The development of automotive industry, the slowly improvement of the roadways andrnthe behavior of the traffic participants increased the number of the road accidents.rnTraffic accident results in loss of life, human injury and financial prejudices. Road TrafficrnSafety which is currently one of the highest priorities may be affected by a number ofrnfactors. One important group of bottlenecks in traffic safety are dangerous accidentrnlocations. Addis Ababa is a city where the number of traffic accident is increasing fromrntime to time. Identification of high crash locations in the city will either protect thernaccident occurrences or minimize the rate of damage to be caused. This paper reports on the findings of a research that had the objective to prioritize highrncrash locations and predict exposure of the society on different crash locations. Thernstudy used data obtained from the Addis Ababa Traffic Office. In order to prioritize highrncrash locations different data mining tools and techniques were used.The data mining process in this research is divided into two major phases. During thernfirst phase data was prepared and formatted into the appropriate format for thernrespective data mining software to be used (Weka 3.5.8). The second phase containsrnmodel building for prioritization using decision tree classification. In the classificationrnphase J4.8 algorithm were employed to generate rules. Traffic accident locations were prioritized based on their degree and number of fatalityrnoccurrence. The patterns obtained from the J-48 algorithm separated these locationsrnas: death, severe injury, and light injury.The outcome of the study is highly useful for the Traffic police office on developingrntraffic management system; for the society, drivers and pedestrians, on per-informingrnthe accident occurrences on those black spots. It also provides valuable information forrnmaking decisions effectively for road safety investment projects.