Analysing the main and interaction effects of commercial vehicle mix and roadway attributes on crash rates using a Bayesian random-parameter Tobit model

Tiantian Chen; N N Sze; Sikai Chen; Samuel Labi; Qiang Zeng

doi:10.1016/j.aap.2021.106089

Analysing the main and interaction effects of commercial vehicle mix and roadway attributes on crash rates using a Bayesian random-parameter Tobit model

Accid Anal Prev. 2021 May:154:106089. doi: 10.1016/j.aap.2021.106089. Epub 2021 Mar 24.

Authors

Tiantian Chen¹, N N Sze², Sikai Chen³, Samuel Labi⁴, Qiang Zeng⁵

Affiliations

¹ Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. Electronic address: tt-nicole.chen@connect.polyu.hk.
² Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. Electronic address: tony.nn.sze@polyu.edu.hk.
³ Lyles School of Civil Eng., Purdue University, W. Lafayette, IN, USA; Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. Electronic address: chen1670@purdue.edu.
⁴ Lyles School of Civil Eng., Purdue University, W. Lafayette, IN, USA. Electronic address: labi@purdue.edu.
⁵ School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong, 510641, PR China. Electronic address: zengqiang@scut.edu.cn.

PMID: 33773197
DOI: 10.1016/j.aap.2021.106089

Abstract

In previous research, the effects of commercial vehicle proportions (CVP) on overall crash propensity have been found to be significant, but the results have been varied in terms of the effect direction. In addition, the mediating or moderating effects of roadway attributes on the CVP-vs-safety relationships, have not been investigated. In addressing this gap in the literature, this study integrates databases on crashes, traffic, and inventory for Hong Kong road segments spanning 2014-2017. The classes of commercial vehicles considered are public buses, taxi, and light-, medium- and heavy-goods vehicles. Random-parameter Tobit models were estimated using the crash rates. The results suggest that the CVP of each class show credible effects on the crash rates, for the various crash severity levels. The results also suggest that the interaction between CVP and roadway attributes is credible enough to mediate the effect of CVP on crash rates, and the magnitude and direction of such mediation varies across the vehicle classes, crash severity levels, and roadway attribute type in four ways. First, the increasing effect of taxi proportion on slight-injury crash rate is magnified at road segments with high intersection density. Second, the increasing effect of light-goods vehicle proportion on slight-injury crash rate is magnified at road segments with on-street parking. Third, the association between the medium- and heavy-goods vehicle proportion and killed/severe injury (KSI) crash rate, is moderated by the roadway width (number of traffic lanes). Finally, a higher proportion of medium- and heavy-goods vehicles generally contributes to increased KSI crash rate at road segments with high intersection density. Overall, the findings of this research are expected not only to help guide commercial vehicle enforcement strategy, licensing policy, and lane control measures, but also to review existing urban roadway designs to enhance safety.

Keywords: Commercial vehicle; Crash rate; Mediating effect; Random-parameter Tobit model; Roadway attribute.

MeSH terms

Accidents, Traffic*
Bayes Theorem
Hong Kong
Humans
Motor Vehicles*
Safety