###### Abstract
[19] built a dataset of traffic accidents using motorcycle track data from GPS and road accident feature data collected through questionnaires and police inquiries, and ranked and predicted feature importance using a random forest algorithm. [PERSON] et al. [20] investigated the effect of the driving environment on attempted accidents, speeding and distraction events based on RF and XGBoost combined with SHAP, aiming to explore the factors affecting the frequency of road accidents and driving risks. The most important contextual factors in predicting these risky events are identified and ranked through Shapley Additive Explanations. [PERSON] et al. [21] used the LGBM-SHAP methodology was used to explore the impact of the importance of accident risk factors on different types of accidents based on accident data in Texarkana. The SHAP methodology quantified the results and yielded more evident results. [PERSON] et al. [22] used XGBoost and SHAP's LCA-based clustering results to identify the main influencing factors in the potential categories and analyzed the interactions between the factors. The SHAP visualization of their results shows the top 15 accident duration influencing factors regarding importance ranking. All these studies have shown that SHAP is a very effective interpretation method for machine learning and is widely used in various industries. Most scholars have found that traffic accident severity is influenced by a multitude of factors, hence multi-factor interactions should be explored. [PERSON] et al. [23] proposed a framework based on association rule mining to identify critical factors influencing motorcycle accident severity, parameter optimization of parameter thresholds in association rule mining is proposed to identify individual key factors from two-item rules and boosting factors from multiple-item rules. [PERSON] et al. [24] conducted a study on bus accidents which occurred in Victoria, Australia between 2006 and 2019, and discovered effective chains of factors influencing fatalities in bus accidents using association rules. [PERSON] et al. [25] applied an association rule mining method on a natural driving dataset to study the correlations between accidents and road features. The item set frequencies after the Apriori algorithm showed that the accidents were strongly associated with roadways without access control, driving during non-peak hours, roadways without a shoulder or a median, roadways with the minor arterial functional class, and roadways with a speed limit between 30 and 60 mph. [PERSON] et al. [26] applied association rule mining techniques on traffic accidents with high number of casualties that occurred in China between 2009 and 2013, and the resultsshowed that traffic accidents with high casualties are affected by the interactions of different complex factors. Based on the relevant research by previous scholars, it is clear that the Random Forest algorithm is superior in exploring the influence of a single factor and the association rule algorithm shows advantages in exploring the multi-factor interaction effects. Therefore, this study adopts an integrated approach of RF and Apriori algorithm to explore the mechanisms of single factors and multi-factor interactions. The remainder of this paper is organized as follows: Section 2 provides an overview of the processing of the data and methodology, Section 3 shows and discusses the results, and Section 4 summarizes the implications of the results for decisions on enhancing road traffic safety.