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dc.contributor.authorSasidharan, Lekshmien_US
dc.contributor.authorWu, Kun-Fengen_US
dc.contributor.authorMenendez, Monicaen_US
dc.date.accessioned2016-03-28T00:04:15Z-
dc.date.available2016-03-28T00:04:15Z-
dc.date.issued2015-12-01en_US
dc.identifier.issn0001-4575en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.aap.2015.09.020en_US
dc.identifier.urihttp://hdl.handle.net/11536/129468-
dc.description.abstractOne of the major challenges in traffic safety analyses is the heterogeneous nature of safety data, due to the sundry factors involved in it. This heterogeneity often leads to difficulties in interpreting results and conclusions due to unrevealed relationships. Understanding the underlying relationship between injury seventies and influential factors is critical for the selection of appropriate safety countermeasures. A method commonly employed to address systematic heterogeneity is to focus on any subgroup of data based on the research purpose. However, this need not ensure homogeneity in the data. In this paper, latent class cluster analysis is applied to identify homogenous subgroups for a specific crash type-pedestrian crashes. The manuscript employs data from police reported pedestrian (2009-2012) crashes in Switzerland. The analyses demonstrate that dividing pedestrian severity data into seven clusters helps in reducing the systematic heterogeneity of the data and to understand the hidden relationships between crash severity levels and socio-demographic, environmental, vehicle, temporal, traffic factors, and main reason for the crash. The pedestrian crash injury severity models were developed for the whole data and individual clusters, and were compared using receiver operating characteristics curve, for which results favored clustering. Overall, the study suggests that latent class clustered regression approach is suitable for reducing heterogeneity and revealing important hidden relationships in traffic safety analyses. (C) 2015 Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectLatent classen_US
dc.subjectCluster analysisen_US
dc.subjectPedestrianen_US
dc.subjectSeverityen_US
dc.subjectBinary logiten_US
dc.subjectReceiver operating characteristic (ROC) curveen_US
dc.subjectSwitzerlanden_US
dc.titleExploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerlanden_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.aap.2015.09.020en_US
dc.identifier.journalACCIDENT ANALYSIS AND PREVENTIONen_US
dc.citation.volume85en_US
dc.citation.spage219en_US
dc.citation.epage228en_US
dc.contributor.department土木工程學系zh_TW
dc.contributor.departmentDepartment of Civil Engineeringen_US
dc.identifier.wosnumberWOS:000364884800021en_US
dc.citation.woscount0en_US
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