A Bayesian Spatial Random Parameters Tobit Model For Analyzing Crash Rates On Roadway Segments

Keywords

Bayesian inference; Crash rate; Random parameters; Spatial correlation; Tobit model

Abstract

This study develops a Bayesian spatial random parameters Tobit model to analyze crash rates on road segments, in which both spatial correlation between adjacent sites and unobserved heterogeneity across observations are accounted for. The crash-rate data for a three-year period on road segments within a road network in Florida, are collected to compare the performance of the proposed model with that of a (fixed parameters) Tobit model and a spatial (fixed parameters) Tobit model in the Bayesian context. Significant spatial effect is found in both spatial models and the results of Deviance Information Criteria (DIC) show that the inclusion of spatial correlation in the Tobit regression considerably improves model fit, which indicates the reasonableness of considering cross-segment spatial correlation. The spatial random parameters Tobit regression has lower DIC value than does the spatial Tobit regression, suggesting that accommodating the unobserved heterogeneity is able to further improve model fit when the spatial correlation has been considered. Moreover, the random parameters Tobit model provides a more comprehensive understanding of the effect of speed limit on crash rates than does its fixed parameters counterpart, which suggests that it could be considered as a good alternative for crash rate analysis.

Publication Date

3-1-2017

Publication Title

Accident Analysis and Prevention

Volume

100

Number of Pages

37-43

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.aap.2016.12.023

Socpus ID

85011883183 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85011883183

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