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Article: A joint-probability approach to crash prediction models

TitleA joint-probability approach to crash prediction models
Authors
Issue Date2011
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 And Prevention, 2011, v. 43 n. 3, p. 1160-1166 How to Cite?
AbstractMany road safety researchers have used crash prediction models, such as Poisson and negative binomial regression models, to investigate the associations between crash occurrence and explanatory factors. Typically, they have attempted to separately model the crash frequencies of different severity levels. However, this method may suffer from serious correlations between the model estimates among different levels of crash severity. Despite efforts to improve the statistical fit of crash prediction models by modifying the data structure and model estimation method, little work has addressed the appropriate interpretation of the effects of explanatory factors on crash occurrence among different levels of crash severity. In this paper, a joint probability model is developed to integrate the predictions of both crash occurrence and crash severity into a single framework. For instance, the Markov chain Monte Carlo (MCMC) approach full Bayesian method is applied to estimate the effects of explanatory factors. As an illustration of the appropriateness of the proposed joint probability model, a case study is conducted on crash risk at signalized intersections in Hong Kong. The results of the case study indicate that the proposed model demonstrates a good statistical fit and provides an appropriate analysis of the influences of explanatory factors. © 2010 Elsevier Ltd All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/150553
ISSN
2015 Impact Factor: 2.07
2015 SCImago Journal Rankings: 1.109
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorPei, Xen_US
dc.contributor.authorWong, SCen_US
dc.contributor.authorSze, NNen_US
dc.date.accessioned2012-06-26T06:05:39Z-
dc.date.available2012-06-26T06:05:39Z-
dc.date.issued2011en_US
dc.identifier.citationAccident Analysis And Prevention, 2011, v. 43 n. 3, p. 1160-1166en_US
dc.identifier.issn0001-4575en_US
dc.identifier.urihttp://hdl.handle.net/10722/150553-
dc.description.abstractMany road safety researchers have used crash prediction models, such as Poisson and negative binomial regression models, to investigate the associations between crash occurrence and explanatory factors. Typically, they have attempted to separately model the crash frequencies of different severity levels. However, this method may suffer from serious correlations between the model estimates among different levels of crash severity. Despite efforts to improve the statistical fit of crash prediction models by modifying the data structure and model estimation method, little work has addressed the appropriate interpretation of the effects of explanatory factors on crash occurrence among different levels of crash severity. In this paper, a joint probability model is developed to integrate the predictions of both crash occurrence and crash severity into a single framework. For instance, the Markov chain Monte Carlo (MCMC) approach full Bayesian method is applied to estimate the effects of explanatory factors. As an illustration of the appropriateness of the proposed joint probability model, a case study is conducted on crash risk at signalized intersections in Hong Kong. The results of the case study indicate that the proposed model demonstrates a good statistical fit and provides an appropriate analysis of the influences of explanatory factors. © 2010 Elsevier Ltd All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/336/description#descriptionen_US
dc.relation.ispartofAccident Analysis and Preventionen_US
dc.subject.meshAccidents, Traffic - Classification - Mortality - Prevention & Control - Statistics & Numerical Dataen_US
dc.subject.meshBayes Theoremen_US
dc.subject.meshHumansen_US
dc.subject.meshMarkov Chainsen_US
dc.subject.meshModels, Statisticalen_US
dc.subject.meshMonte Carlo Methoden_US
dc.subject.meshSafety - Statistics & Numerical Dataen_US
dc.subject.meshSurvival Analysisen_US
dc.subject.meshWounds And Injuries - Classification - Epidemiology - Mortality - Prevention & Controlen_US
dc.titleA joint-probability approach to crash prediction modelsen_US
dc.typeArticleen_US
dc.identifier.emailWong, SC:hhecwsc@hku.hken_US
dc.identifier.authorityWong, SC=rp00191en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.aap.2010.12.026en_US
dc.identifier.pmid21376914-
dc.identifier.scopuseid_2-s2.0-79952438774en_US
dc.identifier.hkuros184813-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79952438774&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume43en_US
dc.identifier.issue3en_US
dc.identifier.spage1160en_US
dc.identifier.epage1166en_US
dc.identifier.isiWOS:000288971200072-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridPei, X=36728058000en_US
dc.identifier.scopusauthoridWong, SC=24323361400en_US
dc.identifier.scopusauthoridSze, NN=8412831200en_US
dc.identifier.citeulike8674751-

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