Urban Traffic Incident Detection for Organic Traffic Control: A Density-based Clustering Approach

Ingo Thomsen, Yannick Zapfe, Sven Tomforde

2021

Abstract

The traffic demands in urban road networks can fluctuate immensely. The Organic Traffic Control (OTC) offers a resilient traffic management to control such traffic demands. An additional challenge is the detection of unforeseen traffic incidents. To enhance the capabilities of OTC accordingly, we outline a traffic incident algorithm based on DBSCAN, a density-based clustering algorithm: In a simulated urban road network, equipped with traffic light controllers at intersections, vehicle detectors are used to gather traffic flow data. The clustering of this time series data to detect simulated road blockages is expanded using various filters. This extension of the initial clustering is the result of an manual evaluation process, which shows the principal applicability of this approach.

Paper Citation

in Harvard Style

Thomsen I., Zapfe Y. and Tomforde S. (2021). Urban Traffic Incident Detection for Organic Traffic Control: A Density-based Clustering Approach. In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-513-5, pages 152-160. DOI: 10.5220/0010454101520160

in Bibtex Style

@conference{vehits21,
author={Ingo Thomsen and Yannick Zapfe and Sven Tomforde},
title={Urban Traffic Incident Detection for Organic Traffic Control: A Density-based Clustering Approach},
booktitle={Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2021},
pages={152-160},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010454101520160},
isbn={978-989-758-513-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Urban Traffic Incident Detection for Organic Traffic Control: A Density-based Clustering Approach
SN - 978-989-758-513-5
AU - Thomsen I.
AU - Zapfe Y.
AU - Tomforde S.
PY - 2021
SP - 152
EP - 160
DO - 10.5220/0010454101520160