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http://dx.doi.org/10.12815/kits.2017.16.4.01

Prediction of Severities of Rental Car Traffic Accidents using Naive Bayes Big Data Classifier  

Jeong, Harim (Dept. of Transportation Eng., Ajou University)
Kim, Honghoi (Ilmile Corp.)
Park, Sangmin (Dept. of Transportation Eng., Ajou University)
Han, Eum (Traffic Science Institute, Road Traffic Authority)
Kim, Kyung Hyun (Transportation Research Division, Korea Expressway Corporation Research Institute)
Yun, Ilsoo (Dept. of Transportation Eng., Ajou University)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.16, no.4, 2017 , pp. 1-12 More about this Journal
Abstract
Traffic accidents are caused by a combination of human factors, vehicle factors, and environmental factors. In the case of traffic accidents where rental cars are involved, the possibility and the severity of traffic accidents are expected to be different from those of other traffic accidents due to the unfamiliar environment of the driver. In this study, we developed a model to forecast the severity of rental car accidents by using Naive Bayes classifier for Busan, Gangneung, and Jeju city. In addition, we compared the prediction accuracy performance of two models where one model uses the variables of which statistical significance were verified in a prior study and another model uses the entire available variables. As a result of the comparison, it is shown that the prediction accuracy is higher when using the variables with statistical significance.
Keywords
Big data; rental car; traffic accident; severity; Naive Bayes; machine learning;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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