The Effects of Traffic Composition on Freeway Crash Frequency by Injury Severity: A Bayesian Multivariate Spatial Modeling Approach

Huiying Wen,Xuan Zhang,Qiang Zeng,Jiaren Sun,Quan Yuan

doi:10.1155/2018/6964828

Huiying Wen, Xuan Zhang + Show 3 more

Open Access

https://doi.org/10.1155/2018/6964828

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Abstract

This study sets out to investigate the effects of traffic composition on freeway crash frequency by injury severity. A crash dataset collected from Kaiyang Freeway, China, is adopted for the empirical analysis, where vehicles are divided into five categories and crashes are classified into no injury and injury levels. In consideration of correlated spatial effects between adjacent segments, a Bayesian multivariate conditional autoregressive model is proposed to link no-injury and injury crash frequencies to the risk factors, including the percentages of different vehicle categories, daily vehicle kilometers traveled (DVKT), and roadway geometry. The model estimation results show that, compared to Category 5 vehicles (e.g., heavy truck), larger percentages of Categories 1 (e.g., passenger car) and 3 (e.g., medium truck) vehicles would lead to less no-injury crashes and more injury crashes. DVKT, horizontal curvature, and vertical grade are also found to be associated with no-injury and/or injury crash frequencies. The significant heterogeneous and spatial effects for no-injury and injury crashes justify the applicability of the proposed model. The findings are helpful to understand the relationship between traffic composition and freeway safety and to provide suggestions for designing strategies of vehicle safety improvement.

Highlights

As a primary component of roadway transportation system, the safety performance of freeways has attracted much attention in the field of traffic safety research in recent years [1,2,3]
One-year crashseverity frequency data from Kaiyang Freeway in China are collected, and a multivariate conditional autoregressive (CAR) model is developed with the proportions of different vehicles and freeway-specific attributes as predictors for predicting crash frequency by severity simultaneously
The results indicate that five variable pairs, Veh(i2) and Veh(i3), Veh(i2) and Veh(i4), Veh(i3) and Veh(i4), Veh(i2) and daily vehicle kilometers traveled (DVKT), and Veh(i4) and DVKT, are significantly correlated with the absolute values of their correlation coefficients over 0.6

Summary

Introduction

As a primary component of roadway transportation system, the safety performance of freeways has attracted much attention in the field of traffic safety research in recent years [1,2,3]. A number of previous studies have demonstrated that traffic composition has statistically significant effects on crash occurrence [3, 10, 14] It would be beneficial for modeling freeway crash frequency by injury severity to incorporate the proportions of each vehicle type in the mixed traffic into the explanatory variables. The key goal is to investigate the effects of traffic composition on freeway crash frequencies at different injury degrees To achieve this goal, one-year crashseverity frequency data from Kaiyang Freeway in China are collected, and a multivariate CAR model is developed with the proportions of different vehicles and freeway-specific attributes as predictors for predicting crash frequency by severity simultaneously.

Data Preparation and Preliminary Analysis

Methodology

Result

Findings

Conclusions and Future Research