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Fast map matching (FMM)

This project is an implementation of the fast map matching (FMM) algorithm introduced in this paper Fast map matching, an algorithm integrating hidden Markov model with precomputation, which acelerates the performance by precomputation. A post-print version of the paper can be downloaded at my home page.

Features of fmm

  • Highly optimized code in C++ using Boost libraries (Graph and Rtree index)
  • Considerable map matching speed
    • 25,000-45,000 points/sec (single processor)
    • 200,000 points/sec (parallel matching with 8 processor)
  • Customized output fields (matched point,traversed path, gps error, etc)
  • Support city level road network (100,000 edges tested) and millions of GPS records
  • Tested on Ubuntu, 🎉 Windows and 🎉 Mac platform (C++ program).
  • Python API (preliminary tested on Ubuntu system)
  • 🎉 A web application under the web_demo folder is designed for map matching on OpenStreetMap. A tutorial is provided at OSM map matching and screenshots are shown below.

Installation, examples and documentation.

Check the wiki for details.

Acknowledgement

Great Acknowledgement is given to Edi Buslovich for improving the installation instructions.

Contact and citation

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

Email: cyang(at)kth.se

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

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   
}
}