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An Intelligent System to Enhance Traffic Safety Analysis

Authors:
Andreas Gregoriades
Kyriacos Mouskos
Neville Parker
Ismini Hadjilambrou
Natalia Ruiz-Juri
Aneesh Krishna

Keywords: Traffic Safety; Dynamic Traffic Assignment; Bayesian Belief Networks

Abstract:
Traffic phenomena are characterized by complexity and uncertainty, hence require sophisticated information management to identify patterns relevant to safety. 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 Belief Networks (BBN). The former generates traffic information that is utilised by a Bayesian engine to quantify accident risk. Dynamic compilation of accident risks is used to gives rise to overall traffic safety. Preliminary results from this research have been validated.

Pages: 42 to 47

Copyright: Copyright (c) IARIA, 2011

Publication date: April 17, 2011

Published in: conference

ISSN: 2308-3700

ISBN: 978-1-61208-132-8

Location: Budapest, Hungary

Dates: from April 17, 2011 to April 22, 2011