Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
Title A Study on Prediction of Passenger's Injury Grade Using Public Traffic Accident Database and Machine Learning
Authors 이용범(Yongbeom Lee) ; 조은기(Eungi Cho) ; 윤창용(Changyong Yoon) ; 박성근(Seongkeun Park)3
Page pp.866-871
ISSN 1975-8359
Keywords NASS-CDS ; Prediction of injury grade ; Machine Learning ; Decision Tree ; Support Vector Machine ; Logistic Regression ; Principal Component Analysis ; Linear Discriminant Analysis
Abstract In this paper, we propose a prediction model for traffic accident injury grade using machine learning algorithm. We used machine learning models such as Decision Tree, Support Vector Machine, and Logistic Regression to predict injury grade. And to improve performance, Principal Component Analysis(PCA) or Linear Discriminant Analysis(LDA) was applied to each machine learning model. The data used in this study are based on NASS-CDS data that collects traffic accident investigation data and medical records of accident victims. According to the results, the proposed method is expected to be used in the automatic traffic accident notification system because of its reliable accuracy.