Characteristic analysis of subway escalator accidents based on disordered multinomial Logistic regression

doi:10.16265/j.cnki.issn1003-3033.2020.04.018

Abstract

Abstract: In order to explore major reasons for subway escalator accidents, with data of such accidents in Beijing subway as an example, disordered multinomial Logistic regression method was used to study characteristics of accidents and to prevent their occurrence by controlling related influencing factors. Then, by analyzing accident forms, types and attributes of explanatory and response variables in 894 subway escalator accidents, a disordered multinomial Logistic regression model was constructed to identify significant correlation factors and calculate their contribution level to accidents. The results show that developing different key control strategies concerning environmental factors, passenger characteristics, passenger behaviors and paths for different types of accidents can effectively reduce occurrence of subway escalator accidents. At the same time, this method can be applied in research of accident predication as well as solve problems of correlation analysis of disordered multivariant variables and quantitative analysis of their contribution.

Key words: subway, escalator accident, disordered multidassification, Logistic regression analysis, accident characteristic, correlation analysis, explanatory variables

CLC Number:

X928

WANG Zhiru, GAO Linyuan, WANG Min. Characteristic analysis of subway escalator accidents based on disordered multinomial Logistic regression[J]. China Safety Science Journal, 2020, 30(4): 114-120.

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