title: A multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity.
abstract: In 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.
authors.affiliation: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641, PR China. Electronic address: zengqiang@scut.edu.cn.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Zeng
authors.affiliation: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641, PR China. Electronic address: hywen@scut.edu.cn.
authors.forename: Huiying
authors.initials: H
authors.lastname: Wen
authors.affiliation: Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, PR China. Electronic address: huanghelai@csu.edu.cn.
authors.forename: Helai
authors.initials: H
authors.lastname: Huang
authors.affiliation: Department of Automation, Tsinghua University, Beijing, PR China. Electronic address: peixin@mail.tsinghua.edu.cn.
authors.forename: Xin
authors.initials: X
authors.lastname: Pei
authors.affiliation: Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong. Electronic address: hhecwsc@hku.hk.
authors.forename: S C
authors.initials: SC
authors.lastname: Wong
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D005260:Female; D006723:Hong Kong / Q000453:epidemiology; D006801:Humans; D008297:Male; D015233:Models, Statistical; D012307:Risk Factors; D012449:Safety / Q000706:statistics & numerical data*; D015599:Trauma Severity Indices; D014947:Wounds and Injuries / Q000145:classification*
keywords: Bayesian inference; Crash rate by severity; Multivariate Tobit model; Random parameters