Probabilistic map-matching using particle filters
Machine Learning
2016-11-30 v1
Abstract
Increasing availability of vehicle GPS data has created potentially transformative opportunities for traffic management, route planning and other location-based services. Critical to the utility of the data is their accuracy. Map-matching is the process of improving the accuracy by aligning GPS data with the road network. In this paper, we propose a purely probabilistic approach to map-matching based on a sequential Monte Carlo algorithm known as particle filters. The approach performs map-matching by producing a range of candidate solutions, each with an associated probability score. We outline implementation details and thoroughly validate the technique on GPS data of varied quality.
Cite
@article{arxiv.1611.09706,
title = {Probabilistic map-matching using particle filters},
author = {Kira Kempinska and Toby Davies and John Shawe-Taylor},
journal= {arXiv preprint arXiv:1611.09706},
year = {2016}
}
Comments
Proceedings of GISRUK 2016 conference