Using data mining to predict road crash count with a focus on skid resistance values

Emerson, Daniel, Nayak, Richi, & Weligamage, Justin (2011) Using data mining to predict road crash count with a focus on skid resistance values. In 3rd International Road Surface Friction Conference, 15-18 May, 2011, Gold Coast, Queensland, Australia.


Road crashes cost world and Australian society a significant proportion of GDP, affecting productivity and causing significant suffering for communities and individuals. This paper presents a case study that generates data mining models that contribute to understanding of road crashes by allowing examination of the role of skid resistance (F60) and other road attributes in road crashes. Predictive data mining algorithms, primarily regression trees, were used to produce road segment crash count models from the road and traffic attributes of crash scenarios. The rules derived from the regression trees provide evidence of the significance of road attributes in contributing to crash, with a focus on the evaluation of skid resistance.

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ID Code: 41458
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Road crashes, predictive data mining, data mining, road asset management
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Transport Engineering (090507)
Divisions: Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: See copyright statement below
Copyright Statement: Copyright Licence Agreement
The Author allows ARRB Group Ltd to publish the work/s submitted for the 3rd International Road Surface Friction Conference 2011, granting ARRB the non-exclusive right to:
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The author retains the right to use their work, illustrations (line art, photographs, figures, plates) and research data in their own future works
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Deposited On: 12 May 2011 01:23
Last Modified: 12 Mar 2013 08:32

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