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

• Published in: Accident analysis and prevention Vol. 99; no. Pt A; pp. 184 - 191
• Main Authors: Zeng, Qiang, Wen, Huiying, Huang, Helai, Pei, Xin, Wong, S.C.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.02.2017
• Subjects: Accidents, Traffic - statistics & numerical data; Bayes Theorem; Bayesian inference; Crash rate by severity; Female; Hong Kong - epidemiology; Humans; Male; Models, Statistical; Multivariate Tobit model; Random parameters; Risk Factors; Safety - statistics & numerical data; Trauma Severity Indices; Wounds and Injuries - classification; Multivariate Tobit model; Crash rate by severity; Bayesian inference; Random parameters
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2016.11.018

•A multivariate random-parameters Tobit model is proposed to analyze crash rate by injury severity.•Crash, traffic and roadway geometric data from Hong Kong are used to demonstrate the proposed model.•Heterogeneous effects on crash rate by severity are found in certain risk factors.•A high and significantly positive correlation between KSI and slight injury crash rates is found.•The proposed model substantially outperforms a multivariate fixed-parameters Tobit model on model fit. 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.