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http://dx.doi.org/10.12815/kits.2020.19.4.13

A Study on Injury Severity Prediction for Car-to-Car Traffic Accidents  

Ko, Changwan (Dept. of Industrial Eng., Chonnam Nat'l. Univ.)
Kim, Hyeonmin (Dept. of Industrial Eng., Chonnam Nat'l. Univ.)
Jeong, Young-Seon (Dept. of Industrial Eng., Chonnam Nat'l. Univ.)
Kim, Jaehee (Dept. of Business Admin., Jeonbuk Nat'l. Univ.)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.19, no.4, 2020 , pp. 13-29 More about this Journal
Abstract
Automobiles have long been an essential part of daily life, but the social costs of car traffic accidents exceed 9% of the national budget of Korea. Hence, it is necessary to establish prevention and response system for car traffic accidents. In order to present a model that can classify and predict the degree of injury in car traffic accidents, we used big data analysis techniques of K-nearest neighbor, logistic regression analysis, naive bayes classifier, decision tree, and ensemble algorithm. The performances of the models were analyzed by using the data on the nationwide traffic accidents over the past three years. In particular, considering the difference in the number of data among the respective injury severity levels, we used down-sampling methods for the group with a large number of samples to enhance the accuracy of the classification of the models and then verified the statistical significance of the models using ANOVA.
Keywords
Traffic incident; Injury severity; Undersampling; Prediction model;
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