Abstract:
The reduction of traffic crashes, as well as their socio-economic consequences, has capti vated 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 RTC
