Citation

Zeng Q, Wang Q, Wang X. Accid. Anal. Prev. 2022; 173: e106717.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.aap.2022.106717

PMID

35643025

Abstract

This paper presents an empirical analysis of factors contributing to roadway infrastructure damage from expressway accidents, using a Bayesian random parameters tobit model. The accident data collected from Kaiyang Expressway, China in 2014 and 2015 are used for the empirical analysis. The results of parameter estimation in the proposed model indicate that: the effects of vehicle types are significantly heterogeneous across observations, and that the effects of horizontal curvature, time of day, vehicle registered province, and accident type are also significant but homogeneous across observations. The marginal effects of these contributing factors are calculated to explicitly quantify their impacts on road infrastructure damage. According to the analysis results, some strategies pertaining to safety education, traffic enforcement, roadway design, and intelligence transportation technology are advocated to reduce road infrastructure damage from expressway accidents.

Language: en

Keywords

Bayesian estimation; Expressway accident; random-parameters Tobit model; Roadway infrastructure damage; Unobserved heterogeneity