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 inmultivehicle 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.

Investigating temporal trends in the explanatory variables related to the severity of drivers'injuries in single-vehicle collisions

This study identifies and quantifies the effects of different explanatory variables that in- crease the severity of drivers＇ injuries related to singie-vehicle collisions involving light- duty vehicles. The research is based on utilizing logistic regression to analyze records of all traffic collisions that occurred in North Carolina for the years from 2007 to 2013. The study also investigates temporal stability of the identified explanatory variables throughout the analysis period. The identified explanatory variables include those related to the roadway, vehicle, driver, and environmental conditions. The explanatory variables related to the roadway include whether the roadway is divided or undivided, and whether it is in an urban or rural area. The explanatory variables related to the vehicle include vehicle＇s age, travel speed, and the type of the light-duty vehicle. The explanatory variables related to the driver include driver＇s age, gender, influence by alcohol or illicit drugs, and the use of seatbelt. The explanatory variables related to the environmental conditions include weather, lighting, and road surface conditions. Three of the investigated explanatory variables were found to be temporally unstable with significantly varying effects on the severity of drivers＇ injuries. Those temporally unstable variables include the travel speed, the type of the light-duty vehicle, and the age of the driver. All other investigated variables were found to be consistently significant throughout the analysis period. The findings of this research have the potential to help decision makers develop policies and counter- measures that reduce the severity of drivers＇ injuries by focusing on explanatory variables that consistently exhibit significant effects on the severity of drivers＇ injuries. The findings of this research also provide quantitative measures that may be used to determine the feasibility of implementing those countermeasures in reducing the severity of drivers＇ in- juries related to single-vehicle collisions. Recommendations for future research are also provided.