## 1 Introduction  
Today, the rapid growth of cities and the increase in the population and number of vehicles pose a significant challenge in terms of traffic management and safety. Traffic incidents are defined as events that interrupt traffic flow and endanger the safety of drivers [1]. These incidents can occur due to numerous factors, including accidents, vehicle breakdowns, roadworks, traffic congestion, and other emergencies. The prediction of the location, timing, and nature of such events is essential, reducing their impact and enhancing traffic management. Traffic incident management is a well-coordinated and planned process that involves multiple disciplines. Its purpose is to identify, respond to, and recover from traffic accidents as safely and efficiently as possible [1; 2; 3]. This process assesses the locations, timing, and potential causative factors of traffic incidents by analyzing historical traffic data, weather conditions, road infrastructure, and other relevant elements [4; 5; 6; 7]. Accurate predictions resulting from this analysis can aid traffic management teams in responding promptly to incidents and recommending alternate routes for drivers. Therefore, precise estimation of the affected area due to road traffic accidents is crucial to enhance traffic flow efficiency and mitigate adverse traffic impacts. This study investigates the geohash-based approach for predicting traffic incident locations. Geohash is a geocoding method used to represent geographic coordinates as a concise alphanumeric string, effectively defining specific areas on a map. This technique holds significant value in geographical data analysis, as highlighted by [PERSON] (2019) [8] and [PERSON] et al. (2022) [9]. This study investigates the potential of a geohash-based methodology for predicting traffic incidents. Numerous studies in the literature address the topic of traffic accidents and their aftermath. These studies present valuable information for professionals in the field. Specifically, recent studies investigate traffic incident management [2; 3; 10], the duration of traffic incidents [11; 12; 13], traffic accident risk [14; 15], traffic congestion [16; 17], and traffic flow [18; 19; 20]. Traffic accident studies have explored topics such as severity [21; 22], injury level [23], the number of accidents, and safety indicators [24; 25; 26]. While this research topic is applicable to various fields, no empirical studies exist on using machine learning algorithms to predict the location or scene of traffic incidents by identifying geohash areas. In this context, this study is the first attempt to use geohash to create spatial context for traffic events. [PERSON] et al. (2022) [27] conducted a comprehensive literature review on the methods used in traffic accidents and accident determinants. They found that in recent years, there has been an increase in prediction studies on traffic accident patterns using artificial intelligence techniques instead of traditional methods, which has garnered more attention. Today, several machine learning algorithms are used to predict traffic incidents [16; 17; 24; 28; 29]. In addition, deep learning [15; 23; 30; 31; 32] and fuzzy logic [19] have also been applied. Several studies in the literature have investigated traffic accidents and employed various methods to identify hotspots [33; 34; 35]. However, studies on the prediction of traffic incident locations have been conducted on traffic accidents or traffic congestion locations. For the first time, our study contributes to the literature by predicting the locations of traffic events, including traffic accidents, vehicle breakdowns, road maintenance, and emergencies, from a broader perspective, rather than focusing only on the locations of traffic accidents or congestion. Identifying hotspots requires extensive analysis. This is because accidents are considered random events and can vary in time and location [34].