Data-Driven Prediction of High-Risk Situations for Cyclists Through Spatiotemporal Patterns and Environmental Conditions

Sarah Di Grande, Mariaelena Berlotti, Salvatore Cavalieri, Daniel G. Costa

2025

Abstract

Enhancing cycling is an increasingly important challenge, especially as it is promoted for its economic, environmental, and health benefits. However, ensuring safety of cyclists is crucial to support this shift in mobility. In this context, machine learning offers promising avenues. This study proposes a novel approach to identifying high-risk locations by dynamically incorporating spatiotemporal patterns and environmental conditions. The method was tested using comprehensive data from Germany, and its design suggests strong potential for generalization to different countries. This work can support urban planners, policymakers, and navigation systems in improving road safety and informing smarter mobility decisions.

Paper Citation

in Harvard Style

Di Grande S., Berlotti M., Cavalieri S. and Costa D. (2025). Data-Driven Prediction of High-Risk Situations for Cyclists Through Spatiotemporal Patterns and Environmental Conditions. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-758-0, SciTePress, pages 677-684. DOI: 10.5220/0013646400003967

in Bibtex Style

@conference{data25,
author={Sarah Di Grande and Mariaelena Berlotti and Salvatore Cavalieri and Daniel Costa},
title={Data-Driven Prediction of High-Risk Situations for Cyclists Through Spatiotemporal Patterns and Environmental Conditions},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2025},
pages={677-684},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013646400003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Data-Driven Prediction of High-Risk Situations for Cyclists Through Spatiotemporal Patterns and Environmental Conditions
SN - 978-989-758-758-0
AU - Di Grande S.
AU - Berlotti M.
AU - Cavalieri S.
AU - Costa D.
PY - 2025
SP - 677
EP - 684
DO - 10.5220/0013646400003967
PB - SciTePress