A data mining driven crash risk profiling method for road asset management

Emerson, Daniel (2013) A data mining driven crash risk profiling method for road asset management. Masters by Research thesis, Queensland University of Technology.

Abstract

This thesis takes a new data mining approach for analyzing road/crash data by developing models for the whole road network and generating a crash risk profile. Roads with an elevated crash risk due to road surface friction deficit are identified. The regression tree model, predicting road segment crash rate, is applied in a novel deployment coined regression tree extrapolation that produces a skid resistance/crash rate curve. Using extrapolation allows the method to be applied across the network and cope with the high proportion of missing road surface friction values. This risk profiling method can be applied in other domains.

Impact and interest:

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312 since deposited on 10 Sep 2013
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ID Code: 61863
Item Type: QUT Thesis (Masters by Research)
Supervisor: Nayak, Richi & Geva, Shlomo
Keywords: data mining, risk managment, data mining project frameworks, information managment, deployment, extrapolation, road crash analysis, skid resistance
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 10 Sep 2013 06:16
Last Modified: 04 Sep 2015 02:51

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