simobility 0.3.0

Lightweight mobility simulation for quick algorithm prototyping

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

• A. Alabbasi, A. Ghosh, and V. Aggarwal, "DeepPool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning", IEEETrans. Intelligent Transportation Systems (to appear). 2019

• C Wang,Y Hou, M Barth, "Data-Driven Multi-step Demand Prediction for Ride-hailing Services Using Convolutional Neural Network". 2019

• J. Ke, F. Xiao, H. Yang, and J. Ye. Optimizing online matching for ride-sourcing services with multi-agent deep reinforcement learning. 2019

Installation

pip install simobility

Contributions and thanks

Thanks to all who contributed to the concept/code:

• Steffen Häußler
• Stephen Privitera
• Sultan Imanhodjaev
• Yábir Benchakhtir

Simulation example

Simple simulation example

Log example

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