Lightweight mobility simulation for quick algorithm prototyping

autonomous-vehicles, fleet-management, mobility, mobility-modeling, optimization-algorithms, python, ridehailing, ridesharing, simulation-framework, simulator, transportation
pip install simobility==0.3.0



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).


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

Some examples:


pip install simobility

Contributions and thanks

Thanks to all who contributed to the concept/code:

Simulation example

Simple simulation example

Log example


Benchmark simulations with LinearRouter. 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 Number of vehicles Bookings per hour Execution time Generated events
1 hour 10 sec 50 100 4 sec 1082
24 hours 1 min 50 100 12 sec 23745
24 hours 10 sec 50 100 20 sec 23880
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

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

Read logs with pandas

import pandas as pd

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

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


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