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http://dx.doi.org/10.7782/JKSR.2017.20.4.482

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

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)
Publication Information
Journal of the Korean Society for Railway / v.20, no.4, 2017 , pp. 482-490 More about this Journal
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.
Keywords
Accident prediction; Railway casualty accident; Logistic regression; Accident investigation; Falling accident;
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Times Cited By KSCI : 3  (Citation Analysis)
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