Driver injury severity analysis of crashes in a western China's rural mountainous county: Taking crash compatibility difference into consideration

China's traffic safety attracts increasing research interest. Official data show that crashes in the western region of China are more severe than those in the eastern region. However, research on crash severity in western China is scarce. This study applied a hierarchical Bayesian logistic model to examine the significant factors related to crash and vehicle/driver levels and their heterogeneous impacts on the severity of drivers' injury. Crash data were collected from Lintao, a rural mountainous county in western China. A variable was proposed to measure the relative difference between the crashworthiness of one vehicle and the aggressivity of the other vehicle in the mixed traffic flow. Results indicated that the majority of the total variance was induced by between-crash variance, showing the suitability of the utilized hierarchical modeling approach. One crash-level variable and six vehicle/driver-level variables, namely, road type, compatibility difference, age, vehicle type, drunk driving, driving unregistered vehicle, and driving years, significantly affected modeling drivers' injury severities. Among these variables, road type (national and provincial), age (young and senior drivers), driving unregistered vehicle, and drunk driving tended to increase the odds of crash-related mortality. Driving years (new drivers with less than six years of driving experience) and vehicle type (heavy vehicle) were likely to decrease the probability of fatal outcomes. Compatibility difference was relatively significant, and the possibilities of mortality in single vehicle crashes were higher than those in multivehicle and pedestrian-involved crashes. The developed methodology and estimation results provided insights into the internal mechanism of rural crashes and effective countermeasures to prevent rural crashes.

Abstract licence: CC BY-NC-ND

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.