneapolis - Hybrid,"@(CBSA == 'MSP') & (df.fuel_type == 'Hybrid')",coef_msp_hyb +util_msp_ev,Minneapolis - Electric,"@(CBSA == 'MSP') & ((df.fuel_type == 'PEV') | (df.fuel_type == 'BEV'))",coef_msp_ev +util_msp_age,Minneapolis - Age,"@(CBSA == 'MSP') * df.age",coef_msp_age +util_dca_van,Washington DC - Van,"@(CBSA == 'DCA') & (df.body_type.str.contains('Van'))",coef_dca_van +util_dca_suv,Washington DC - SUV,"@(CBSA == 'DCA') & (df.body_type.str.contains('SUV'))",coef_dca_suv +util_dca_pu,Washington DC - Pickup,"@(CBSA == 'DCA') & (df.body_type.str.contains('Pickup'))",coef_dca_pu +util_dca_mc,Washington DC - Motorcycle,"@(CBSA == 'DCA') & (df.body_type.str.contains('Motorcycle'))",coef_dca_mc +util_dca_hyb,Washington DC - Hybrid,"@(CBSA == 'DCA') & (df.fuel_type == 'Hybrid')",coef_dca_hyb +util_dca_ev,Washington DC - Electric,"@(CBSA == 'DCA') & ((df.fuel_type == 'PEV') | (df.fuel_type == 'BEV'))",coef_dca_ev +util_dca_age,Washington DC - Age,"@(CBSA == 'DCA') * df.age",coef_dca_age +util_oreg_van,Oregon - Van,"@(CBSA == 'OREG') & (df.body_type.str.contains('Van'))",coef_oreg_van +util_oreg_suv,Oregon - SUV,"@(CBSA == 'OREG') & (df.body_type.str.contains('SUV'))",coef_oreg_suv +util_oreg_pu,Oregon - Pickup,"@(CBSA == 'OREG') & (df.body_type.str.contains('Pickup'))",coef_oreg_pu +util_oreg_mc,Oregon - Motorcycle,"@(CBSA == 'OREG') & (df.body_type.str.contains('Motorcycle'))",coef_oreg_mc +util_oreg_hyb,Oregon - Hybrid,"@(CBSA == 'OREG') & (df.fuel_type == 'Hybrid')",coef_oreg_hyb +util_oreg_ev,Oregon - Electric,"@(CBSA == 'OREG') & ((df.fuel_type == 'PEV') | (df.fuel_type == 'BEV'))",coef_oreg_ev +util_oreg_age,Oregon - Age,"@(CBSA == 'OREG') * df.age",coef_oreg_age +util_ohio_van,Ohio - Van,"@(CBSA == 'OHIO') & (df.body_type.str.contains('Van'))",coef_ohio_van +util_ohio_suv,Ohio - SUV,"@(CBSA == 'OHIO') & (df.body_type.str.contains('SUV'))",coef_ohio_suv +util_ohio_pu,Ohio - Pickup,"@(CBSA == 'OHIO') & (df.body_type.str.contains('Pickup'))",coef_ohio_pu +util_ohio_mc,Ohio - Motorcycle,"@(CBSA == 'OHIO') & (df.body_type.str.contains('Motorcycle'))",coef_ohio_mc +util_ohio_hyb,Ohio - Hybrid,"@(CBSA == 'OHIO') & (df.fuel_type == 'Hybrid')",coef_ohio_hyb +util_ohio_ev,Ohio - Electric,"@(CBSA == 'OHIO') & ((df.fuel_type == 'PEV') | (df.fuel_type == 'BEV'))",coef_ohio_ev +util_ohio_age,Ohio - Age,"@(CBSA == 'OHIO') * df.age",coef_ohio_age diff --git a/configs/resident/vehicle_type_choice_op4_coefficients.csv b/configs/resident/vehicle_type_choice_op4_coefficients.csv new file mode 100644 index 0000000..d2fdf46 --- /dev/null +++ b/configs/resident/vehicle_type_choice_op4_coefficients.csv @@ -0,0 +1,190 @@ +coefficient_name,value,constrain +coef_unavail,-999,F +coef_ln_nmods,.612,F +coef_ln_nmakes,.247,F +coef_mpg,0.007,F +coef_crange,0.018,F +coef_crangeltwk,-0.781,F +coef_ln_chpc_ev,1686.871,F +coef_cprice0,-0.00010,F +coef_cprice25,-0.00009,F +coef_cprice50,-0.00008,F +coef_cprice100,-0.00007,F +coef_cprice150,-0.00005,F +coef_cpricem,-0.00007,F +coef_i025_age,0.107,F +coef_i2550_age,0.047,F +coef_i100_age,-0.037,F +coef_i150p_age,-0.071,F +coef_imiss_age,0.001,F +coef_den3_van,-0.019,F +coef_den4_van,-0.089,F +coef_den5_van,-0.193,F +coef_den6_van,-0.413,F +coef_den7_van,-0.710,F +coef_den8_van,-0.509,F +coef_den3_suv,0.005,F +coef_den4_suv,-0.084,F +coef_den5_suv,-0.208,F +coef_den6_suv,-0.422,F +coef_den7_suv,-0.465,F +coef_den8_suv,-0.463,F +coef_den3_pu,-0.167,F +coef_den4_pu,-0.219,F +coef_den5_pu,-0.313,F +coef_den6_pu,-0.720,F +coef_den7_pu,-1.140,F +coef_den8_pu,-2.006,F +coef_den3_mc,-0.030,F +coef_den4_mc,0.157,F +coef_den5_mc,0.185,F +coef_den6_mc,0.436,F +coef_den7_mc,1.106,F +coef_den8_mc,1.533,F +coef_den3_hyb,-0.018,F +coef_den4_hyb,0.070,F +coef_den5_hyb,0.239,F +coef_den6_hyb,0.290,F +coef_den7_hyb,0.406,F +coef_den8_hyb,0.445,F +coef_den34_ev,0.107,F +coef_den5_ev,0.323,F +coef_den6_ev,0.272,F +coef_den78_ev,0.030,F +coef_oneveh_age,-0.028,F +coef_vhgtdr_van,-.108,F +coef_vhgtdr_suv,-0.126,F +coef_vhgtdr_pu,0.287,F +coef_vhgtdr_mc,2.408,F +coef_vhgtdr_hy,-0.168,F +coef_vhgtdr_ev,0.165,F +coef_vhgtdr_age,0.068,F +coef_nchld_van,0.560,F +coef_nchld_suv,0.193,F +coef_nchld_pu,0.016,F +coef_nchld_mc,0.028,F +coef_nchld_hy,-0.045,F +coef_nchld_ev,0.174,F +coef_nchld_age,-0.001,F +coef_dstwkt_age,-0.00013,F +coef_van_van,0.028,F +coef_van_suv,-0.477,F +coef_van_pu,-0.075,F +coef_van_mc,0.527,F +coef_suv_suv,-0.115,F +coef_suv_pu,0.194,F +coef_suv_mc,1.048,F +coef_pu_pu,-0.164,F +coef_pu_mc,1.360,F +coef_mc_mc,3.469,F +coef_hyb_hyb,1.611,F +coef_hyb_ev,0.940,F +coef_only_van,-0.259,F +coef_only_suv,-0.102,F +coef_only_pu,-0.781,F +coef_only_mc,-1.233,F +coef_only_bev,-0.491,F +coef_van,-0.629,F +coef_suv,0.413,F +coef_pu,0.224,F +coef_mc,-5.259,F +coef_dsl,-0.649,F +coef_hyb,-1.055,F +coef_pev,-1.406,F +coef_bev,-4.640,F +coef_age2,0.163,F +coef_age3,0.107,F +coef_age4,0.009,F +coef_age5,-0.079,F +coef_age6,-0.307,F +coef_age7,-0.216,F +coef_age8,-0.346,F +coef_age9,-0.133,F +coef_age10,-0.163,F +coef_age11,-0.060,F +coef_age12,-0.032,F +coef_age13,-0.248,F +coef_age14,-0.350,F +coef_age15,-0.510,F +coef_age16,-0.658,F +coef_age17,-0.772,F +coef_age18,-0.885,F +coef_age19,-1.187,F +coef_age20,0.484,F +coef_rural_van,0.234,F +coef_rural_suv,0.150,F +coef_rural_pu,0.824,F +coef_rural_mc,-0.150,F +coef_rural_hyb,-0.280,F +coef_rural_ev,-0.483,F +coef_rural_age,0.004,F +coef_smsa_van,0.130,F +coef_smsa_suv,0.003,F +coef_smsa_pu,0.357,F +coef_smsa_mc,0.011,F +coef_smsa_hyb,-0.063,F +coef_smsa_ev,-0.341,F +coef_smsa_age,0.011,F +coef_sfo_van,0.047,F +coef_sfo_suv,-0.386,F +coef_sfo_pu,-0.527,F +coef_sfo_mc,-0.021,F +coef_sfo_hyb,0.942,F +coef_sfo_ev,1.497,F +coef_sfo_age,0.058,F +coef_san_van,0.055,F +coef_san_suv,-0.132,F +coef_san_pu,-0.187,F +coef_san_mc,-0.129,F +coef_san_hyb,0.0629,F +coef_san_ev,0.0922,F +coef_san_age,0.030,F +coef_atl_van,-0.158,F +coef_atl_suv,-0.072,F +coef_atl_pu,-0.107,F +coef_atl_mc,-0.284,F +coef_atl_hyb,-0.180,F +coef_atl_ev,0.547,F +coef_atl_age,0.016,F +coef_sea_van,0.088,F +coef_sea_suv,-0.134,F +coef_sea_pu,-0.131,F +coef_sea_mc,-0.275,F +coef_sea_hyb,0.421,F +coef_sea_ev,0.706,F +coef_sea_age,0.060,F +coef_det_van,0.703,F +coef_det_suv,0.260,F +coef_det_pu,-0.150,F +coef_det_mc,0.427,F +coef_det_hyb,-0.982,F +coef_det_ev,0.531,F +coef_det_age,-0.045,F +coef_msp_van,0.263,F +coef_msp_suv,-0.052,F +coef_msp_pu,-0.087,F +coef_msp_mc,0.083,F +coef_msp_hyb,-0.111,F +coef_msp_ev,-0.733,F +coef_msp_age,0.008,F +coef_dca_van,0.062,F +coef_dca_suv,0.273,F +coef_dca_pu,-0.411,F +coef_dca_mc,0.062,F +coef_dca_hyb,-0.428,F +coef_dca_ev,-1.605,F +coef_dca_age,-0.040,F +coef_oreg_van,0.037,F +coef_oreg_suv,-0.121,F +coef_oreg_pu,0.185,F +coef_oreg_mc,0.044,F +coef_oreg_hyb,0.819,F +coef_oreg_ev,0.278,F +coef_oreg_age,0.056,F +coef_ohio_van,0.170,F +coef_ohio_suv,-0.147,F +coef_ohio_pu,-0.355,F +coef_ohio_mc,0.140,F +coef_ohio_hyb,-0.142,F +coef_ohio_ev,-1.590,F +coef_ohio_age,-0.008,F diff --git a/configs/resident/vehicle_type_data.csv b/configs/resident/vehicle_type_data.csv new file mode 100644 index 0000000..7831b52 --- /dev/null +++ b/configs/resident/vehicle_type_data.csv @@ -0,0 +1,501 @@ +body_type,fuel_type,vehicle_year,NumMakes,NumModels,MPG,Range,NewPrice,auto_operating_cost,co2gpm +Car,Gas,2017,39,738,24,0,32926.61346,20.17,388.85 +Car,Gas,2016,39,734,23.7,0,33383.61537,20.32822785,392.27 +Car,Gas,2015,39,740,23.4,0,33369.12233,20.49051282,398.43 +Car,Gas,2014,41,717,23.2,0,32571.33539,20.60103448,402.86 +Car,Gas,2013,40,702,23.1,0,32976.62731,20.65701299,402.94 +Car,Gas,2012,42,668,22.4,0,32959.56219,21.06285714,417.59 +Car,Gas,2011,42,617,21.7,0,34409.88113,21.49488479,428.48 +Car,Gas,2010,44,641,21.5,0,35279.71318,21.62348837,434.37 +Car,Gas,2009,45,637,21,0,35638.52401,21.95571429,444.46 +Car,Gas,2008,46,649,20.5,0,35745.78309,22.30414634,454.02 +Car,Gas,2007,43,594,20.3,0,37613.76774,22.44832512,454.23 +Car,Gas,2006,43,595,20.2,0,38581.65596,22.52148515,456.18 +Car,Gas,2005,41,661,20.4,0,37537.36453,22.37588235,452.05 +Car,Gas,2004,43,628,20.6,0,37988.14511,22.2331068,445.25 +Car,Gas,2003,42,573,20.5,0,37597.37624,22.30414634,450.5 +Car,Gas,2002,42,543,20.8,0,37380.8733,22.09307692,439.76 +Car,Gas,2001,39,505,21,0,37361.09207,21.95571429,437.72 +Car,Gas,2000,39,473,21,0,38856.86312,21.95571429,437.7 +Car,Gas,1999,40,483,21.2,0,38447.21363,21.8209434,435.35 +Car,Gas,1998,42,464,21.3,0,38952.55256,21.75450704,433.86 +Car,Diesel,2017,3,9,35.2,0,38787.1746,16.19272727,290 +Car,Diesel,2016,2,8,31.4,0,39325.51763,17.22414013,325.13 +Car,Diesel,2015,4,23,32.8,0,39308.445,16.81634146,311.57 +Car,Diesel,2014,4,20,31.5,0,38368.66109,17.19380952,322.3 +Car,Diesel,2013,2,12,31.8,0,38846.09034,17.10396226,319.17 +Car,Diesel,2012,2,8,32.3,0,38825.98783,16.9579257,314.55 +Car,Diesel,2011,3,8,31.4,0,40534.44697,17.22414013,324.25 +Car,Diesel,2010,3,8,31.4,0,41559.0992,17.22414013,324.25 +Car,Diesel,2009,3,6,30.2,0,41981.77425,17.60377483,339.1 +Car,Diesel,2008,1,1,26,0,42108.12422,19.20846154,391.54 +Car,Diesel,2007,1,1,26,0,44308.58881,19.20846154,391.54 +Car,Diesel,2006,2,7,32.3,0,45448.75007,16.9579257,317.06 +Car,Diesel,2005,2,13,32,0,44218.5867,17.045,321.07 +Car,Diesel,2004,1,10,32.6,0,44749.60108,16.87245399,315.61 +Car,Diesel,2003,1,8,35.6,0,44289.27981,16.09696629,287.33 +Car,Diesel,2002,1,8,35.6,0,44034.24182,16.09696629,287.33 +Car,Diesel,2001,1,6,35.5,0,44010.93975,16.12070423,288.19 +Car,Diesel,2000,1,6,35.5,0,45772.94097,16.12070423,288.19 +Car,Diesel,1999,2,8,34.5,0,45290.37855,16.36565217,299.48 +Car,Diesel,1998,2,8,33.8,0,45885.66203,16.54573964,306.26 +Car,Hybrid,2017,11,29,40.4,0,34769.7517,15.09574257,242.55 +Car,Hybrid,2016,14,31,36.8,0,35252.33528,15.82217391,253.65 +Car,Hybrid,2015,12,33,35.5,0,35237.03096,16.12070423,261.42 +Car,Hybrid,2014,15,40,34.8,0,34394.58617,16.29068966,270.95 +Car,Hybrid,2013,15,34,35.3,0,34822.56518,16.16858357,265.21 +Car,Hybrid,2012,12,25,34.2,0,34804.54481,16.44192982,280.1 +Car,Hybrid,2011,11,20,33.6,0,36336.04847,16.59857143,285.2 +Car,Hybrid,2010,8,13,34.5,0,37254.57126,16.36565217,276.5 +Car,Hybrid,2009,6,8,32.3,0,37633.4673,16.9579257,293.75 +Car,Hybrid,2008,6,8,31.8,0,37746.73043,17.10396226,299.43 +Car,Hybrid,2007,5,7,33.3,0,39719.27956,16.67900901,281.75 +Car,Hybrid,2006,2,5,42.4,0,40741.3474,14.7454717,224.05 +Car,Hybrid,2005,2,8,41.9,0,39638.59951,14.82990453,218.55 +Car,Hybrid,2004,2,7,43.9,0,40114.61351,14.50371298,204.44 +Car,Hybrid,2003,2,7,43.3,0,39701.9705,14.59840647,207.34 +Car,Hybrid,2002,2,3,47,0,39473.34834,14.05297872,191.17 +Car,Hybrid,2001,2,4,47,0,39452.45982,14.05297872,190.65 +Car,Hybrid,2000,1,1,53,0,41031.95988,13.33037736,167.68 +Car,Hybrid,1999,0,0,0,0,40599.37938,13.33037736,0 +Car,Hybrid,1998,0,0,0,0,41133.00574,13.33037736,0 +Car,PEV,2017,9,14,35.1,0,56882.64178,16.21700855,138.64 +Car,PEV,2016,8,14,33.8,0,65356.24918,16.54573964,142.21 +Car,PEV,2015,8,11,32.1,0,55150.60558,17.01579439,181.55 +Car,PEV,2014,8,10,35.1,0,44172.85443,16.21700855,160 +Car,PEV,2013,3,4,40.8,0,52370.99125,15.02294118,118 +Car,PEV,2012,3,3,35.7,0,47444.73245,16.07336134,129.67 +Car,PEV,2011,1,1,37,0,38741.26195,15.77810811,84 +Car,PEV,2010,0,0,0,0,38741.26195,15.77810811,0 +Car,PEV,2009,0,0,0,0,38741.26195,15.77810811,0 +Car,PEV,2008,0,0,0,0,38741.26195,15.77810811,0 +Car,PEV,2007,0,0,0,0,38741.26195,15.77810811,0 +Car,PEV,2006,0,0,0,0,38741.26195,15.77810811,0 +Car,PEV,2005,0,0,0,0,38741.26195,15.77810811,0 +Car,PEV,2004,0,0,0,0,38741.26195,15.77810811,0 +Car,PEV,2003,0,0,0,0,38741.26195,15.77810811,0 +Car,PEV,2002,0,0,0,0,38741.26195,15.77810811,0 +Car,PEV,2001,0,0,0,0,38741.26195,15.77810811,0 +Car,PEV,2000,0,0,0,0,38741.26195,15.77810811,0 +Car,PEV,1999,0,0,0,0,38741.26195,15.77810811,0 +Car,PEV,1998,0,0,0,0,38741.26195,15.77810811,0 +Car,BEV,2017,13,23,108.7,169,65091.20275,10.4298896,0 +Car,BEV,2016,11,25,103.1,173,74787.61065,10.57979631,0 +Car,BEV,2015,10,18,103.2,157,63109.22167,10.57697674,0 +Car,BEV,2014,10,13,106.1,115,50547.30465,10.49752121,0 +Car,BEV,2013,9,12,104.3,105,59928.48966,10.54631831,0 +Car,BEV,2012,5,5,95.6,113,54291.33743,10.80807531,0 +Car,BEV,2011,3,4,93.8,73,44331.89559,10.86829424,0 +Car,BEV,2010,0,0,0,0,44331.89559,10.86829424,0 +Car,BEV,2009,0,0,0,0,44331.89559,10.86829424,0 +Car,BEV,2008,0,0,0,0,44331.89559,10.86829424,0 +Car,BEV,2007,0,0,0,0,44331.89559,10.86829424,0 +Car,BEV,2006,0,0,0,0,44331.89559,10.86829424,0 +Car,BEV,2005,0,0,0,0,44331.89559,10.86829424,0 +Car,BEV,2004,0,0,0,0,44331.89559,10.86829424,0 +Car,BEV,2003,0,0,0,0,44331.89559,10.86829424,0 +Car,BEV,2002,0,0,0,0,44331.89559,10.86829424,0 +Car,BEV,2001,0,0,0,0,44331.89559,10.86829424,0 +Car,BEV,2000,0,0,0,0,44331.89559,10.86829424,0 +Car,BEV,1999,0,0,0,0,44331.89559,10.86829424,0 +Car,BEV,1998,0,0,0,0,44331.89559,10.86829424,0 +Van,Gas,2017,9,17,18.4,0,30190.16873,24.11434783,510.06 +Van,Gas,2016,9,17,18,0,30609.19041,24.47666667,516.47 +Van,Gas,2015,11,32,16.3,0,30595.90185,26.21490798,577.5 +Van,Gas,2014,12,48,15.7,0,29864.41689,26.91828025,587.94 +Van,Gas,2013,11,48,15.9,0,30236.02606,26.67792453,587.33 +Van,Gas,2012,12,51,15.4,0,30220.37918,27.29051948,603.9 +Van,Gas,2011,11,47,15.1,0,31550.1659,27.67754967,611 +Van,Gas,2010,10,30,17.2,0,32347.70849,25.25186047,530.66 +Van,Gas,2009,11,43,16.4,0,32676.69949,26.10268293,553.56 +Van,Gas,2008,11,46,16.5,0,32775.04455,25.99181818,550.05 +Van,Gas,2007,13,49,16.3,0,34487.78588,26.21490798,553.75 +Van,Gas,2006,14,52,16.7,0,35375.23544,25.77407186,542.24 +Van,Gas,2005,14,65,16.4,0,34417.73234,26.10268293,549.76 +Van,Gas,2004,13,64,16,0,34831.04973,26.56,565.75 +Van,Gas,2003,12,68,15.8,0,34472.75663,26.79734177,574.52 +Van,Gas,2002,14,65,16,0,34274.24668,26.56,564.71 +Van,Gas,2001,13,62,15.8,0,34256.10942,26.79734177,571.5 +Van,Gas,2000,14,69,16.1,0,35627.57086,26.44354037,562.93 +Van,Gas,1999,13,67,15.9,0,35251.96627,26.67792453,566.55 +Van,Gas,1998,4,37,14.5,0,35715.30779,28.49965517,615.25 +Van,Diesel,2017,0,0,0,0,36228.20248,24.11434783,0 +Van,Diesel,2016,0,0,0,0,36731.02849,24.47666667,0 +Van,Diesel,2015,0,0,0,0,36715.08222,26.21490798,0 +Van,Diesel,2014,0,0,0,0,35837.30027,26.91828025,0 +Van,Diesel,2013,0,0,0,0,36283.23127,26.67792453,0 +Van,Diesel,2012,0,0,0,0,36264.45502,27.29051948,0 +Van,Diesel,2011,0,0,0,0,37860.19908,27.67754967,0 +Van,Diesel,2010,0,0,0,0,38817.25019,25.25186047,0 +Van,Diesel,2009,0,0,0,0,39212.03939,26.10268293,0 +Van,Diesel,2008,0,0,0,0,39330.05345,25.99181818,0 +Van,Diesel,2007,0,0,0,0,41385.34306,26.21490798,0 +Van,Diesel,2006,0,0,0,0,42450.28253,25.77407186,0 +Van,Diesel,2005,0,0,0,0,41301.2788,26.10268293,0 +Van,Diesel,2004,0,0,0,0,41797.25967,26.56,0 +Van,Diesel,2003,0,0,0,0,41367.30796,26.79734177,0 +Van,Diesel,2002,0,0,0,0,41129.09602,26.56,0 +Van,Diesel,2001,0,0,0,0,41107.3313,26.79734177,0 +Van,Diesel,2000,0,0,0,0,42753.08503,26.44354037,0 +Van,Diesel,1999,0,0,0,0,42302.35952,26.67792453,0 +Van,Diesel,1998,0,0,0,0,42858.36935,28.49965517,0 +Van,Hybrid,2017,0,0,0,0,33209.1856,15.23574257,0 +Van,Hybrid,2016,0,0,0,0,33670.10945,15.96217391,0 +Van,Hybrid,2015,0,0,0,0,33655.49203,16.26070423,0 +Van,Hybrid,2014,0,0,0,0,32850.85858,16.43068966,0 +Van,Hybrid,2013,0,0,0,0,33259.62867,16.30858357,0 +Van,Hybrid,2012,0,0,0,0,33242.4171,16.58192982,0 +Van,Hybrid,2011,0,0,0,0,34705.18249,16.73857143,0 +Van,Hybrid,2010,0,0,0,0,35582.47934,16.50565217,0 +Van,Hybrid,2009,0,0,0,0,35944.36944,17.0979257,0 +Van,Hybrid,2008,0,0,0,0,36052.549,17.24396226,0 +Van,Hybrid,2007,0,0,0,0,37936.56447,16.81900901,0 +Van,Hybrid,2006,0,0,0,0,38912.75898,14.8854717,0 +Van,Hybrid,2005,0,0,0,0,37859.50557,14.96990453,0 +Van,Hybrid,2004,0,0,0,0,38314.1547,14.64371298,0 +Van,Hybrid,2003,0,0,0,0,37920.03229,14.73840647,0 +Van,Hybrid,2002,0,0,0,0,37701.67135,14.19297872,0 +Van,Hybrid,2001,0,0,0,0,37681.72036,14.19297872,0 +Van,Hybrid,2000,0,0,0,0,39190.32794,13.47037736,0 +Van,Hybrid,1999,0,0,0,0,38777.16289,13.47037736,0 +Van,Hybrid,1998,0,0,0,0,39286.83857,13.47037736,0 +Van,PEV,2017,1,1,32,0,53599.94056,17.185,106 +Van,PEV,2016,0,0,0,0,61584.53548,17.185,0 +Van,PEV,2015,0,0,0,0,51967.86028,17.185,0 +Van,PEV,2014,0,0,0,0,41623.63591,17.185,0 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+Motorcycle,Diesel,2015,0,0,0,0,11685.99273,12.49037736,0 +Motorcycle,Diesel,2014,0,0,0,0,11406.60473,12.49037736,0 +Motorcycle,Diesel,2013,0,0,0,0,11548.53949,12.49037736,0 +Motorcycle,Diesel,2012,0,0,0,0,11542.56322,12.49037736,0 +Motorcycle,Diesel,2011,0,0,0,0,12050.47039,12.49037736,0 +Motorcycle,Diesel,2010,0,0,0,0,12355.08887,12.49037736,0 +Motorcycle,Diesel,2009,0,0,0,0,12480.74577,12.49037736,0 +Motorcycle,Diesel,2008,0,0,0,0,12518.30831,12.49037736,0 +Motorcycle,Diesel,2007,0,0,0,0,13172.48359,12.49037736,0 +Motorcycle,Diesel,2006,0,0,0,0,13511.4417,12.49037736,0 +Motorcycle,Diesel,2005,0,0,0,0,13145.7269,12.49037736,0 +Motorcycle,Diesel,2004,0,0,0,0,13303.59196,12.49037736,0 +Motorcycle,Diesel,2003,0,0,0,0,13166.74322,12.49037736,0 +Motorcycle,Diesel,2002,0,0,0,0,13090.92308,12.49037736,0 +Motorcycle,Diesel,2001,0,0,0,0,13083.99561,12.49037736,0 +Motorcycle,Diesel,2000,0,0,0,0,13607.82029,12.49037736,0 +Motorcycle,Diesel,1999,0,0,0,0,13464.35949,12.49037736,0 +Motorcycle,Diesel,1998,0,0,0,0,13641.33109,12.49037736,0 +Motorcycle,Hybrid,2017,0,0,0,0,11531.02445,8.08,0 +Motorcycle,Hybrid,2016,0,0,0,0,11691.06825,8.08,0 +Motorcycle,Hybrid,2015,0,0,0,0,11685.99273,8.08,0 +Motorcycle,Hybrid,2014,0,0,0,0,11406.60473,8.08,0 +Motorcycle,Hybrid,2013,0,0,0,0,11548.53949,8.08,0 +Motorcycle,Hybrid,2012,0,0,0,0,11542.56322,8.08,0 +Motorcycle,Hybrid,2011,0,0,0,0,12050.47039,8.08,0 +Motorcycle,Hybrid,2010,0,0,0,0,12355.08887,8.08,0 +Motorcycle,Hybrid,2009,0,0,0,0,12480.74577,8.08,0 +Motorcycle,Hybrid,2008,0,0,0,0,12518.30831,8.08,0 +Motorcycle,Hybrid,2007,0,0,0,0,13172.48359,8.08,0 +Motorcycle,Hybrid,2006,0,0,0,0,13511.4417,8.08,0 +Motorcycle,Hybrid,2005,0,0,0,0,13145.7269,8.08,0 +Motorcycle,Hybrid,2004,0,0,0,0,13303.59196,8.08,0 +Motorcycle,Hybrid,2003,0,0,0,0,13166.74322,8.08,0 +Motorcycle,Hybrid,2002,0,0,0,0,13090.92308,8.08,0 +Motorcycle,Hybrid,2001,0,0,0,0,13083.99561,8.08,0 +Motorcycle,Hybrid,2000,0,0,0,0,13607.82029,8.08,0 +Motorcycle,Hybrid,1999,0,0,0,0,13464.35949,8.08,0 +Motorcycle,Hybrid,1998,0,0,0,0,13641.33109,8.08,0 +Motorcycle,PEV,2017,0,0,0,0,16712.08255,8.08,0 +Motorcycle,PEV,2016,0,0,0,0,19201.6228,8.08,0 +Motorcycle,PEV,2015,0,0,0,0,16203.21145,8.08,0 +Motorcycle,PEV,2014,0,0,0,0,12977.95542,8.08,0 +Motorcycle,PEV,2013,0,0,0,0,15386.5626,8.08,0 +Motorcycle,PEV,2012,0,0,0,0,13939.23102,8.08,0 +Motorcycle,PEV,2011,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,2010,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,2009,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,2008,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,2007,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,2006,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,2005,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,2004,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,2003,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,2002,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,2001,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,2000,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,1999,0,0,0,0,11382.15714,8.08,0 +Motorcycle,PEV,1998,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2017,2,2,240,80,16712.08255,8.08,0 +Motorcycle,BEV,2016,2,2,240,80,19201.6228,8.08,0 +Motorcycle,BEV,2015,1,1,240,80,16203.21145,8.08,0 +Motorcycle,BEV,2014,1,1,240,80,12977.95542,8.08,0 +Motorcycle,BEV,2013,1,1,240,80,15386.5626,8.08,0 +Motorcycle,BEV,2012,1,1,240,80,13939.23102,8.08,0 +Motorcycle,BEV,2011,1,1,240,80,11382.15714,8.08,0 +Motorcycle,BEV,2010,1,1,240,80,11382.15714,8.08,0 +Motorcycle,BEV,2009,1,1,240,80,11382.15714,8.08,0 +Motorcycle,BEV,2008,1,1,240,80,11382.15714,8.08,0 +Motorcycle,BEV,2007,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2006,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2005,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2004,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2003,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2002,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2001,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,2000,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,1999,0,0,0,0,11382.15714,8.08,0 +Motorcycle,BEV,1998,0,0,0,0,11382.15714,8.08,0 diff --git a/configs/resident/vehicle_type_data_extended.csv b/configs/resident/vehicle_type_data_extended.csv new file mode 100644 index 0000000..c996ee7 --- /dev/null +++ b/configs/resident/vehicle_type_data_extended.csv @@ -0,0 +1,1116 @@ +body_type,fuel_type,vehicle_year,MPG,NewPrice,NumMakes,NumModels,Range,auto_operating_cost,co2gpm +Car,BEV,2011,93.8,44331.89559,3.0,4.0,73.0,10.86829424,0.0 +Car,BEV,2012,95.6,54291.33743,5.0,5.0,113.0,10.80807531,0.0 +Car,BEV,2013,104.3,59928.48966,9.0,12.0,105.0,10.54631831,0.0 +Car,BEV,2014,106.1,50547.30465,10.0,13.0,115.0,10.49752121,0.0 +Car,BEV,2015,103.2,63109.22167,10.0,18.0,157.0,10.57697674,0.0 +Car,BEV,2016,103.1,74787.61065,11.0,25.0,173.0,10.57979631,0.0 +Car,BEV,2017,108.7,65091.20275,13.0,23.0,169.0,10.4298896,0.0 +Car,BEV,2018,115.03098584261784,65202.70828428572,13.923076923076923,44.30769230769231,145.639398695536,10.36546280178571, +Car,BEV,2019,116.75645063025709,65202.70828428572,14.846153846153847,65.61538461538461,147.82398967596902,10.302922147738087, +Car,BEV,2020,118.50779738971093,65202.70828428572,15.76923076923077,86.92307692307692,150.04134952110854,10.240381493690478, +Car,BEV,2021,120.28541435055658,65202.70828428572,16.692307692307693,108.23076923076923,152.29196976392515,10.177840839642855, +Car,BEV,2022,122.08969556581492,65202.70828428572,17.615384615384617,129.53846153846155,154.57634931038402,10.115300185595231, +Car,BEV,2023,123.92104099930214,65202.70828428572,18.53846153846154,150.84615384615384,156.89499455003977,10.052759531547622, +Car,BEV,2024,125.77985661429163,65202.70828428572,19.46153846153846,172.15384615384613,159.24841946829034,9.990218877499998, +Car,BEV,2025,127.66655446350602,65202.70828428572,20.384615384615387,193.46153846153845,161.6371457603147,9.927678223452375, +Car,BEV,2026,129.5815527804586,65202.70828428572,21.307692307692307,214.76923076923077,164.06170294671938,9.865137569404752, +Car,BEV,2027,131.52527607216544,65202.70828428572,22.230769230769234,236.07692307692307,166.52262849092014,9.802596915357142, +Car,BEV,2028,133.4981552132479,65202.70828428572,23.153846153846153,257.38461538461536,169.02046791828394,9.740056261309519, +Car,BEV,2029,135.50062754144662,65202.70828428572,24.076923076923077,278.6923076923077,171.55577493705817,9.677515607261896, +Car,BEV,2030,137.53313695456833,65202.70828428572,25.0,300.0,174.12911156111406,9.614974953214286, +Car,BEV,2031,139.59613400888682,65202.70828428572,25.25,297.5,176.74104823453075,9.552434299166663, +Car,BEV,2032,141.6900760190201,65202.70828428572,25.5,295.0,179.39216395804866,9.48989364511904, +Car,BEV,2033,143.8154271593054,65202.70828428572,25.75,292.5,182.08304641741938,9.42735299107143, +Car,BEV,2034,145.97265856669497,65202.70828428572,26.0,290.0,184.81429211368066,9.364812337023807, +Car,BEV,2035,148.16224844519536,65202.70828428572,26.25,287.5,187.58650649538583,9.302271682976183, +Car,BEV,2036,150.3846821718733,65202.70828428572,26.5,285.0,190.4003040928166,9.23973102892856, +Car,BEV,2037,152.64045240445137,65202.70828428572,26.75,282.5,193.25630865420885,9.17719037488095, +Car,BEV,2038,154.93005919051814,65202.70828428572,27.0,280.0,196.15515328402196,9.114649720833327, +Car,BEV,2039,157.2540100783759,65202.70828428572,27.25,277.5,199.09748058328228,9.052109066785704, +Car,BEV,2040,159.61282022955152,65202.70828428572,27.5,275.0,202.08394279203148,8.989568412738095, +Car,BEV,2041,162.00701253299476,65202.70828428572,27.75,272.5,205.11520193391195,8.927027758690471, +Car,BEV,2042,164.43711772098968,65202.70828428572,28.0,270.0,208.1919299629206,8.864487104642848, +Car,BEV,2043,166.9036744868045,65202.70828428572,28.25,267.5,211.3148089123644,8.801946450595239, +Car,BEV,2044,169.40722960410656,65202.70828428572,28.5,265.0,214.48453104604982,8.739405796547615, +Car,BEV,2045,171.94833804816815,65202.70828428572,28.75,262.5,217.70179901174058,8.676865142499992, +Car,BEV,2046,174.52756311889064,65202.70828428572,29.0,260.0,220.96732599691666,8.614324488452368, +Car,BEV,2047,177.145476565674,65202.70828428572,29.25,257.5,224.28183588687037,8.551783834404745, +Car,BEV,2048,179.8026587141591,65202.70828428572,29.5,255.0,227.64606342517342,8.489243180357136, +Car,BEV,2049,182.49969859487146,65202.70828428572,29.75,252.5,231.060754376551,8.426702526309526, +Car,BEV,2050,185.2371940737945,65202.70828428572,30.0,250.0,234.52666569219926,8.364161872261889, +Car,Diesel,1998,33.8,45885.66203,2.0,8.0,0.0,16.54573964,306.26 +Car,Diesel,1999,34.5,45290.37855,2.0,8.0,0.0,16.36565217,299.48 +Car,Diesel,2000,35.5,45772.94097,1.0,6.0,0.0,16.12070423,288.19 +Car,Diesel,2001,35.5,44010.93975,1.0,6.0,0.0,16.12070423,288.19 +Car,Diesel,2002,35.6,44034.24182,1.0,8.0,0.0,16.09696629,287.33 +Car,Diesel,2003,35.6,44289.27981,1.0,8.0,0.0,16.09696629,287.33 +Car,Diesel,2004,32.6,44749.60108,1.0,10.0,0.0,16.87245399,315.61 +Car,Diesel,2005,32.0,44218.5867,2.0,13.0,0.0,17.045,321.07 +Car,Diesel,2006,32.3,45448.75007,2.0,7.0,0.0,16.9579257,317.06 +Car,Diesel,2007,26.0,44308.58881,1.0,1.0,0.0,19.20846154,391.54 +Car,Diesel,2008,26.0,42108.12422,1.0,1.0,0.0,19.20846154,391.54 +Car,Diesel,2009,30.2,41981.77425,3.0,6.0,0.0,17.60377483,339.1 +Car,Diesel,2010,31.4,41559.0992,3.0,8.0,0.0,17.22414013,324.25 +Car,Diesel,2011,31.4,40534.44697,3.0,8.0,0.0,17.22414013,324.25 +Car,Diesel,2012,32.3,38825.98783,2.0,8.0,0.0,16.9579257,314.55 +Car,Diesel,2013,31.8,38846.09034,2.0,12.0,0.0,17.10396226,319.17 +Car,Diesel,2014,31.5,38368.66109,4.0,20.0,0.0,17.19380952,322.3 +Car,Diesel,2015,32.8,39308.445,4.0,23.0,0.0,16.81634146,311.57 +Car,Diesel,2016,31.4,39325.51763,2.0,8.0,0.0,17.22414013,325.13 +Car,Diesel,2017,35.2,38787.1746,3.0,9.0,0.0,16.19272727,290.0 +Car,Diesel,2018,41.48711268918141,45079.34609428571,2.769230769230769,8.307692307692308,0.0,16.5874271385714, +Car,Diesel,2019,42.109419379519125,45079.34609428571,2.5384615384615383,7.615384615384615,0.0,16.497489097142847, +Car,Diesel,2020,42.74106067021191,45079.34609428571,2.3076923076923075,6.923076923076923,0.0,16.407551055714265, +Car,Diesel,2021,43.38217658026508,45079.34609428571,2.0769230769230766,6.230769230769231,0.0,16.317613014285683, +Car,Diesel,2022,44.032909228969054,45079.34609428571,1.846153846153846,5.538461538461538,0.0,16.22767497285713, +Car,Diesel,2023,44.69340286740359,45079.34609428571,1.6153846153846154,4.846153846153847,0.0,16.13773693142855, +Car,Diesel,2024,45.36380391041463,45079.34609428571,1.3846153846153846,4.153846153846154,0.0,16.047798889999967, +Car,Diesel,2025,46.044260969070855,45079.34609428571,1.1538461538461537,3.4615384615384617,0.0,15.957860848571414, 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+Motorcycle,Gas,2048,106.65406728428015,13401.621732857142,4.4,10.5,0.0,12.49037736, +Motorcycle,Gas,2049,108.25387829354435,13401.621732857142,4.199999999999999,10.25,0.0,12.49037736, +Motorcycle,Gas,2050,109.8776864679475,13401.621732857142,4.0,10.0,0.0,12.49037736, +Pickup,BEV,2018,90.1194069276245,66267.63264428571,0.3076923076923077,2.3076923076923075,168.97388798929592,26.51181818, +Pickup,BEV,2019,91.47119803153885,66267.63264428571,0.6153846153846154,4.615384615384615,171.50849630913535,26.51181818, +Pickup,BEV,2020,92.84326600201192,66267.63264428571,0.9230769230769231,6.9230769230769225,174.08112375377235,26.51181818, +Pickup,BEV,2021,94.23591499204208,66267.63264428571,1.2307692307692308,9.23076923076923,176.6923406100789,26.51181818, +Pickup,BEV,2022,95.64945371692272,66267.63264428571,1.5384615384615385,11.538461538461537,179.3427257192301,26.51181818, +Pickup,BEV,2023,97.08419552267655,66267.63264428571,1.8461538461538463,13.846153846153845,182.03286660501854,26.51181818, +Pickup,BEV,2024,98.54045845551667,66267.63264428571,2.153846153846154,16.153846153846153,184.76335960409375,26.51181818, +Pickup,BEV,2025,100.01856533234943,66267.63264428571,2.4615384615384617,18.46153846153846,187.53480999815517,26.51181818, +Pickup,BEV,2026,101.51884381233465,66267.63264428571,2.769230769230769,20.769230769230766,190.34783214812748,26.51181818, +Pickup,BEV,2027,103.04162646951966,66267.63264428571,3.076923076923077,23.076923076923073,193.20304963034937,26.51181818, +Pickup,BEV,2028,104.58725086656244,66267.63264428571,3.384615384615385,25.384615384615383,196.10109537480457,26.51181818, +Pickup,BEV,2029,106.15605962956087,66267.63264428571,3.6923076923076925,27.69230769230769,199.04261180542662,26.51181818, +Pickup,BEV,2030,107.74840052400428,66267.63264428571,4.0,30.0,202.02825098250804,26.51181818, +Pickup,BEV,2031,109.36462653186433,66267.63264428571,3.95,30.75,205.05867474724562,26.51181818, +Pickup,BEV,2032,111.00509592984227,66267.63264428571,3.9,31.5,208.13455486845427,26.51181818, +Pickup,BEV,2033,112.6701723687899,66267.63264428571,3.85,32.25,211.25657319148107,26.51181818, +Pickup,BEV,2034,114.36022495432175,66267.63264428571,3.8,33.0,214.42542178935327,26.51181818, +Pickup,BEV,2035,116.07562832863655,66267.63264428571,3.75,33.75,217.64180311619353,26.51181818, +Pickup,BEV,2036,117.8167627535661,66267.63264428571,3.7,34.5,220.90643016293643,26.51181818, +Pickup,BEV,2037,119.58401419486958,66267.63264428571,3.65,35.25,224.22002661538045,26.51181818, +Pickup,BEV,2038,121.3777744077926,66267.63264428571,3.6,36.0,227.58332701461111,26.51181818, +Pickup,BEV,2039,123.19844102390948,66267.63264428571,3.55,36.75,230.99707691983028,26.51181818, +Pickup,BEV,2040,125.04641763926811,66267.63264428571,3.5,37.5,234.4620330736277,26.51181818, +Pickup,BEV,2041,126.92211390385711,66267.63264428571,3.45,38.25,237.9789635697321,26.51181818, +Pickup,BEV,2042,128.82594561241496,66267.63264428571,3.4,39.0,241.54864802327808,26.51181818, +Pickup,BEV,2043,130.7583347966012,66267.63264428571,3.35,39.75,245.17187774362722,26.51181818, +Pickup,BEV,2044,132.71970981855017,66267.63264428571,3.3,40.5,248.8494559097816,26.51181818, +Pickup,BEV,2045,134.71050546582842,66267.63264428571,3.25,41.25,252.5821977484283,26.51181818, +Pickup,BEV,2046,136.73116304781584,66267.63264428571,3.2,42.0,256.37093071465466,26.51181818, +Pickup,BEV,2047,138.78213049353306,66267.63264428571,3.15,42.75,260.21649467537446,26.51181818, +Pickup,BEV,2048,140.86386245093604,66267.63264428571,3.1,43.5,264.1197420955051,26.51181818, +Pickup,BEV,2049,142.9768203877001,66267.63264428571,3.05,44.25,268.08153822693765,26.51181818, +Pickup,BEV,2050,145.12147269351556,66267.63264428571,3.0,45.0,272.1027613003417,26.51181818, +Pickup,Diesel,1998,15.0,47126.2457,2.0,4.0,0.0,28.33,678.67 +Pickup,Diesel,2011,20.0,41630.35298,1.0,1.0,0.0,23.33,509.0 +Pickup,Diesel,2014,22.5,39406.01202,1.0,2.0,0.0,21.66333333,450.5 +Pickup,Diesel,2015,23.0,40371.20431,1.0,3.0,0.0,21.37347826,443.67 +Pickup,Diesel,2016,23.6,40388.73853,3.0,7.0,0.0,21.04186441,434.71 +Pickup,Diesel,2017,23.0,39835.84063,3.0,7.0,0.0,21.37347826,448.43 +Pickup,Diesel,2018,18.039974136761973,46298.12986714286,2.9444444444444446,6.888888888888889,0.0,20.536679650357087, +Pickup,Diesel,2019,18.3105737488134,46298.12986714286,2.888888888888889,6.777777777777778,0.0,20.227085658214264, +Pickup,Diesel,2020,18.585232355045598,46298.12986714286,2.8333333333333335,6.666666666666667,0.0,19.91749166607144, +Pickup,Diesel,2021,18.86401084037128,46298.12986714286,2.7777777777777777,6.555555555555555,0.0,19.607897673928505, +Pickup,Diesel,2022,19.146971002976848,46298.12986714286,2.7222222222222223,6.444444444444445,0.0,19.298303681785683, +Pickup,Diesel,2023,19.434175568021498,46298.12986714286,2.6666666666666665,6.333333333333333,0.0,18.98870968964286, +Pickup,Diesel,2024,19.725688201541818,46298.12986714286,2.611111111111111,6.222222222222222,0.0,18.679115697499924, +Pickup,Diesel,2025,20.021573524564946,46298.12986714286,2.5555555555555554,6.111111111111111,0.0,18.3695217053571, +Pickup,Diesel,2026,20.321897127433417,46298.12986714286,2.5,6.0,0.0,18.05992771321428, +Pickup,Diesel,2027,20.626725584344914,46298.12986714286,2.4444444444444446,5.888888888888889,0.0,17.750333721071343, +Pickup,Diesel,2028,20.936126468110086,46298.12986714286,2.388888888888889,5.777777777777778,0.0,17.44073972892852, +Pickup,Diesel,2029,21.250168365131735,46298.12986714286,2.3333333333333335,5.666666666666667,0.0,17.131145736785697, +Pickup,Diesel,2030,21.568920890608712,46298.12986714286,2.2777777777777777,5.555555555555555,0.0,16.82155174464276, +Pickup,Diesel,2031,21.89245470396784,46298.12986714286,2.2222222222222223,5.444444444444445,0.0,16.51195775249994, +Pickup,Diesel,2032,22.220841524527355,46298.12986714286,2.166666666666667,5.333333333333334,0.0,16.202363760357116, +Pickup,Diesel,2033,22.554154147395263,46298.12986714286,2.111111111111111,5.222222222222222,0.0,15.892769768214293, +Pickup,Diesel,2034,22.89246645960619,46298.12986714286,2.0555555555555554,5.111111111111111,0.0,15.583175776071357, +Pickup,Diesel,2035,23.23585345650028,46298.12986714286,2.0,5.0,0.0,15.273581783928535, +Pickup,Diesel,2036,23.584391258347782,46298.12986714286,2.0,5.0,0.0,14.963987791785712, +Pickup,Diesel,2037,23.938157127222997,46298.12986714286,2.0,5.0,0.0,14.654393799642776, +Pickup,Diesel,2038,24.297229484131336,46298.12986714286,2.0,5.0,0.0,14.344799807499953, +Pickup,Diesel,2039,24.661687926393306,46298.12986714286,2.0,5.0,0.0,14.03520581535713, +Pickup,Diesel,2040,25.031613245289204,46298.12986714286,2.0,5.0,0.0,13.725611823214194, +Pickup,Diesel,2041,25.40708744396854,46298.12986714286,2.0,5.0,0.0,13.416017831071372, +Pickup,Diesel,2042,25.788193755628065,46298.12986714286,2.0,5.0,0.0,13.10642383892855, +Pickup,Diesel,2043,26.175016661962484,46298.12986714286,2.0,5.0,0.0,12.796829846785727, +Pickup,Diesel,2044,26.56764191189192,46298.12986714286,2.0,5.0,0.0,12.48723585464279, +Pickup,Diesel,2045,26.966156540570296,46298.12986714286,2.0,5.0,0.0,12.177641862499968, +Pickup,Diesel,2046,27.370648888678843,46298.12986714286,2.0,5.0,0.0,11.868047870357145, +Pickup,Diesel,2047,27.781208622009025,46298.12986714286,2.0,5.0,0.0,11.558453878214209, +Pickup,Diesel,2048,28.19792675133916,46298.12986714286,2.0,5.0,0.0,11.248859886071386, +Pickup,Diesel,2049,28.620895652609246,46298.12986714286,2.0,5.0,0.0,10.939265893928564, +Pickup,Diesel,2050,29.050209087398382,46298.12986714286,2.0,5.0,0.0,10.629671901785628, +Pickup,Gas,1998,16.1,39271.87141,8.0,124.0,0.0,26.96354037,561.74 +Pickup,Gas,1999,16.0,38762.38991,8.0,119.0,0.0,27.08,566.51 +Pickup,Gas,2000,16.5,39175.39755,8.0,119.0,0.0,26.51181818,550.73 +Pickup,Gas,2001,16.0,37667.36471,8.0,126.0,0.0,27.08,566.42 +Pickup,Gas,2002,15.7,37687.3081,9.0,127.0,0.0,27.43828025,577.81 +Pickup,Gas,2003,15.8,37905.58585,8.0,127.0,0.0,27.31734177,571.99 +Pickup,Gas,2004,16.3,38299.55811,7.0,140.0,0.0,26.73490798,557.48 +Pickup,Gas,2005,16.4,37845.08219,7.0,127.0,0.0,26.62268293,556.04 +Pickup,Gas,2006,16.2,38897.93434,12.0,151.0,0.0,26.84851852,561.0 +Pickup,Gas,2007,16.1,37922.11173,12.0,162.0,0.0,26.96354037,558.22 +Pickup,Gas,2008,16.3,36038.81401,14.0,148.0,0.0,26.73490798,556.44 +Pickup,Gas,2009,16.8,35930.67567,11.0,138.0,0.0,26.18714286,534.88 +Pickup,Gas,2010,17.2,35568.92344,10.0,108.0,0.0,25.77186047,525.8 +Pickup,Gas,2011,17.3,34691.96081,8.0,110.0,0.0,25.67104046,522.85 +Pickup,Gas,2012,17.3,33229.7527,8.0,100.0,0.0,25.67104046,520.49 +Pickup,Gas,2013,16.9,33246.9577,7.0,69.0,0.0,26.08147929,532.93 +Pickup,Gas,2014,17.1,32838.34335,8.0,65.0,0.0,25.87385965,531.12 +Pickup,Gas,2015,18.6,33642.67026,6.0,66.0,0.0,24.45903226,485.79 +Pickup,Gas,2016,18.5,33657.28211,7.0,72.0,0.0,24.54621622,488.18 +Pickup,Gas,2017,18.5,33196.53386,8.0,84.0,0.0,24.54621622,488.67 +Pickup,Gas,2018,22.755150249225185,38581.77489,7.722222222222222,80.44444444444444,0.0,24.360774814999957, +Pickup,Gas,2019,23.09647750296356,38581.77489,7.444444444444445,76.88888888888889,0.0,24.14592630083331, +Pickup,Gas,2020,23.44292466550801,38581.77489,7.166666666666667,73.33333333333333,0.0,23.931077786666663, +Pickup,Gas,2021,23.794568535490626,38581.77489,6.888888888888889,69.77777777777777,0.0,23.71622927249996, +Pickup,Gas,2022,24.151487063522985,38581.77489,6.611111111111111,66.22222222222223,0.0,23.501380758333312, +Pickup,Gas,2023,24.513759369475828,38581.77489,6.333333333333333,62.66666666666667,0.0,23.286532244166665, +Pickup,Gas,2024,24.88146576001796,38581.77489,6.055555555555555,59.111111111111114,0.0,23.07168372999996, +Pickup,Gas,2025,25.254687746418227,38581.77489,5.777777777777778,55.55555555555556,0.0,22.856835215833314, +Pickup,Gas,2026,25.6335080626145,38581.77489,5.5,52.0,0.0,22.641986701666667, +Pickup,Gas,2027,26.01801068355371,38581.77489,5.222222222222222,48.44444444444444,0.0,22.427138187499963, +Pickup,Gas,2028,26.408280843807017,38581.77489,4.944444444444445,44.88888888888889,0.0,22.212289673333316, +Pickup,Gas,2029,26.80440505646412,38581.77489,4.666666666666666,41.333333333333336,0.0,21.99744115916667, +Pickup,Gas,2030,27.20647113231108,38581.77489,4.388888888888889,37.77777777777778,0.0,21.782592644999966, +Pickup,Gas,2031,27.614568199295743,38581.77489,4.111111111111111,34.22222222222223,0.0,21.56774413083332, +Pickup,Gas,2032,28.028786722285172,38581.77489,3.833333333333333,30.66666666666667,0.0,21.352895616666615, +Pickup,Gas,2033,28.44921852311945,38581.77489,3.5555555555555554,27.111111111111114,0.0,21.138047102499968, +Pickup,Gas,2034,28.87595680096624,38581.77489,3.2777777777777777,23.555555555555557,0.0,20.92319858833332, 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+Van-AV,PEV,2035,43.528360623238704,800000.0,1.5,1.75,0.0,17.0, +Van-AV,PEV,2036,44.18128603258729,800000.0,1.6,1.9,0.0,17.0, +Van-AV,PEV,2037,44.84400532307609,800000.0,1.7000000000000002,2.05,0.0,17.0, +Van-AV,PEV,2038,45.51666540292223,800000.0,1.8,2.2,0.0,17.0, +Van-AV,PEV,2039,46.199415383966056,800000.0,1.9,2.3499999999999996,0.0,17.0, +Van-AV,PEV,2040,46.892406614725545,800000.0,2.0,2.5,0.0,17.0, +Van-AV,PEV,2041,47.595792713946416,800000.0,2.1,2.65,0.0,17.0, +Van-AV,PEV,2042,48.309729604655615,800000.0,2.2,2.8,0.0,17.0, +Van-AV,PEV,2043,49.03437554872545,800000.0,2.3,2.95,0.0,17.0, +Van-AV,PEV,2044,49.76989118195632,800000.0,2.4000000000000004,3.1,0.0,17.0, +Van-AV,PEV,2045,50.516439549685664,800000.0,2.5,3.25,0.0,17.0, +Van-AV,PEV,2046,51.27418614293094,800000.0,2.6,3.4,0.0,17.0, +Van-AV,PEV,2047,52.0432989350749,800000.0,2.7,3.55,0.0,17.0, +Van-AV,PEV,2048,52.82394841910102,800000.0,2.8,3.6999999999999997,0.0,17.0, +Van-AV,PEV,2049,53.61630764538753,800000.0,2.9000000000000004,3.85,0.0,17.0, +Van-AV,PEV,2050,54.42055226006834,800000.0,3.0,4.0,0.0,17.0, diff --git a/configs/resident/work_from_home.csv b/configs/resident/work_from_home.csv new file mode 100644 index 0000000..7b27934 --- /dev/null +++ b/configs/resident/work_from_home.csv @@ -0,0 +1,30 @@ +Label,Description,Expression,work_at_home,work_away_from_home +util_work_from_home_constant,Constant for Working from home,1,coef_work_from_home_constant, +util_part_time_worker,Part time worker,@df.ptype==2,coef_part_time_worker, +util_access_to_workplaces,Accessibility to workplaces of the home mgra,@df.workplace_location_accessibility,,coef_access_to_workplaces +util_2016,Year 2016 survey,@PRE_COVID,coef_2016, +util_age_35_to_44,Age Group - 35 yrs to 44 yrs,"@df.age.between(35, 44)",coef_age_35_to_44, +util_age_45_to_54,Age Group - 45 yrs to 54 yrs,"@df.age.between(45, 54)",coef_age_45_to_54, +util_age_55_to_64,Age Group - 55 yrs to 64 yrs,"@df.age.between(55, 64)",coef_age_55_to_64, +util_age_65_79,Age 65-79,"@df.age.between(65, 79)",coef_age_65_79, +util_age_80_plus,Age 80 plus,@df.age > 79,coef_age_80_plus, +util_inc_lt_15,Household income less than 15k,@df.income<15000,coef_inc_lt_15, +util_inc_150_250,Household income between 150k-249999k,"@df.income.between(150000,249999)",coef_inc_150_250, +util_ind_accom,Industry type is accomodation,@df.naics_code==721,coef_ind_accom, +util_ind_bus_srv,Industry type is business services,@df.naics_code==54,coef_ind_bus_srv, +util_ind_construct,Industry type is construction,@df.naics_code==23,coef_ind_construct, +util_ind_edu,Industry type is education,@df.naics_code==61,coef_ind_edu, +util_ind_enter,Industry type is entertainment,@df.naics_code==71,coef_ind_enter, +util_ind_food_srv,Industry type is food services,@df.naics_code==722,coef_ind_food_srv, +util_ind_gov,Industry type is government,@df.naics_code==92,coef_ind_gov, +util_ind_health,Industry type is healthcare,@df.naics_code==62,coef_ind_health, +util_ind_manu,Industry type is manufacturing,"@df.naics_code.isin([31,32,33])",coef_ind_manu, +util_ind_mgmt_srv,Industry type is management services,@df.naics_code==55,coef_ind_mgmt_srv, +util_ind_mil,Industry type is military,@df.naics_code==9000,coef_ind_mil, +util_regional_calibration, Regional calibration factor,1,coef_regional_calibration, +#util_cbd,PMSA 1 - CBD,@df.pseudomsa==1,coef_cbd, +#util_central,PMSA 2 - Central,@df.pseudomsa==2,coef_central, +#util_Ssuburb,PMSA 4 - South