Application of Data Mining Technology to Support the Prioritization of Dangerous Crash Locations the Case of Addis Ababa Traffic Office
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Date
2009-01
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Addis Ababa University
Abstract
The development of automotive industry, the slowly improvement of the roadways and the behavior of the traffic participants increased the number of the road accidents. Traffic accident results in loss of life, human injury and financial prejudices. Road Traffic Safety which is currently one of the highest priorities may be affected by a number of factors. One important group of bottlenecks in traffic safety are dangerous accident locations. Addis Ababa is a city where the number of traffic accident is increasing from time to time. Identification of high crash locations in the city will either protect the accident 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 high crash locations and predict exposure of the society on different crash locations. The study used data obtained from the Addis Ababa Traffic Office. In order to prioritize high crash locations different data mining tools and techniques were used.
The data mining process in this research is divided into two major phases. During the first phase data was prepared and formatted into the appropriate format for the respective data mining software to be used (Weka 3.5.8). The second phase contains model building for prioritization using decision tree classification. In the classification phase J4.8 algorithm were employed to generate rules.
Traffic accident locations were prioritized based on their degree and number of fatality occurrence. The patterns obtained from the J-48 algorithm separated these locations as: death, severe injury, and light injury.The outcome of the study is highly useful for the Traffic police office on developing traffic management system; for the society, drivers and pedestrians, on pre-informing the accident occurrences on those black spots. It also provides valuable information for making decisions effectively for road safety investment projects.
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Data Mining Technology to Support