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toruseo / UXsim

Vehicular traffic flow simulator in road network, written in pure Python
https://toruseo.jp/UXsim/docs/
MIT License
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dynamic-traffic-assignment simulation traffic traffic-flow traffic-flow-simulation traffic-simulation transportation transportation-network

readme

UXsim: Network traffic flow simulator in pure Python

PyPi Conda Version Demo in Colab codecov arXiv Static Badge

UXsim is a free, open-source macroscopic and mesoscopic network traffic flow simulator written in Python. It simulates the movements of car travelers and traffic congestion in road networks. It is suitable for simulating large-scale (e.g., city-scale) traffic phenomena. UXsim is especially useful for scientific and educational purposes because of its simple, lightweight, and customizable features, but users are free to use UXsim for any purpose.

If you are interested, please see:

Main Features

Simulation Examples

Large-scale scenario

Below are simulation results where approximately 60000 vehicles pass through a 10km x 10km grid network in 2 hours. The computation time was about 30 seconds on a standard desktop PC.

Visualization of link traffic states (thicker lines mean more vehicles, darker colors mean slower speeds) and some vehicle trajectories:

Vehicle trajectory diagram on a corridor of the above network:

Deep reinforcement learning signal control using PyTorch

A traffic signal controller is trained by deep reinforcement learning (DRL) using PyTorch. The left (or upper) scenario shows no control with fixed signal timing; the traffic demand exceeds the network capacity with the naive signal setting, and a gridlock occurs. The right (or bottom) scenario shows DRL control, where the traffic signal can be changed by observing queue length; although the demand level is the same, traffic flows smoothly. A Jupyter Notebook of this example is available.

Interactive GUI for exploring a simulation result

https://github.com/toruseo/UXsim/assets/34780089/ec780a33-d9ba-4068-a005-0b06127196d9

Install

Using pip

The simplest way is to use pip to install from PyPI:

pip install uxsim

Using conda

You can also install with conda from conda-forge channel:

conda install uxsim

For the details, please see here.

Alternative methods for advanced users (click to see) ### Using pip with custom configuration You can also use `pip` to install the GitHub version: ``` pip install -U -e git+https://github.com/toruseo/uxsim@main#egg=uxsim ``` Or any other (development) branch on this repo or your own fork: ``` pip install -U -e git+https://github.com/YOUR_FORK/uxsim@YOUR_BRANCH#egg=uxsim ``` ### Manual install Download the `uxsim` directory from this Github repo or [the latest release](https://github.com/toruseo/UXsim/releases/latest/download/uxsim.zip) and place it in your local directory as follows: ``` your_project_directory/ ├── uxsim/ # The uxsim directory │ ├── uxsim.py # The main code of UXsim. You can customize this as you wish │ └── ... # Other files and directories in uxsim ├── your_simulation_code.py # Your code if necessary ├── your_simulation_notebook.ipynb # Your Jupyter notebook if necessary ├── ... # Other files if necessary ``` This way, you can flexibly customize UXsim on your own.

Getting Started

As a simple example, the following code will simulate traffic flow in a Y-shaped network. 

from uxsim import *

# Define the main simulation
# Units are standardized to seconds (s) and meters (m)
W = World(
    name="",    # Scenario name
    deltan=5,   # Simulation aggregation unit delta n
    tmax=1200,  # Total simulation time (s)
    print_mode=1, save_mode=1, show_mode=0,    # Various options
    random_seed=0    # Set the random seed
)

# Define the scenario
## Create nodes
W.addNode(name="orig1", x=0, y=0)
W.addNode("orig2", 0, 2)
W.addNode("merge", 1, 1)
W.addNode("dest", 2, 1)
## Create links between nodes
W.addLink(name="link1", start_node="orig1", end_node="merge",
          length=1000, free_flow_speed=20, number_of_lanes=1)
W.addLink("link2", "orig2", "merge", length=1000, free_flow_speed=20, number_of_lanes=1)
W.addLink("link3", "merge", "dest", length=1000, free_flow_speed=20, number_of_lanes=1)
## Create OD traffic demand between nodes
W.adddemand(orig="orig1", dest="dest", t_start=0, t_end=1000, flow=0.45)
W.adddemand("orig2", "dest", 400, 1000, 0.6)

# Run the simulation to the end
W.exec_simulation()

# Print summary of simulation result
W.analyzer.print_simple_stats()

# Visualize snapshots of network traffic state for several timesteps
W.analyzer.network(100, detailed=1, network_font_size=12)
W.analyzer.network(600, detailed=1, network_font_size=12)
W.analyzer.network(800, detailed=1, network_font_size=12)

It will output text to the terminal and images to the out directory like below:

simulation setting:
 scenario name:
 simulation duration:    1200 s
 number of vehicles:     810 veh
 total road length:      3000 m
 time discret. width:    5 s
 platoon size:           5 veh
 number of timesteps:    240
 number of platoons:     162
 number of links:        3
 number of nodes:        4
 setup time:             0.00 s
simulating...
      time| # of vehicles| ave speed| computation time
       0 s|        0 vehs|   0.0 m/s|     0.00 s
     600 s|      130 vehs|  13.7 m/s|     0.03 s
    1195 s|       75 vehs|  12.3 m/s|     0.06 s
 simulation finished
results:
 average speed:  11.6 m/s
 number of completed trips:      735 / 810
 average travel time of trips:   162.6 s
 average delay of trips:         62.6 s
 delay ratio:                    0.385

Further Reading

To learn more about UXsim, please see:

Main Files

Terms of Use & License

UXsim is released under the MIT License. You are free to use it as long as the source is acknowledged.

When publishing works based on UXsim, please cite:

Works using UXsim is summarized on the Github Wiki page. Please feel free to edit.

Contributing and Discussion

Contributions are welcome! Please see the Contributing Guideline.

If you have any questions or suggestions, please post them to the Issues or Discussions (in English or Japanese).

I (Toru Seo) work on this project in my spare time. Please understand that my response may be delayed.

Acknowledgments

UXsim is based on various works in traffic flow theory and related fields. We acknowledge the contributions of the research community in advancing this field. Specifically, UXsim directly uses the following works:

Related Links