###### Abstract  
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. c 2023 13 859 1 2023 12 July 2023  
elderly pedestrians in North Carolina from 2007 to 2019. For exploring the influencing factors affecting the severity of road traffic accidents, we have mainly referred to the selection of variables in many relevant papers, with the academic literature emphasizing five key dimensions, namely the at-fault party characteristics, road characteristics, crash characteristics, time of day, and environmental conditions [4]. Among these, the at-fault party characteristics include the driver's age [5], gender [6], driving experience [7], and mode of transport [8]. Road characteristics include physical separation of the road [9], and road type [10]. Crash characteristics include cause of accident [11], cross-sectional location [12], and whether the crash occurred at an intersection [13]. Temporal and environmental conditions include seasonality [14], weather [15], climatic factors [16], and built environment related variables. With the rapid development of machine learning, more than 20 machine learning-related algorithms have been adopted to explore factors influencing traffic accident severity, such as artificial neural networks, support vector machines, decision trees, Bayesian networks, and K-Nearest Neighbors. Compared with traditional regression models, machine learning can better handle different types of data, and are capable of analyzing the complex nonlinear relationships among variables [17]. In a study by [PERSON] et al. [18], six different machine learning algorithms, namely Random Forest (RF), Decision Jungle (DJ), Adaptive Boost (AdaBoost), Extreme Gradient Boost (XGBoost), Light Gradient Boost Machine (LGBM), and Classification Boost (CatBoost), were used to analyze road accidents in New Zealand from 2016 to 2020, and they showed RF has the best prediction results. [PERSON] et al.