Suburban,@df.pseudomsa==4,coef_SSuburb, +#util_NWcounty,PMSA 6 - North County West,@df.pseudomsa==6,coef_NWCounty, +#util_Ncounty,PMSA 7 - North County East,@df.pseudomsa==7,coef_NCounty, +#util_Ecounty,PMSA 8 - East County,@df.pseudomsa==8,coef_ECounty, diff --git a/configs/resident/work_from_home.yaml b/configs/resident/work_from_home.yaml new file mode 100644 index 0000000..c4f265c --- /dev/null +++ b/configs/resident/work_from_home.yaml @@ -0,0 +1,12 @@ + +# borrowed from free parking model + +SPEC: work_from_home.csv +COEFFICIENTS: work_from_home_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +WORK_FROM_HOME_ALT: 0 + +DEST_CHOICE_COLUMN_NAME: workplace_zone_id diff --git a/configs/resident/work_from_home_coeffs.csv b/configs/resident/work_from_home_coeffs.csv new file mode 100644 index 0000000..a2e2e6c --- /dev/null +++ b/configs/resident/work_from_home_coeffs.csv @@ -0,0 +1,31 @@ +coefficient_name,value,constrain +coef_work_from_home_constant,-0.386629856,F +coef_part_time_worker,-1.4513809,F +coef_access_to_workplaces,0.1,T +coef_age_35_to_44,0.353530241,F +coef_age_45_to_54,0.468133972,F +coef_age_55_to_64,0.495172418,F +coef_2016,-1.06294934,F +coef_age_65_79,1.243578944,F +coef_age_80_plus,0.770722144,F +coef_inc_lt_15,0.613583177,F +coef_inc_150_250,0.419020695,F +coef_ind_accom,-1.639185004,F +coef_ind_bus_srv,0.721333596,F +coef_ind_construct,-0.738399459,F +coef_ind_edu,-0.508510677,F +coef_ind_enter,1.027744147,F +coef_ind_food_srv,-1.204685952,F +coef_ind_gov,-0.75445486,F +coef_ind_health,-0.441707723,F +coef_ind_manu,-0.566600437,F +coef_ind_mgmt_srv,0.386847827,F +coef_ind_mil,-2.257002339,F +coef_external_worker_identification,-999.0,T +coef_regional_calibration,-0.82,T +coef_cbd,0.8653139314986638,F +coef_central,0.45025084346579664,F +coef_SSuburb,-0.4,F +coef_NWCounty,-0.38900940018141394,F +coef_NCounty,-0.7103700573089826,F +coef_ECounty,-5.0,F diff --git a/configs/resident/workplace_location.csv b/configs/resident/workplace_location.csv new file mode 100644 index 0000000..13f7edf --- /dev/null +++ b/configs/resident/workplace_location.csv @@ -0,0 +1,31 @@ +Label,Description,Expression,coefficient +local_dist,,_DIST@skims['DIST'],1 +alt_dest_pmsa,,"_ALT_PMSA@reindex(land_use.pseudomsa, df.alt_dest)",1 +#,,, +util_dist,Distance,@_DIST,coef_dist +util_dist_sqrt,Square root of distance,@_DIST**0.5,coef_dist_sqrt +util_dist_sqrd,Distance squared,@_DIST**2,coef_dist_sqrd +util_dist_cubed,Distance cubed,@_DIST**3,coef_dist_cubed +util_dist_lowincome,Distance - low income,@_DIST * (df.income<=2),coef_dist_lowincme +util_dist_sqrt_lowincome,Distance sqrt - low income,@(_DIST**0.5) * (df.income<=2),coef_dist_sqrt_lowincme +util_dist_sqrd_lowincome,Distance squared - low income,@(_DIST**2) * (df.income<=2),coef_dist_sqrd__lowincme +util_dist_highincome,Distance - high income,@_DIST * (df.income>=4),coef_dist_highincme +util_dist_sqrd_highincome,Distance squared - high income,@(_DIST**2) * (df.income>=4),coef_dist_sqrd_highincme +util_dist_pt,Distance - parttime worker,@_DIST * (df.ptype==2),coef_dist_ptworker +util_dist_sqrd_pt,Distance squared - parttime worker,@(_DIST**2) * (df.ptype==2),coef_dist_sqrd_ptworker +util_dist_femaleworker,Distance - female worker,@_DIST * (df.ptype<3) * (df.female==1),coef_dist_femaleworker +#,,, +util_size_variable,Size variable,@df['size_term'].apply(np.log1p),1 +util_size_unavailable,Size variable,@df['size_term']==0,-999 +util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1 +#,,, +util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum +#,,, +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1 +#calibration constants,,, +util_Calibration 0-2 - miles,Calibration 0-2 - miles,@_DIST<=2,coef_distance_0_2miles +util_Calibration 2-5 - miles,Calibration 2-5 - miles,@(_DIST>2) * (_DIST<=5),coef_distance_2_5miles +util_Calibration 5-10 - miles,Calibration 5-10 - miles,@(_DIST>5) * (_DIST<=10),coef_distance_5_10miles +util_ABM2 calibration 10-20 - miles,ABM2 calibration 10-20 - miles,@(_DIST>10) * (_DIST<=20),coef_distance_10_20miles +util_ABM2 calibration 20-30 - miles,ABM2 calibration 20-30 - miles,@(_DIST>20) * (_DIST<=30),coef_distance_20_30miles +util_ABM2 calibration >30 - miles,ABM2 calibration >30 - miles,@(_DIST>30),coef_distance_30plusmiles diff --git a/configs/resident/workplace_location.yaml b/configs/resident/workplace_location.yaml new file mode 100644 index 0000000..8720254 --- /dev/null +++ b/configs/resident/workplace_location.yaml @@ -0,0 +1,87 @@ +SAMPLE_SIZE: 30 +ESTIMATION_SAMPLE_SIZE: 10 + +SIMULATE_CHOOSER_COLUMNS: + - income_segment + - home_zone_id + - income + - age + - ptype + - female + +SAMPLE_SPEC: workplace_location_sample.csv +SPEC: workplace_location.csv +COEFFICIENTS: workplace_location_coefficients.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor +LOGSUM_TOUR_PURPOSE: work + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 17 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: workplace_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: workplace_location_logsum +# comment out MODE_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +MODE_CHOICE_LOGSUM_COLUMN_NAME: workplace_modechoice_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: workplace_location_sample + + +annotate_persons: + SPEC: annotate_persons_workplace + DF: persons + TABLES: + - land_use + +annotate_households: + SPEC: annotate_households_workplace + DF: households + TABLES: + - persons + +# - shadow pricing + + +# income_segment is in households, but we want to count persons +CHOOSER_TABLE_NAME: persons_merged + +# size_terms model_selector +MODEL_SELECTOR: workplace + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: occupation #income_segment + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_internal_worker + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +# FIXME - these are not needed for this model and should be re/factored out +SEGMENT_IDS: + mngt_busi_scic_arts: mngt_busi_scic_arts + services: services + health: health + sales_office: sales_office + prod_trans_move: prod_trans_move + constr_maint: constr_maint + military: military + # work_low: 1 # INCOME_SEGMENT_LOW + # work_med: 2 # INCOME_SEGMENT_MED + # work_high: 3 # INCOME_SEGMENT_HIGH + # work_veryhigh: 4 # INCOME_SEGMENT_VERYHIGH + +CONSTANTS: + WORK_HIGH_SEGMENT_ID: 3 + + +# model adds these tables (informational - not added if commented out) +SHADOW_PRICE_TABLE: workplace_shadow_prices +MODELED_SIZE_TABLE: workplace_modeled_size + +# not loaded if commented out +SAVED_SHADOW_PRICE_TABLE_NAME: workplace_shadow_prices.csv diff --git a/configs/resident/workplace_location_coefficients.csv b/configs/resident/workplace_location_coefficients.csv new file mode 100644 index 0000000..62a8593 --- /dev/null +++ b/configs/resident/workplace_location_coefficients.csv @@ -0,0 +1,21 @@ +coefficient_name,value,constrain +coef_dist,0.2658,F +coef_dist_sqrt,-1.604,F +coef_dist_sqrd,-0.004362,F +coef_dist_cubed,0.00002365,F +coef_dist_lowincme,0.1941,F +coef_dist_sqrt_lowincme,-0.8721,F +coef_dist_sqrd__lowincme,-0.002409,F +coef_dist_highincme,0,F +coef_dist_sqrd_highincme,0.0001919,F +coef_dist_ptworker,-0.116,F +coef_dist_sqrd_ptworker,0.0003934,F +coef_dist_femaleworker,-0.02515,F +coef_mode_logsum,0.5468,F +#work distance calibration,, +coef_distance_0_2miles,-0.770,F +coef_distance_2_5miles,-0.870,F +coef_distance_5_10miles,0.0270,F +coef_distance_10_20miles,0.720,F +coef_distance_20_30miles,0.852,F +coef_distance_30plusmiles,0.231,F diff --git a/configs/resident/workplace_location_sample.csv b/configs/resident/workplace_location_sample.csv new file mode 100644 index 0000000..d8469fe --- /dev/null +++ b/configs/resident/workplace_location_sample.csv @@ -0,0 +1,19 @@ +Label,Description,Expression,coefficient +local_dist,,_DIST@skims['DIST'],1 +#,,, +util_dist,Distance,@_DIST,coef_dist +util_dist_sqrt,Square root of distance,@_DIST**0.5,coef_dist_sqrt +util_dist_sqrd,Distance squared,@_DIST**2,coef_dist_sqrd +util_dist_cubed,Distance cubed,@_DIST**3,coef_dist_cubed +util_dist_lowincome,Distance - low income,@_DIST * (df.income<=2),coef_dist_lowincme +util_dist_sqrt_lowincome,Distance sqrt - low income,@(_DIST**0.5) * (df.income<=2),coef_dist_sqrt_lowincme +util_dist_sqrd_lowincome,Distance squared - low income,@(_DIST**2) * (df.income<=2),coef_dist_sqrd__lowincme +util_dist_highincome,Distance - high income,@_DIST * (df.income>=4),coef_dist_highincme +util_dist_sqrd_highincome,Distance squared - high income,@(_DIST**2) * (df.income>=4),coef_dist_sqrd_highincme +util_dist_pt,Distance - parttime worker,@_DIST * (df.ptype==2),coef_dist_ptworker +util_dist_sqrd_pt,Distance squared - parttime worker,@(_DIST**2) * (df.ptype==2),coef_dist_sqrd_ptworker +util_dist_femaleworker,Distance - female worker,@_DIST * (df.ptype<3) * (df.female==1),coef_dist_femaleworker +#,,, +util_size_variable,Size variable,@df['size_term'].apply(np.log1p),1 +util_size_unavailable,Size variable,@df['size_term']==0,-999 +util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1 \ No newline at end of file diff --git a/configs/resident/write_data_dictionary.yaml b/configs/resident/write_data_dictionary.yaml new file mode 100644 index 0000000..6d6ed39 --- /dev/null +++ b/configs/resident/write_data_dictionary.yaml @@ -0,0 +1,13 @@ + + +txt_format: data_dict.txt +csv_format: data_dict.csv + +tables: + - land_use + - accessibility + - households + - persons + - tours + - trips + - joint_tour_participants diff --git a/configs/resident/write_trip_matrices.yaml b/configs/resident/write_trip_matrices.yaml new file mode 100644 index 0000000..109de76 --- /dev/null +++ b/configs/resident/write_trip_matrices.yaml @@ -0,0 +1,505 @@ +# read trips table post preprocessor and run expressions to code +# additional data fields, with one data fields for each matrix specified below + +preprocessor: + SPEC: write_trip_matrices_annotate_trips_preprocessor + DF: trips + TABLES: + - tours + - households + - land_use + +# divide trip counts by household expansion factor +HH_EXPANSION_WEIGHT_COL: sample_rate # added when households read in + +# save preprocessed trips table to pipeline if desired +SAVE_TRIPS_TABLE: True + +MATRICES: + - file_name: autotrips_EA_low.omx + tables: + - name: SOVNOTRPDR_EA + data_field: DRIVEALONE_NOTR_EA_LOW + - name: SOVTRPDR_EA + data_field: DRIVEALONE_TR_EA_LOW + - name: SR2NOTRPDR_EA + data_field: SHARED2_NOTR_EA_LOW + - name: SR2TRPDR_EA + data_field: SHARED2_TR_EA_LOW + - name: SR3NOTRPDR_EA + data_field: SHARED3_NOTR_EA_LOW + - name: SR3TRPDR_EA + data_field: SHARED3_TR_EA_LOW + - file_name: autotrips_EA_med.omx + tables: + - name: SOVNOTRPDR_EA + data_field: DRIVEALONE_NOTR_EA_MED + - name: SOVTRPDR_EA + data_field: DRIVEALONE_TR_EA_MED + - name: SR2NOTRPDR_EA + data_field: SHARED2_NOTR_EA_MED + - name: SR2TRPDR_EA + data_field: SHARED2_TR_EA_MED + - name: SR3NOTRPDR_EA + data_field: SHARED3_NOTR_EA_MED + - name: SR3TRPDR_EA + data_field: SHARED3_TR_EA_MED + - file_name: autotrips_EA_high.omx + tables: + - name: SOVNOTRPDR_EA + data_field: DRIVEALONE_NOTR_EA_HIGH + - name: SOVTRPDR_EA + data_field: DRIVEALONE_TR_EA_HIGH + - name: SR2NOTRPDR_EA + data_field: SHARED2_NOTR_EA_HIGH + - name: SR2TRPDR_EA + data_field: SHARED2_TR_EA_HIGH + - name: SR3NOTRPDR_EA + data_field: SHARED3_NOTR_EA_HIGH + - name: SR3TRPDR_EA + data_field: SHARED3_TR_EA_HIGH + - file_name: autotrips_AM_low.omx + tables: + - name: SOVNOTRPDR_AM + data_field: DRIVEALONE_NOTR_AM_LOW + - name: SOVTRPDR_AM + data_field: DRIVEALONE_TR_AM_LOW + - name: SR2NOTRPDR_AM + data_field: SHARED2_NOTR_AM_LOW + - name: SR2TRPDR_AM + data_field: SHARED2_TR_AM_LOW + - name: SR3NOTRPDR_AM + data_field: SHARED3_NOTR_AM_LOW + - name: SR3TRPDR_AM + data_field: SHARED3_TR_AM_LOW + - file_name: autotrips_AM_med.omx + tables: + - name: SOVNOTRPDR_AM + data_field: DRIVEALONE_NOTR_AM_MED + - name: SOVTRPDR_AM + data_field: DRIVEALONE_TR_AM_MED + - name: SR2NOTRPDR_AM + data_field: SHARED2_NOTR_AM_MED + - name: SR2TRPDR_AM + data_field: SHARED2_TR_AM_MED + - name: SR3NOTRPDR_AM + data_field: SHARED3_NOTR_AM_MED + - name: SR3TRPDR_AM + data_field: SHARED3_TR_AM_MED + - file_name: autotrips_AM_high.omx + tables: + - name: SOVNOTRPDR_AM + data_field: DRIVEALONE_NOTR_AM_HIGH + - name: SOVTRPDR_AM + data_field: DRIVEALONE_TR_AM_HIGH + - name: SR2NOTRPDR_AM + data_field: SHARED2_NOTR_AM_HIGH + - name: SR2TRPDR_AM + data_field: SHARED2_TR_AM_HIGH + - name: SR3NOTRPDR_AM + data_field: SHARED3_NOTR_AM_HIGH + - name: SR3TRPDR_AM + data_field: SHARED3_TR_AM_HIGH + - file_name: autotrips_MD_low.omx + tables: + - name: SOVNOTRPDR_MD + data_field: DRIVEALONE_NOTR_MD_LOW + - name: SOVTRPDR_MD + data_field: DRIVEALONE_TR_MD_LOW + - name: SR2NOTRPDR_MD + data_field: SHARED2_NOTR_MD_LOW + - name: SR2TRPDR_MD + data_field: SHARED2_TR_MD_LOW + - name: SR3NOTRPDR_MD + data_field: SHARED3_NOTR_MD_LOW + - name: SR3TRPDR_MD + data_field: SHARED3_TR_MD_LOW + - file_name: autotrips_MD_med.omx + tables: + - name: SOVNOTRPDR_MD + data_field: DRIVEALONE_NOTR_MD_MED + - name: SOVTRPDR_MD + data_field: DRIVEALONE_TR_MD_MED + - name: SR2NOTRPDR_MD + data_field: SHARED2_NOTR_MD_MED + - name: SR2TRPDR_MD + data_field: SHARED2_TR_MD_MED + - name: SR3NOTRPDR_MD + data_field: SHARED3_NOTR_MD_MED + - name: SR3TRPDR_MD + data_field: SHARED3_TR_MD_MED + - file_name: autotrips_MD_high.omx + tables: + - name: SOVNOTRPDR_MD + data_field: DRIVEALONE_NOTR_MD_HIGH + - name: SOVTRPDR_MD + data_field: DRIVEALONE_TR_MD_HIGH + - name: SR2NOTRPDR_MD + data_field: SHARED2_NOTR_MD_HIGH + - name: SR2TRPDR_MD + data_field: SHARED2_TR_MD_HIGH + - name: SR3NOTRPDR_MD + data_field: SHARED3_NOTR_MD_HIGH + - name: SR3TRPDR_MD + data_field: SHARED3_TR_MD_HIGH + - file_name: autotrips_PM_low.omx + tables: + - name: SOVNOTRPDR_PM + data_field: DRIVEALONE_NOTR_PM_LOW + - name: SOVTRPDR_PM + data_field: DRIVEALONE_TR_PM_LOW + - name: SR2NOTRPDR_PM + data_field: SHARED2_NOTR_PM_LOW + - name: SR2TRPDR_PM + data_field: SHARED2_TR_PM_LOW + - name: SR3NOTRPDR_PM + data_field: SHARED3_NOTR_PM_LOW + - name: SR3TRPDR_PM + data_field: SHARED3_TR_PM_LOW + - file_name: autotrips_PM_med.omx + tables: + - name: SOVNOTRPDR_PM + data_field: DRIVEALONE_NOTR_PM_MED + - name: SOVTRPDR_PM + data_field: DRIVEALONE_TR_PM_MED + - name: SR2NOTRPDR_PM + data_field: SHARED2_NOTR_PM_MED + - name: SR2TRPDR_PM + data_field: SHARED2_TR_PM_MED + - name: SR3NOTRPDR_PM + data_field: SHARED3_NOTR_PM_MED + - name: SR3TRPDR_PM + data_field: SHARED3_TR_PM_MED + - file_name: autotrips_PM_high.omx + tables: + - name: SOVNOTRPDR_PM + data_field: DRIVEALONE_NOTR_PM_HIGH + - name: SOVTRPDR_PM + data_field: DRIVEALONE_TR_PM_HIGH + - name: SR2NOTRPDR_PM + data_field: SHARED2_NOTR_PM_HIGH + - name: SR2TRPDR_PM + data_field: SHARED2_TR_PM_HIGH + - name: SR3NOTRPDR_PM + data_field: SHARED3_NOTR_PM_HIGH + - name: SR3TRPDR_PM + data_field: SHARED3_TR_PM_HIGH + - file_name: autotrips_EV_low.omx + tables: + - name: SOVNOTRPDR_EV + data_field: DRIVEALONE_NOTR_EV_LOW + - name: SOVTRPDR_EV + data_field: DRIVEALONE_TR_EV_LOW + - name: SR2NOTRPDR_EV + data_field: SHARED2_NOTR_EV_LOW + - name: SR2TRPDR_EV + data_field: SHARED2_TR_EV_LOW + - name: SR3NOTRPDR_EV + data_field: SHARED3_NOTR_EV_LOW + - name: SR3TRPDR_EV + data_field: SHARED3_TR_EV_LOW + - file_name: autotrips_EV_med.omx + tables: + - name: SOVNOTRPDR_EV + data_field: DRIVEALONE_NOTR_EV_MED + - name: SOVTRPDR_EV + data_field: DRIVEALONE_TR_EV_MED + - name: SR2NOTRPDR_EV + data_field: SHARED2_NOTR_EV_MED + - name: SR2TRPDR_EV + data_field: SHARED2_TR_EV_MED + - name: SR3NOTRPDR_EV + data_field: SHARED3_NOTR_EV_MED + - name: SR3TRPDR_EV + data_field: SHARED3_TR_EV_MED + - file_name: autotrips_EV_high.omx + tables: + - name: SOVNOTRPDR_EV + data_field: DRIVEALONE_NOTR_EV_HIGH + - name: SOVTRPDR_EV + data_field: DRIVEALONE_TR_EV_HIGH + - name: SR2NOTRPDR_EV + data_field: SHARED2_NOTR_EV_HIGH + - name: SR2TRPDR_EV + data_field: SHARED2_TR_EV_HIGH + - name: SR3NOTRPDR_EV + data_field: SHARED3_NOTR_EV_HIGH + - name: SR3TRPDR_EV + data_field: SHARED3_TR_EV_HIGH + - file_name: trantrips_EA.omx + tables: + - name: WALK_SET_set1_EA + data_field: WALK_LOC_EA + - name: KNROUT_SET_set1_EA + data_field: KNR_LOCOUT_EA + - name: PNROUT_SET_set1_EA + data_field: PNR_LOCOUT_EA + - name: TNCOUT_SET_set1_EA + data_field: TNC_LOCOUT_EA + - name: KNRIN_SET_set1_EA + data_field: KNR_LOCIN_EA + - name: PNRIN_SET_set1_EA + data_field: PNR_LOCIN_EA + - name: TNCIN_SET_set1_EA + data_field: TNC_LOCIN_EA + - name: WALK_SET_set2_EA + data_field: WALK_PRM_EA + - name: KNROUT_SET_set2_EA + data_field: KNR_PRMOUT_EA + - name: PNROUT_SET_set2_EA + data_field: PNR_PRMOUT_EA + - name: TNCOUT_SET_set2_EA + data_field: TNC_PRMOUT_EA + - name: KNRIN_SET_set2_EA + data_field: KNR_PRMIN_EA + - name: PNRIN_SET_set2_EA + data_field: PNR_PRMIN_EA + - name: TNCIN_SET_set2_EA + data_field: TNC_PRMIN_EA + - name: WALK_SET_set3_EA + data_field: WALK_MIX_EA + - name: KNROUT_SET_set3_EA + data_field: KNR_MIXOUT_EA + - name: PNROUT_SET_set3_EA + data_field: PNR_MIXOUT_EA + - name: TNCOUT_SET_set3_EA + data_field: TNC_MIXOUT_EA + - name: KNRIN_SET_set3_EA + data_field: KNR_MIXIN_EA + - name: PNRIN_SET_set3_EA + data_field: PNR_MIXIN_EA + - name: TNCIN_SET_set3_EA + data_field: TNC_MIXIN_EA + - file_name: trantrips_AM.omx + tables: + - name: WALK_SET_set1_AM + data_field: WALK_LOC_AM + - name: KNROUT_SET_set1_AM + data_field: KNR_LOCOUT_AM + - name: PNROUT_SET_set1_AM + data_field: PNR_LOCOUT_AM + - name: TNCOUT_SET_set1_AM + data_field: TNC_LOCOUT_AM + - name: KNRIN_SET_set1_AM + data_field: KNR_LOCIN_AM + - name: PNRIN_SET_set1_AM + data_field: PNR_LOCIN_AM + - name: TNCIN_SET_set1_AM + data_field: TNC_LOCIN_AM + - name: WALK_SET_set2_AM + data_field: WALK_PRM_AM + - name: KNROUT_SET_set2_AM + data_field: KNR_PRMOUT_AM + - name: PNROUT_SET_set2_AM + data_field: PNR_PRMOUT_AM + - name: TNCOUT_SET_set2_AM + data_field: TNC_PRMOUT_AM + - name: KNRIN_SET_set2_AM + data_field: KNR_PRMIN_AM + - name: PNRIN_SET_set2_AM + data_field: PNR_PRMIN_AM + - name: TNCIN_SET_set2_AM + data_field: TNC_PRMIN_AM + - name: WALK_SET_set3_AM + data_field: WALK_MIX_AM + - name: KNROUT_SET_set3_AM + data_field: KNR_MIXOUT_AM + - name: PNROUT_SET_set3_AM + data_field: PNR_MIXOUT_AM + - name: TNCOUT_SET_set3_AM + data_field: TNC_MIXOUT_AM + - name: KNRIN_SET_set3_AM + data_field: KNR_MIXIN_AM + - name: PNRIN_SET_set3_AM + data_field: PNR_MIXIN_AM + - name: TNCIN_SET_set3_AM + data_field: TNC_MIXIN_AM + - file_name: trantrips_MD.omx + tables: + - name: WALK_SET_set1_MD + data_field: WALK_LOC_MD + - name: KNROUT_SET_set1_MD + data_field: KNR_LOCOUT_MD + - name: PNROUT_SET_set1_MD + data_field: PNR_LOCOUT_MD + - name: TNCOUT_SET_set1_MD + data_field: TNC_LOCOUT_MD + - name: KNRIN_SET_set1_MD + data_field: KNR_LOCIN_MD + - name: PNRIN_SET_set1_MD + data_field: PNR_LOCIN_MD + - name: TNCIN_SET_set1_MD + data_field: TNC_LOCIN_MD + - name: WALK_SET_set2_MD + data_field: WALK_PRM_MD + - name: KNROUT_SET_set2_MD + data_field: KNR_PRMOUT_MD + - name: PNROUT_SET_set2_MD + data_field: PNR_PRMOUT_MD + - name: TNCOUT_SET_set2_MD + data_field: TNC_PRMOUT_MD + - name: KNRIN_SET_set2_MD + data_field: KNR_PRMIN_MD + - name: PNRIN_SET_set2_MD + data_field: PNR_PRMIN_MD + - name: TNCIN_SET_set2_MD + data_field: TNC_PRMIN_MD + - name: WALK_SET_set3_MD + data_field: WALK_MIX_MD + - name: KNROUT_SET_set3_MD + data_field: KNR_MIXOUT_MD + - name: PNROUT_SET_set3_MD + data_field: PNR_MIXOUT_MD + - name: TNCOUT_SET_set3_MD + data_field: TNC_MIXOUT_MD + - name: KNRIN_SET_set3_MD + data_field: KNR_MIXIN_MD + - name: PNRIN_SET_set3_MD + data_field: PNR_MIXIN_MD + - name: TNCIN_SET_set3_MD + data_field: TNC_MIXIN_MD + - file_name: trantrips_PM.omx + tables: + - name: WALK_SET_set1_PM + data_field: WALK_LOC_PM + - name: KNROUT_SET_set1_PM + data_field: KNR_LOCOUT_PM + - name: PNROUT_SET_set1_PM + data_field: PNR_LOCOUT_PM + - name: TNCOUT_SET_set1_PM + data_field: TNC_LOCOUT_PM + - name: KNRIN_SET_set1_PM + data_field: KNR_LOCIN_PM + - name: PNRIN_SET_set1_PM + data_field: PNR_LOCIN_PM + - name: TNCIN_SET_set1_PM + data_field: TNC_LOCIN_PM + - name: WALK_SET_set2_PM + data_field: WALK_PRM_PM + - name: KNROUT_SET_set2_PM + data_field: KNR_PRMOUT_PM + - name: PNROUT_SET_set2_PM + data_field: PNR_PRMOUT_PM + - name: TNCOUT_SET_set2_PM + data_field: TNC_PRMOUT_PM + - name: KNRIN_SET_set2_PM + data_field: KNR_PRMIN_PM + - name: PNRIN_SET_set2_PM + data_field: PNR_PRMIN_PM + - name: TNCIN_SET_set2_PM + data_field: TNC_PRMIN_PM + - name: WALK_SET_set3_PM + data_field: WALK_MIX_PM + - name: KNROUT_SET_set3_PM + data_field: KNR_MIXOUT_PM + - name: PNROUT_SET_set3_PM + data_field: PNR_MIXOUT_PM + - name: TNCOUT_SET_set3_PM + data_field: TNC_MIXOUT_PM + - name: KNRIN_SET_set3_PM + data_field: KNR_MIXIN_PM + - name: PNRIN_SET_set3_PM + data_field: PNR_MIXIN_PM + - name: TNCIN_SET_set3_PM + data_field: TNC_MIXIN_PM + - file_name: trantrips_EV.omx + tables: + - name: WALK_SET_set1_EV + data_field: WALK_LOC_EV + - name: KNROUT_SET_set1_EV + data_field: KNR_LOCOUT_EV + - name: PNROUT_SET_set1_EV + data_field: PNR_LOCOUT_EV + - name: TNCOUT_SET_set1_EV + data_field: TNC_LOCOUT_EV + - name: KNRIN_SET_set1_EV + data_field: KNR_LOCIN_EV + - name: PNRIN_SET_set1_EV + data_field: PNR_LOCIN_EV + - name: TNCIN_SET_set1_EV + data_field: TNC_LOCIN_EV + - name: WALK_SET_set2_EV + data_field: WALK_PRM_EV + - name: KNROUT_SET_set2_EV + data_field: KNR_PRMOUT_EV + - name: PNROUT_SET_set2_EV + data_field: PNR_PRMOUT_EV + - name: TNCOUT_SET_set2_EV + data_field: TNC_PRMOUT_EV + - name: KNRIN_SET_set2_EV + data_field: KNR_PRMIN_EV + - name: PNRIN_SET_set2_EV + data_field: PNR_PRMIN_EV + - name: TNCIN_SET_set2_EV + data_field: TNC_PRMIN_EV + - name: WALK_SET_set3_EV + data_field: WALK_MIX_EV + - name: KNROUT_SET_set3_EV + data_field: KNR_MIXOUT_EV + - name: PNROUT_SET_set3_EV + data_field: PNR_MIXOUT_EV + - name: TNCOUT_SET_set3_EV + data_field: TNC_MIXOUT_EV + - name: KNRIN_SET_set3_EV + data_field: KNR_MIXIN_EV + - name: PNRIN_SET_set3_EV + data_field: PNR_MIXIN_EV + - name: TNCIN_SET_set3_EV + data_field: TNC_MIXIN_EV + - file_name: nmottrips_EA.omx + tables: + - name: WALK_EA + data_field: WALK_EA + - name: BIKE_EA + data_field: BIKE_EA + - file_name: nmottrips_AM.omx + tables: + - name: WALK_AM + data_field: WALK_AM + - name: BIKE_AM + data_field: BIKE_AM + - file_name: nmottrips_MD.omx + tables: + - name: WALK_MD + data_field: WALK_MD + - name: BIKE_MD + data_field: BIKE_MD + - file_name: nmottrips_PM.omx + tables: + - name: WALK_PM + data_field: WALK_PM + - name: BIKE_PM + data_field: BIKE_PM + - file_name: nmottrips_EV.omx + tables: + - name: WALK_EV + data_field: WALK_EV + - name: BIKE_EV + data_field: BIKE_EV + +CONSTANTS: + time_periods: + EA: + first_hour: 1 + last_hour: 6 + AM: + first_hour: 7 + last_hour: 12 + MD: + first_hour: 13 + last_hour: 25 + PM: + first_hour: 26 + last_hour: 32 + EV: + first_hour: 33 + last_hour: 48 + # SHARED2 and SHARED3 Occupancies + OCC_SHARED2: 2.0 + OCC_SHARED3: 3.33 + vot_threshold_low: 8.81 + vot_threshold_med: 18.00 + # add TNC and Taxi Occupancies + OCC_TAXI: 1.1 + OCC_TNC_SINGLE: 1.2 + OCC_TNC_SHARED: 2.0 \ No newline at end of file diff --git a/configs/resident/write_trip_matrices_annotate_trips_preprocessor.csv b/configs/resident/write_trip_matrices_annotate_trips_preprocessor.csv new file mode 100644 index 0000000..721b645 --- /dev/null +++ b/configs/resident/write_trip_matrices_annotate_trips_preprocessor.csv @@ -0,0 +1,498 @@ +Description,Target,Expression +# add additional fields,, +,tour_participants,trips.tour_id.map(tours.number_of_participants) +,distance,od_skims['DIST'] +,costOperatingDrive,"np.where(trips.trip_mode.isin(['DRIVEALONE', 'SHARED2','SHARED3']),costPerMile * distance/100, 0)" +,_sample_rate,trips.household_id.map(households.sample_rate) +,weightTrip,"np.where(trips.trip_mode == 'SHARED2', tour_participants/OCC_SHARED2, np.where(trips.trip_mode == 'SHARED3',tour_participants/OCC_SHARED3, np.where(trips.trip_mode == 'TAXI', tour_participants/OCC_TAXI, np.where(trips.trip_mode == 'TNC_SINGLE', tour_participants/OCC_TNC_SINGLE, np.where(trips.trip_mode == 'TNC_SHARED', tour_participants/OCC_TNC_SHARED, np.where(trips.trip_mode.isin(['WALK','BIKE','EBIKE','ESCOOTER']), tour_participants, 1))))))/_sample_rate" +,weightPersonTrip,tour_participants/_sample_rate +# code time periods,, +,is_ea,"trips.depart.between(time_periods['EA']['first_hour'], time_periods['EA']['last_hour'])" +,is_am,"trips.depart.between(time_periods['AM']['first_hour'], time_periods['AM']['last_hour'])" +,is_md,"trips.depart.between(time_periods['MD']['first_hour'], time_periods['MD']['last_hour'])" +,is_pm,"trips.depart.between(time_periods['PM']['first_hour'], time_periods['PM']['last_hour'])" +,is_ev,"trips.depart.between(time_periods['EV']['first_hour'], time_periods['EV']['last_hour'])" +,ownTrp,trips.ownsTransponder ==1 +,_vot_bin_da,"np.where(trips.trip_mode.isin(['DRIVEALONE']),np.where(trips.vot_da < vot_threshold_low, 1,np.where(trips.vot_da < vot_threshold_med, 2, 3)),0)" +,_vot_bin_s2,"np.where(trips.trip_mode.isin(['SHARED2','TAXI','TNC_SINGLE']),np.where(trips.vot_s2 < vot_threshold_low,1,np.where(trips.vot_s2 < vot_threshold_med, 2, 3)),0)" +,_vot_bin_s3,"np.where(trips.trip_mode.isin(['SHARED3','TNC_SHARED']),np.where(trips.vot_s3 < vot_threshold_low,1,np.where(trips.vot_s3 < vot_threshold_med, 2, 3)),0)" +,vot1,"np.where((_vot_bin_da == 1) |( _vot_bin_s2 == 1) |( _vot_bin_s3 ==1),1,0)" +,vot2,"np.where((_vot_bin_da == 2 )|( _vot_bin_s2 == 2) |( _vot_bin_s3 ==2),1,0)" +,vot3,"np.where((_vot_bin_da == 3) | (_vot_bin_s2 == 3) | (_vot_bin_s3 ==3),1,0)" +,outbound,trips.outbound +,inbound,~trips.outbound +# ea trips,, +,DRIVEALONE_NOTR_EA_LOW,((trips.trip_mode == 'DRIVEALONE') & is_ea & ~ownTrp & vot1) * tour_participants +,SHARED2_NOTR_EA_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_ea * ~ownTrp * vot1 * tour_participants +,SHARED3_NOTR_EA_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_ea * ~ownTrp * vot1 * tour_participants +,DRIVEALONE_TR_EA_LOW,((trips.trip_mode == 'DRIVEALONE') & is_ea & ownTrp & vot1) * tour_participants +,SHARED2_TR_EA_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_ea * ownTrp * vot1 * tour_participants +,SHARED3_TR_EA_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_ea * ownTrp * vot1 * tour_participants +,DRIVEALONE_NOTR_EA_MED,((trips.trip_mode == 'DRIVEALONE') & is_ea & ~ownTrp & vot2) * tour_participants +,SHARED2_NOTR_EA_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_ea * ~ownTrp * vot2 * tour_participants +,SHARED3_NOTR_EA_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_ea * ~ownTrp * vot2 * tour_participants +,DRIVEALONE_TR_EA_MED,((trips.trip_mode == 'DRIVEALONE') & is_ea & ownTrp & vot2) * tour_participants +,SHARED2_TR_EA_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_ea * ownTrp * vot2 * tour_participants +,SHARED3_TR_EA_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_ea * ownTrp * vot2 * tour_participants +,DRIVEALONE_NOTR_EA_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_ea & ~ownTrp & vot3) * tour_participants +,SHARED2_NOTR_EA_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_ea * ~ownTrp * vot3 * tour_participants +,SHARED3_NOTR_EA_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_ea * ~ownTrp * vot3 * tour_participants +,DRIVEALONE_TR_EA_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_ea & ownTrp & vot3) * tour_participants +,SHARED2_TR_EA_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_ea * ownTrp * vot3 * tour_participants +,SHARED3_TR_EA_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_ea * ownTrp * vot3 * tour_participants +,WALK_LOC_EA,((trips.trip_mode == 'WALK_LOC') & is_ea) * tour_participants +,WALK_PRM_EA,((trips.trip_mode == 'WALK_PRM') & is_ea) * tour_participants +,WALK_MIX_EA,((trips.trip_mode == 'WALK_MIX') & is_ea) * tour_participants +,PNR_LOCOUT_EA,((trips.trip_mode == 'PNR_LOC') & is_ea) * tour_participants * outbound +,PNR_PRMOUT_EA,((trips.trip_mode == 'PNR_PRM') & is_ea) * tour_participants * outbound +,PNR_MIXOUT_EA,((trips.trip_mode == 'PNR_MIX') & is_ea) * tour_participants * outbound +,KNR_LOCOUT_EA,((trips.trip_mode == 'KNR_LOC') & is_ea) * tour_participants * outbound +,KNR_PRMOUT_EA,((trips.trip_mode == 'KNR_PRM') & is_ea) * tour_participants * outbound +,KNR_MIXOUT_EA,((trips.trip_mode == 'KNR_MIX') & is_ea) * tour_participants * outbound +,TNC_LOCOUT_EA,((trips.trip_mode == 'TNC_LOC') & is_ea) * tour_participants * outbound +,TNC_PRMOUT_EA,((trips.trip_mode == 'TNC_PRM') & is_ea) * tour_participants * outbound +,TNC_MIXOUT_EA,((trips.trip_mode == 'TNC_MIX') & is_ea) * tour_participants * outbound +,PNR_LOCIN_EA,((trips.trip_mode == 'PNR_LOC') & is_ea) * tour_participants * inbound +,PNR_PRMIN_EA,((trips.trip_mode == 'PNR_PRM') & is_ea) * tour_participants * inbound +,PNR_MIXIN_EA,((trips.trip_mode == 'PNR_MIX') & is_ea) * tour_participants * inbound +,KNR_LOCIN_EA,((trips.trip_mode == 'KNR_LOC') & is_ea) * tour_participants * inbound +,KNR_PRMIN_EA,((trips.trip_mode == 'KNR_PRM') & is_ea) * tour_participants * inbound +,KNR_MIXIN_EA,((trips.trip_mode == 'KNR_MIX') & is_ea) * tour_participants * inbound +,TNC_LOCIN_EA,((trips.trip_mode == 'TNC_LOC') & is_ea) * tour_participants * inbound +,TNC_PRMIN_EA,((trips.trip_mode == 'TNC_PRM') & is_ea) * tour_participants * inbound +,TNC_MIXIN_EA,((trips.trip_mode == 'TNC_MIX') & is_ea) * tour_participants * inbound +,BIKE_EA,((trips.trip_mode == 'BIKE') & is_ea) * tour_participants +,WALK_EA,((trips.trip_mode == 'WALK') & is_ea) * tour_participants +# am trips,, +,DRIVEALONE_NOTR_AM_LOW,((trips.trip_mode == 'DRIVEALONE') & is_am & ~ownTrp & vot1) * tour_participants +,SHARED2_NOTR_AM_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_am * ~ownTrp * vot1 * tour_participants +,SHARED3_NOTR_AM_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_am * ~ownTrp * vot1 * tour_participants +,DRIVEALONE_TR_AM_LOW,((trips.trip_mode == 'DRIVEALONE') & is_am & ownTrp & vot1) * tour_participants +,SHARED2_TR_AM_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_am * ownTrp * vot1 * tour_participants +,SHARED3_TR_AM_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_am * ownTrp * vot1 * tour_participants +,DRIVEALONE_NOTR_AM_MED,((trips.trip_mode == 'DRIVEALONE') & is_am & ~ownTrp & vot2) * tour_participants +,SHARED2_NOTR_AM_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_am * ~ownTrp * vot2 * tour_participants +,SHARED3_NOTR_AM_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_am * ~ownTrp * vot2 * tour_participants +,DRIVEALONE_TR_AM_MED,((trips.trip_mode == 'DRIVEALONE') & is_am & ownTrp & vot2) * tour_participants +,SHARED2_TR_AM_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_am * ownTrp * vot2 * tour_participants +,SHARED3_TR_AM_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_am * ownTrp * vot2 * tour_participants +,DRIVEALONE_NOTR_AM_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_am & ~ownTrp & vot3) * tour_participants +,SHARED2_NOTR_AM_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_am * ~ownTrp * vot3 * tour_participants +,SHARED3_NOTR_AM_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_am * ~ownTrp * vot3 * tour_participants +,DRIVEALONE_TR_AM_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_am & ownTrp & vot3) * tour_participants +,SHARED2_TR_AM_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_am * ownTrp * vot3 * tour_participants +,SHARED3_TR_AM_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_am * ownTrp * vot3 * tour_participants +,WALK_LOC_AM,((trips.trip_mode == 'WALK_LOC') & is_am) * tour_participants +,WALK_PRM_AM,((trips.trip_mode == 'WALK_PRM') & is_am) * tour_participants +,WALK_MIX_AM,((trips.trip_mode == 'WALK_MIX') & is_am) * tour_participants +,PNR_LOCOUT_AM,((trips.trip_mode == 'PNR_LOC') & is_am) * tour_participants * outbound +,PNR_PRMOUT_AM,((trips.trip_mode == 'PNR_PRM') & is_am) * tour_participants * outbound +,PNR_MIXOUT_AM,((trips.trip_mode == 'PNR_MIX') & is_am) * tour_participants * outbound +,KNR_LOCOUT_AM,((trips.trip_mode == 'KNR_LOC') & is_am) * tour_participants * outbound +,KNR_PRMOUT_AM,((trips.trip_mode == 'KNR_PRM') & is_am) * tour_participants * outbound +,KNR_MIXOUT_AM,((trips.trip_mode == 'KNR_MIX') & is_am) * tour_participants * outbound +,TNC_LOCOUT_AM,((trips.trip_mode == 'TNC_LOC') & is_am) * tour_participants * outbound +,TNC_PRMOUT_AM,((trips.trip_mode == 'TNC_PRM') & is_am) * tour_participants * outbound +,TNC_MIXOUT_AM,((trips.trip_mode == 'TNC_MIX') & is_am) * tour_participants * outbound +,PNR_LOCIN_AM,((trips.trip_mode == 'PNR_LOC') & is_am) * tour_participants * inbound +,PNR_PRMIN_AM,((trips.trip_mode == 'PNR_PRM') & is_am) * tour_participants * inbound +,PNR_MIXIN_AM,((trips.trip_mode == 'PNR_MIX') & is_am) * tour_participants * inbound +,KNR_LOCIN_AM,((trips.trip_mode == 'KNR_LOC') & is_am) * tour_participants * inbound +,KNR_PRMIN_AM,((trips.trip_mode == 'KNR_PRM') & is_am) * tour_participants * inbound +,KNR_MIXIN_AM,((trips.trip_mode == 'KNR_MIX') & is_am) * tour_participants * inbound +,TNC_LOCIN_AM,((trips.trip_mode == 'TNC_LOC') & is_am) * tour_participants * inbound +,TNC_PRMIN_AM,((trips.trip_mode == 'TNC_PRM') & is_am) * tour_participants * inbound +,TNC_MIXIN_AM,((trips.trip_mode == 'TNC_MIX') & is_am) * tour_participants * inbound +,BIKE_AM,((trips.trip_mode == 'BIKE') & is_am) * tour_participants +,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants +# md trips,, +,DRIVEALONE_NOTR_MD_LOW,((trips.trip_mode == 'DRIVEALONE') & is_md & ~ownTrp & vot1) * tour_participants +,SHARED2_NOTR_MD_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_md * ~ownTrp * vot1 * tour_participants +,SHARED3_NOTR_MD_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_md * ~ownTrp * vot1 * tour_participants +,DRIVEALONE_TR_MD_LOW,((trips.trip_mode == 'DRIVEALONE') & is_md & ownTrp & vot1) * tour_participants +,SHARED2_TR_MD_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_md * ownTrp * vot1 * tour_participants +,SHARED3_TR_MD_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_md * ownTrp * vot1 * tour_participants +,DRIVEALONE_NOTR_MD_MED,((trips.trip_mode == 'DRIVEALONE') & is_md & ~ownTrp & vot2) * tour_participants +,SHARED2_NOTR_MD_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_md * ~ownTrp * vot2 * tour_participants +,SHARED3_NOTR_MD_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_md * ~ownTrp * vot2 * tour_participants +,DRIVEALONE_TR_MD_MED,((trips.trip_mode == 'DRIVEALONE') & is_md & ownTrp & vot2) * tour_participants +,SHARED2_TR_MD_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_md * ownTrp * vot2 * tour_participants +,SHARED3_TR_MD_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_md * ownTrp * vot2 * tour_participants +,DRIVEALONE_NOTR_MD_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_md & ~ownTrp & vot3) * tour_participants +,SHARED2_NOTR_MD_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_md * ~ownTrp * vot3 * tour_participants +,SHARED3_NOTR_MD_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_md * ~ownTrp * vot3 * tour_participants +,DRIVEALONE_TR_MD_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_md & ownTrp & vot3) * tour_participants +,SHARED2_TR_MD_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_md * ownTrp * vot3 * tour_participants +,SHARED3_TR_MD_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_md * ownTrp * vot3 * tour_participants +,WALK_LOC_MD,((trips.trip_mode == 'WALK_LOC') & is_md) * tour_participants +,WALK_PRM_MD,((trips.trip_mode == 'WALK_PRM') & is_md) * tour_participants +,WALK_MIX_MD,((trips.trip_mode == 'WALK_MIX') & is_md) * tour_participants +,PNR_LOCOUT_MD,((trips.trip_mode == 'PNR_LOC') & is_md) * tour_participants * outbound +,PNR_PRMOUT_MD,((trips.trip_mode == 'PNR_PRM') & is_md) * tour_participants * outbound +,PNR_MIXOUT_MD,((trips.trip_mode == 'PNR_MIX') & is_md) * tour_participants * outbound +,KNR_LOCOUT_MD,((trips.trip_mode == 'KNR_LOC') & is_md) * tour_participants * outbound +,KNR_PRMOUT_MD,((trips.trip_mode == 'KNR_PRM') & is_md) * tour_participants * outbound +,KNR_MIXOUT_MD,((trips.trip_mode == 'KNR_MIX') & is_md) * tour_participants * outbound +,TNC_LOCOUT_MD,((trips.trip_mode == 'TNC_LOC') & is_md) * tour_participants * outbound +,TNC_PRMOUT_MD,((trips.trip_mode == 'TNC_PRM') & is_md) * tour_participants * outbound +,TNC_MIXOUT_MD,((trips.trip_mode == 'TNC_MIX') & is_md) * tour_participants * outbound +,PNR_LOCIN_MD,((trips.trip_mode == 'PNR_LOC') & is_md) * tour_participants * inbound +,PNR_PRMIN_MD,((trips.trip_mode == 'PNR_PRM') & is_md) * tour_participants * inbound +,PNR_MIXIN_MD,((trips.trip_mode == 'PNR_MIX') & is_md) * tour_participants * inbound +,KNR_LOCIN_MD,((trips.trip_mode == 'KNR_LOC') & is_md) * tour_participants * inbound +,KNR_PRMIN_MD,((trips.trip_mode == 'KNR_PRM') & is_md) * tour_participants * inbound +,KNR_MIXIN_MD,((trips.trip_mode == 'KNR_MIX') & is_md) * tour_participants * inbound +,TNC_LOCIN_MD,((trips.trip_mode == 'TNC_LOC') & is_md) * tour_participants * inbound +,TNC_PRMIN_MD,((trips.trip_mode == 'TNC_PRM') & is_md) * tour_participants * inbound +,TNC_MIXIN_MD,((trips.trip_mode == 'TNC_MIX') & is_md) * tour_participants * inbound +,BIKE_MD,((trips.trip_mode == 'BIKE') & is_md) * tour_participants +,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants +# pm trips,, +,DRIVEALONE_NOTR_PM_LOW,((trips.trip_mode == 'DRIVEALONE') & is_pm & ~ownTrp & vot1) * tour_participants +,SHARED2_NOTR_PM_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_pm * ~ownTrp * vot1 * tour_participants +,SHARED3_NOTR_PM_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_pm * ~ownTrp * vot1 * tour_participants +,DRIVEALONE_TR_PM_LOW,((trips.trip_mode == 'DRIVEALONE') & is_pm & ownTrp & vot1) * tour_participants +,SHARED2_TR_PM_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_pm * ownTrp * vot1 * tour_participants +,SHARED3_TR_PM_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_pm * ownTrp * vot1 * tour_participants +,DRIVEALONE_NOTR_PM_MED,((trips.trip_mode == 'DRIVEALONE') & is_pm & ~ownTrp & vot2) * tour_participants +,SHARED2_NOTR_PM_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_pm * ~ownTrp * vot2 * tour_participants +,SHARED3_NOTR_PM_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_pm * ~ownTrp * vot2 * tour_participants +,DRIVEALONE_TR_PM_MED,((trips.trip_mode == 'DRIVEALONE') & is_pm & ownTrp & vot2) * tour_participants +,SHARED2_TR_PM_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_pm * ownTrp * vot2 * tour_participants +,SHARED3_TR_PM_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_pm * ownTrp * vot2 * tour_participants +,DRIVEALONE_NOTR_PM_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_pm & ~ownTrp & vot3) * tour_participants +,SHARED2_NOTR_PM_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_pm * ~ownTrp * vot3 * tour_participants +,SHARED3_NOTR_PM_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_pm * ~ownTrp * vot3 * tour_participants +,DRIVEALONE_TR_PM_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_pm & ownTrp & vot3) * tour_participants +,SHARED2_TR_PM_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_pm * ownTrp * vot3 * tour_participants +,SHARED3_TR_PM_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_pm * ownTrp * vot3 * tour_participants +,WALK_LOC_PM,((trips.trip_mode == 'WALK_LOC') & is_pm) * tour_participants +,WALK_PRM_PM,((trips.trip_mode == 'WALK_PRM') & is_pm) * tour_participants +,WALK_MIX_PM,((trips.trip_mode == 'WALK_MIX') & is_pm) * tour_participants +,PNR_LOCOUT_PM,((trips.trip_mode == 'PNR_LOC') & is_pm) * tour_participants * outbound +,PNR_PRMOUT_PM,((trips.trip_mode == 'PNR_PRM') & is_pm) * tour_participants * outbound +,PNR_MIXOUT_PM,((trips.trip_mode == 'PNR_MIX') & is_pm) * tour_participants * outbound +,KNR_LOCOUT_PM,((trips.trip_mode == 'KNR_LOC') & is_pm) * tour_participants * outbound +,KNR_PRMOUT_PM,((trips.trip_mode == 'KNR_PRM') & is_pm) * tour_participants * outbound +,KNR_MIXOUT_PM,((trips.trip_mode == 'KNR_MIX') & is_pm) * tour_participants * outbound +,TNC_LOCOUT_PM,((trips.trip_mode == 'TNC_LOC') & is_pm) * tour_participants * outbound +,TNC_PRMOUT_PM,((trips.trip_mode == 'TNC_PRM') & is_pm) * tour_participants * outbound +,TNC_MIXOUT_PM,((trips.trip_mode == 'TNC_MIX') & is_pm) * tour_participants * outbound +,PNR_LOCIN_PM,((trips.trip_mode == 'PNR_LOC') & is_pm) * tour_participants * inbound +,PNR_PRMIN_PM,((trips.trip_mode == 'PNR_PRM') & is_pm) * tour_participants * inbound +,PNR_MIXIN_PM,((trips.trip_mode == 'PNR_MIX') & is_pm) * tour_participants * inbound +,KNR_LOCIN_PM,((trips.trip_mode == 'KNR_LOC') & is_pm) * tour_participants * inbound +,KNR_PRMIN_PM,((trips.trip_mode == 'KNR_PRM') & is_pm) * tour_participants * inbound +,KNR_MIXIN_PM,((trips.trip_mode == 'KNR_MIX') & is_pm) * tour_participants * inbound +,TNC_LOCIN_PM,((trips.trip_mode == 'TNC_LOC') & is_pm) * tour_participants * inbound +,TNC_PRMIN_PM,((trips.trip_mode == 'TNC_PRM') & is_pm) * tour_participants * inbound +,TNC_MIXIN_PM,((trips.trip_mode == 