Map Matching Algorithm for Large-scale Datasets

David Fiedler, Michal Čáp, Jan Nykl, Pavol Žilecký

2022

Abstract

GPS receivers embedded in cell phones and connected vehicles generate series of location measurements that can be used for various analytical purposes. A common preprocessing step of this data is the so-called map matching. The goal of map matching is to infer the trajectory that the device followed in a road network from a potentially sparse series of noisy location measurements. Although accurate and robust map matching algorithms based on probabilistic models exist, they are computationally heavy and thus impractical for processing large datasets. In this paper, we present a scalable map matching algorithm based on Dijkstra’s shortest path method, that is both accurate and applicable to large datasets. Our experiments on a publicly available dataset showed that the proposed method achieves accuracy that is comparable to that of the existing map matching methods using only a fraction of computational resources. As a result, our algorithm can be used to efficiently process large datasets of noisy and potentially sparse location data that would be unexploitable using existing techniques due to their high computational requirements.

Paper Citation

in Harvard Style

Fiedler D., Čáp M., Nykl J. and Žilecký P. (2022). Map Matching Algorithm for Large-scale Datasets. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 500-508. DOI: 10.5220/0010849100003116

in Bibtex Style

@conference{icaart22,
author={David Fiedler and Michal Čáp and Jan Nykl and Pavol Žilecký},
title={Map Matching Algorithm for Large-scale Datasets},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={500-508},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010849100003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Map Matching Algorithm for Large-scale Datasets
SN - 978-989-758-547-0
AU - Fiedler D.
AU - Čáp M.
AU - Nykl J.
AU - Žilecký P.
PY - 2022
SP - 500
EP - 508
DO - 10.5220/0010849100003116