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Updated SEMCOG Example (ActivitySim#603)
* initial example_semcog commit * documentation * single process passing but not multiprocess * testing infrastructure * blacken * formatting * implemented stable sample for university extensions * adding ignored output folder * removed squared term and re-estimated * updating test env * calibrated configs * fixing bad merge * updated configs * reading in extension folder * updated shadowpricing yaml file * updated test regress * Revert "updated test regress" This reverts commit 8d5af7d. * updated regress test table * updated test regress table * updated cropped land use and skim * updated shadow pricing method * updated network_los to read segmented skims if needed * updated example_manifest.yaml * fixing conflicts * making fixes per review comments * push mistake! * Revert "making fixes per review comments" This reverts commit c05c7f3. * Revert "Revert "making fixes per review comments"" This reverts commit d83019a. * added back accidentally untracked output folder + updated regress table * misc small fixes --------- Co-authored-by: Ali Etezady <[email protected]>
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...ototype_semcog/configs/accessibility.yaml → ...duction_semcog/configs/accessibility.yaml
100755 → 100644
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activitysim/examples/production_semcog/configs/auto_ownership.csv
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,23 @@ | ||
Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 | ||
util_drivers_1,1 Adult (age 16+),num_drivers==1,,coef_cars1_drivers_1,coef_cars2_drivers_1,coef_cars3_drivers_1,coef_cars4_drivers_1 | ||
util_drivers_2,2 Adult (age 16+),num_drivers==2,,coef_cars1_drivers_2,coef_cars2_drivers_2,coef_cars3_drivers_2,coef_cars4_drivers_2 | ||
util_drivers_3_up,3+ Adults (age 16+),num_drivers>=3,,coef_cars1_drivers_3_up,coef_cars2_drivers_3_up,coef_cars3_drivers_3_up,coef_cars4_drivers_3_up | ||
util_persons_16_17,Persons age 16-17,num_children_16_to_17,,coef_cars1_persons_16_17,coef_cars2_persons_16_17,coef_cars34_persons_16_17,coef_cars34_persons_16_17 | ||
util_persons_18_24,Persons age 18-24,num_college_age,,coef_cars1_persons_18_24,coef_cars2_persons_18_24,coef_cars34_persons_18_24,coef_cars34_persons_18_24 | ||
util_persons_25_34,Persons age 35-34,num_young_adults,,coef_cars1_persons_25_34,coef_cars2_persons_25_34,coef_cars34_persons_25_34,coef_cars34_persons_25_34 | ||
util_presence_children_0_4,Presence of children age 0-4,num_young_children>0,,coef_cars1_presence_children_0_4,coef_cars2_presence_children_0_4,coef_cars34_presence_children_0_4,coef_cars34_presence_children_0_4 | ||
util_presence_children_5_17,Presence of children age 5-17,(num_children_5_to_15+num_children_16_to_17)>0,,coef_cars1_presence_children_5_17,coef_cars2_presence_children_5_17,coef_cars34_presence_children_5_17,coef_cars34_presence_children_5_17 | ||
util_num_workers_clip_3,Number of workers capped at 3,@df.num_workers.clip(upper=3),,coef_cars1_num_workers_clip_3,coef_cars2_num_workers_clip_3,coef_cars3_num_workers_clip_3,coef_cars4_num_workers_clip_3 | ||
util_hh_income_0_15k,Piecewise Linear household income $0-15k,income_in_thousands < 15,,coef_cars1_hh_income_0_15k,coef_cars2_hh_income_0_15k,coef_cars3_hh_income_0_15k,coef_cars4_hh_income_0_15k | ||
util_hh_income_15_35k,Piecewise Linear household income $15-35k,(income_in_thousands >= 15) & (income_in_thousands<35),,coef_cars1_hh_income_15_35k,coef_cars2_hh_income_15_35k,coef_cars3_hh_income_15_35k,coef_cars4_hh_income_15_35k | ||
util_hh_income_35_50k,Piecewise Linear household income $35-50k,(income_in_thousands >= 35) & (income_in_thousands<50),,coef_cars1_hh_income_35_50k,coef_cars2_hh_income_35_50k,coef_cars3_hh_income_35_50k,coef_cars4_hh_income_35_50k | ||
util_hh_income_50_75k,Piecewise Linear household income $50-75k,(income_in_thousands >= 50) & (income_in_thousands<75),,coef_cars1_hh_income_50_75k,coef_cars2_hh_income_50_75k,coef_cars3_hh_income_50_75k,coef_cars4_hh_income_50_75k | ||
util_density,Density index,@df.density_index,,coef_cars1_density,coef_cars2_density,coef_cars3_density,coef_cars4_density | ||
util_retail_transit,Retail accessibility (0.66*PK + 0.34*OP) by transit,(0.66*trPkRetail+0.34*trOpRetail),,coef_cars1_retail_transit,coef_cars2_retail_transit,coef_cars3_retail_transit,coef_cars4_retail_transit | ||
util_retail_non_motor,Retail accessibility by non-motorized,nmRetail,,coef_cars1_retail_non_motor,coef_cars2_retail_non_motor,coef_cars3_retail_non_motor,coef_cars4_retail_non_motor | ||
util_auto_time_saving_per_worker,Auto time savings per worker to work,"@np.where(df.num_workers > 0, df.hh_work_auto_savings_ratio / df.num_workers, 0)",,coef_cars1_auto_time_saving_per_worker,coef_cars2_auto_time_saving_per_worker,coef_cars3_auto_time_saving_per_worker,coef_cars4_auto_time_saving_per_worker | ||
util_univ_students_GQ,University Students living in GQ households,(num_college_age > 0) & (type == 3),,coef_cars1_univ_GQ,coef_unavailable,coef_unavailable,coef_unavailable | ||
util_constants,Alternative-specific constants,1,,coef_cars1,coef_cars2,coef_cars3,coef_cars4 | ||
util_detroit,district specific constant,home_in_detroit,coef_detroit,0,0,,0 | ||
#,,,,,,, | ||
calib_constant,calibration constant,1,coef_cars0_asc,coef_cars1_asc,coef_cars2_asc,coef_cars3_asc,coef_cars4_asc |
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