An Intelligent Transportation System for Accident Risk Index Quantification

Andreas Gregoriades, Kyriacos Mouskos, Harris Michail

2012

Abstract

Traffic phenomena are characterized by complexity and uncertainty, hence require sophisticated information management to identify patterns relevant to safety and reliability. Traffic information systems have emerged with the aim to ease traffic congestion and improve road safety. However, assessment of traffic safety and congestion requires significant amount of data which in most cases is not available. This work illustrates an approach that aims to alleviate this problem through the integration of two mature technologies namely, simulation-based Dynamic Traffic Assignment (DTA) and Bayesian Networks (BN). The former generates traffic flow data, utilised by a BN model that quantifies accident risk. Traffic flow data is used to assess the accident risk index per road section and hence, escape from the limitation of traditional approaches that use only accident frequencies to quantify accident risk. The development of the BN model combines historical accident records obtained from the Cyprus police and domain knowledge from road safety.

Paper Citation

in Harvard Style

Gregoriades A., Mouskos K. and Michail H. (2012). An Intelligent Transportation System for Accident Risk Index Quantification . In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-10-5, pages 318-321. DOI: 10.5220/0003989203180321

in Bibtex Style

@conference{iceis12,
author={Andreas Gregoriades and Kyriacos Mouskos and Harris Michail},
title={An Intelligent Transportation System for Accident Risk Index Quantification},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2012},
pages={318-321},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003989203180321},
isbn={978-989-8565-10-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - An Intelligent Transportation System for Accident Risk Index Quantification
SN - 978-989-8565-10-5
AU - Gregoriades A.
AU - Mouskos K.
AU - Michail H.
PY - 2012
SP - 318
EP - 321
DO - 10.5220/0003989203180321