V.N.V, Manoj (PhD)Mossie, Getnet2018-11-282023-11-292018-11-282023-11-292009-04http://etd.aau.edu.et/handle/123456789/14588Road 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 socio-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 preprocessing and model building and evaluating. The dataset used for this research work was collected from Addis Ababa traffic control and investigation office, 6107 road traffic accident records. Since the collected dataset was not suitable as it is for experiment, data preprocessing activities were done. In data preprocessing 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.In the experiment section models were built and rules also generated with decision tree (using J48 algorithm) and rule induction (using PART algorithm) techniques. The experiment of this research proves that the performance of J48 algorithm is slightly better than PART algorithm. In this research the variables LicenseGrade, VehicleServiceyear, Typesofvehicles and Experience were identified as the most important variables to predict accident severity pattern.In this research work, the researcher has proved that the road traffic accident database could be successfully mined to identify determinant risk factors of drivers and vehicles that cause accidentenData Mining to Identify Determinant Factors of DriversThesis