Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

The effect of risk factors on crash severity varies across vehicle types. objective this study was to explore the associated with rural single-vehicle (SV) crashes. Four types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity spatial correlation in data, a novel Bayesian random parameters logit (SRP-logit) model is proposed. Rural SV data Shandong Province extracted calibrate model. Three traditional approaches—multinomial model, parameter intercept model—were also established compared proposed results indicated that SRP-logit exhibits best fit performance other models, highlighting simultaneously accommodating promising modeling approach. Further, there significant positive between weekend, dark (without street lighting) conditions, collision fixed object severe crashes negative pedestrians findings can provide valuable information for policy makers improve traffic safety areas.