'TNC_MIX') & is_pm) * tour_participants * inbound +,BIKE_PM,((trips.trip_mode == 'BIKE') & is_pm) * tour_participants +,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants +# ev trips,, +,DRIVEALONE_NOTR_EV_LOW,((trips.trip_mode == 'DRIVEALONE') & is_ev & ~ownTrp & vot1) * tour_participants +,SHARED2_NOTR_EV_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_ev * ~ownTrp * vot1 * tour_participants +,SHARED3_NOTR_EV_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_ev * ~ownTrp * vot1 * tour_participants +,DRIVEALONE_TR_EV_LOW,((trips.trip_mode == 'DRIVEALONE') & is_ev & ownTrp & vot1) * tour_participants +,SHARED2_TR_EV_LOW,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_ev * ownTrp * vot1 * tour_participants +,SHARED3_TR_EV_LOW,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_ev * ownTrp * vot1 * tour_participants +,DRIVEALONE_NOTR_EV_MED,((trips.trip_mode == 'DRIVEALONE') & is_ev & ~ownTrp & vot2) * tour_participants +,SHARED2_NOTR_EV_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_ev * ~ownTrp * vot2 * tour_participants +,SHARED3_NOTR_EV_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_ev * ~ownTrp * vot2 * tour_participants +,DRIVEALONE_TR_EV_MED,((trips.trip_mode == 'DRIVEALONE') & is_ev & ownTrp & vot2) * tour_participants +,SHARED2_TR_EV_MED,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_ev * ownTrp * vot2 * tour_participants +,SHARED3_TR_EV_MED,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_ev * ownTrp * vot2 * tour_participants +,DRIVEALONE_NOTR_EV_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_ev & ~ownTrp & vot3) * tour_participants +,SHARED2_NOTR_EV_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_ev * ~ownTrp * vot3 * tour_participants +,SHARED3_NOTR_EV_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_ev * ~ownTrp * vot3 * tour_participants +,DRIVEALONE_TR_EV_HIGH,((trips.trip_mode == 'DRIVEALONE') & is_ev & ownTrp & vot3) * tour_participants +,SHARED2_TR_EV_HIGH,(trips.trip_mode.isin(['SHARED2'])/OCC_SHARED2+trips.trip_mode.isin(['TAXI'])/OCC_TAXI+trips.trip_mode.isin(['TNC_SINGLE'])/OCC_TNC_SINGLE)* is_ev * ownTrp * vot3 * tour_participants +,SHARED3_TR_EV_HIGH,(trips.trip_mode.isin(['SHARED3'])/OCC_SHARED3+trips.trip_mode.isin(['TNC_SHARED'])/OCC_TNC_SHARED)* is_ev * ownTrp * vot3 * tour_participants +,WALK_LOC_EV,((trips.trip_mode == 'WALK_LOC') & is_ev) * tour_participants +,WALK_PRM_EV,((trips.trip_mode == 'WALK_PRM') & is_ev) * tour_participants +,WALK_MIX_EV,((trips.trip_mode == 'WALK_MIX') & is_ev) * tour_participants +,PNR_LOCOUT_EV,((trips.trip_mode == 'PNR_LOC') & is_ev) * tour_participants * outbound +,PNR_PRMOUT_EV,((trips.trip_mode == 'PNR_PRM') & is_ev) * tour_participants * outbound +,PNR_MIXOUT_EV,((trips.trip_mode == 'PNR_MIX') & is_ev) * tour_participants * outbound +,KNR_LOCOUT_EV,((trips.trip_mode == 'KNR_LOC') & is_ev) * tour_participants * outbound +,KNR_PRMOUT_EV,((trips.trip_mode == 'KNR_PRM') & is_ev) * tour_participants * outbound +,KNR_MIXOUT_EV,((trips.trip_mode == 'KNR_MIX') & is_ev) * tour_participants * outbound +,TNC_LOCOUT_EV,((trips.trip_mode == 'TNC_LOC') & is_ev) * tour_participants * outbound +,TNC_PRMOUT_EV,((trips.trip_mode == 'TNC_PRM') & is_ev) * tour_participants * outbound +,TNC_MIXOUT_EV,((trips.trip_mode == 'TNC_MIX') & is_ev) * tour_participants * outbound +,PNR_LOCIN_EV,((trips.trip_mode == 'PNR_LOC') & is_ev) * tour_participants * inbound +,PNR_PRMIN_EV,((trips.trip_mode == 'PNR_PRM') & is_ev) * tour_participants * inbound +,PNR_MIXIN_EV,((trips.trip_mode == 'PNR_MIX') & is_ev) * tour_participants * inbound +,KNR_LOCIN_EV,((trips.trip_mode == 'KNR_LOC') & is_ev) * tour_participants * inbound +,KNR_PRMIN_EV,((trips.trip_mode == 'KNR_PRM') & is_ev) * tour_participants * inbound +,KNR_MIXIN_EV,((trips.trip_mode == 'KNR_MIX') & is_ev) * tour_participants * inbound +,TNC_LOCIN_EV,((trips.trip_mode == 'TNC_LOC') & is_ev) * tour_participants * inbound +,TNC_PRMIN_EV,((trips.trip_mode == 'TNC_PRM') & is_ev) * tour_participants * inbound +,TNC_MIXIN_EV,((trips.trip_mode == 'TNC_MIX') & is_ev) * tour_participants * inbound +,BIKE_EV,((trips.trip_mode == 'BIKE') & is_ev) * tour_participants +,WALK_EV,((trips.trip_mode == 'WALK') & is_ev) * tour_participants +#auto skims,, +,_timeDrive,"odt_skims['SOV_NT_L_TIME'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ~ownTrp & vot1),1,0)" +,_timeDrive,"_timeDrive + odt_skims['SOV_TR_L_TIME'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ownTrp & vot1),1,0)" +,_timeDrive,"_timeDrive + odt_skims['HOV2_L_TIME'] * np.where(((trips.trip_mode == 'SHARED2') & vot1),1,0)" +,_timeDrive,"_timeDrive + odt_skims['HOV3_L_TIME'] * np.where(((trips.trip_mode == 'SHARED3') & vot1),1,0)" +,_timeDrive,"_timeDrive + odt_skims['SOV_NT_M_TIME'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ~ownTrp & vot2),1,0)" +,_timeDrive,"_timeDrive + odt_skims['SOV_TR_M_TIME'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ownTrp & vot2),1,0)" +,_timeDrive,"_timeDrive + odt_skims['HOV2_M_TIME'] * np.where(((trips.trip_mode == 'SHARED2') & vot2),1,0)" +#,_timeDrive,"_timeDrive + (odt_skims['HOV2_M_TIME'] + _TAXI_WAIT_TIME) * np.where((trips.trip_mode == 'TAXI'),1,0)" +#,_timeDrive,"_timeDrive + (odt_skims['HOV2_M_TIME'] + _SINGLE_TNC_WAIT_TIME) * np.where((trips.trip_mode == 'TNC_SINGLE'),1,0)" +,_timeDrive,"_timeDrive + odt_skims['HOV3_M_TIME'] * np.where(((trips.trip_mode == 'SHARED3') & vot2),1,0)" +#,_timeDrive,"_timeDrive + (odt_skims['HOV3_M_TIME'] + _SHARED_TNC_WAIT_TIME) * np.where((trips.trip_mode == 'TNC_SHARED'),1,0) * _TNC_SHARED_IVT_FACTOR" +,_timeDrive,"_timeDrive + odt_skims['SOV_NT_H_TIME'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ~ownTrp & vot3),1,0)" +,_timeDrive,"_timeDrive + odt_skims['SOV_TR_H_TIME'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ownTrp & vot3),1,0)" +,_timeDrive,"_timeDrive + odt_skims['HOV2_H_TIME'] * np.where(((trips.trip_mode == 'SHARED2') & vot3),1,0)" +,timeDrive,"_timeDrive + odt_skims['HOV3_H_TIME'] * np.where(((trips.trip_mode == 'SHARED3') & vot3),1,0)" +,_distanceDrive,"odt_skims['SOV_NT_L_DIST'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ~ownTrp & vot1),1,0)" +,_distanceDrive,"_distanceDrive + odt_skims['SOV_TR_L_DIST'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ownTrp & vot1),1,0)" +,_distanceDrive,"_distanceDrive + odt_skims['HOV2_L_DIST'] * np.where(((trips.trip_mode == 'SHARED2') & vot1),1,0)" +,_distanceDrive,"_distanceDrive + odt_skims['HOV3_L_DIST'] * np.where(((trips.trip_mode == 'SHARED3') & vot1),1,0)" +,_distanceDrive,"_distanceDrive + odt_skims['SOV_NT_M_DIST'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ~ownTrp & vot2),1,0)" +,_distanceDrive,"_distanceDrive + odt_skims['SOV_TR_M_DIST'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ownTrp & vot2),1,0)" +,_distanceDrive,"_distanceDrive + odt_skims['HOV2_M_DIST'] * np.where(((trips.trip_mode == 'SHARED2') & vot2),1,0)" +,_distanceDrive,"_distanceDrive + odt_skims['HOV3_M_DIST'] * np.where(((trips.trip_mode == 'SHARED3') & vot2),1,0)" +,_distanceDrive,"_distanceDrive + odt_skims['SOV_NT_H_DIST'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ~ownTrp & vot3),1,0)" +,_distanceDrive,"_distanceDrive + odt_skims['SOV_TR_H_DIST'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ownTrp & vot3),1,0)" +,_distanceDrive,"_distanceDrive + odt_skims['HOV2_H_DIST'] * np.where(((trips.trip_mode == 'SHARED2') & vot3),1,0)" +,distanceDrive,"_distanceDrive + odt_skims['HOV3_H_DIST'] * np.where(((trips.trip_mode == 'SHARED3') & vot3),1,0)" +,_costTollDrive,"odt_skims['SOV_NT_L_TOLLCOST'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ~ownTrp & vot1),1,0)" +,_costTollDrive,"_costTollDrive + odt_skims['SOV_TR_L_TOLLCOST'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ownTrp & vot1),1,0)" +,_costTollDrive,"_costTollDrive + odt_skims['HOV2_L_TOLLCOST'] * np.where(((trips.trip_mode == 'SHARED2') & vot1),1,0)" +,_costTollDrive,"_costTollDrive + odt_skims['HOV3_L_TOLLCOST'] * np.where(((trips.trip_mode == 'SHARED3') & vot1),1,0)" +,_costTollDrive,"_costTollDrive + odt_skims['SOV_NT_M_TOLLCOST'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ~ownTrp & vot2),1,0)" +,_costTollDrive,"_costTollDrive + odt_skims['SOV_TR_M_TOLLCOST'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ownTrp & vot2),1,0)" +,_costTollDrive,"_costTollDrive + odt_skims['HOV2_M_TOLLCOST'] * np.where(((trips.trip_mode == 'SHARED2') & vot2),1,0)" +,_costTollDrive,"_costTollDrive + odt_skims['HOV3_M_TOLLCOST'] * np.where(((trips.trip_mode == 'SHARED3') & vot2),1,0)" +,_costTollDrive,"_costTollDrive + odt_skims['SOV_NT_H_TOLLCOST'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ~ownTrp & vot3),1,0)" +,_costTollDrive,"_costTollDrive + odt_skims['SOV_TR_H_TOLLCOST'] * np.where(((trips.trip_mode == 'DRIVEALONE') & ownTrp & vot3),1,0)" +,_costTollDrive,"_costTollDrive + odt_skims['HOV2_H_TOLLCOST'] * np.where(((trips.trip_mode == 'SHARED2') & vot3),1,0)" +,costTollDrive,"_costTollDrive + odt_skims['HOV3_H_TOLLCOST'] * np.where(((trips.trip_mode == 'SHARED3') & vot3),1,0)" +#transit skims,, +,_timeTransitInVehicle,"odt_skims['WALK_LOC_TOTALIVTT'] * np.where(trips.trip_mode == 'WALK_LOC',1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['WALK_PRM_TOTALIVTT'] * np.where(trips.trip_mode == 'WALK_PRM',1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['WALK_MIX_TOTALIVTT'] * np.where(trips.trip_mode == 'WALK_MIX',1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['PNROUT_LOC_TOTALIVTT'] * np.where(trips.trip_mode == 'PNR_LOC',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['PNROUT_PRM_TOTALIVTT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['PNROUT_MIX_TOTALIVTT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['KNROUT_LOC_TOTALIVTT'] * np.where(trips.trip_mode == 'KNR_LOC',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['KNROUT_PRM_TOTALIVTT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['KNROUT_MIX_TOTALIVTT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['TNCOUT_LOC_TOTALIVTT'] * np.where(trips.trip_mode == 'TNC_LOC',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['TNCOUT_PRM_TOTALIVTT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['TNCOUT_MIX_TOTALIVTT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['PNRIN_LOC_TOTALIVTT'] * np.where(trips.trip_mode == 'PNR_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['PNRIN_PRM_TOTALIVTT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['PNRIN_MIX_TOTALIVTT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['KNRIN_LOC_TOTALIVTT'] * np.where(trips.trip_mode == 'KNR_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['KNRIN_PRM_TOTALIVTT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['KNRIN_MIX_TOTALIVTT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['TNCIN_LOC_TOTALIVTT'] * np.where(trips.trip_mode == 'TNC_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['TNCIN_PRM_TOTALIVTT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(~trips.outbound,1,0)" +,timeTransitInVehicle,"_timeTransitInVehicle + odt_skims['TNCIN_MIX_TOTALIVTT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(~trips.outbound,1,0)" +#,, +,_timeRapidTransitInVehicle,"odt_skims['WALK_PRM_BRTIVTT'] * np.where(trips.trip_mode == 'WALK_PRM',1,0)" +,_timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['WALK_MIX_BRTIVTT'] * np.where(trips.trip_mode == 'WALK_MIX',1,0)" +,_timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['PNROUT_PRM_BRTIVTT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['PNROUT_MIX_BRTIVTT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['KNROUT_PRM_BRTIVTT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['KNROUT_MIX_BRTIVTT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['TNCOUT_PRM_BRTIVTT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['TNCOUT_MIX_BRTIVTT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['PNRIN_PRM_BRTIVTT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['PNRIN_MIX_BRTIVTT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['KNRIN_PRM_BRTIVTT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['KNRIN_MIX_BRTIVTT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['TNCIN_PRM_BRTIVTT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(~trips.outbound,1,0)" +,timeRapidTransitInVehicle,"_timeRapidTransitInVehicle + odt_skims['TNCIN_MIX_BRTIVTT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(~trips.outbound,1,0)" +#,, +,_timeExpressBusTransitInVehicle,"odt_skims['WALK_PRM_EXPIVTT'] * np.where(trips.trip_mode == 'WALK_PRM',1,0)" +,_timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['WALK_MIX_EXPIVTT'] * np.where(trips.trip_mode == 'WALK_MIX',1,0)" +,_timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['PNROUT_PRM_EXPIVTT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['PNROUT_MIX_EXPIVTT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['KNROUT_PRM_EXPIVTT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['KNROUT_MIX_EXPIVTT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['TNCOUT_PRM_EXPIVTT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['TNCOUT_MIX_EXPIVTT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['PNRIN_PRM_EXPIVTT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['PNRIN_MIX_EXPIVTT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['KNRIN_PRM_EXPIVTT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['KNRIN_MIX_EXPIVTT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['TNCIN_PRM_EXPIVTT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(~trips.outbound,1,0)" +,timeExpressBusTransitInVehicle,"_timeExpressBusTransitInVehicle + odt_skims['TNCIN_MIX_EXPIVTT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(~trips.outbound,1,0)" +#,, +,_timeLocalBusTransitInVehicle,"odt_skims['WALK_LOC_BUSIVTT'] * np.where(trips.trip_mode == 'WALK_LOC',1,0)" +,_timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['WALK_MIX_BUSIVTT'] * np.where(trips.trip_mode == 'WALK_MIX',1,0)" +,_timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['PNROUT_LOC_BUSIVTT'] * np.where(trips.trip_mode == 'PNR_LOC',1,0) * np.where(trips.outbound,1,0)" +,_timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['PNROUT_MIX_BUSIVTT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['KNROUT_LOC_BUSIVTT'] * np.where(trips.trip_mode == 'KNR_LOC',1,0) * np.where(trips.outbound,1,0)" +,_timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['KNROUT_MIX_BUSIVTT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['TNCOUT_LOC_BUSIVTT'] * np.where(trips.trip_mode == 'TNC_LOC',1,0) * np.where(trips.outbound,1,0)" +,_timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['TNCOUT_MIX_BUSIVTT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['PNRIN_LOC_BUSIVTT'] * np.where(trips.trip_mode == 'PNR_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['PNRIN_MIX_BUSIVTT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['KNRIN_LOC_BUSIVTT'] * np.where(trips.trip_mode == 'KNR_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['KNRIN_MIX_BUSIVTT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['TNCIN_LOC_BUSIVTT'] * np.where(trips.trip_mode == 'TNC_LOC',1,0) * np.where(~trips.outbound,1,0)" +,timeLocalBusTransitInVehicle,"_timeLocalBusTransitInVehicle + odt_skims['TNCIN_MIX_BUSIVTT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(~trips.outbound,1,0)" +#,, +,_timeLightRailTransitInVehicle,"odt_skims['WALK_PRM_LRTIVTT'] * np.where(trips.trip_mode == 'WALK_PRM',1,0)" +,_timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['WALK_MIX_LRTIVTT'] * np.where(trips.trip_mode == 'WALK_MIX',1,0)" +,_timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['PNROUT_PRM_LRTIVTT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['PNROUT_MIX_LRTIVTT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['KNROUT_PRM_LRTIVTT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['KNROUT_MIX_LRTIVTT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['TNCOUT_PRM_LRTIVTT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['TNCOUT_MIX_LRTIVTT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['PNRIN_PRM_LRTIVTT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['PNRIN_MIX_LRTIVTT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['KNRIN_PRM_LRTIVTT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['KNRIN_MIX_LRTIVTT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['TNCIN_PRM_LRTIVTT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(~trips.outbound,1,0)" +,timeLightRailTransitInVehicle,"_timeLightRailTransitInVehicle + odt_skims['TNCIN_MIX_LRTIVTT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(~trips.outbound,1,0)" +#,, +,_timeCommuterRailTransitInVehicle,"odt_skims['WALK_PRM_CMRIVTT'] * np.where(trips.trip_mode == 'WALK_PRM',1,0)" +,_timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['WALK_MIX_CMRIVTT'] * np.where(trips.trip_mode == 'WALK_MIX',1,0)" +,_timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['PNROUT_PRM_CMRIVTT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['PNROUT_MIX_CMRIVTT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['KNROUT_PRM_CMRIVTT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['KNROUT_MIX_CMRIVTT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['TNCOUT_PRM_CMRIVTT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['TNCOUT_MIX_CMRIVTT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['PNRIN_PRM_CMRIVTT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['PNRIN_MIX_CMRIVTT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['KNRIN_PRM_CMRIVTT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['KNRIN_MIX_CMRIVTT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['TNCIN_PRM_CMRIVTT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(~trips.outbound,1,0)" +,timeCommuterRailTransitInVehicle,"_timeCommuterRailTransitInVehicle + odt_skims['TNCIN_MIX_CMRIVTT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(~trips.outbound,1,0)" +#,, +,_timeTransitInitialWait,"odt_skims['WALK_LOC_FIRSTWAIT'] * np.where(trips.trip_mode == 'WALK_LOC',1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['WALK_PRM_FIRSTWAIT'] * np.where(trips.trip_mode == 'WALK_PRM',1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['WALK_MIX_FIRSTWAIT'] * np.where(trips.trip_mode == 'WALK_MIX',1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['PNROUT_LOC_FIRSTWAIT'] * np.where(trips.trip_mode == 'PNR_LOC',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['PNROUT_PRM_FIRSTWAIT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['PNROUT_MIX_FIRSTWAIT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['KNROUT_LOC_FIRSTWAIT'] * np.where(trips.trip_mode == 'KNR_LOC',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['KNROUT_PRM_FIRSTWAIT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['KNROUT_MIX_FIRSTWAIT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['TNCOUT_LOC_FIRSTWAIT'] * np.where(trips.trip_mode == 'TNC_LOC',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['TNCOUT_PRM_FIRSTWAIT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['TNCOUT_MIX_FIRSTWAIT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['PNRIN_LOC_FIRSTWAIT'] * np.where(trips.trip_mode == 'PNR_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['PNRIN_PRM_FIRSTWAIT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['PNRIN_MIX_FIRSTWAIT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['KNRIN_LOC_FIRSTWAIT'] * np.where(trips.trip_mode == 'KNR_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['KNRIN_PRM_FIRSTWAIT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['KNRIN_MIX_FIRSTWAIT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['TNCIN_LOC_FIRSTWAIT'] * np.where(trips.trip_mode == 'TNC_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['TNCIN_PRM_FIRSTWAIT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(~trips.outbound,1,0)" +,timeTransitInitialWait,"_timeTransitInitialWait + odt_skims['TNCIN_MIX_FIRSTWAIT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(~trips.outbound,1,0)" +#,, +,_costFareTransit,"odt_skims['WALK_LOC_FARE'] * np.where(trips.trip_mode == 'WALK_LOC',1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['WALK_PRM_FARE'] * np.where(trips.trip_mode == 'WALK_PRM',1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['WALK_MIX_FARE'] * np.where(trips.trip_mode == 'WALK_MIX',1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['PNROUT_LOC_FARE'] * np.where(trips.trip_mode == 