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Road crash proneness prediction using data mining

By Richi Nayak, Daniel Emerson, Justin Weligamage and Noppadol Piyatrapoomi

Abstract

Developing safe and sustainable road systems is a common goal in all countries. Applications to assist with road asset management and crash minimization are sought universally. This paper presents a data mining methodology using decision trees for modeling the crash proneness of road segments using available road and crash attributes. The models quantify the concept of crash proneness and demonstrate that road segments with only a few crashes have more in common with non-crash roads than roads with higher crash counts. This paper also examines ways of dealing with highly unbalanced data sets encountered in the study

Topics: 090507 Transport Engineering, road crashes, road crash proneness, predictive data mining, data mining
Publisher: Association for Computing Machinery (ACM)
Year: 2011
OAI identifier: oai:eprints.qut.edu.au:41343

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