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simobility

simobility is a light-weight mobility simulation framework. Best for quick prototyping

simobility is a human-friendly Python framework that helps scientists and engineers to prototype and compare fleet optimization algorithms (autonomous and human-driven vehicles). It provides a set of building blocks that can be used to design different simulation scenarious, run simulations and calculate metrics. It is easy to plug in custom demand models, customer behavior models, fleet types, spatio-temporal models (for example, use OSRM for routing vehicles and machine learning models trained on historical data to predict ETA).

Motivation

Create an environment for experiments with machine learning algorithms for decision-making problems in mobility services and compare them to classical solutions.

Some examples:

Installation

pip install simobility

Contributions and thanks

Thanks to all who contributed to the concept/code:

Examples

Grid world simulation

Simple simulation

Taxi service

Log example

Benchmarks

Benchmark simulations with LinearRouter and GreedyMatcher. Simulations will run slower with OSRMRouter because OSRM cannot process requests as fast as the linear router.

Processor: 2,3 GHz Dual-Core Intel Core i5; Memory: 8 GB 2133 MHz LPDDR3

Simulated time Simulation step Vehicles Bookings per hour Execution time Generated events Pickup rate
1 hour 10 sec 50 100 4 sec 1082 96.97%
24 hours 1 min 50 100 12 sec 23745 88.37%
24 hours 10 sec 50 100 20 sec 23880 88.84%
12 hours 10 sec 200 100 18 sec 13337 99.89%
12 hours 10 sec 50 500 31 sec 40954 53.92%
12 hours 10 sec 200 500 46 sec 65444 99.3%
12 hours 10 sec 1000 500 1 min 48 sec 66605 99.98%
1 hour 1 min 1000 1000 14 sec 11486
1 hour 10 sec 1000 1000 18 sec 11631
24 hours 1 min 1000 1000 5 min 1 sec 262384
24 hours 10 sec 1000 1000 6 min 20 sec 262524

A heuristic that allows estimating a maximum number of booking a fleet of N vehicles can handle: assume that an avarage trip duration is 15 minute, than 1 vehicle can not more then handle 4 booking per hour and the upper limit for 1000 vehicles is 4000 bookings per hour.

Metrics example

{
    "avg_paid_utilization": 63.98,
    "avg_utilization": 96.87,
    "avg_waiting_time": 292.92,
    "created": 3998,
    "dropoffs": 589,
    "empty_distance": 640.37,
    "empty_distance_pcnt": 33.67,
    "fleet_paid_utilization": 63.98,
    "fleet_utilization": 96.87,
    "num_vehicles": 50,
    "pickup_rate": 15.48,
    "pickups": 619,
    "total_distance": 1902.04,
}

Simulation logs

The are multiple ways to collect simulation log - use CSV or InMemory log handler or implement your own handler: loggers

Read CSV logs with pandas:

import pandas as pd

data = pd.read_csv(
    "simulation_output.csv",
    sep=";",
    converters={"details": lambda v: eval(v)},
)

details = data.details.apply(pd.Series)

Run OSRM

wget http://download.geofabrik.de/north-america/us/new-york-latest.osm.pbf
docker run -t -v "${PWD}:/data" osrm/osrm-backend osrm-extract -p /opt/car.lua /data/new-york-latest.osm.pbf
docker run -t -v "${PWD}:/data" osrm/osrm-backend osrm-partition /data/new-york-latest.osrm
docker run -t -v "${PWD}:/data" osrm/osrm-backend osrm-customize /data/new-york-latest.osrm
docker run -d -t -i -p 5010:5000 -v "${PWD}:/data" osrm/osrm-backend osrm-routed --algorithm mld /data/new-york-latest.osrm