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 per-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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Information Science