utdf2gmns

Authors: Xiangyong Luo, Xuesong (Simon) Zhou

utdf2gmns is a tool to convert utdf file to GMNS format: synchro utdf format to gmns signal timing format at movement layer

If you use utdf2gmns in your research, please cite: https://github.com/xyluo25/utdf2gmns

Required Data Input Files:

  • [X] UTDF.csv

  • [X] node.csv (GMNS format)

  • [X] movement.csv (GMNS format)

Produced outputs

If input folder have UTDF.csv only, outputs are:

  • A dictionary store utdf data with keys: Networks, Node, Links, Timeplans, Lanes, and utdf_intersection

  • A file named utdf2gmns.pickle to store dictionary object.

If input folder have extra node.csv and movement.csv, outputs are:

  • Two files named: movement_utdf.csv and utdf_intersection.csv

Sample results: datasets

Package dependencies

  • [X] geocoder==1.38.1

  • [X] pandas==1.4.4

Data Conversion Steps

Step 1: Read UTDF.csv file and perform geocoding, then produce utdf_geo, utdf_lane, and utdf_phase_timeplans.

Step 2: Match four files (utdf_geo, node, utdf_lane, utdf_pahse_timeplans, movement) to produce movement_utdf

Call for Contributions

The utdf2gmns project welcomes your expertise and enthusiasm!

Small improvements or fixes are always appreciated. If you are considering larger contributions to the source code, please contact us through email:

Xiangyong Luo : luoxiangyong01@gmail.com

Dr. Xuesong Simon Zhou : xzhou74@asu.edu

Writing code isn’t the only way to contribute to utdf2gmns. You can also:

  • review pull requests

  • help us stay on top of new and old issues

  • develop tutorials, presentations, and other educational materials

  • develop graphic design for our brand assets and promotional materials

  • translate website content

  • help with outreach and onboard new contributors

  • write grant proposals and help with other fundraising efforts

For more information about the ways you can contribute to utdf2gmns, visit [our GitHub](https://github.com/asu-trans-ai-lab/utdf2gmns). If you’ re unsure where to start or how your skills fit in, reach out! You can ask by opening a new issue or leaving a comment on a relevant issue that is already open on GitHub.

Contents

For program source code and sample network files, readers can visit the project homepage at ASU Trans+AI Lab Github.