Automatic Identification and Classification of Map-Matching Anomalies in Cycling Routes

Carlos Carvalho, Moisés Ramires, Rui José

2024

Abstract

Road network data models are a key element for many cycling services. However, cyclists often ride unconventional paths that may not be properly represented in those models. This may cause various types of map-matching anomalies, where the map-matched route does not correspond to the real route. In this work, we assess a set of classification models to automatically detect and classify these map-matching anomalies. Using OpenStreetMap road network, we generated the map-matched routes for a dataset of 98 cycling GPS traces. To produce ground-truth data, we visually inspected each result to identify and classify every map-matching anomaly, and computed several similarity measures between each GPS trace and the respective map-matched segment. Based on this data, we trained several classification models with different feature engineering approaches to perform binary and multi-class classification. The results show that binary classifiers can be very effective in the identification of map-matching anomalies. The best model, a XGBoost classifier, obtained an F1 Score of 0.906 and an accuracy of 0.893, which outperform other methods. However, the multi-class classifiers had lower performance. This ability to automatically detect and classify map-matching anomalies may help to systematically improve road network models and consequently improve information provided to cyclists and decision-makers.

Paper Citation

in Harvard Style

Carvalho C., Ramires M. and José R. (2024). Automatic Identification and Classification of Map-Matching Anomalies in Cycling Routes. In Proceedings of the 13th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS; ISBN 978-989-758-702-3, SciTePress, pages 17-28. DOI: 10.5220/0012627700003714

in Bibtex Style

@conference{smartgreens24,
author={Carlos Carvalho and Moisés Ramires and Rui José},
title={Automatic Identification and Classification of Map-Matching Anomalies in Cycling Routes},
booktitle={Proceedings of the 13th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS},
year={2024},
pages={17-28},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012627700003714},
isbn={978-989-758-702-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS
TI - Automatic Identification and Classification of Map-Matching Anomalies in Cycling Routes
SN - 978-989-758-702-3
AU - Carvalho C.
AU - Ramires M.
AU - José R.
PY - 2024
SP - 17
EP - 28
DO - 10.5220/0012627700003714
PB - SciTePress