MobilityTwin-PT

MobilityTwin-PT (OPDTwin)

An open-source digital twin platform for public transport systems.

MobilityTwin-PT enables the creation of interactive digital representations of public transport networks using open data. It transforms network, operational, and built environmental datasets, as well as simulation outputs into a structured digital twin that can be explored, analyzed, and extended for research or planning purposes. The platform is designed to be transparent, modular, and reproducible — lowering the barrier to developing digital twins for public transport. 

What It Does

MobilityTwin-PT provides:

  • Automated processing of open transport data (e.g., GTFS)
  • A structured digital model of routes, stops, and services
  • An interactive web-based visualization interface
  • A backend architecture that can connect to simulation and analytical tools

The layered design allows the digital twin to serve both as a visualization environment and as a foundation for model integration.

Use Cases

MobilityTwin-PT can be used to:

  • Explore and visualize public transport networks and operations (Nowcasting/Monitoring)
  • Collection and analysis of historical data (Retrospective/Understanding)
  • Prototype service or network changes integrating with simulation (Forecasting/Planning)
  • Support teaching and demonstration of digital twin concepts, and extendable integration of computing models 

The platform was demonstrated in a real-world case study in Kista, Stockholm, showing how open data can be transformed into an operational digital twin environment.

Open Resources

Software (Open Source)
https://github.com/MobilityInformaticsLab/opdtwin

Documentation
https://mobilityinformaticslab.github.io/opdtwin/

Scientific Publication
https://www.sciencedirect.com/science/article/pii/S1077291X26000032

Contacts

Jonas Jostmann (jostmann@kth.se), Tong Mo (tong33576@gmail.com), Zhenliang Ma (zhema@kth.se)

MobilityTwin-PT was developed at the Mobility Informatics Lab, KTH Royal Institute of Technology, with support from Digital Futures and the Senseable Stockholm Lab (a collaboration between Stockholm Stad, KTH, and MIT).