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Road crash proneness prediction using data mining

Nayak, Richi, Emerson, Daniel, Weligamage, Justin, & Piyatrapoomi, Noppadol (2011) Road crash proneness prediction using data mining. In Ailamaki, Anastasia & Amer-Yahia , Sihem (Eds.) Proceedings of the 14th International Conference on Extending Database Technology, Association for Computing Machinery (ACM), Uppsala, Sweden., pp. 521-526.

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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.

Impact and interest:

1 citations in Scopus
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ID Code: 41343
Item Type: Conference Paper
Keywords: road crashes, road crash proneness, predictive data mining, data mining
ISBN: 978-1-4503-0528-0
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Transport Engineering (090507)
Divisions: Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright ACM 2011
Copyright Statement: This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published In Ailamaki, Anastasia & Amer-Yahia , Sihem (Eds.) Proceedings of the 14th International Conference on Extending Database Technology, Association for Computing Machinery (ACM), Uppsala, Sweden. http://www.edbt.org/Proceedings/2011-Uppsala/
Deposited On: 12 May 2011 11:38
Last Modified: 13 Mar 2013 09:37

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