Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

Fulu Wei,Yongqing Guo,Xiaoyan Wu,Xin Li,Zhenyu Wang,Zhenggan Cai

doi:10.3390/app11177819

Fulu Wei, Yongqing Guo + Show 4 more

Open Access

https://doi.org/10.3390/app11177819

Copy DOI

Abstract

The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit model, random parameter logit model, and random intercept logit model—were also established and compared with the proposed model. The results indicated that the SRP-logit model exhibits the best fit performance compared with other models, highlighting that simultaneously accommodating unobserved heterogeneity and spatial correlation is a promising modeling approach. Further, there is a significant positive correlation between weekend, dark (without street lighting) conditions, and collision with fixed object and severe crashes and a significant negative correlation between collision with pedestrians and severe crashes. The findings can provide valuable information for policy makers to improve traffic safety performance in rural areas.

Highlights

IntroductionDifferent from developed countries, there is serious latent danger in rural areas of
Different from developed countries, there is serious latent danger in rural areas ofChina, such as a complex traffic environment, inadequate infrastructure, high speed, and sluggish rescue response
A diffused normal distribution was specified for fixed parameters ( β l, β 0 ) and random parameters ( β i,l, β i,0 ), which can be expressed as: β l ∼ normal(0τ, 104 Iτ )

Summary

Introduction

Different from developed countries, there is serious latent danger in rural areas of. China, such as a complex traffic environment, inadequate infrastructure, high speed, and sluggish rescue response. These adverse factors increase the possibility of serious traffic crashes. In 2017, the number of traffic crashes and fatalities in rural areas in China accounted for 48.43% and 40.59% of the respective totals for all crashes [1]. Rural SV crashes usually have serious consequences; the number of fatalities has increased significantly in recent years, showing an average growth rate of 4.8% from 2013 to 2017. There were 24 rural crashes resulting in 10 or more fatalities, including

Objectives

Methods

Results

Discussion

Conclusion