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- Publisher Website: 10.1016/j.aap.2010.12.026
- Scopus: eid_2-s2.0-79952438774
- PMID: 21376914
- WOS: WOS:000288971200072
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Article: A joint-probability approach to crash prediction models
Title | A joint-probability approach to crash prediction models |
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Authors | |
Keywords | Crash frequency Crash severity Full Bayesian method Joint probability Markov chain Monte Carlo (MCMC) approach |
Issue Date | 2011 |
Publisher | Elsevier 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? |
Abstract | Many 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 Identifier | http://hdl.handle.net/10722/150553 |
ISSN | 2023 Impact Factor: 5.7 2023 SCImago Journal Rankings: 1.897 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Pei, X | en_US |
dc.contributor.author | Wong, SC | en_US |
dc.contributor.author | Sze, NN | en_US |
dc.date.accessioned | 2012-06-26T06:05:39Z | - |
dc.date.available | 2012-06-26T06:05:39Z | - |
dc.date.issued | 2011 | en_US |
dc.identifier.citation | Accident Analysis And Prevention, 2011, v. 43 n. 3, p. 1160-1166 | en_US |
dc.identifier.issn | 0001-4575 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/150553 | - |
dc.description.abstract | Many 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.language | eng | en_US |
dc.publisher | Elsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/336/description#description | en_US |
dc.relation.ispartof | Accident Analysis and Prevention | en_US |
dc.subject | Crash frequency | - |
dc.subject | Crash severity | - |
dc.subject | Full Bayesian method | - |
dc.subject | Joint probability | - |
dc.subject | Markov chain Monte Carlo (MCMC) approach | - |
dc.subject.mesh | Accidents, Traffic - Classification - Mortality - Prevention & Control - Statistics & Numerical Data | en_US |
dc.subject.mesh | Bayes Theorem | en_US |
dc.subject.mesh | Humans | en_US |
dc.subject.mesh | Markov Chains | en_US |
dc.subject.mesh | Models, Statistical | en_US |
dc.subject.mesh | Monte Carlo Method | en_US |
dc.subject.mesh | Safety - Statistics & Numerical Data | en_US |
dc.subject.mesh | Survival Analysis | en_US |
dc.subject.mesh | Wounds And Injuries - Classification - Epidemiology - Mortality - Prevention & Control | en_US |
dc.title | A joint-probability approach to crash prediction models | en_US |
dc.type | Article | en_US |
dc.identifier.email | Wong, SC:hhecwsc@hku.hk | en_US |
dc.identifier.authority | Wong, SC=rp00191 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1016/j.aap.2010.12.026 | en_US |
dc.identifier.pmid | 21376914 | - |
dc.identifier.scopus | eid_2-s2.0-79952438774 | en_US |
dc.identifier.hkuros | 184813 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-79952438774&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 43 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.spage | 1160 | en_US |
dc.identifier.epage | 1166 | en_US |
dc.identifier.isi | WOS:000288971200072 | - |
dc.publisher.place | United Kingdom | en_US |
dc.identifier.scopusauthorid | Pei, X=36728058000 | en_US |
dc.identifier.scopusauthorid | Wong, SC=24323361400 | en_US |
dc.identifier.scopusauthorid | Sze, NN=8412831200 | en_US |
dc.identifier.citeulike | 8674751 | - |
dc.identifier.issnl | 0001-4575 | - |