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Survey on Analysis and Prediction of Road Traffic Accident Severity Levels using Data Mining Techniques in Maharashtra, India

Author : Baye Atnafu and Gagandeep Kaur

Pages : 1973-1978
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Traffic accidents are the main cause of death as well as serious injuries in the world. India is among the emerging countries where the rate at which traffic accident occurs is more than the critical limit. As a human being, we all want to avoid traffic accidents and stay safe. In order to stay safe, careful analysis of roadway traffic accident data is important to find out factors that are related to fatal, grievous injury, minor injuries, and non-injury. For this purpose, there are a number of classification association rule mining algorithms. From these, the survey paper discusses the algorithms that show better performance in the previous studies and also the survey examines the most widely used data mining tools. The proposed model implements by using the algorithm that shows better performance during the experiment to overcome the shortcomings of previous studies on accident severity prediction. Road traffic accident historical data is obtained from National Highway Authority of India (NHAI).

Keywords: Road accident, data mining, random tree, J48, Naive Baye’s, association rule mining

Article published in International Journal of Current Engineering and Technology, Vol.7, No.6 (Nov/Dec 2017)

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