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Article: Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

TitleRevisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach
Authors
KeywordsBayesian inference
Conditional autoregressive prior
Crash frequency
Spatial heterogeneity
Unobserved heterogeneity
Issue Date2017
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/336/description#description
Citation
Accident Analysis & Prevention, 2017, v. 98, p. 330-337 How to Cite?
AbstractThis study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness.
Persistent Identifierhttp://hdl.handle.net/10722/237010
ISSN
2023 Impact Factor: 5.7
2023 SCImago Journal Rankings: 1.897
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXu, P-
dc.contributor.authorHuang, H-
dc.contributor.authorDong, N-
dc.contributor.authorWong, SC-
dc.date.accessioned2016-12-20T06:14:46Z-
dc.date.available2016-12-20T06:14:46Z-
dc.date.issued2017-
dc.identifier.citationAccident Analysis & Prevention, 2017, v. 98, p. 330-337-
dc.identifier.issn0001-4575-
dc.identifier.urihttp://hdl.handle.net/10722/237010-
dc.description.abstractThis study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness.-
dc.languageeng-
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/336/description#description-
dc.relation.ispartofAccident Analysis & Prevention-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectBayesian inference-
dc.subjectConditional autoregressive prior-
dc.subjectCrash frequency-
dc.subjectSpatial heterogeneity-
dc.subjectUnobserved heterogeneity-
dc.titleRevisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach-
dc.typeArticle-
dc.identifier.emailWong, SC: hhecwsc@hku.hk-
dc.identifier.authorityWong, SC=rp00191-
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.aap.2016.10.015-
dc.identifier.pmid27816012-
dc.identifier.scopuseid_2-s2.0-84994357592-
dc.identifier.hkuros270794-
dc.identifier.volume98-
dc.identifier.spage330-
dc.identifier.epage337-
dc.identifier.isiWOS:000390965500037-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0001-4575-

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