Authors: Jiawei Lu, Xuesong (Simon) Zhou

osm2gmns is an open-source Python package that enables users to conveniently obtain and manipulate any networks from OpenStreetMap (OSM). With a single line of Python code, users can obtain and model drivable, bikeable, walkable, railway, and aeroway networks for any region in the world and output networks to CSV files in GMNS format for seamless data sharing and research collaboration. osm2gmns mainly focuses on providing researchers and practitioners with flexible, standard and ready-to-use multi-modal transportation networks, as well as a bunch of customized and practical functions to facilitate various research and applications on traffic modeling.


Lu, J., & Zhou, X.S. (2023). Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (CAM) system design based on general modeling network specification (GMNS). Transportation Research Part C: Emerging Technologies, 153, 104223. paper link

Main Features

  • Obtain any networks from OSM. osm2gmns parses map data from OSM and output networks to csv files in GMNS format.

  • Standard network format. osm2gmns adopts GMNS as the network format for seamless data sharing and research collaboration.

  • Ready-to-use network. osm2gmns cleans erroneous information from OSM map data and is able to fill up critical missing values, e.g., lanes, speed and capacity, to quickly provide ready-to-use networks.

  • Directed network. two directed links are generated for each bi-directional osm ways identified by osm2gmns.

  • Multi-modal support. five different network types are supported, including auto, bike, walk, railway, and aeroway

  • Customized and practical functions to facilitate traffic modeling. functions include complex intersection consolidation, moevement generation, traffic zone creation, short link combination, network visualization.

  • Multi-resolution modeling. osm2gmns automatically constructs the corresponding mesoscopic and microscopic networks for any macroscopic networks in GMNS format.


For program source code and sample network files, readers can visit the project homepage at ASU Trans+AI Lab Github. Interested readers can also check the link for our online transportation modelling visualization platform, in which network data are provided by osm2gmns.