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:
-
Deep Reinforcement Learning with Applications in Transportation
-
T. Oda and C. Joe-Wong, "Movi: A model-free approach to dynamic fleet management". 2018
Installation
pip install simobility
Contributions and thanks
Thanks to all who contributed to the concept/code:
Simulation example
Benchmarks
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(
"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