## 1 Introduction  
As the global population ages, the safety of elderly pedestrians has become an increasingly critical issue in transportation research [1]. Older adults, particularly those aged 65 and above, are at a disproportionately high risk for traffic accidents, with pedestrian fatalities being a leading cause of injury-related death in this age group [2, 3]. While elderly individuals make up a smaller percentage of total pedestrian traffic, they account for a significantly higher proportion of pedestrian fatalities and serious injuries compared to younger populations [4]. This vulnerability is due to a combination of age-related physical, cognitive, and sensory impairments, which make older pedestrians less able to navigate traffic safely or react quickly in hazardous situations [5]. While urban environments and high-traffic areas tend to increase the risks for all pedestrians, elderly pedestrians in particular face additional challenges, such as inadequate infrastructure and insufficient crossing times at traffic signals [6]. However, one of the most significant threats to the safety of elderly pedestrians is aggressive driving behavior. Aggressive driving behavior, including actions such as speeding, tailgating, and road rage, is a well-documented risk factor for traffic accidents and road safety concerns [7; 8]. Older pedestrians are particularly susceptible to accidents involving aggressive drivers because they often require more time to cross streets, make decisions, and react to fast-moving traffic [9]. Aggressive drivers, who tend to disregard speed limits, traffic laws, and the safety of others, pose a significant threat to these pedestrians. Previous studies have shown a clear link between reckless driving and increased risks for older pedestrians. They found that intersections and crosswalks, where elderly pedestrians are most likely to be injured, are often the sites of high-speed driving, abrupt lane changes, and failures to yield to pedestrians, behaviors that are characteristic of aggressive driving [10]. Additionally, older pedestrians have been found more likely to be involved in accidents during nighttime, when visibility is reduced, and aggressive drivers may be more likely to engage in risky behaviors such as running red lights or failing to stop for pedestrians [11]. These findings collectively suggest that the combination of aggressive driving behavior and the physical vulnerabilities of elderly pedestrians creates a dangerous environment. Nonetheless, the association between aggressive driving behavior and elderly pedestrian traffic accidents still has some room for further investigation. The existing studies often focus on general pedestrian safety [12; 13], without adequately distinguishing the unique risks faced by elderly individuals, who may have slower reaction times and reduced mobility. Additionally, while AI-driven models have been employed to analyze traffic accident patterns, their reliance on black-box algorithms limits interpretability and actionable insights for policymakers and urban planners [14; 15]. The application of XAI in this context is still underdeveloped, with a lack of studies leveraging explainable models to uncover the nuanced interactions between aggressive driving behaviors and elderly pedestrian accident risks. Addressing this gap could lead to the development of more targeted and transparent interventions, improving road safety and guiding data-driven urban planning strategies tailored to aging populations. This paper aims to explore various factors that mediate the association between them, such as the built environment and sociodemographic characteristics around the accident site. Also, we aim to identify the spatial match and mismatch between risky driving behavior and elderly pedestrian car crashes and examine how the aforementioned factors affect their relationships differently.