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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Addis Ababa University


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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