Using data mining to predict road crash count with a focus on skid resistance values
| Data(s) |
16/05/2011
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| Resumo |
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. |
| Formato |
application/pdf |
| Identificador | |
| Relação |
http://eprints.qut.edu.au/41458/1/2011_ARRB11_DM_Crash_Count_and_Skid_Rresistance_Emerson_D.pdf 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. |
| Direitos |
See copyright statement below 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: • publish the work in printed format • publish the work in electronic format • publish the work online. The author retains the right to use their work, illustrations (line art, photographs, figures, plates) and research data in their own future works The Author warrants that they are entitled to deal with the Intellectual Property Rights in the works submitted, including clearing all third party intellectual property rights and obtaining formal permission from their respective institutions or employers before submission, where necessary. |
| Fonte |
Computer Science; Faculty of Science and Technology |
| Palavras-Chave | #090507 Transport Engineering #Road crashes #predictive data mining #data mining #road asset management |
| Tipo |
Conference Paper |
