## 4 Discussion  
The findings of this study demonstrate the critical role of aggressive driving behaviors, such as rapid acceleration, rapid deceleration, and speeding, in explaining both the frequency and severity of elderly pedestrian traffic accidents. This aligns with prior studies emphasizing the disproportionate risks posed by aggressive driving behaviors, especially for vulnerable road users like elderly pedestrians [3,48]. More specifically, this study highlights the differences in feature importance across the 'total','match', and'mismatch' groups, depending on the spatial match or mismatch between a risky driving area and the accident spots. Figure 8: SHAP values of XGBoost model estimating the deaths of elderly pedestrians from traffic accidents. (**a,c,e**) The relative importance of the total, match, and mismatch groups; (**b,d,f**) the impact of the total, match, and mismatch groups. Compared to previous studies that may have either lacked spatial differentiation or used simpler correlation-based methods, this approach provides a more structured framework for a spatial analysis. It allows for a clear and intuitive classification of areas, enabling researchers to visually and statistically identify spatial patterns where aggressive driving behavior and elderly pedestrian accidents align or diverge [29]. This facilitates targeted interventions by highlighting the high-risk areas where both factors are prevalent, as well as the locations where aggressive driving occurs without corresponding pedestrian accidents or vice versa. However, quadrant classification depends on the chosen threshold (the average values), which may not capture nuanced variations within each category, potentially oversimplifying complex spatial relationships [49]. As a result, for the 'total' group, not only did aggressive driving behaviors show substantial influence on elderly pedestrian crashes but built environment and demographic factors did as well. These findings emphasize the role of well-planned urban infrastructure in reducing traffic accidents [50]. The'match' group, characterized by an alignment between aggressive driving behaviors and accident hotspots, places greater emphasis on driving dynamics, including rapid lane changes and speeding. This underscores the critical need for targeted interventions such as stricter law enforcement and driver education in areas with a high match between driving risk and accident frequency [51]. In the'mismatch' group, aggressive driving behaviors overwhelmingly dominate, with demographic and environmental factors showing diminished relevance. This group's unique dynamics suggest that inconsistent infrastructure or demographic mismatches amplify the risks posed by driving behaviors. The results aligned with the previous literature indicating that mismatched environments exacerbate accident risks due to a lack of adequate infrastructure or safety measures [52]. Additionally, this study highlights key distinctions between the influencing factors of accident frequency and severity. While aggressive driving behaviors dominate both models, their influence is heightened in predicting deaths. Features like speeding and rapid deceleration gain greater importance for accident fatalities, reflecting their direct correlation with fatal outcomes. Conversely, built environment factors, such as the number of crosswalks and the elderly population, play a more significant role in the frequency models. These results align with the research that links speeding with higher accident fatality rates and demographic factors with accident likelihood [53; 54]. Lastly, the application of Explainable Artificial Intelligence (XAI) in examining the association between aggressive driving behavior and elderly pedestrian traffic accidents offers significant ethical and user confidence benefits.