title: Traffic accident segmentation by means of latent class clustering.
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.
authors.affiliation: Transportation Research Institute, Hasselt University, Wetenschapspark 5 bus 6, Diepenbeek, Belgium. benoit.depaire@uhasselt.be
authors.forename: Benoît
authors.initials: B
authors.lastname: Depaire
authors.forename: Geert
authors.initials: G
authors.lastname: Wets
authors.forename: Koen
authors.initials: K
authors.lastname: Vanhoof
mesh_terms: D000063:Accidents, Traffic / Q000145:classification / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001530:Belgium / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007223:Infant; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D018570:Risk Assessment; D014947:Wounds and Injuries / Q000453:epidemiology*