Abstract
The reduction of traffic crashes, as well as their socio-economic consequences, has captivated the attention of safety professionals and transportation agencies. The most important activity for an effective road safety practice is to identify hazardous roadway areas based on a spatial pattern analysis of crashes and an evaluation of crash spatial relations with neighboring areas and other relevant factors. For decades, safety researchers have adopted several techniques to analyze historical road traffic crash (RTC) information using the advanced GIS-based hot spot analysis. The objective of this study is to present a GIS technique for identifying crash hot spots based on spatial autocorrelation analysis using a four-year (2014–2017) crash data across Ethiopian regions, as well as zones and towns in the Oromia region. The study considered the corresponding severity values of RTCs for the analysis and ranking of crash hot spot areas. The spatial autocorrelation tool in ArcGIS 10.5 was used to analyze the spatial patterns of RTCs and then the Getis Ord Gi* statistics tool was used to identify high and low crash severity cluster zones. The results showed that the methods used in this analysis, which incorporated Moran’s I spatial autocorrelation of crash incidents, Getis Ord Gi* and crash severity index, proved to be a fruitful strategy for identifying and ranking crash hot spots. The identified crash hot spot areas are along the entrance to and exit from Addis Ababa, Ethiopia’s capital city, so the responsible bodies and traffic management agencies should give top priority attention and conduct a thorough study to reduce the socio-economic effect of RTCs.
Highlights
Road traffic safety has become a major apprehension for human beings since the emerging of roadway transport and motor vehicles
The analysis to figure out the trends of fatalities that occurred due to road traffic crashes (RTCs) in Ethiopia was done based on eight consecutive years (2010–2017)
The advantage of using advanced Geographical Information System (GIS)-based hot spot analysis is not limited to the simple presentation of hot spot roadway areas; it provides the ability to investigate the spatial dependency of crash incidents and spatial connections with other factors
Summary
Road traffic safety has become a major apprehension for human beings since the emerging of roadway transport and motor vehicles. According to the World Health Organization (WHO), RTC (road traffic crash) is the 8th most common means of death for all age groups. Road traffic safety analysis has been employed to save the loss of lives by understanding the cause of traffic crashes and coming up with safety mitigations. The analysis aims to investigate pieces of information needed by decision makers to apply suitable safety measures to eliminate and minimize the occurrence of traffic crashes [2]. Because of the presence of distance in the road networks, the spatial pattern of crashes must be examined in traffic safety studies. Spatial analysis is the inspection of crash occurrence patterns by considering their relative locations or zones. Traffic crashes meet the main characteristics of spatial heterogeneity and spatial dependence of point data. Spatial dependence belongs to the influence of events at a location by neighboring events, while spatial heterogeneity happens when the spatial relationships among observed incidents and random parameters in the developed model are not established spatially [3]
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