Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors

Abstract

This study develops a Bayesian spatial generalized ordered logit model with conditional autoregressive priors to examine severity of freeway crashes. Our model can simultaneously account for the ordered nature in discrete crash severity levels and the spatial correlation among adjacent crashes without fixing the thresholds between crash severity levels. The crash data from Kaiyang Freeway, China in 2014 are collected for the analysis, where crash severity levels are defined considering the combination of injury severity, financial loss, and numbers of injuries and deaths. We calibrate the proposed spatial model and compare it with a traditional generalized ordered logit model via Bayesian inference. The superiority of the spatial model is indicated by its better model fit and the statistical significance of the spatial term. Estimation results show that driver type, season, traffic volume and composition, response time for emergency medical services, and crash type have significant effects on crash severity propensity. In addition, vehicle type, season, time of day, weather condition, vertical grade, bridge, traffic volume and composition, and crash type have significant impacts on the threshold between median and severe crash levels. The average marginal effects of the contributing factors on each crash severity level are also calculated. Based on the estimation results, several countermeasures regarding driver education, traffic rule enforcement, vehicle and roadway engineering, and emergency services are proposed to mitigate freeway crash severity.