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Article: A multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity

TitleA multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity
Authors
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. 99 n. pt. A, p. 184-191 How to Cite?
AbstractIn this study, a multivariate random-parameters Tobit model is proposed for the analysis of crash rates by injury severity. In the model, both correlation across injury severity and unobserved heterogeneity across road-segment observations are accommodated. The proposed model is compared with a multivariate (fixed-parameters) Tobit model in the Bayesian context, by using a crash dataset collected from the Traffic Information System of Hong Kong. The dataset contains crash, road geometric and traffic information on 224 directional road segments for a five-year period (2002–2006). The multivariate random-parameters Tobit model provides a much better fit than its fixed-parameters counterpart, according to the deviance information criteria and Bayesian R2, while it reveals a higher correlation between crash rates at different severity levels. The parameter estimates show that a few risk factors (bus stop, lane changing opportunity and lane width) have heterogeneous effects on crash-injury-severity rates. For the other factors, the variances of their random parameters are insignificant at the 95% credibility level, then the random parameters are set to be fixed across observations. Nevertheless, most of these fixed coefficients are estimated with higher precisions (i.e., smaller variances) in the random-parameters model. Thus, the random-parameters Tobit model, which provides a more comprehensive understanding of the factors’ effects on crash rates by injury severity, is superior to the multivariate Tobit model and should be considered a good alternative for traffic safety analysis.
Persistent Identifierhttp://hdl.handle.net/10722/237012
ISSN
2017 Impact Factor: 2.584
2015 SCImago Journal Rankings: 1.109
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZeng, Q-
dc.contributor.authorWen, H-
dc.contributor.authorHuang, H-
dc.contributor.authorPei, X-
dc.contributor.authorWong, SC-
dc.date.accessioned2016-12-20T06:14:48Z-
dc.date.available2016-12-20T06:14:48Z-
dc.date.issued2017-
dc.identifier.citationAccident Analysis & Prevention, 2017, v. 99 n. pt. A, p. 184-191-
dc.identifier.issn0001-4575-
dc.identifier.urihttp://hdl.handle.net/10722/237012-
dc.description.abstractIn this study, a multivariate random-parameters Tobit model is proposed for the analysis of crash rates by injury severity. In the model, both correlation across injury severity and unobserved heterogeneity across road-segment observations are accommodated. The proposed model is compared with a multivariate (fixed-parameters) Tobit model in the Bayesian context, by using a crash dataset collected from the Traffic Information System of Hong Kong. The dataset contains crash, road geometric and traffic information on 224 directional road segments for a five-year period (2002–2006). The multivariate random-parameters Tobit model provides a much better fit than its fixed-parameters counterpart, according to the deviance information criteria and Bayesian R2, while it reveals a higher correlation between crash rates at different severity levels. The parameter estimates show that a few risk factors (bus stop, lane changing opportunity and lane width) have heterogeneous effects on crash-injury-severity rates. For the other factors, the variances of their random parameters are insignificant at the 95% credibility level, then the random parameters are set to be fixed across observations. Nevertheless, most of these fixed coefficients are estimated with higher precisions (i.e., smaller variances) in the random-parameters model. Thus, the random-parameters Tobit model, which provides a more comprehensive understanding of the factors’ effects on crash rates by injury severity, is superior to the multivariate Tobit model and should be considered a good alternative for traffic safety analysis.-
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.titleA multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity-
dc.typeArticle-
dc.identifier.emailWong, SC: hhecwsc@hku.hk-
dc.identifier.authorityWong, SC=rp00191-
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.aap.2016.11.018-
dc.identifier.hkuros270796-
dc.identifier.volume99-
dc.identifier.issuept. A-
dc.identifier.spage184-
dc.identifier.epage191-
dc.identifier.isiWOS:000394063400019-
dc.publisher.placeUnited Kingdom-

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