'PNR_LOC',1,0) * np.where(trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['PNROUT_PRM_FARE'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['PNROUT_MIX_FARE'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['KNROUT_LOC_FARE'] * np.where(trips.trip_mode == 'KNR_LOC',1,0) * np.where(trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['KNROUT_PRM_FARE'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['KNROUT_MIX_FARE'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['TNCOUT_LOC_FARE'] * np.where(trips.trip_mode == 'TNC_LOC',1,0) * np.where(trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['TNCOUT_PRM_FARE'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['TNCOUT_MIX_FARE'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['PNRIN_LOC_FARE'] * np.where(trips.trip_mode == 'PNR_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['PNRIN_PRM_FARE'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['PNRIN_MIX_FARE'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['KNRIN_LOC_FARE'] * np.where(trips.trip_mode == 'KNR_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['KNRIN_PRM_FARE'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['KNRIN_MIX_FARE'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['TNCIN_LOC_FARE'] * np.where(trips.trip_mode == 'TNC_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_costFareTransit,"_costFareTransit + odt_skims['TNCIN_PRM_FARE'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(~trips.outbound,1,0)" +,costFareTransit,"_costFareTransit + odt_skims['TNCIN_MIX_FARE'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(~trips.outbound,1,0)" +#,, +,_transfersTransit,"odt_skims['WALK_LOC_XFERS'] * np.where(trips.trip_mode == 'WALK_LOC',1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['WALK_PRM_XFERS'] * np.where(trips.trip_mode == 'WALK_PRM',1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['WALK_MIX_XFERS'] * np.where(trips.trip_mode == 'WALK_MIX',1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['PNROUT_LOC_XFERS'] * np.where(trips.trip_mode == 'PNR_LOC',1,0) * np.where(trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['PNROUT_PRM_XFERS'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['PNROUT_MIX_XFERS'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['KNROUT_LOC_XFERS'] * np.where(trips.trip_mode == 'KNR_LOC',1,0) * np.where(trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['KNROUT_PRM_XFERS'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['KNROUT_MIX_XFERS'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['TNCOUT_LOC_XFERS'] * np.where(trips.trip_mode == 'TNC_LOC',1,0) * np.where(trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['TNCOUT_PRM_XFERS'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['TNCOUT_MIX_XFERS'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['PNRIN_LOC_XFERS'] * np.where(trips.trip_mode == 'PNR_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['PNRIN_PRM_XFERS'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['PNRIN_MIX_XFERS'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['KNRIN_LOC_XFERS'] * np.where(trips.trip_mode == 'KNR_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['KNRIN_PRM_XFERS'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['KNRIN_MIX_XFERS'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['TNCIN_LOC_XFERS'] * np.where(trips.trip_mode == 'TNC_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_transfersTransit,"_transfersTransit + odt_skims['TNCIN_PRM_XFERS'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(~trips.outbound,1,0)" +,transfersTransit,"_transfersTransit + odt_skims['TNCIN_MIX_XFERS'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(~trips.outbound,1,0)" +#bikeskim,, +,timeBike,"od_skims['BIKE_TIME'] * np.where(trips.trip_mode == 'BIKE',1,0)" +,timeWalk,"od_skims['walkTime'] * np.where(trips.trip_mode == 'WALK',1,0)" +# totals,, +,cost_total, costOperatingDrive + costTollDrive + costFareTransit +,time_total, timeDrive + timeBike + timeWalk + timeTransitInitialWait + timeTransitInVehicle + transfersTransit +#,, +,_time_transit_wait,"odt_skims['WALK_LOC_XFERWAIT'] * np.where(trips.trip_mode == 'WALK_LOC',1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['WALK_PRM_XFERWAIT'] * np.where(trips.trip_mode == 'WALK_PRM',1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['WALK_MIX_XFERWAIT'] * np.where(trips.trip_mode == 'WALK_MIX',1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['PNROUT_LOC_XFERWAIT'] * np.where(trips.trip_mode == 'PNR_LOC',1,0) * np.where(trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['PNROUT_PRM_XFERWAIT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['PNROUT_MIX_XFERWAIT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['KNROUT_LOC_XFERWAIT'] * np.where(trips.trip_mode == 'KNR_LOC',1,0) * np.where(trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['KNROUT_PRM_XFERWAIT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['KNROUT_MIX_XFERWAIT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['TNCOUT_LOC_XFERWAIT'] * np.where(trips.trip_mode == 'TNC_LOC',1,0) * np.where(trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['TNCOUT_PRM_XFERWAIT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['TNCOUT_MIX_XFERWAIT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['PNRIN_LOC_XFERWAIT'] * np.where(trips.trip_mode == 'PNR_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['PNRIN_PRM_XFERWAIT'] * np.where(trips.trip_mode == 'PNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['PNRIN_MIX_XFERWAIT'] * np.where(trips.trip_mode == 'PNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['KNRIN_LOC_XFERWAIT'] * np.where(trips.trip_mode == 'KNR_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['KNRIN_PRM_XFERWAIT'] * np.where(trips.trip_mode == 'KNR_PRM',1,0) * np.where(~trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['KNRIN_MIX_XFERWAIT'] * np.where(trips.trip_mode == 'KNR_MIX',1,0) * np.where(~trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['TNCIN_LOC_XFERWAIT'] * np.where(trips.trip_mode == 'TNC_LOC',1,0) * np.where(~trips.outbound,1,0)" +,_time_transit_wait,"_time_transit_wait + odt_skims['TNCIN_PRM_XFERWAIT'] * np.where(trips.trip_mode == 'TNC_PRM',1,0) * np.where(~trips.outbound,1,0)" +,time_transit_wait,"_time_transit_wait + odt_skims['TNCIN_MIX_XFERWAIT'] * np.where(trips.trip_mode == 'TNC_MIX',1,0) * np.where(~trips.outbound,1,0)" +#,, +,time_total,timeDrive+timeBike+timeWalk+timeTransitInitialWait+timeTransitInVehicle+time_transit_wait+df.time_mm+df.time_wait_drive +,cost_total,costOperatingDrive+costTollDrive+costFareTransit+df.cost_parking+df.cost_fare_drive+df.cost_fare_mm +#,, +,value_of_time_category_id,"np.where(vot1,1,np.where(vot2,2,3))" +#,, +,origin_micro_prm_dist,"reindex(land_use.micro_dist_premium_transit, trips.origin)" +,dest_micro_prm_dist,"reindex(land_use.micro_dist_premium_transit, trips.destination)" +microtransit available at origin,microtransit_orig,"reindex(land_use.microtransit, trips.origin)" +microtransit available at destination,microtransit_dest,"reindex(land_use.microtransit, trips.destination)" +microtransit available,microtransit_available,(microtransit_orig > 0) & (microtransit_orig == microtransit_dest) & (od_skims['DIST'] < microtransitMaxDist) +nev available at origin,nev_orig,"reindex(land_use.nev, trips.origin)" +nev available at destination,nev_dest,"reindex(land_use.nev, trips.destination)" +nev available,nev_available,(nev_orig > 0) & (nev_orig == nev_dest) & (od_skims['DIST'] < nevMaxDist) +outbound microtransit access available,microtransit_access_available_out,trips.outbound & (microtransit_orig>0) & (origin_micro_prm_dist0) & (origin_micro_prm_dist0) & (dest_micro_prm_dist>maxWalkIfMTAccessAvailable) & (dest_micro_prm_dist0) & (dest_micro_prm_dist>maxWalkIfMTAccessAvailable) & (dest_micro_prm_dist0) & (origin_micro_prm_dist>maxWalkIfMTAccessAvailable) & (origin_micro_prm_dist0) & (origin_micro_prm_dist>maxWalkIfMTAccessAvailable) & (origin_micro_prm_dist0) & (dest_micro_prm_dist0) & (dest_micro_prm_dist 1) & (trips.trip_num==1) & (trips.outbound), 'home', origin_purpose)" +,origin_purpose,"np.where(((trips.trip_count > 1) & (trips.trip_num>1)) | ((trips.trip_count > 1) & (trips.trip_num==1) & ~(trips.outbound)), trips['purpose'].shift(1), origin_purpose)" diff --git a/data/households.csv b/data/households.csv new file mode 100644 index 0000000..9a9f8c0 --- /dev/null +++ b/data/households.csv @@ -0,0 +1,55270 @@ +hhid,household_serial_no,taz,mgra,hinccat1,hinc,hworkers,veh,persons,hht,bldgsz,unittype,version,poverty +479044,0,1502,5721,5,188475,2,4,2,1,2,0,0,10.93242459 +479045,0,1502,5721,1,13031,0,1,2,5,9,0,0,0.755899072 +479046,0,1502,5721,1,10770,0,1,1,6,4,0,0,0.844043887 +479047,0,1502,5721,1,26925,0,1,1,6,9,0,0,2.110109718 +479048,0,1502,5721,3,94776,0,2,1,6,3,0,0,7.427586207 +479049,0,1502,5721,2,53850,0,0,1,4,7,0,0,4.220219436 +479050,0,1502,5721,3,68497,0,2,2,5,5,0,0,3.973155452 +479051,0,1502,5721,2,57081,0,1,2,1,5,0,0,3.310962877 +479052,0,1502,5721,4,109423,0,2,2,1,2,0,0,6.347053364 +479053,0,1502,5721,5,586965,2,2,2,1,2,0,0,34.04669374 +505740,0,1448,24282,1,10770,0,1,1,4,2,0,0,0.844043887 +505741,0,1448,24282,1,21249,0,2,1,6,5,0,0,1.665298589 +505742,0,1448,24282,4,107700,2,2,2,1,2,0,0,6.247099768 +505743,0,1470,24283,1,5385,0,1,1,4,2,0,0,0.422021944 +505744,0,1470,24283,1,24340,1,1,2,3,6,0,0,1.411844548 +505745,0,1470,24283,1,10770,0,2,2,5,2,0,0,0.624709977 +505746,0,1470,24283,1,25632,0,2,1,4,7,0,0,2.008824451 +784481,0,1797,2,5,296175,2,2,2,1,9,0,0,17.17952436 +784482,0,1797,2,5,296175,2,2,2,1,9,0,0,17.17952436 +784483,0,1797,2,5,296175,2,2,2,1,9,0,0,17.17952436 +784484,0,1797,2,4,144156,0,1,1,4,6,0,0,11.29752743 +784485,0,1797,2,3,64835,0,1,1,6,9,0,0,5.081144201 +784486,0,1797,2,3,91545,0,1,1,4,9,0,0,7.174373041 +784487,0,1797,2,5,617121,0,2,2,5,9,0,0,35.79588167 +784488,0,1797,2,4,124932,2,1,3,1,9,0,0,5.751933702 +784489,0,1797,2,4,124932,2,1,3,1,9,0,0,5.751933702 +784490,0,1797,2,4,107700,0,1,1,6,4,0,0,8.440438871 +784491,0,1797,2,1,15767,0,0,1,4,9,0,0,1.235680251 +784492,0,1797,2,1,4631,0,0,1,6,9,0,0,0.362938871 +784493,0,1797,2,3,97683,0,1,1,6,9,0,0,7.655478056 +784494,0,1797,2,2,38987,0,0,1,4,9,0,0,3.055438871 +784495,0,1797,2,1,0,0,1,1,4,2,0,0,0 +784496,0,1797,2,5,306945,0,1,1,4,3,0,0,24.05525078 +784497,0,1797,2,2,34356,0,1,1,6,7,0,0,2.6925 +784498,0,1797,2,5,285405,2,2,2,1,2,0,0,16.55481439 +784499,0,1797,2,1,17232,0,1,1,6,2,0,0,1.350470219 +784500,0,1797,2,1,24232,0,1,1,6,4,0,0,1.899098746 +784501,0,1797,2,5,165858,0,1,1,4,2,0,0,12.99827586 +784502,0,1797,2,1,0,0,0,1,6,9,0,0,0 +784503,0,1797,2,1,12062,0,0,1,6,9,0,0,0.945329154 +784504,0,1797,2,1,20463,0,1,1,6,9,0,0,1.603683386 +784505,0,1797,2,1,20463,0,1,1,6,9,0,0,1.603683386 +784506,0,1797,2,4,107700,0,1,1,6,8,0,0,8.440438871 +784507,0,1797,2,2,38772,0,1,1,6,9,0,0,3.038557994 +784508,0,1797,2,4,107700,0,1,1,4,4,0,0,8.440438871 +784509,0,1797,2,4,107700,0,1,1,4,4,0,0,8.440438871 +784510,0,1797,2,3,86160,0,1,1,4,7,0,0,6.752351097 +784511,0,1797,2,3,94237,2,2,2,1,2,0,0,5.466212297 +784512,0,1797,2,5,202476,2,2,2,1,9,0,0,11.74454756 +784513,0,1797,2,1,26063,0,0,1,6,9,0,0,2.042586207 +784514,0,1797,2,3,66698,0,1,1,6,3,0,0,5.227163793 +784515,0,1797,2,2,33171,0,1,1,6,7,0,0,2.599655172 +784516,0,1797,2,5,250294,0,1,1,4,9,0,0,19.61557994 +784517,0,1797,2,3,63866,0,0,1,4,9,0,0,5.005180251 +784518,0,1797,2,2,51157,0,1,1,4,9,0,0,4.009208464 +784519,0,1797,2,2,33063,0,0,1,4,9,0,0,2.591214734 +784520,0,1797,2,5,484650,0,1,1,4,7,0,0,37.98197492 +784521,0,1797,2,1,3231,0,2,2,2,6,0,0,0.187412993 +784522,0,1797,2,1,3231,0,2,2,2,6,0,0,0.187412993 +784523,0,1797,2,1,3231,0,2,2,2,6,0,0,0.187412993 +784524,0,1797,2,5,214323,0,1,1,4,9,0,0,16.79647335 +784525,0,1797,2,2,50619,0,2,1,6,9,0,0,3.96700627 +784526,0,1797,2,4,140010,0,1,1,6,5,0,0,10.97257053 +784527,0,1797,2,5,241248,2,3,3,1,2,0,0,11.10718232 +784528,0,1797,2,5,193860,0,1,1,4,9,0,0,15.19278997 +784529,0,1629,12,3,95853,0,1,1,6,6,0,0,7.511990596 +784530,0,1629,12,3,80775,1,2,2,1,2,0,0,4.685324826 +784531,0,1629,12,5,254172,0,2,2,1,2,0,0,14.74315545 +784532,0,1629,12,5,161550,1,1,2,1,2,0,0,9.370649652 +784578,0,1601,19,2,43080,1,2,2,1,2,0,0,2.498839907 +784579,0,1601,19,3,62789,0,0,1,6,9,0,0,4.920775862 +784580,0,1601,19,1,17339,0,0,1,6,9,0,0,1.358910658 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-0,0 +1,6 @@ +from . import av_ownership +from . import external_identification +from . import external_location_choice +from . import transponder_ownership +from . import airport_returns +from . import adjust_auto_operating_cost diff --git a/extensions/__pycache__/__init__.cpython-39.pyc b/extensions/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000..d4d54a8 Binary files /dev/null and b/extensions/__pycache__/__init__.cpython-39.pyc differ diff --git a/extensions/__pycache__/adjust_auto_operating_cost.cpython-39.pyc b/extensions/__pycache__/adjust_auto_operating_cost.cpython-39.pyc new file mode 100644 index 0000000..58b86eb Binary files /dev/null and b/extensions/__pycache__/adjust_auto_operating_cost.cpython-39.pyc differ diff --git a/extensions/__pycache__/airport_returns.cpython-39.pyc b/extensions/__pycache__/airport_returns.cpython-39.pyc new file mode 100644 index 0000000..bcacad9 Binary files /dev/null and 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a/extensions/adjust_auto_operating_cost.py b/extensions/adjust_auto_operating_cost.py new file mode 100644 index 0000000..2c924f2 --- /dev/null +++ b/extensions/adjust_auto_operating_cost.py @@ -0,0 +1,38 @@ +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import ( + config, + inject, + pipeline, +) + +logger = logging.getLogger(__name__) + + +@inject.step() +def adjust_auto_operating_cost(vehicles): + """Adjusts the `auto_operating_cost` field in the vehicles table + so that the average is a desired value set as costPerMile in the + settings + + Parameters + ---------- + vehicles : orca.DataFrameWrapper + """ + target_auto_operating_cost = config.get_global_constants()["costPerMile"] + vehicles = vehicles.to_frame() + + adjustment_factor = ( + target_auto_operating_cost / vehicles["auto_operating_cost"].mean() + ) + logger.info( + "Adjusting auto operating costs in vehicles table by a factor of {}".format( + adjustment_factor + ) + ) + vehicles["auto_operating_cost"] *= adjustment_factor + + pipeline.replace_table("vehicles", vehicles) diff --git a/extensions/airport_returns.py b/extensions/airport_returns.py new file mode 100644 index 0000000..ac195cb --- /dev/null +++ b/extensions/airport_returns.py @@ -0,0 +1,62 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np + +from activitysim.core import tracing +from activitysim.core import config +from activitysim.core import pipeline +from activitysim.core import simulate +from activitysim.core import inject +from activitysim.core import expressions + +from activitysim.abm.models.util import estimation + +logger = logging.getLogger(__name__) + + +@inject.step() +def airport_returns(trips, chunk_size, trace_hh_id): + """ + This model updates the airport trip list to include return trips for drop off + passengers. The output is a larger trip list duplicating the trips which are dropped + off at the airport to return to their origin. + The main interface to the airport returns model is the airport_returns() function. + """ + + trace_label = "airport_returns" + model_settings_file_name = "airport_returns.yaml" + + trip_list = trips.to_frame() + logger.info("Running %s with %d trips", trace_label, len(trip_list)) + + model_settings = config.read_model_settings(model_settings_file_name) + + returning_modes = model_settings["RETURN_MODE_SEGMENTS"] + print(trips.trip_mode.unique()) + trip_returns = trip_list.copy() + trip_returns = trip_returns[trip_returns.trip_mode.isin(returning_modes)] + trip_returns["return_origin"] = trip_returns["destination"] + trip_returns["return_dest"] = trip_returns["origin"] + trip_returns["origin"] = trip_returns["return_origin"] + trip_returns["destination"] = trip_returns["return_dest"] + trip_returns["outbound"] = ~trip_returns["outbound"] + trip_returns["trip_num"] = 2 + trip_returns["trip_count"] = 2 + trip_returns["primary_purpose"] = trip_returns["primary_purpose"].map( + lambda n: "{}_return".format(n) + ) + trip_returns = trip_returns.drop(["return_origin", "return_dest"], axis=1) + trip_returns["trip_id"] = np.arange( + trip_list.index.max() + 1, trip_list.index.max() + 1 + len(trip_returns) + ) + trip_returns = trip_returns.set_index("trip_id") + trip_list = trip_list.append(trip_returns) + + pipeline.replace_table("trips", trip_list) + + # tracing.print_summary('airport_returns', trips.returns, value_counts=True) + + if trace_hh_id: + tracing.trace_df(trip_list, label=trace_label, warn_if_empty=True) diff --git a/extensions/av_ownership.py b/extensions/av_ownership.py new file mode 100644 index 0000000..5947a4e --- /dev/null +++ b/extensions/av_ownership.py @@ -0,0 +1,152 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np + +from activitysim.abm.models.util import estimation +from activitysim.core import config, expressions, inject, pipeline, simulate, tracing + +logger = logging.getLogger("activitysim") + + +@inject.step() +def av_ownership(households_merged, households, chunk_size, trace_hh_id): + """ + This model predicts whether a household owns an autonomous vehicle. + The output from this model is TRUE or FALSE. + """ + + trace_label = "av_ownership" + model_settings_file_name = "av_ownership.yaml" + + choosers = households_merged.to_frame() + model_settings = config.read_model_settings(model_settings_file_name) + + logger.info("Running %s with %d households", trace_label, len(choosers)) + + estimator = estimation.manager.begin_estimation("av_ownership") + + constants = config.get_model_constants(model_settings) + av_ownership_alt = model_settings.get("AV_OWNERSHIP_ALT", 0) + + # - preprocessor + preprocessor_settings = model_settings.get("preprocessor", None) + if preprocessor_settings: + + locals_d = {} + if constants is not None: + locals_d.update(constants) + + expressions.assign_columns( + df=choosers, + model_settings=preprocessor_settings, + locals_dict=locals_d, + trace_label=trace_label, + ) + + model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"]) + coefficients_df = simulate.read_model_coefficients(model_settings) + nest_spec = config.get_logit_model_settings(model_settings) + + if estimator: + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df) + estimator.write_choosers(choosers) + + # - iterative single process what-if adjustment if specified + iterations = model_settings.get("AV_OWNERSHIP_ITERATIONS", 1) + iterations_coefficient_constant = model_settings.get( + "AV_OWNERSHIP_COEFFICIENT_CONSTANT", None + ) + iterations_target_percent = model_settings.get("AV_OWNERSHIP_TARGET_PERCENT", None) + iterations_target_percent_tolerance = model_settings.get( + "AV_OWNERSHIP_TARGET_PERCENT_TOLERANCE", 0.01 + ) + + for iteration in range(iterations): + + logger.info( + "Running %s with %d households iteration %d", + trace_label, + len(choosers), + iteration, + ) + + # re-read spec to reset substitution + model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"]) + model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) + + choices = simulate.simple_simulate( + choosers=choosers, + spec=model_spec, + nest_spec=nest_spec, + locals_d=constants, + chunk_size=chunk_size, + trace_label=trace_label, + trace_choice_name="av_ownership", + estimator=estimator, + ) + + if iterations_target_percent is not None: + # choices_for_filter = choices[choosers[iterations_chooser_filter]] + + current_percent = (choices == av_ownership_alt).sum() / len(choosers) + logger.info( + "Running %s iteration %i choosers %i current percent %f target percent %f", + trace_label, + iteration, + len(choosers), + current_percent, + iterations_target_percent, + ) + + if current_percent <= ( + iterations_target_percent + iterations_target_percent_tolerance + ) and current_percent >= ( + iterations_target_percent - iterations_target_percent_tolerance + ): + logger.info( + "Running %s iteration %i converged with coefficient %f", + trace_label, + iteration, + coefficients_df.value[iterations_coefficient_constant], + ) + break + + else: + new_value = ( + np.log( + iterations_target_percent / np.maximum(current_percent, 0.0001) + ) + + coefficients_df.value[iterations_coefficient_constant] + ) + coefficients_df.value[iterations_coefficient_constant] = new_value + logger.info( + "Running %s iteration %i new coefficient for next iteration %f", + trace_label, + iteration, + new_value, + ) + iteration = iteration + 1 + + choices = choices == av_ownership_alt + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "households", "av_ownership") + estimator.write_override_choices(choices) + estimator.end_estimation() + + households = households.to_frame() + households["av_ownership"] = ( + choices.reindex(households.index).fillna(0).astype(bool) + ) + + pipeline.replace_table("households", households) + + tracing.print_summary("av_ownership", households.av_ownership, value_counts=True) + + if trace_hh_id: + tracing.trace_df(households, label=trace_label, warn_if_empty=True) diff --git a/extensions/external_identification.py b/extensions/external_identification.py new file mode 100644 index 0000000..be36ce5 --- /dev/null +++ b/extensions/external_identification.py @@ -0,0 +1,326 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import tracing +from activitysim.core import config +from activitysim.core import pipeline +from activitysim.core import simulate +from activitysim.core import inject +from activitysim.core import expressions + +from activitysim.abm.models.util import estimation + +logger = logging.getLogger(__name__) + + +def determine_closest_external_station(choosers, skim_dict, origin_col="home_zone_id"): + + unique_origin_zones = choosers[origin_col].unique() + landuse = inject.get_table("land_use").to_frame() + ext_zones = landuse[landuse.external_MAZ > 0].index.to_numpy() + + choosers["closest_external_zone"] = -1 + choosers["dist_to_external_zone"] = 0.0 + + for origin_zone in unique_origin_zones: + # FIXME in_skim check in skim_dictionary.py requires orig and dests to be the same shape in lookup + orig_zones = np.full(shape=len(ext_zones), fill_value=origin_zone, dtype=int) + zone_distances = skim_dict.lookup(orig_zones, ext_zones, "DIST") + + closest_zone_idx = np.argmin(zone_distances) + closest_zone = int(ext_zones[closest_zone_idx]) + dist_to_closest_zone = zone_distances[closest_zone_idx] + + choosers.loc[ + choosers[origin_col] == origin_zone, "closest_external_zone" + ] = closest_zone + choosers.loc[ + choosers[origin_col] == origin_zone, "dist_to_external_zone" + ] = dist_to_closest_zone + + return choosers + + +def external_identification( + model_settings, estimator, choosers, network_los, chunk_size, trace_label +): + + constants = config.get_model_constants(model_settings) + + locals_d = {} + if constants is not None: + locals_d.update(constants) + locals_d.update({"land_use": inject.get_table("land_use").to_frame()}) + + skim_dict = network_los.get_default_skim_dict() + # print('skim_dict', skim_dict) + choosers = determine_closest_external_station(choosers, skim_dict) + + # - preprocessor + preprocessor_settings = model_settings.get("preprocessor", None) + if preprocessor_settings: + expressions.assign_columns( + df=choosers, + model_settings=preprocessor_settings, + locals_dict=locals_d, + trace_label=trace_label, + ) + + model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"]) + coefficients_df = simulate.read_model_coefficients(model_settings) + model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) + + nest_spec = config.get_logit_model_settings(model_settings) + + if estimator: + estimator.write_model_settings( + model_settings, model_settings["_yaml_file_name"] + ) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df, model_settings) + estimator.write_choosers(choosers) + + choices = simulate.simple_simulate( + choosers=choosers, + spec=model_spec, + nest_spec=nest_spec, + locals_d=locals_d, + chunk_size=chunk_size, + trace_label=trace_label, + trace_choice_name=trace_label, + estimator=estimator, + ) + + return choices + + +@inject.step() +def external_worker_identification( + persons_merged, persons, network_los, chunk_size, trace_hh_id +): + """ + This model predicts the whether a worker has an external work location. + The output from this model is TRUE (if external) or FALSE (if internal). + + The main interface to the external worker model is the external_worker_identification() function. + This function is registered as an orca step in the example Pipeline. + """ + + trace_label = "external_worker_identification" + model_settings_file_name = "external_worker_identification.yaml" + model_settings = config.read_model_settings(model_settings_file_name) + model_settings["_yaml_file_name"] = model_settings_file_name + + estimator = estimation.manager.begin_estimation(trace_label) + + choosers = persons_merged.to_frame() + filter_col = model_settings.get("CHOOSER_FILTER_COLUMN_NAME") + choosers = choosers[choosers[filter_col]] + logger.info("Running %s with %d persons", trace_label, len(choosers)) + + choices = external_identification( + model_settings, estimator, choosers, network_los, chunk_size, trace_label + ) + + external_col_name = model_settings["EXTERNAL_COL_NAME"] + internal_col_name = model_settings["INTERNAL_COL_NAME"] + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "persons", trace_label) + estimator.write_override_choices(choices) + estimator.end_estimation() + + persons = persons.to_frame() + persons[external_col_name] = ( + (choices == 0).reindex(persons.index).fillna(False).astype(bool) + ) + persons[internal_col_name] = persons[filter_col] & ~persons[external_col_name] + + pipeline.replace_table("persons", persons) + + tracing.print_summary( + external_col_name, persons[external_col_name], value_counts=True + ) + + if trace_hh_id: + tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + +@inject.step() +def external_student_identification( + persons_merged, persons, network_los, chunk_size, trace_hh_id +): + """ + This model predicts the whether a student has an external work location. + The output from this model is TRUE (if external) or FALSE (if internal). + + The main interface to the external student model is the external_student_identification() function. + This function is registered as an orca step in the example Pipeline. + """ + + trace_label = "external_student_identification" + model_settings_file_name = "external_student_identification.yaml" + model_settings = config.read_model_settings(model_settings_file_name) + model_settings["_yaml_file_name"] = model_settings_file_name + + estimator = estimation.manager.begin_estimation(trace_label) + + choosers = persons_merged.to_frame() + filter_col = model_settings.get("CHOOSER_FILTER_COLUMN_NAME") + choosers = choosers[choosers[filter_col]] + + choices = external_identification( + model_settings, estimator, choosers, network_los, chunk_size, trace_label + ) + + external_col_name = model_settings["EXTERNAL_COL_NAME"] + internal_col_name = model_settings["INTERNAL_COL_NAME"] + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "persons", trace_label) + estimator.write_override_choices(choices) + estimator.end_estimation() + + persons = persons.to_frame() + persons[external_col_name] = ( + (choices == 0).reindex(persons.index).fillna(False).astype(bool) + ) + persons[internal_col_name] = persons[filter_col] & ~persons[external_col_name] + + pipeline.replace_table("persons", persons) + + tracing.print_summary( + external_col_name, persons[external_col_name], value_counts=True + ) + + if trace_hh_id: + tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + +def set_external_tour_variables(tours, choices, model_settings, trace_label): + """ + Set the internal and external tour indicator columns in the tours file + """ + external_col_name = model_settings["EXTERNAL_COL_NAME"] + internal_col_name = model_settings["INTERNAL_COL_NAME"] + + tours = tours.to_frame() + + tours.loc[choices.index, external_col_name] = ( + (choices == 0).reindex(tours.index).fillna(False).astype(bool) + ) + tours.loc[choices.index, internal_col_name] = np.where( + tours.loc[choices.index, external_col_name], False, True + ) + + # - annotate tours table + if "annotate_tours" in model_settings: + expressions.assign_columns( + df=tours, + model_settings=model_settings.get("annotate_tours"), + trace_label=tracing.extend_trace_label(trace_label, "annotate_tours"), + ) + + return tours + + +@inject.step() +def external_non_mandatory_identification( + tours_merged, tours, network_los, chunk_size, trace_hh_id +): + """ + This model predicts the whether a non-mandatory tour is external. + The output from this model is TRUE (if external) or FALSE (if internal). + + The main interface to the external student model is the external_nonmandatory_identification() function. + This function is registered as an orca step in the example Pipeline. + """ + + trace_label = "external_non_mandatory_identification" + model_settings_file_name = "external_non_mandatory_identification.yaml" + model_settings = config.read_model_settings(model_settings_file_name) + model_settings["_yaml_file_name"] = model_settings_file_name + + estimator = estimation.manager.begin_estimation(trace_label) + + choosers = tours_merged.to_frame() + choosers = choosers[choosers["tour_category"] == "non_mandatory"] + + choices = external_identification( + model_settings, estimator, choosers, network_los, chunk_size, trace_label + ) + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "tours", trace_label) + estimator.write_override_choices(choices) + estimator.end_estimation() + + tours = set_external_tour_variables(tours, choices, model_settings, trace_label) + + pipeline.replace_table("tours", tours) + + external_col_name = model_settings["EXTERNAL_COL_NAME"] + tracing.print_summary( + external_col_name, tours[external_col_name], value_counts=True + ) + + if trace_hh_id: + tracing.trace_df(tours, label=trace_label, warn_if_empty=True) + + +@inject.step() +def external_joint_tour_identification( + tours_merged, tours, network_los, chunk_size, trace_hh_id +): + """ + This model predicts the whether a joint tour is external. + The output from this model is TRUE (if external) or FALSE (if internal). + + The main interface to the external student model is the external_nonmandatory_identification() function. + This function is registered as an orca step in the example Pipeline. + """ + + trace_label = "external_joint_tour_identification" + model_settings_file_name = "external_joint_tour_identification.yaml" + model_settings = config.read_model_settings(model_settings_file_name) + model_settings["_yaml_file_name"] = model_settings_file_name + + estimator = estimation.manager.begin_estimation(trace_label) + + choosers = tours_merged.to_frame() + choosers = choosers[choosers["tour_category"] == "joint"] + + # - if no choosers + if choosers.shape[0] > 0: + choices = external_identification( + model_settings, estimator, choosers, network_los, chunk_size, trace_label + ) + else: + # everything is internal, still want to set internal or external columns in df + choices = pd.Series(1, index=choosers.index) + tracing.no_results(trace_label) + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "tours", trace_label) + estimator.write_override_choices(choices) + estimator.end_estimation() + + tours = set_external_tour_variables(tours, choices, model_settings, trace_label) + + pipeline.replace_table("tours", tours) + + external_col_name = model_settings["EXTERNAL_COL_NAME"] + tracing.print_summary( + external_col_name, tours[external_col_name], value_counts=True + ) + + if trace_hh_id: + tracing.trace_df(tours, label=trace_label, warn_if_empty=True) diff --git a/extensions/external_location_choice.py b/extensions/external_location_choice.py new file mode 100644 index 0000000..ba0ab54 --- /dev/null +++ b/extensions/external_location_choice.py @@ -0,0 +1,300 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np + +from activitysim.core import tracing +from activitysim.core import config +from activitysim.core import pipeline +from activitysim.core import simulate +from activitysim.core import inject +from activitysim.core import expressions + +from activitysim.abm.models.util import estimation +from activitysim.abm.models.util import tour_destination +from activitysim.abm.models.location_choice import ( + iterate_location_choice, + write_estimation_specs, +) + +from activitysim.core.util import assign_in_place + + +logger = logging.getLogger(__name__) + + +@inject.step() +def external_school_location( + persons_merged, persons, households, network_los, chunk_size, trace_hh_id, locutor +): + """ + External school location choice model + + iterate_location_choice adds location choice column and annotations to persons table + """ + + trace_label = "external_school_location" + model_settings = config.read_model_settings("external_school_location.yaml") + + estimator = estimation.manager.begin_estimation("external_school_location") + if estimator: + write_estimation_specs( + estimator, model_settings, "external_school_location.yaml" + ) + + persons_df = iterate_location_choice( + model_settings, + persons_merged, + persons, + households, + network_los, + estimator, + chunk_size, + trace_hh_id, + locutor, + trace_label, + ) + + pipeline.replace_table("persons", persons_df) + + if estimator: + estimator.end_estimation() + + +@inject.step() +def external_workplace_location( + persons_merged, persons, households, network_los, chunk_size, trace_hh_id, locutor +): + """ + External workplace location choice model + + iterate_location_choice adds location choice column and annotations to persons table + """ + + trace_label = "external_workplace_location" + model_settings = config.read_model_settings("external_workplace_location.yaml") + + estimator = estimation.manager.begin_estimation("external_workplace_location") + if estimator: + write_estimation_specs( + estimator, model_settings, "external_workplace_location.yaml" + ) + + persons_df = iterate_location_choice( + model_settings, + persons_merged, + persons, + households, + network_los, + estimator, + chunk_size, + trace_hh_id, + locutor, + trace_label, + ) + + pipeline.replace_table("persons", persons_df) + + if estimator: + estimator.end_estimation() + + +@inject.step() +def external_non_mandatory_destination( + tours, persons_merged, network_los, chunk_size, trace_hh_id +): + + """ + Given the tour generation from the above, each tour needs to have a + destination, so in this case tours are the choosers (with the associated + person that's making the tour) + """ + + trace_label = "external_non_mandatory_destination" + model_settings_file_name = "external_non_mandatory_destination.yaml" + model_settings = config.read_model_settings(model_settings_file_name) + + logsum_column_name = model_settings.get("DEST_CHOICE_LOGSUM_COLUMN_NAME") + want_logsums = logsum_column_name is not None + + sample_table_name = model_settings.get("DEST_CHOICE_SAMPLE_TABLE_NAME") + want_sample_table = ( + config.setting("want_dest_choice_sample_tables") + and sample_table_name is not None + ) + + tours = tours.to_frame() + + persons_merged = persons_merged.to_frame() + + # choosers are tours - in a sense tours are choosing their destination + non_mandatory_ext_tours = tours[ + (tours.tour_category == "non_mandatory") & (tours.is_external_tour) + ] + + # if no external non-mandatory tours + if non_mandatory_ext_tours.shape[0] == 0: + tracing.no_results(trace_label) + return + + estimator = estimation.manager.begin_estimation( + "external_non_mandatory_destination" + ) + if estimator: + estimator.write_coefficients(model_settings=model_settings) + # estimator.write_spec(model_settings, tag='SAMPLE_SPEC') + estimator.write_spec(model_settings, tag="SPEC") + estimator.set_alt_id(model_settings["ALT_DEST_COL_NAME"]) + estimator.write_table( + inject.get_injectable("size_terms"), "size_terms", append=False + ) + estimator.write_table( + inject.get_table("land_use").to_frame(), "landuse", append=False + ) + estimator.write_model_settings(model_settings, model_settings_file_name) + + choices_df, save_sample_df = tour_destination.run_tour_destination( + non_mandatory_ext_tours, + persons_merged, + want_logsums, + want_sample_table, + model_settings, + network_los, + estimator, + chunk_size, + trace_hh_id, + trace_label, + ) + + if estimator: + estimator.write_choices(choices_df.choice) + choices_df.choice = estimator.get_survey_values( + choices_df.choice, "tours", "destination" + ) + estimator.write_override_choices(choices_df.choice) + estimator.end_estimation() + + non_mandatory_ext_tours["destination"] = choices_df.choice + + assign_in_place(tours, non_mandatory_ext_tours[["destination"]]) + + if want_logsums: + non_mandatory_ext_tours[logsum_column_name] = choices_df["logsum"] + assign_in_place(tours, non_mandatory_ext_tours[[logsum_column_name]]) + + pipeline.replace_table("tours", tours) + + if want_sample_table: + assert len(save_sample_df.index.get_level_values(0).unique()) == len(choices_df) + # save_sample_df.set_index(model_settings['ALT_DEST_COL_NAME'], append=True, inplace=True) + pipeline.extend_table(sample_table_name, save_sample_df) + + if trace_hh_id: + tracing.trace_df( + tours[tours.tour_category == "non_mandatory"], + label="external_non_mandatory_destination", + slicer="person_id", + index_label="tour", + columns=None, + warn_if_empty=True, + ) + + +@inject.step() +def external_joint_tour_destination( + tours, persons_merged, network_los, chunk_size, trace_hh_id +): + + """ + Given the tour generation from the above, each tour needs to have a + destination, so in this case tours are the choosers (with the associated + person that's making the tour) + """ + + trace_label = "external_joint_tour_destination" + model_settings_file_name = "external_joint_tour_destination.yaml" + model_settings = config.read_model_settings(model_settings_file_name) + + logsum_column_name = model_settings.get("DEST_CHOICE_LOGSUM_COLUMN_NAME") + want_logsums = logsum_column_name is not None + + sample_table_name = model_settings.get("DEST_CHOICE_SAMPLE_TABLE_NAME") + want_sample_table = ( + config.setting("want_dest_choice_sample_tables") + and sample_table_name is not None + ) + + tours = tours.to_frame() + + persons_merged = persons_merged.to_frame() + + joint_ext_tours = tours[ + (tours.tour_category == "joint") + & (tours.get("is_external_tour", False) == True) + ] # get needed incase no joint tours + + # if no external joint tours + if joint_ext_tours.shape[0] == 0: + tracing.no_results(trace_label) + return + + estimator = estimation.manager.begin_estimation("external_joint_tour_destination") + if estimator: + estimator.write_coefficients(model_settings=model_settings) + # estimator.write_spec(model_settings, tag='SAMPLE_SPEC') + estimator.write_spec(model_settings, tag="SPEC") + estimator.set_alt_id(model_settings["ALT_DEST_COL_NAME"]) + estimator.write_table( + inject.get_injectable("size_terms"), "size_terms", append=False + ) + estimator.write_table( + inject.get_table("land_use").to_frame(), "landuse", append=False + ) + estimator.write_model_settings(model_settings, model_settings_file_name) + + choices_df, save_sample_df = tour_destination.run_tour_destination( + joint_ext_tours, + persons_merged, + want_logsums, + want_sample_table, + model_settings, + network_los, + estimator, + chunk_size, + trace_hh_id, + trace_label, + ) + + if estimator: + estimator.write_choices(choices_df.choice) + choices_df.choice = estimator.get_survey_values( + choices_df.choice, "tours", "destination" + ) + estimator.write_override_choices(choices_df.choice) + estimator.end_estimation() + + joint_ext_tours["destination"] = choices_df.choice + + assign_in_place(tours, joint_ext_tours[["destination"]]) + + if want_logsums: + joint_ext_tours[logsum_column_name] = choices_df["logsum"] + assign_in_place(tours, joint_ext_tours[[logsum_column_name]]) + + pipeline.replace_table("tours", tours) + + if want_sample_table: + assert len(save_sample_df.index.get_level_values(0).unique()) == len(choices_df) + # save_sample_df.set_index(model_settings['ALT_DEST_COL_NAME'], append=True, inplace=True) + pipeline.extend_table(sample_table_name, save_sample_df) + + if trace_hh_id: + tracing.trace_df( + tours[tours.tour_category == "non_mandatory"], + label="external_joint_tour_destination", + slicer="person_id", + index_label="tour", + columns=None, + warn_if_empty=True, + ) diff --git a/extensions/transponder_ownership.py b/extensions/transponder_ownership.py new file mode 100644 index 0000000..03cc765 --- /dev/null +++ b/extensions/transponder_ownership.py @@ -0,0 +1,100 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np + +from activitysim.core import tracing +from activitysim.core import config +from activitysim.core import pipeline +from activitysim.core import simulate +from activitysim.core import inject +from activitysim.core