Using data mining to predict road crash count with a focus on skid resistance values
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Description
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 | ||
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| Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||
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| Measurements or Duration: | 18 pages | ||
| Keywords: | Data Mining, Predictive Data Mining, Road Asset Management, Road Crashes | ||
| ISBN: | 1-876592-68-0 | ||
| Pure ID: | 32033648 | ||
| Divisions: | Past > QUT Faculties & Divisions > Faculty of Science and Technology Past > QUT Faculties & Divisions > Science & Engineering Faculty |
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| Copyright Owner: | Consult author(s) regarding copyright matters | ||
| Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||
| Deposited On: | 12 May 2011 01:23 | ||
| Last Modified: | 09 Mar 2024 11:15 |
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