Citation

Atnafu B, Kaur G. Int. J. Eng. Technol. Sci. Res. 2017; 4(10): 1153-1162.

Copyright

(Copyright © 2017, IJETSR)

DOI

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PMID

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Abstract

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 identify the nature of road traffic accident that causes for fatal, grievous injury, minor injuries, and non-injury. For this purpose, there are a number of classification and association rule mining algorithms. From this in the proposed method, Random tree, J48, and Naive Baye's algorithms are selected that were show better performance in the previousstudies and applied on data set to analyze road accident data of Maharashtra state in India. The results of the three algorithms are compared and then the prediction model is done using the algorithm which proves to be the best. Further, the Apriori association rule mining algorithm is applied to find out the relationship between independent variablesrespects with nature of accidents. This study also investigates the influence of other contributing factors (like road related, driver related and environmental related factors) that influence the likelihood of severe accidents in Maharashtra, India.

Keywords--road accident, data mining, random tree, J48, Naive Baye's, association rule mining

Language: en