## 2 Literature Review  
There is no universally acknowledged definition of Road Traffic Crash (RTC) hot spot identification [10]. Some safety researchers prioritize sites by crash rate (i.e., crashes per vehicle-kilometers for segments and crashes per entering vehicles for intersections), others use crash frequency (i.e., crashes per year), some use spatial analysis of crashes and others adopt the integration of the above and the rest uses regression modeling (i.e., the predictive approach of Crash Prediction Model, CPM) [24]. RTC hot spot analysis, in general, attempted to identify and prioritize road segments in need of immediate safety improvements to achieve valuable crash reduction through effective safety mitigation. In addition to crash analysis, local expertise and expert judgment are used to identify RTC hot spots. This work (road safety management) comprises the following tasks [25; 26]:  
1. Targeting or identifying crash hot spots on the road network;
2. Studying safety issues in each hot spot;Identifying contributing factors and design mitigations;  
Evaluating the safety effects of the possible mitigations;  
Prioritizing the hot spots to apply cost-effective safety mitigations; and  
Evaluating the effectiveness of applied treatments. This review has mainly focused on the first task of identifying crash hot spots on the road network. Since RTC analysis is such an important activity for improving roadway safety, it has been extensively researched in the academic press. Despite the lack of consensus on crash hot spot analysis, researchers and experts have developed various methods for crash event analysis. The simplest and most straightforward method for crash hot spot analysis is detecting where the crash rate per unit exposure exceeds a given normal threshold [27]. Although most of the traditional approaches of crash analysis focus on the time dimension, nowadays, the spatial dimension of traffic crashes has got more attention from researchers [16; 28]. This facilitates the application of GIS into crash hot spot analysis. In recent years, several RTC databases recorded the precise locations of crashes through the application of GPS devices; hence, it is no longer necessary to identify the crash hot spot section of a road from aggregated data [2; 29]. By having these accurate locations of RTC, road safety analysts can focus on the highly clustered crash locations. Generally, the most frequently used methods of crash hot spot analysis can be categorized into two. These are:  
* Non-Spatial Model Analysis (NSMA): which uses the traditional approaches of statistics such as regression models [30], Empirical Bayes [31], and full Bayesian [32];
* Geo-Statistical Analysis (GSA): by analyzing the spatial units of crashes (i.e., Density Estimation, DE) [33; 34] or spatial arrangement of each crash attribute value (i.e., Spatial-Autocorrelation, SA) [35; 36; 37]. As compared to NSMA, Geo-Statistical crash hot spot analysis needs fewer data and is easier to apply due to the simplified mathematical calculations [38] and its integration of crash incidence with spatial factors and, also, the presentation of clear visualization in the result. The adoption of the spatial arrangement of attribute values from each unit is more advantageous than using a spatial unit of crashes due to its consideration of spatial dependence of attribute values and the geographical location of incident points [39]. For a thorough understanding of the Geo-Statistical analysis of crashes, readers are recommended to a review paper [40] dedicated to reviewing the scientific literature on spatial approaches and spatial analysis in road safety. GSA can be further divided into two groups such as Global Indexes (GI) for instance Global _Moran's I_ (Spatial-Autocorrelation), Getis-Ord G statistic and Geary's C; and Local Indexes (LI) these are Local Anselin Moran's I (Cluster and Outlier Analysis), planar Kernel Density Estimation (KDE), Getis Ord \(Gi^{\star}\) and kriging.