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Applying data mining to assess crash risk on curves

Chen, Samantha, Rakotonirainy, Andry, & Loke, Seng Wai (2009) Applying data mining to assess crash risk on curves. In Grzebieta, Raphael & McTiernan, David (Eds.) Proceedings of the 2009 Australasian Road Safety Research, Policing and Education Conference and the 2009 Intelligent Speed Adaptation (ISA) Conference, Roads and Traffic Authority of New South Wales, Australia, Sydney Convention and Exhibition Centre, Sydney, New South Wales, pp. 500-507.

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Abstract

The wide range of contributing factors and circumstances surrounding crashes on road curves suggest that no single intervention can prevent these crashes. This paper presents a novel methodology, based on data mining techniques, to identify contributing factors and the relationship between them. It identifies contributing factors that influence the risk of a crash. Incident records, described using free text, from a large insurance company were analysed with rough set theory. Rough set theory was used to discover dependencies among data, and reasons using the vague, uncertain and imprecise information that characterised the insurance dataset. The results show that male drivers, who are between 50 and 59 years old, driving during evening peak hours are involved with a collision, had a lowest crash risk. Drivers between 25 and 29 years old, driving from around midnight to 6 am and in a new car has the highest risk. The analysis of the most significant contributing factors on curves suggests that drivers with driving experience of 25 to 42 years, who are driving a new vehicle have the highest crash cost risk, characterised by the vehicle running off the road and hitting a tree. This research complements existing statistically based tools approach to analyse road crashes. Our data mining approach is supported with proven theory and will allow road safety practitioners to effectively understand the dependencies between contributing factors and the crash type with the view to designing tailored countermeasures.

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ID Code: 29396
Item Type: Conference Paper
Keywords: data mining, text mining, rough set analysis, crash risk, relationships, road curve
ISBN: 9781921692260
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Divisions: Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Psychology & Counselling
Copyright Owner: Copyright 2009 [please consult the authors]
Deposited On: 08 Jan 2010 09:56
Last Modified: 01 Mar 2012 00:03

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