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Study on Accident Prediction Models in Urban Railway Casualty Accidents Using Logistic Regression Analysis Model

로지스틱회귀분석 모델을 활용한 도시철도 사상사고 사고예측모형 개발에 대한 연구

  • Jin, Soo-Bong (Department of Railway Electrical and Signaling Engineering, Graduate School of Railway, Seoul National University of Science and Technology) ;
  • Lee, Jong-Woo (Department of Railway Electrical and Signaling Engineering, Graduate School of Railway, Seoul National University of Science and Technology)
  • Received : 2017.08.17
  • Accepted : 2017.08.25
  • Published : 2017.08.31

Abstract

This study is a railway accident investigation statistic study with the purpose of prediction and classification of accident severity. Linear regression models have some difficulties in classifying accident severity, but a logistic regression model can be used to overcome the weaknesses of linear regression models. The logistic regression model is applied to escalator (E/S) accidents in all stations on 5~8 lines of the Seoul Metro, using data mining techniques such as logistic regression analysis. The forecasting variables of E/S accidents in urban railway stations are considered, such as passenger age, drinking, overall situation, behavior, and handrail grip. In the overall accuracy analysis, the logistic regression accuracy is explained 76.7%. According to the results of this analysis, it has been confirmed that the accuracy and the level of significance of the logistic regression analysis make it a useful data mining technique to establish an accident severity prediction model for urban railway casualty accidents.

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