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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8396

Title: Traffic accident segmentation by means of latent class clustering
Authors: DEPAIRE, Benoit
WETS, Geert
Issue Date: 2008
Publisher: Elsevier
Citation: Accident Analysis and Prevention, 40(4). p. 1257-1266
Abstract: Traffic accident data are often heterogeneous, which can cause certain relationships to remain hidden. Therefore, traffic accident analysis is often performed on a small subset of traffic accidents or several models are built for various traffic accident types. In this paper, we examine the effectiveness of a clustering technique, i.e. latent class clustering, for identifying homogenous traffic accident types. Firstly, a heterogeneous traffic accident data set is segmented into seven clusters, which are translated into seven traffic accident types. Secondly, injury analysis is performed for each cluster. The results of these cluster-based analyses are compared with the results of a full-data analysis. This shows that applying latent class clustering as a preliminary analysis can reveal hidden relationships and can help the domain expert or traffic safety researcher to segment traffic accidents.
URI: http://hdl.handle.net/1942/8396
DOI: 10.1016/j.aap.2008.01.007
ISI #: 000258166800001
ISSN: 0001-4575
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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