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This study explores risk factors influencing the at-fault party in traffic accidents and analyzes their impact on traffic accident severity. Based on the traffic accident data of Shenyang City, Liaoning Province, China, from 2018 to 2020, 19 attribute variables including road attributes, time attributes, environmental attributes, and characteristics of the at-fault parties with either full responsibility, primary responsibility, or equal responsibility of the traffic accidents were extracted and analyzed in conjunction with the built environment attributes, such as road network density and POI (points of interest) density at the sites of traffic accidents. Using the RF-SHAP method to determine the relative importance of risk factors influencing the severity of traffic accidents with either motor vehicles or vulnerable groups at-fault, the top ten risk factors influencing the severity of traffic accidents with vulnerable road users as the at-fault parties are: functional zone, density of shopping POI, density of services POI, cause of accident, travel mode, collision type, season, road type, age of driver, and physical isolation. Travel mode, season, and road speed limit are more important risk factors for traffic accidents, with motor vehicle drivers as the at-fault parties. The density of service POI and cause of the accident are less critical for traffic accidents with motor vehicle drivers than traffic accidents with vulnerable road users who are at-fault. Subsequently, the Apriori algorithm based on association rules is used to analyze the important causal factors of traffic accidents, so as to explore the influence mechanism of multiple causal factors and their implied strong association rules. Our results show that most combined factors are associated with the matched Service and Shopping POI features. This study provides valuable information on the perceived risk of fatal accidents and highlights the built environment's significant influence on fatal traffic accidents. Management strategies targeting the most typical combinations of accident risk factors are proposed for preventing fatalities and injuries in serious traffic accidents. [ABSTRACT FROM AUTHOR]

Bicycle–motor vehicle (BMV) crashes have been identified as a major type of traffic accident affecting transportation safety. In order to determine the characteristics of BMV crashes in cold regions, this study presents an analysis using police-reported data from 2015 to 2017 on BMV crashes in Shenyang, China. A two-stage approach integrating latent class analysis (LCA) and the random parameter logit (RP-logit) model is proposed to identify specific crash groups and explore their contributing factors. First, LCA was used to classify data into several homogenous clusters, and then the RP-logit model was established to identify significant factors in the whole data model and the cluster-based model from LCA. The proposed two-stage approach can maximize the heterogeneity effects both among clusters and within clusters. Results show that three significant factors in the cluster-based model are obscured by the whole data model in which male cyclists are associated with a higher risk of fatality, especially in the winter. Additionally, differences exist in the exploration of factors due to the characteristics of clusters; thus, countermeasures for specific crash groups should be implemented. This research can provide references for regulators to develop targeted policies and reduce injury severity in BMV crashes in cold regions. [ABSTRACT FROM AUTHOR]

• Contribution factors on vulnerable road user (VRU) involved crashes are explored. • A hybrid method integrating random parameter logit model and Bayesian network is used. • Model estimation results shown great differences on different seasons. • Target measures on different seasons are proposed. Recently, vulnerable road user to motor vehicle (VRU-MV) crashes have been the focus of much attention. Data from Shenyang, China were used for crashes analysis and significant differences existed in VRU-MV crashes across seasons. Therefore, this study analysed three crash datasets (whole, "spring & summer", "fall & winter") by using a hybrid method integrating random parameter logit (RP-logit) model and Bayesian network (BN). First, RP-logit model was established to find out significant factors and heterogeneity considering those three datasets. Second, significant factors identified from RP-logit model were utilized to establish a BN to investigate statistical associations between injury severity and explanatory attributes. Results showed seven significant factors were found in "spring & summer" dataset while five significant factors in "fall & winter" dataset, four hidden factors were found by comparative analysis of those three datasets. Besides, five factors were found to be random and normally distributed. Results of BN indicated that some factors could significantly increase high possibility of fatality when combined with other factors. For example, functional zone (ZON) in "spring & summer" dataset and motor vehicle type (MVT) in "fall & winter" dataset. The proposed hybrid method demonstrated both the consistence of methods (RP-logit model & BN) and the differences across seasons for VRU-MV crashes analysis. Three personalized factors including physical isolation (PI), crash type (CT) and motor vehicle type (MVT) made great differences across seasons. [ABSTRACT FROM AUTHOR]

• A hybrid approach of Random Forest based SHAP algorithm (RF-SHAP) and random parameters logit modeling framework is developed to analyze Vulnerable Road users involved crashes. • The unobserved heterogeneity and unobserved heterogeneity correlations of factors with random parameters is revealed by random parameters logit modeling framework. • The interaction effects of factors with random parameters and factors with fixed parameters are uncovered based on the hybrid approach. • Single variable, for example, rural and primary roads increase the risk of being fatal individually, however, their interaction effects decrease the probability of fatal crash occurrence to some extent. Vulnerable road users (VRUs) involved crashes are a major road safety concern due to the high likelihood of fatal and severe injury. The use of data-driven methods and heterogeneity models separately have limitations in crash data analysis. This study develops a hybrid approach of Random Forest based SHAP algorithm (RF-SHAP) and random parameters logit modeling framework to explore significant factors and identify the underlying interaction effects on injury severity of VRUs-involved crashes in Shenyang (China) from 2015 to 2017. The results show that the hybrid approach can uncover more underlying causality, which not only quantifies the impact of individual factors on injury severity, but also finds the interaction effects between the factors with random parameters and fixed parameters. Seven factors are found to have significant effect on crash injury severity. Two factors, including primary roads and rural areas produce random parameters. The interaction effects reveal interesting combination features. For example, even though rural areas and primary roads increase the likelihood of fatal crash occurrence individually, the interaction effect of the two factors decreases the likelihood of being fatal. The findings form the foundation for developing safety countermeasures targeted at specific crash groups for reducing fatalities in future crashes. [ABSTRACT FROM AUTHOR]