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

Abstract EN

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.