## 1 Introduction
In recent years, studies have been carried out to map road accidents or congestion hotspots using geographic information system (GIS)-based methods and spatial analysis [36; 37; 38; 39; 40; 41]. With the advancement of data collection and processing technologies, GIS accident information systems have been increasingly used to understand traffic accidents. GIS-based systems aim to reduce the number of accidents by identifying accident hazards using spatial data analysis and statistical analysis methods [42]. However, it is an important problem to divide the regions where traffic incidents occur into reasonable dimensions and to collect and process data by considering these regions. Another contribution of this research is the utilization of geohash zones with suitable dimensions within the three-stage model to identify the locations of traffic incidents. Traffic data were collected and pre-processed for various variables that impact traffic events, including time, region, vehicles, traffic index, road structure, and weather conditions, specifically for these areas. In recent years, there has been an increasing use of GIS-based systems and data-driven models in the study of traffic incidents. [PERSON] et al. (2005) [43] provide an introduction to a GIS-based system for accident analysis, which is considered to be one of the earlier works in this area of research. The system aims to determine the accident location and sequence. A prototype Geographic Information System and Road Accident View System (GIS-RAVS) has been developed to reduce accidents. It allows users to quickly identify high-accident locations, view relevant road accident and location details, input and retrieve accident data, and conduct statistical analyses on selected accident locations. [PERSON] et al. (2018) [38] analyzed traffic accident data over a four-year period to determine if there was a clustered pattern of accidents in a particular area. To identify hotspots, the data were visualized using ArcGIS software, and two spatial analysis techniques were employed: nearest-neighbor hierarchical clustering and spatial-temporal clustering. In [PERSON]'s (2020) [44] research, which developed a GIS-based model for detecting traffic accident hotspots using XY coordinate data, traffic accident records reported by road users and police officers were the sensors used to determine the location of the application. Thisanalysis supports the strategic deployment of traffic police resources in areas of high accident frequency, commonly referred to as \"hot spots\". In their study, [PERSON] and [PERSON] (2020) [45] introduced an innovative spatial autocorrelation method to identify traffic accident hot zones. Using ARC-GIS software and a spatial autocorrelation algorithm, the method takes into account accident characteristics and attributes. It works on road sections of 100 m and allows the identification of accident locations independent of their occurrence rates. [PERSON] et al. (2021) [46] conducted a study to analyze the temporal and spatial patterns of traffic accidents in Amman, with a focus on hotspot detection. They used statistical analysis to examine temporal factors such as accident year, severity, road type, and lighting conditions. Additionally, they used GIS to evaluate the spatial distribution over three years and revealed high-density clusters. The study utilized the Nearest Neighbor Index (NNI) to identify clusters among all groups and hotspots in the study area through an experimental study. [PERSON] et al. (2021) [47] presented a cell-grid model for mapping cyclist risks in their study. The study employed a Bayesian framework to construct a random parameter model that links bicycle accident costs in Manhattan, New York, to land use, transportation, and sociodemographic data. The proposed approach was compared with various models and was claimed to be more effective. [PERSON] et al. (2021) [41] conducted a study to identify high-risk road areas in terms of heavy vehicle crashes despite low traffic volumes.