Applying Data Mining to Identify Determinant Factors of Drivers and Vehicles In Support Of Reducing and Controlling Road Traffic Accident: In the Case Of Addis Ababa City
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Date
2009-04
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Addis Ababa University
Abstract
Road transport plays vital roles in the effort of enriching the economic growth of the society,
especially in developing countries. An efficient transport system is decisive factor to promote
sociology-economic development of Ethiopia. Although the transport sector is important in
facilitating economic growth and development, a very negative phenomenon, namely road traffic
accident, has increased thereby highly threatening the safety of every traveler in Ethiopia, in
particular at Addis Ababa city.
Traditionally, simple manual and statistical techniques are used for traffic accident analysis at
Addis Ababa traffic control and investigation office. These methods are inefficient and
impractical as the volume of road traffic accident data increases. Thus this research work will
discuss how to investigate the potential application of data mining tool and techniques to develop
models that can support to reduce and control road traffic accident by identifying and predicting
the major drivers and vehicles determinant risk factors (attributes) that causes road traffic
accident.
The methodology used for this research work had three basic steps namely, data collection, data
reprocessing and model building and evaluating. The datasets used for this research work was
collected from Addis Ababa traffic control and investigation office, 6 107 road traffic accident
records. Since the collected datasets was not suitable as it is for experiment, data reprocessing
activities were done. In data reprocessing steps data cleaning and data reduction were
undertaken. To build models decision tree and rule induction techniques were employed using
Weka, version 3-5-8, data mining tool.
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Information Science