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FMM is an open source map matching framework in C++ and Python. It solves the problem of matching noisy GPS data to a road network. The design considers maximizing performance, scalability and functionality.

Check the online demo.

• High performance: C++ implementation using Rtree, optimized routing, parallel computing (OpenMP).
• Python API: jupyter-notebook and web app
• Scalibility: millions of GPS points and millions of road edges.
• Multiple data format:
  • Road network in OpenStreetMap or ESRI shapefile.
  • GPS data in Point CSV, Trajectory CSV and Trajectory Shapefile (more details).
• Detailed matching information: traversed path, geometry, individual matched edges, GPS error, etc. More information at here.
• Multiple algorithms: FMM (for small and middle scale network) and STMatch (for large scale road network)
• Platform support: Unix (ubuntu) , Mac and Windows(cygwin environment).
• Hexagon match: 🎉 Match to the uber's h3 Hexagonal Hierarchical Geospatial Indexing System. Check the demo.

We encourage contribution with feature request, bug report or developping new map matching algorithms using the framework.

Map match to OSM road network by drawing

Explore the factor of candidate size k, search radius and GPS error

Explore detailed map matching information

Explore with dual map

Map match to hexagon by drawing

Explore the factor of hexagon level and interpolate

Source code of these screenshots are available at https://github.com/cyang-kth/fmm-examples.

• Check https://fmm-wiki.github.io/ for installation, documentation.
• Check example for simple examples of fmm.
• 🎉 Check https://github.com/cyang-kth/fmm-examples for interactive map matching in notebook.

Check https://cyang-kth.github.io/fmm/

Can Yang, Ph.D. student at KTH, Royal Institute of Technology in Sweden

Email: cyang(at)kth.se

Homepage: https://people.kth.se/~cyang/

FMM originates from an implementation of this paper Fast map matching, an algorithm integrating hidden Markov model with precomputation. A post-print version of the paper can be downloaded at link. Substaintial new features have been added compared with the original paper.

Please cite fmm in your publications if it helps your research:

Can Yang & Gyozo Gidofalvi (2018) Fast map matching, an algorithm
integrating hidden Markov model with precomputation, International Journal of Geographical Information Science, 32:3, 547-570, DOI: 10.1080/13658816.2017.1400548

Bibtex file

@article{doi:10.1080/13658816.2017.1400548,
author = {Can Yang and Gyozo Gidofalvi},
title = {Fast map matching, an algorithm integrating hidden Markov model with precomputation},
journal = {International Journal of Geographical Information Science},
volume = {32},
number = {3},
pages = {547-570},
year  = {2018},
publisher = {Taylor & Francis},
doi = {10.1080/13658816.2017.1400548},
URL = {
        https://doi.org/10.1080/13658816.2017.1400548
},
eprint = {
        https://doi.org/10.1080/13658816.2017.1400548   
}
}