## 4 Conclusions  
This study uses police-reported traffic accident data from Shenyang, Liaoning Province, China. The accidents were separated into two categories based on the at-fault parties: vulnerable road users or motor vehicle drivers. A random forest model was developed to analyze the importance of factors influencing traffic accident severity. Then the association rule analysis is used to study the combination mechanism of traffic accident influencing factors. This paper introduces Random Forest and SHAP to determine the critical risk factors. RF algorithm improves model accuracy using less training time when dealing with categorical variables and about a thousand datasets. Introducing a priori algorithms helps us find the association between different factors more clearly. The main findings are summarized as follows:  
1. Descriptive statistical analysis and classification of variables were performed on the dataset, and the importance of 24 characteristic factors was assessed using RF-SHAP. The results show that for accidents in the vulnerable road user category, factors such as area division, restaurant and shopping POI density, life service POI density, cause of accident and traffic mode exert a key influence on accident severity. For accidents in the motor vehicle driver group, factors such as mode of transport, the density of restaurant and shopping POIs, zoning, season, road speed limit, and type of collision significantly influence the fatality of traffic accidents. 2. This paper focuses on the first ten characteristic variables for the critical influencing factors under the dual perspective of accidents. The Apriori algorithm was used to delve into the mechanism of multi-factor interactions in fatal accidents. Our results \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **head\_set** & **tail\_set** & **Support** & **Confidence** & **Lift** \\ \hline \multicolumn{6}{c}{(a) vulnerable road user} \\ \hline
1 & [’Collision Type is Vehicle-vehicle accident’ & & [’Service-POI is 50–500 pcs/km\({}^{2}\)’] & 0.122 & 1 & 2.46 \\
2 & [’Functional Zone is Urban District’ and & & [’Shopping-POI is 500 pcs/km\({}^{2}\)’] & 0.115 & 1 & 2.46 \\
3 & [’Shopping-POI is 500 pcs/km\({}^{2}\)’] & 0.15 & 1 & 2.46 \\ Type is Trunk Road’ & & & & & \\
4 & [’Traffic Mode is Non-Motorized Vehicle’ and & & & & \\
5 & [’Srive-POI is 50 pcs/km\({}^{2}\)’] & 0.11 & 1 & 2.46 \\
6 & [’Road Type is Secondary and Tertiary Roads’ & & & & \\ and ‘Service-POI is \(\leq\)50 pcs/km\({}^{2}\)’] & 0.185 & 0.949 & 2.149 \\
7 & [’Traffic Mode is Motorcycle’ and ’Functional & & & & \\ Zone is Rural District’] & & & & & \\ \hline \multicolumn{6}{c}{(b) motor vehicle driver} \\ \hline \hline \multicolumn{6}{c}{(c) [Traffic Mode is Motorcar’ and & & & \\ \multicolumn{6}{c}{(Shopping-POI is \(>\)500 pcs/km\({}^{2}\)’]} & [’Service-POI is 50–500 pcs/km\({}^{2}\)’] & 0.109 & 0.994 & 2.323 \\
2 & [’Road Type is Trunk Road’ and & & & & \\ Slope-POI is \(>\)500 pcs/km\({}^{2}\)’] & [’Service-POI is 50–500 pcs/km\({}^{2}\)’] & 0.141 & 0.991 & 2.315 \\
3 & [’Functional Zone is Urban District’ and & & & & \\ Slope-POI is \(\leq\)50 pcs/km\({}^{2}\)’] & [’Service-POI is 50–500 pcs/km\({}^{2}\)’] & 0.126 & 0.99 & 2.313 \\
4 & [’Functional Zone is Rural District and & & & & \\ Slope-POI is \(\leq\)50 pcs/km\({}^{2}\)’] & 0.13 & 0.934 & 2.187 \\
5 & [’Service-POI is \(\leq\)50 pcs/km\({}^{2}\)’ and Road & & & \\ Slope is Secondary and Tertiary Roads’ & & & & \\ Slope-POI is \(\leq\)50 pcs/km\({}^{2}\)’] & 0.11 & 0.879 & 2.06 \\
6 & [’Road Type is Secondary and Tertiary Roads’ & & & \\ and ‘Network is \(\leq\)10 km/km\({}^{2}\)’] & [’Shopping-POI is \(\leq\)50 pcs/km\({}^{2}\)’] & 0.11 & 0.861 & 2.016 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Three association rules. show that most combinations of the factors that occur contain Service and Shopping POI density features.