Skip to main content

DLSim is an open-source, cross-platform, lightweight, and fast Python traffic assignment tool adopted and modified from ASU TransAI Lab

Project description

"Dynamic Traffic Simulation Package with Multi-Resolution Modelling" (DLSim-MRM) is an open source, high-fidelity multi-resolution (i.e., macroscopic, mesoscopic, and microscopic simulation) traffic simulation package which users jointly apply varying temporal and spatial resolutions to solve a single question or set of questions that mirror the physical world with complex intersections. Users can perform traffic assignments and feed results from one model to another while maintaining consistency between the model assumptions. DLSim-MRM typically takes the following steps for simulation based on General Modeling Network Specification (GMNS) format:

  1. Use demand forecasting models to determine overall trip patterns in a regional network, including trip generation, trip distribution, mode split, and initial O-D matrices.
  2. Use mesoscopic simulation-based dynamic traffic assignment (DTA) to realistically assign traffic to the network by accounting for strategic traveler behavior.
  3. Use microscopic analysis of traffic at the corridor level or subnetwork level.

DLSim-MRM uses 3 open-source packages; OSM2GMNS, Path4GMNS and Vol2Timing with the additional developments along the multi-resolution modelling and dyanmic traffic simulation.

-OSM2GMNS can help users easily convert networks from OpenStreetMap to .csv files with standard GMNS format for visualization, traffic simulation and planning purpose.

-Path4GMNS is an open-source AMS library for efficiently macroscopic and mesoscopic traffic assignment based on General Modeling Network Specification (GMNS) format.

-Vol2Timing is a python tool aims to offer a light-weight computational engine to generate optimize signal control timing data, and analyze the effectiveness of signal control strategies.

Installation:

DLSim has been published on PyPI, and can be installed by using package manager pip to install DLSim.

pip install DLSim

If you need a specific version of DLSim, say, 0.2.1,

$ pip install dlsim==0.2.11

Usage

Find the shortest path (based on distance) and output it in the format of a sequence of node/link IDs.

from DLSim import DLSim

# load the DLSim class
DL = DLSim()

# check the working directory
DL.check_working_directory()

# check all the required files exist
DL.check_DLSim_input_files()

# load and update settings
DL.DLSim_settings

# perform kernel network assignment simulation
DL.perform_kernel_network_assignment_simulation()



Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

License

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dlsim-0.2.12.tar.gz (401.3 kB view details)

Uploaded Source

Built Distribution

DLSim-0.2.12-py3-none-any.whl (400.7 kB view details)

Uploaded Python 3

File details

Details for the file dlsim-0.2.12.tar.gz.

File metadata

  • Download URL: dlsim-0.2.12.tar.gz
  • Upload date:
  • Size: 401.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.11.7

File hashes

Hashes for dlsim-0.2.12.tar.gz
Algorithm Hash digest
SHA256 892fbd38dbc22e0a6ac6aaba12abee18292bc01eacec0d7421fd6cb72258f85f
MD5 29f9576a48a70485af152e454af65c1c
BLAKE2b-256 ad7e776353eeec82c72885e050ce12bc767417c73141815810aa80720bbffd48

See more details on using hashes here.

File details

Details for the file DLSim-0.2.12-py3-none-any.whl.

File metadata

  • Download URL: DLSim-0.2.12-py3-none-any.whl
  • Upload date:
  • Size: 400.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.11.7

File hashes

Hashes for DLSim-0.2.12-py3-none-any.whl
Algorithm Hash digest
SHA256 8105fb73ff8d0743ebf9e38fc024ebdb2d8602716ee206f0ee0dfa199fa2cdf1
MD5 380e455a20ae0c8fda73e94e33164e36
BLAKE2b-256 c396d7c4182350b4d1c6e4699bdd8d92d4c12049968e272ac97762a6818d79bb

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page