Align a trace of GPS measurements to a map or road segments.
The matching is based on a Hidden Markov Model (HMM) with non-emitting states. The model can deal with missing data and you can plug in custom transition and emission probability distributions.
Main reference:
Meert Wannes, Mathias Verbeke, "HMM with Non-Emitting States for Map Matching", European Conference on Data Analysis (ECDA), Paderborn, Germany, 2018.
Other references:
Devos Laurens, Vandebril Raf (supervisor), Meert Wannes (supervisor), "Traffic patterns revealed through matrix functions and map matching", Master thesis, Faculty of Engineering Science, KU Leuven, 2018
$ pip install leuvenmapmatching
More information and examples:
leuvenmapmatching.readthedocs.io
Required:
Optional (only loaded when methods are called to rely on these packages):
- matplotlib: For visualisation
- smopy: For visualisation
- nvector: For latitude-longitude computations
- gpxpy: To import GPX files
- pykalman: So smooth paths using a Kalman filter
- pyproj: To project latitude-longitude coordinates to an XY-plane
- rtree: To quickly search locations
Wannes Meert, DTAI, KU Leuven
[email protected]
https://dtai.cs.kuleuven.be
Mathias Verbeke, Sirris
[email protected]
http://www.sirris.be/expertise/data-innovation
Developed with the support of Elucidata.be.
Copyright 2015-2022, KU Leuven - DTAI Research Group, Sirris - Elucidata Group
Apache License, Version 2.0.
