## 6 Conclusions  
The primary goal of crash hot spot analysis is to answer a critical question in road safety practice: where are the hazardous street areas located? This inquiry is addressed scientifically by studying the spatial pattern of crashes. The advantage of using advanced GIS-based hot spot analysis is not limited to the simple presentation of hot spot roadway areas; it also provides the ability to investigate the spatial dependency of crash incidents and spatial connections with other factors. For the past five decades, a GIS application in the field of traffic safety has aided in the advanced realization of crash characteristics, which is then used as a piece of information to improve traffic safety. This study aimed to present a GIS application to identify and quantify statistically significant spatial patterns based on crash numbers and severity. Based on the severity of the crashes, crash hot spots were identified using the integration of spatial autocorrelation of crashes and Getis Ord \(Gi^{\star}\). The use of spatial autocorrelation has the advantage of allowing statistical analysis of crash spatial patterns. The ability of \(Gi^{\star}\) statistics to differentiate high crash clusters from low crash clusters makes it a better approach for identifying crash hot spots than _Moran's I_ index. In this study, rather than the total number of collisions, crash counts and severity values were used. Following the analysis of spatial autocorrelation of crashes, \(Gi^{\star}\) statistics were used to identify high and low crash severity clusters. The first analysis result of crash hot spot identification for all Ethiopian regions and federal territories identified Addis Ababa and Oromia region as high crash severity clusters with confidence intervals of 99 percent and 95 percent, respectively. Second, six statistically significant crash hot spots in Oromia were detected through the integration of spatial autocorrelation and \(Gi^{\star}\) statistics. With a confidence level of 99%, the quantitative results of Z-Scores prioritized East 'Shewa' zone and 'Burayu' town as the most crash hot spots. All of the six identified crash hot spot zones and towns are near the entrance to Addis Ababa, Ethiopia's capital city. Addis Ababa is linked to other towns, zones and regions by five major roads and all of these connecting roads pass through the identified crash hot spots in Oromia. It can be concluded that the identified crash hot spot locations are along the entrance and exit of Addis Ababa city; therefore, the concerned bodies and traffic management agencies should give these areas top priority and conduct a thorough study in order to reduce the socio-economic impact of traffic collisions. The results of the study acknowledged that the applied method of crash hot spot identification (such as the integration of spatial autocorrelation of crashes and Getis Ord \(Gi^{\star}\)) has the ability to analyze spatial patterns of crashes and identify highly clustered crash severity with a statistical significance. The study demonstrated that using GIS in Ethiopia has a significant benefit when it comes to prioritizing a promising site for safety improvements. Thus, the application of GIS in crash hot spot analysis must have to be considered and need to be used as a tool for road safety study in Ethiopia in the future. The recommendations for future road safety improvements include the development of a national crash hot spot (blackspot) identification manual, the frequent spatial and temporal mapping of crash patterns and the provision of training for safety engineers on the methodologies for identifying crash hot spots, as well as on the advancements of GIS technology to be implemented in the field of road safety.