UXsim is a free, open-source macroscopic and mesoscopic network traffic flow simulator developed in Python. It is suitable for simulating large-scale (e.g., city-scale) vehicular transportation. It computes dynamic traffic flow in a network by using traffic flow models commonly utilized by transportation research. UXsim would be especially useful for scientific and educational purposes because of its simple, lightweight, and customizable features; but of course users are free to use UXsim for any purpose.
- Simple and easy-to-use Python implementation of the modern standard models of dynamic network traffic flow.
- Dynamic network traffic simulation with a given network and time-dependent OD demand (i.e., dynamic traffic assignment). Specifically, the following models are used jointly:
- Newell's simplified car-following model (X-Model)
- Lagrangian Incremental Node Model
- Dynamic User Optimum-type Route Choice Model (with inertia)
- Implementation of traffic management schemes (e.g., traffic signals, inflow control, route guidance, congestion pricing).
- Basic analysis of simulation results (e.g., trip completion rate, total travel time, delay), and their export to pandas.DataFrame and CSV files.
- Visualization of simulation results (e.g., time-space diagram, MFD, network traffic animation).
- Can be flexibly customized by users thanks to pure Python implementation.
- Can also be directly integrated with other Python-based frameworks, such as PyTorch for deep reinforcement learning traffic control.
uxsimdirectory: UXsim main packageuxsim/uxsim.py: UXsim main codeuxsim/utils.py: UXsim utilities codeuxsim/utilsdirectory: UXsim utilities files
demos_and_examplesdirectory: Tutorials and examples of UXsimdatdirectory: Sample scenario files
The simplest way is using pip to install from PyPI.
pip install uxsim
Alternative methods (click to see)
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
Download the uxsim directory from this Github repo or the latest release and place it to your local directory as follows:
your_project_directory/
├── uxsim/ # The uxsim directory
│ ├── utils/ # Utility files of UXsim
│ ├── uxsim.py # The main code of UXsim. You can customize this as you wish
│ ├── utils.py # Utility funcsions of UXsim
│ └── ... # Other files in uxsim
├── your_simulation_code.py # Your code if nessesary
├── your_simulation_notebook.ipynb # Your Jupyter notebook if nessesary
├── ... # Other files if nessesary
In this way, you can flexibly customize UXsim by your own.
Import the module using:
from uxsim import *and then define your simulation scenario.
A simple example (click to see)
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
W.addNode("orig1", 0, 0) # Create a node
W.addNode("orig2", 0, 2)
W.addNode("merge", 1, 1)
W.addNode("dest", 2, 1)
W.addLink("link1", "orig1", "merge", length=1000, free_flow_speed=20, jam_density=0.2, merge_priority=0.5) # Create a link
W.addLink("link2", "orig2", "merge", length=1000, free_flow_speed=20, jam_density=0.2, merge_priority=2)
W.addLink("link3", "merge", "dest", length=1000, free_flow_speed=20, jam_density=0.2)
W.adddemand("orig1", "dest", 0, 1000, 0.4) # Create OD traffic demand. Parameters: origin node, destination node, start time, end time, demand flow rate
W.adddemand("orig2", "dest", 500, 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(0, detailed=1, network_font_size=0)
W.analyzer.network(500, detailed=1, network_font_size=0)
W.analyzer.network(1000, detailed=1, network_font_size=0)This code will simulate traffic flow in a Y-shaped network. It would output text to the terminal and images to out directory like below:
simulation setting:
scenario name:
simulation duration: 1200 s
number of vehicles: 700 veh
total road length: 3000 m
time discret. width: 5 s
platoon size: 5 veh
number of timesteps: 240
number of platoons: 140
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| 100 vehs| 17.5 m/s| 0.03 s
1195 s| 25 vehs| 20.0 m/s| 0.05 s
simulation finished
results:
average speed: 13.8 m/s
number of completed trips: 675 / 700
average travel time of trips: 142.7 s
average delay of trips: 42.7 s
delay ratio: 0.299
The Jupyter Notebook Demo summarizes the basic usage and features. For the further details, please see demos_and_examples and UXsim technical documentation.
Belows are simulation result 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:
Traffic signal controller is trained by deep reinforcement learning (DRL) of PyTorch. The left is no control scenario with fixed signal timing; the traffic demand exceeds the network capacity with naive signal setting, and a gridlock occurs. The right is with DRL control scenario, where traffic signal can be changed by observing queue length; although the demand level is the same, traffic is smoothly flowing. Jupyter Notebook of this example is available.
- multi-lane link
- day-to-day dynamics
- taxi and shared mobility (i.e., vehicles travel through a network by passing through specific nodes that are dynamically updated)
- network import from OSMnx
- Done, but still experimental.
- basemap for visualization
- modern packaging
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 from UXsim, please cite:
- Toru Seo. Macroscopic Traffic Flow Simulation: Fundamental Mathematical Theory and Python Implementation. Corona Publishing Co., Ltd., 2023.
- Toru Seo. UXsim: An open source macroscopic and mesoscopic traffic simulator in Python-a technical overview. arXiv preprint arXiv: 2309.17114, 2023
Contribution is welcome! For minor changes including bug fixes, please submit a pull request. If you want a major change, please start a discussion at Issues page first.
If you have any questions or suggestions, please start a discussion at Issues page (in English or Japanese).
I (Toru Seo) work on this project in my spare time. Please understand that my response may be delayed.
UXsim is based on various works in traffic flow theory. We would like to acknowledge the contributions of the research community in advancing this field.
- Toru Seo (Author)
- Collection of related simulators by Seo
- Japanese book "Macroscopic Traffic Simulation: Fundamental Mathematical Theory and Python Implementation" (Author: Toru Seo, Publisher: Corona Publishing Co., Ltd.): UXsim is a significant expansion of the traffic flow simulator UroborosX described in this book.
- Seo Laboratory, Tokyo Institute of Technology
- Interactive Traffic Flow Simulator that Runs on a Web Browser: Play with the same link traffic flow model used in this simulator interactively, and learn the basics of traffic flow and its simulation.