import expressions + +from activitysim.abm.models.util import estimation + +logger = logging.getLogger(__name__) + + +@inject.step() +def transponder_ownership( + households_merged, households, network_los, chunk_size, trace_hh_id +): + """ + This model predicts whether the household owns a transponder. + The output from this model is TRUE (if yes) or FALSE (if no) and is stored + in the transponder_ownership column in the households table + + The main interface to the Transponder Ownership model is the transponder_ownership() function. + This function is registered as an orca step in the example Pipeline. + """ + + trace_label = "transponder_ownership" + model_settings_file_name = "transponder_ownership.yaml" + model_settings = config.read_model_settings(model_settings_file_name) + transponder_own_alt = model_settings["TRANSPONDER_OWNERSHIP_ALT"] + + estimator = estimation.manager.begin_estimation("transponder_ownership") + constants = config.get_model_constants(model_settings) + + choosers = households_merged.to_frame() + logger.info("Running %s with %d households", trace_label, len(choosers)) + + # - preprocessor + preprocessor_settings = model_settings.get("preprocessor", None) + if preprocessor_settings: + locals_d = {} + if constants is not None: + locals_d.update(constants) + + expressions.assign_columns( + df=choosers, + model_settings=preprocessor_settings, + locals_dict=locals_d, + trace_label=trace_label, + ) + + model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"]) + coefficients_df = simulate.read_model_coefficients(model_settings) + model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) + + nest_spec = config.get_logit_model_settings(model_settings) + + if estimator: + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df, model_settings) + estimator.write_choosers(choosers) + + choices = simulate.simple_simulate( + choosers=choosers, + spec=model_spec, + nest_spec=nest_spec, + locals_d=constants, + chunk_size=chunk_size, + trace_label=trace_label, + trace_choice_name="transponder_ownership", + estimator=estimator, + ) + choices = choices == transponder_own_alt + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values( + choices, "households", "transponder_ownership" + ) + estimator.write_override_choices(choices) + estimator.end_estimation() + + households = households.to_frame() + households["transponder_ownership"] = ( + choices.reindex(households.index).fillna(0).astype(bool) + ) + pipeline.replace_table("households", households) + + tracing.print_summary( + "transponder_ownership", households["transponder_ownership"], value_counts=True + ) + + if trace_hh_id: + tracing.trace_df(households, label=trace_label, warn_if_empty=True) diff --git a/extensions/update_tables.py b/extensions/update_tables.py new file mode 100644 index 0000000..edf646a --- /dev/null +++ b/extensions/update_tables.py @@ -0,0 +1,269 @@ +# ActivitySim +# See full license in LICENSE.txt. +import git +import logging +import os +import yaml + +import numpy as np +import pandas as pd + +from activitysim.core import config, inject, pipeline, tracing +from activitysim.core.config import setting + +# from io import StringIO + +logger = logging.getLogger("activitysim") + + +def find_git_folder(code_folder, path_level): + """ + Returns the path to the .git folder + + Parameters + ---------- + code folder: str + path_level: str + + Returns + ------- + git_dir: str + The path to the .git folder. + """ + + git_dir = os.path.abspath(os.path.join(code_folder, path_level)) + git_folder = os.path.join(git_dir, ".git") + return git_folder + + +def get_commit_info(repo_path): + """ + Returns a dictionary containing the short commit hash and branch name of the Git repository + at the specified path. + + Parameters + ---------- + repo_path (str): The path to the Git repository. + + Returns + ------- + dict: A dictionary with the following keys: + - short_commit_hash (str): The first 7 characters of the current commit hash. + - branch_name (str): The name of the current active branch. + If the repository path is not a Git repository, both values will be empty strings. + """ + + commit_hash = "" + branch_name = "" + + if os.path.isdir(repo_path): + try: + repo = git.Repo(repo_path) + if repo.head.is_valid(): + commit_hash = repo.head.commit.hexsha[:7] + if not repo.head.is_detached: + branch_name = repo.active_branch.name + else: + branch_name = repo.active_branch.name + branch_file = open(repo_path + "\\refs\\heads\\" + branch_name, "r") + commit_hash = branch_file.read()[:7] + # commit_hash = branch_file.read(7) + branch_file.close() + except (git.InvalidGitRepositoryError, AttributeError, FileNotFoundError): + pass + + return {"short_commit_hash": commit_hash, "branch_name": branch_name} + + +def write_metadata(prefix): + + output_dir = inject.get_injectable("output_dir") + + # repo branch name and commit hash: activitysim + asim_git_folder = find_git_folder(pipeline.__file__, "../../..") + asim_commit_info = get_commit_info(asim_git_folder) + + # repo branch name and commit hash: abm3 + abm_git_path = os.path.abspath( + os.path.join(output_dir, "..", "..", "git_info.yaml") + ) + if os.path.isfile(abm_git_path): + with open(abm_git_path, "r") as stream: + abm_git_info = yaml.safe_load(stream) + abm_git_info["commit"] = abm_git_info["commit"][:7] + else: + abm_git_info = {"branch": "", "commit": ""} + + trip_settings = config.read_model_settings("write_trip_matrices.yaml") + constants = trip_settings.get("CONSTANTS") + + model_metadata_dict = { + "asim_branch_name": asim_commit_info["branch_name"], + "asim_commit_hash": asim_commit_info["short_commit_hash"], + "abm_branch_name": abm_git_info["branch"], + "abm_commit_hash": abm_git_info["commit"], + "constants": constants, + "prefix": prefix, + } + model_metadata_path = os.path.join(output_dir, "model_metadata.yaml") + with open(model_metadata_path, "w") as file: + yaml.dump(model_metadata_dict, file, default_flow_style=False) + + +def remove_columns(table_settings, df): + # remove columns from a final table + setting = "remove_columns" + if setting not in table_settings: + return df + remove_cols = table_settings[setting] + + remove_filter = df.filter(remove_cols) + df_removed = df.drop(columns=remove_filter) + # df_removed.name = df.name + + return df_removed + + +def reorder_columns(table_settings, df): + # reorder columns in a final table + setting = "reorder_columns" + if setting not in table_settings: + return df + reorder_cols = table_settings[setting] + + # index will not get reordered + if df.index.name in reorder_cols: + reorder_cols.remove(df.index.name) + + existing_cols = df.columns.values.tolist() + for col in existing_cols: + if col not in reorder_cols: + reorder_cols.append(col) + + for col in reorder_cols: + if col not in existing_cols: + df[col] = np.nan + + df_reorder = df[reorder_cols] + # df_reorder.name = df.name + + return df_reorder + + +def rename_columns(table_settings, df): + # rename columns in a final table + setting = "rename_columns" + if setting not in table_settings: + return df + rename_cols = table_settings[setting] + + df_rename = df.rename(columns=rename_cols) + + return df_rename + + +def get_output_table_names(output_tables_settings, output_tables_settings_name): + """ """ + action = output_tables_settings.get("action") + tables = output_tables_settings.get("tables") + registered_tables = pipeline.registered_tables() + if action == "include": + # interpret empty or missing tables setting to mean include all registered tables + output_tables_list = tables if tables is not None else registered_tables + elif action == "skip": + output_tables_list = [t for t in registered_tables if t not in tables] + else: + raise "expected %s action '%s' to be either 'include' or 'skip'" % ( + output_tables_settings_name, + action, + ) + return output_tables_list + + +@inject.step() +def update_tables(): + # get list of model outputs to update + output_dir = inject.get_injectable("output_dir") + input_dir = os.path.abspath(os.path.join(output_dir, "..", "..", "input")) + # input_dir = inject.get_injectable("data_dir") + output_tables_settings_name = "output_tables" + output_tables_settings = setting(output_tables_settings_name) + if output_tables_settings is None: + logger.info("No output_tables specified in settings file. Nothing to update.") + return + output_tables_list = get_output_table_names( + output_tables_settings, output_tables_settings_name + ) + + common_settings_file_name = "..\common\outputs.yaml" + common_settings = config.read_model_settings(common_settings_file_name) + + for table_name in output_tables_list: + if not isinstance(table_name, str): + table_name = table_name["tablename"] + + if not ( + table_name in common_settings + or table_name == "households" + or table_name == "vehicles" + or table_name == "persons" + ): + continue + + output_table = pipeline.get_table(table_name) + + # set sample rate to float + if table_name == "households": + output_table["sample_rate"] = output_table["sample_rate"].astype(float) + input_households = pd.read_csv( + os.path.join(input_dir, "households.csv"), + usecols=["hhid", "poverty"], + dtype={"hhid": "int32", "poverty": "float"}, + ) + output_table = output_table.merge( + input_households, how="inner", left_on="household_id", right_on="hhid" + ) + + # split vehicle_type column + if table_name == "vehicles": + output_table[ + ["vehicle_category", "num_occupants", "fuel_type"] + ] = output_table["vehicle_type"].str.split(pat="_", expand=True) + # output_table.drop(columns={'vehicle_type'}, inplace=True) ## TODO decide whether to drop column here or in bronze -> silver filter + # add missing columns from input persons file + if table_name == "persons": + input_persons = pd.read_csv( + os.path.join(input_dir, "persons.csv"), + usecols=[ + "perid", + "miltary", + "grade", + "weeks", + "hours", + "rac1p", + "hisp", + ], + dtype={ + "perid": "int32", + "miltary": "int8", + "grade": "int8", + "weeks": "int8", + "hours": "int8", + "rac1p": "int8", + "hisp": "int8", + }, + ) + output_table = output_table.merge( + input_persons, how="inner", left_on="person_id", right_on="perid" + ) + + if table_name in common_settings: + table_settings = common_settings[table_name] + output_table = remove_columns(table_settings, output_table) + output_table = reorder_columns(table_settings, output_table) + output_table = rename_columns(table_settings, output_table) + + pipeline.replace_table(table_name, output_table) + + prefix = output_tables_settings.get("prefix", "final_") + write_metadata(prefix) diff --git a/simulation.py b/simulation.py new file mode 100644 index 0000000..5d31553 --- /dev/null +++ b/simulation.py @@ -0,0 +1,24 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import sys +import argparse +import os +import pandas as pd +import warnings + +from activitysim.cli.run import add_run_args, run + +import extensions + +if __name__ == "__main__": + + warnings.simplefilter(action="ignore", category=pd.errors.PerformanceWarning) + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + os.environ["MKL_NUM_THREADS"] = "1" + + sys.exit(run(args)) diff --git a/test/configs/network_los.yaml b/test/configs/network_los.yaml new file mode 100644 index 0000000..1c4cd79 --- /dev/null +++ b/test/configs/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/test/configs/settings.yaml b/test/configs/settings.yaml new file mode 100644 index 0000000..c4e7b5b --- /dev/null +++ b/test/configs/settings.yaml @@ -0,0 +1,30 @@ +inherit_settings: True + +# treat warnings as errors +strict: True + +# number of households to simulate +households_sample_size: 10 +chunk_size: 0 + +# - shadow pricing global switches +use_shadow_pricing: True + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +cleanup_pipeline_after_run: True + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + +recode_pipeline_columns: True diff --git a/test/configs_chunkless/network_los.yaml b/test/configs_chunkless/network_los.yaml new file mode 100644 index 0000000..1c4cd79 --- /dev/null +++ b/test/configs_chunkless/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/test/configs_chunkless/settings.yaml b/test/configs_chunkless/settings.yaml new file mode 100644 index 0000000..996364a --- /dev/null +++ b/test/configs_chunkless/settings.yaml @@ -0,0 +1,28 @@ +inherit_settings: True + +# treat warnings as errors +strict: True + +# number of households to simulate +households_sample_size: 10 +chunk_size: 0 +chunk_training_mode: disabled + +# - shadow pricing global switches +use_shadow_pricing: True + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +cleanup_pipeline_after_run: True + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - trips + +recode_pipeline_columns: False diff --git a/test/configs_mp/network_los.yaml b/test/configs_mp/network_los.yaml new file mode 100644 index 0000000..1c4cd79 --- /dev/null +++ b/test/configs_mp/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/test/configs_mp/settings.yaml b/test/configs_mp/settings.yaml new file mode 100644 index 0000000..9806dc3 --- /dev/null +++ b/test/configs_mp/settings.yaml @@ -0,0 +1,33 @@ +inherit_settings: True + +# treat warnings as errors +strict: True + +# raise error if any sub-process fails without waiting for others to complete +fail_fast: True + +multiprocess: True + +use_shadow_pricing: True + +chunk_size: 0 +num_processes: 2 + +# number of households to simulate +households_sample_size: 10 + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +cleanup_pipeline_after_run: True + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - trips + +recode_pipeline_columns: False diff --git a/test/configs_sharrow/network_los.yaml b/test/configs_sharrow/network_los.yaml new file mode 100644 index 0000000..1c4cd79 --- /dev/null +++ b/test/configs_sharrow/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/test/configs_sharrow/settings.yaml b/test/configs_sharrow/settings.yaml new file mode 100644 index 0000000..ae940bd --- /dev/null +++ b/test/configs_sharrow/settings.yaml @@ -0,0 +1,31 @@ +inherit_settings: True + +# treat warnings as errors +strict: True + +# number of households to simulate +households_sample_size: 10 +chunk_size: 0 + +# - shadow pricing global switches +use_shadow_pricing: True + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +cleanup_pipeline_after_run: True + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - tablename: trips + decode_columns: + origin: land_use.zone_id + destination: land_use.zone_id + +sharrow: require +recode_pipeline_columns: True diff --git a/test/regress/final_trips.csv b/test/regress/final_trips.csv new file mode 100644 index 0000000..b38a085 --- /dev/null +++ b/test/regress/final_trips.csv @@ -0,0 +1,57 @@ 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diff --git a/test/test_sandag_abm3.py b/test/test_sandag_abm3.py new file mode 100644 index 0000000..ce1fa43 --- /dev/null +++ b/test/test_sandag_abm3.py @@ -0,0 +1,222 @@ +# ActivitySim +# See full license in LICENSE.txt. +import os +import subprocess +import sys +from pathlib import Path + +import pandas as pd +import pandas.testing as pdt + +from activitysim.core import testing, workflow + + +def _example_path(dirname): + """Paths to things in the top-level directory of this repository.""" + return os.path.normpath(os.path.join(os.path.dirname(__file__), "..", dirname)) + + +def _test_path(dirname): + """Paths to things in the `test` directory.""" + return os.path.join(os.path.dirname(__file__), dirname) + + +def run_test_sandag_abm3( + multiprocess=False, chunkless=False, sharrow=False +): + + def regress(ext): + regress_trips_df = pd.read_csv(_test_path("regress/final_trips.csv")) + final_trips_df = pd.read_csv(_test_path("output/final_trips.csv")) + + # column order may not match, so fix it before checking + assert sorted(regress_trips_df.columns) == sorted(final_trips_df.columns) + final_trips_df = final_trips_df[regress_trips_df.columns] + + # person_id,household_id,tour_id,primary_purpose,trip_num,outbound,trip_count,purpose, + # destination,origin,destination_logsum,depart,trip_mode,mode_choice_logsum + # compare_cols = [] + pdt.assert_frame_equal(final_trips_df, regress_trips_df) + + file_path = os.path.join(os.path.dirname(__file__), '..', "simulation.py") + + run_args = [] + + if multiprocess: + run_args.extend( + [ + "-c", + _test_path("configs_mp"), + "-c", + _example_path("configs_mp"), + ] + ) + elif chunkless: + run_args.extend( + [ + "-c", + _test_path("configs_chunkless"), + ] + ) + elif sharrow: + run_args.extend( + [ + "-c", + _test_path("configs_sharrow"), + ] + ) + if os.environ.get("GITHUB_ACTIONS") != "true": + run_args.append("--persist-sharrow-cache") + else: + run_args.extend( + [ + "-c", + _example_path(r"configs\resident"), + ] + ) + + out_dir = _test_path( + f"output-{'mp' if multiprocess else 'single'}" + f"-{'chunkless' if chunkless else 'chunked'}" + f"-{'sharrow' if sharrow else 'no_sharrow'}" + ) + + # create output directory if it doesn't exist and add .gitignore + Path(out_dir).mkdir(exist_ok=True) + Path(out_dir).joinpath(".gitignore").write_text("**\n") + + run_args.extend( + [ + "-c", + _example_path("configs"), + "-d", + _example_path("data"), + "-o", + out_dir, + ] + ) + + if os.environ.get("GITHUB_ACTIONS") == "true": + subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + else: + subprocess.run([sys.executable, file_path] + run_args, check=True) + + regress() + + +def test_sandag_abm3(): + run_test_sandag_abm3(multiprocess=False) + + +def test_sandag_abm3_chunkless(): + run_test_sandag_abm3(multiprocess=False, chunkless=True) + + +def test_sandag_abm3_mp(): + run_test_sandag_abm3(multiprocess=True) + + +def test_sandag_abm3_sharrow(): + run_test_sandag_abm3(sharrow=True) + + +EXPECTED_MODELS = [ + "initialize_proto_population", + "compute_disaggregate_accessibility", + "initialize_landuse", + "initialize_households", + "compute_accessibility", + "av_ownership", + "auto_ownership_simulate", + "work_from_home", + "external_worker_identification", + "external_workplace_location", + "school_location", + "workplace_location", + "transit_pass_subsidy", + "transit_pass_ownership", + "vehicle_type_choice", + "adjust_auto_operating_cost", + "transponder_ownership", + "free_parking", + "telecommute_frequency", + "cdap_simulate", + "mandatory_tour_frequency", + "mandatory_tour_scheduling", + "school_escorting", + "joint_tour_frequency_composition", + "external_joint_tour_identification", + "joint_tour_participation", + "joint_tour_destination", + "external_joint_tour_destination", + "joint_tour_scheduling", + "non_mandatory_tour_frequency", + "external_non_mandatory_identification", + "non_mandatory_tour_destination", + "external_non_mandatory_destination", + "non_mandatory_tour_scheduling", + "vehicle_allocation", + "tour_mode_choice_simulate", + "atwork_subtour_frequency", + "atwork_subtour_destination", + "atwork_subtour_scheduling", + "atwork_subtour_mode_choice", + "stop_frequency", + "trip_purpose", + "trip_destination", + "trip_purpose_and_destination", + "trip_scheduling", + "trip_mode_choice", + "parking_location", + "write_data_dictionary", + "track_skim_usage", + "write_trip_matrices", + "write_tables", +] + + +@testing.run_if_exists("reference-pipeline-abm3.zip") +def test_sandag_abm3_progressive(): + + import activitysim.abm # register components # noqa: F401 + + out_dir = _test_path("output-progressive") + Path(out_dir).mkdir(exist_ok=True) + Path(out_dir).joinpath(".gitignore").write_text("**\n") + + state = workflow.State.make_default( + configs_dir=( + _test_path("configs_recode"), + _test_path("configs"), + _example_path("resident\configs"), + ), + data_dir=_example_path("data"), + # data_model_dir=_example_path("data_model"), + output_dir=out_dir, + ) + state.filesystem.persist_sharrow_cache() + + assert state.settings.models == EXPECTED_MODELS + assert state.settings.chunk_size == 0 + assert not state.settings.sharrow + + for step_name in EXPECTED_MODELS: + state.run.by_name(step_name) + try: + state.checkpoint.check_against( + Path(__file__).parent.joinpath("reference-pipeline-abm3.zip"), + checkpoint_name=step_name, + ) + except Exception: + print(f"> prototype_mtc_extended {step_name}: ERROR") + raise + else: + print(f"> prototype_mtc_extended {step_name}: ok") + + +if __name__ == "__main__": + run_test_sandag_abm3(multiprocess=False) + run_test_sandag_abm3(multiprocess=True) + run_test_sandag_abm3(multiprocess=False, chunkless=True) + run_test_sandag_abm3(sharrow=True) + test_sandag_abm3_progressive()