Using data mining on road asset management data in analysing road crashes

Nayak, Richi, Emerson, Daniel, Weligamage, Justin, & Piyatrapoomi, Noppadol (2010) Using data mining on road asset management data in analysing road crashes. In 16th annual TMR Engineering & Technology Forum, 3-5 August 2010, Brisbane, Qld.

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Road safety is a major concern worldwide. Road safety will improve as road conditions and their effects on crashes are continually investigated. This paper proposes to use the capability of data mining to include the greater set of road variables for all available crashes with skid resistance values across the Queensland state main road network in order to understand the relationships among crash, traffic and road variables. This paper presents a data mining based methodology for the road asset management data to find out the various road properties that contribute unduly to crashes. The models demonstrate high levels of accuracy in predicting crashes in roads when various road properties are included. This paper presents the findings of these models to show the relationships among skid resistance, crashes, crash characteristics and other road characteristics such as seal type, seal age, road type, texture depth, lane count, pavement width, rutting, speed limit, traffic rates intersections, traffic signage and road design and so on.

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ID Code: 41334
Item Type: Conference Item (Presentation)
Refereed: Yes
Additional URLs:
Keywords: data mining, road crash
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Transport Engineering (090507)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2010 [please consult the authors]
Deposited On: 21 Apr 2011 03:55
Last Modified: 20 Jul 2017 14:41

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