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

Abstract

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

Analysis of Factors Affecting the Severity of Injuries in Electric Two-Wheeled Vehicle Crashes with or without Violation: A Random Parametric Logit Model considering Heterogeneity of Means and Variances

“…In the no violation scenario, riders in other-angle collision crashes sufered minor injuries, and the probability decreased in the two scenarios of other-angle collisions impacting the severity of the crash. Te diference between the two scenarios is relatively small, which may be due to the occurrence of other angle collision compared to the side of the collision, the rider sufered a small direct impact, and the proportion of the occurrence of a small In the two scenarios, the infuence of this factor is smaller, so the probability of riders being injured in crashes is reduced in both scenarios, and in the study on the angle of collision, it was also found that the driving route of electric two-way vehicles is more unstable, which makes it easy to have side crashes, and the side collision is the most common collision mode [25][26][27].…”

Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis

“…Bayesian random parameter model has been widely applied in crash injury severity analysis of traditional human-driven vehicles, in which Bayesian multilevel/hierarchical random parameter models (Castro and Kim, 2016; Alarifi et al , 2017; Han et al , 2018; Li et al , 2019; Fu and Sayed, 2021) were developed to address the unobserved heterogeneity issue by allowing parameters of risk factors to vary randomly, while Bayesian multivariate random parameter models with spatio-temporal regression (Guo et al , 2019; Zeng et al , 2019; Huang et al , 2019; Wei et al , 2021) were proposed to accommodate the unobserved heterogeneity issue as well as spatial correlation. All of these provide the foundation for the crash analysis of AVs.…”

FACTORS AFFECTING ANALYSIS of THE SEVERITY of AUTOMOBILE AND ELECTRIC BICYCLE ACCIDENTS USING RANDOM FOREST MODEL

“…Experts and scholars have done a lot of research on electric bicycle accidents, but most of them tend to analyze the injured parts. It includes for vehicle and pedestrian accident reconstruction, the accident severity is analyzed from collision angle as well as to assessment the between automobile and pedestrian accident risk, research shows that through the optimization of the structure of city road, improve road traffic design can significantly reduce automobiles and pedestrian traffic accident happened [6][7][8]. Soria analyzed the injury characteristics by studying the cases of fatal traffic accidents between motorcycles and pedestrians.…”