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• W2559285600 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." @default.
• W2559285600 created "2016-12-08" @default.
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• W2559285600 date "2017-02-01" @default.
• W2559285600 modified "2023-10-03" @default.
• W2559285600 title "A multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity" @default.
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• W2559285600 doi "https://doi.org/10.1016/j.aap.2016.11.018" @default.
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