## 3 Results and Discussion
### Association Rules  
Association rules are used to identify a set of risk factors that are often present concurrently in traffic accidents and examine how various accident characteristics are related [31]. Figure 2: SHAP summary plots. (**a**) vulnerable road user; (**b**) motor vehicle driver. This study investigated the effects of interactions between different influencing factors on specific outcomes. The top ten characteristic variables from RF-SHAP's importance ranking results were selected for multifactor interaction analysis. Significant features between different factors were found using association rule analysis. Our study focuses on reducing fatal accidents; this section selects 137 and 665 fatal accidents with vulnerable road users and motor vehicle drivers as parties of primary responsibility, respectively. The classical Apriori algorithm in association rules is used to perform the analysis and the support, confidence and boosting thresholds were set. According to past research which applied the association rules to analyze traffic safety and in order to improve the strength and accuracy of association rules, the thresholds of the three indicators in this study were set as support \(\geq\) 10%, confidence \(\geq\) 80%, and boost \(\geq\) 1.5, respectively. Two, three and four association rule results of traffic accidents and the corresponding support, confidence, and boost values are calculated. The association rules were ranked according to the elevation values in each table from highest to lowest. It should be emphasized that some association rules may have low support in the bumper-to-bumper database, but they may be necessary because the features contained in the rules are rarely present. This means the elevation value is more important than the other two criteria for determining the strength of association rules. It can be seen from Table 2a,b that among the dichotomous association rules, significant association rules in traffic accidents with vulnerable road users at-fault are related to road type, road physical isolation, collision type, and functional zone. Rules #1 and #3 have the highest elevation values in Table 2a, which indicate that if a fatal accident occurred on a median-separated road, it is likely to have happened in the urban area and on a trunk road. Rules #2 and #5 indicate that fatal accidents are more likely to occur on a trunk road when the density of life service POI is in the range of 50 to 500 pcs/km\({}^{2}\) or in urban areas. Compared with high-grade roads, trunk roads in urban areas are generally connected to residential and commercial areas, with more blind spots for drivers and more chaotic road appurtenances. The higher density of life service POI also equates to more vulnerable road users and more random travel paths, which are likely to result in fatal accidents on roads with only middle separation and no guarantees of motor vehicle right-of-way. Therefore, the road environment should be analyzed, with appropriate additional machine-non-motorized separation zones and clear signage. Traffic control should be strengthened in accident-prone areas, and the safety awareness of non-motorized plans and pedestrians should be enhanced to avoid illegal crossings to prevent fatal accidents. The essential association rules in Table 2b from the viewpoint of motor vehicle drivers are related to the POI density of shopping, POI density of service, road type, and functional zone. Four association rules are obtained by excluding rules which the preceding and following items are POI density. The rules show that when a fatal accident take place in urban areas or on secondary and tertiary roads, these are also more likely to be areas with