Exploring the potential of data mining techniques for the analysis of accident patterns

Prato, Carlo Giacomo, Bekhor, Shlomo, Galtzur, Ayelet, Mahalel, David and Prashker, Joseph N. (2010). Exploring the potential of data mining techniques for the analysis of accident patterns. In: Proceedings of the 12th WCTR Conference. WCTR 2010: 12th World Conference on Transportation Research, Lisbon, Portugal, (). 11-15 July 2010.

Author Prato, Carlo Giacomo
Bekhor, Shlomo
Galtzur, Ayelet
Mahalel, David
Prashker, Joseph N.
Title of paper Exploring the potential of data mining techniques for the analysis of accident patterns
Conference name WCTR 2010: 12th World Conference on Transportation Research
Conference location Lisbon, Portugal
Conference dates 11-15 July 2010
Proceedings title Proceedings of the 12th WCTR Conference
Publication Year 2010
Sub-type Other
Language eng
Abstract/Summary Research in road safety faces major challenges: individuation of the most significant determinants of traffic accidents, recognition of the most recurrent accident patterns, and allocation of resources necessary to address the most relevant issues. This paper intends to comprehend which data mining techniques appear more suitable for the objective of providing a broad picture of the road safety situation and individuating specific problems that the allocation of resources should address first. Descriptive (i.e., K-means and Kohonen clustering) and predictive (i.e., decision trees, neural networks and association rules) data mining techniques are implemented for the analysis of traffic accidents occurred in Israel between 2001 and 2004. Results show that descriptive techniques are useful to classify the large amount of analyzed accidents, even though introduce problems with respect to the clear-cut definition of the clusters and the triviality of the description of the main accident characteristics. Results also show that prediction techniques present problems with respect to the large number of rules produced by decision trees, the interpretation of neural network results in terms of relative importance of input and intermediate neurons, and the relative importance of hundreds of association rules. Further research should investigate whether limiting the analysis to fatal accidents would simplify the task of data mining techniques in recognizing accident patterns without the “noise” probably created by considering also severe and light injury accidents.
Q-Index Code EX
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Conference Paper
Collection: School of Civil Engineering Publications
 
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Created: Tue, 19 Apr 2016, 16:13:12 EST by Carlo Prato on behalf of School of Civil Engineering