## 2 Literature Review  
### Road Accident Hotspots  
The spatial clustering of hotspots can be mapped in point forms using statistical mapping approaches (tools or programs in the GIS), such as K-means [39], Moran I Incremental Spatial Autocorrelation Method [40; 41; 42] Statistics on Getis Ord \(G_{i}\)[43; 44; 45; 46; 47; 48; 49; 50; 51; 52] and Kernel Density Estimation (KDE) [53; 54; 55; 56]. In their study, [PERSON] et al. [57] utilized Getis-Ord \(G_{i}\)* to detect 25 hotspots along the North-South Expressway in Kuala Lumpur. The study was based on a case analysis spanning from 2016 to 2019 which encompassed over 47,359 accidents. Sixty-four percent of the total hotspots were located at exit ramps, existing interchanges, rest areas, or slip roads, which had a total of 16 hotspot locations. The study found that accident hotspots occurred when traffic flow was disrupted. [PERSON] et al. [39] identified the road accident hotspots usingGetis-Ord \(G_{i}\)* from another perspective. Between 2010 and 2018, a total of 55,296 road accidents were analyzed in rural sections of the primary roads (3933 km) of the Czech road network. The road network's length was found to be covered with identified road accident hotspots, ranging between 6.8% and 8.2%. The proportion of road accidents occurring in hotspots exhibited a consistent trend of approximately 50% over an extended period. Spatiotemporal crash patterns were examined, and three types of hotspots were discovered: those that emerge, those that persist, and those that vanish. Only 100 hotspots exhibited stability throughout the complete nine-year duration. [PERSON] et al. (2009) compared statistical techniques in the town of Varanasi using five years of accident data (2009-2013). Using Getis Ord \(G_{i}\)*, Kernel Density Estimation, and Moran's I, the spatial clustering of road accidents and the spatial densities of hotspots were analyzed. With a variety of severity grading systems, this approach assessed the impact of minor and property-damage-only accidents on overall outcomes. The z-value, which is associated with statistical significance, was used to rank hotspots using \(G_{i}\)*. Based on pixel values collected from various locations, K was used to rank hot spots. The hot spot analysis results identified specific road segments and intersections with a high concentration of hotspots. The study conducted by [PERSON] et al. (2017) investigated the incidence of fatal road accidents in Bangladesh through the utilization of a novel newspaper-based approach that employed grids to establish a comprehensive database of road traffic fatalities. The Getis-Ord \(G_{i}\)* was employed to delineate spatiotemporal hotspots and trends. The results indicate discernible trends in road accident occurrences between urban and rural regions of Bangladesh, with Dhaka and Sir Aiganj in the northern part of the country exhibiting elevated incidence rates. The study conducted by [PERSON] et al. (2019) investigated the temporal and spatial patterns of traffic accidents in Irbid Governorate over a period of five years, from 2015 to 2019, based on available data. The study utilized the Global Moran I index and hotspot analysis (Getis-Ord \(G_{i}\)*) techniques in ArcGIS to identify the road accident hotspots. The research findings revealed a significant rise of 38% in a year-on-year comparison. The analysis of severe road accidents led to the identification of hotspot locations. The Irbid Governorate's internal road network, which has the area's highest traffic volume, was found to be the primary site of minor road accidents. The research used Getis-Ord \(G_{i}\)* and spatial autocorrelation at 90% and 95% confidence intervals to identify a clustering trend among arterial and internal road network segments. The National Highway (NH-66) in Alappuzha, Kerala, India, is an 8 km flat stretch. To begin, accident data from the NHAI office and the police station in the area above was gathered. The data were then analyzed using the severity index.