Technical gazette, Vol. 29 No. 2, 2022.
Original scientific paper
https://doi.org/10.17559/TV-20211102092922
Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks
Jin Wujie
; School of Transportation and Logistics, East China Jiaotong University, No. 808, East Shuanggang Road, Nanchang City, Jiangxi Province, China, State Grid Zhoushan Power Supply Company, Zhoushan, Zhejiang Province, China
Jia Le
; School of Transportation and Logistics, East China Jiaotong University, No. 808, East Shuanggang Road, Nanchang City, Jiangxi Province, China
Yan Lixin*
; School of Transportation and Logistics, East China Jiaotong University, No. 808, East Shuanggang Road, Nanchang City, Jiangxi Province, China
Zhang Cheng
; School of Transportation and Logistics, East China Jiaotong University, No. 808, East Shuanggang Road, Nanchang City, Jiangxi Province, China
Abstract
The prevention of railway operational accidents has become one of the leading issues in railway safety. Identifying the impact factors which significantly affect railway operating is critical for decreasing the occurrence of railway accidents. In this study, 8440 samples of accident data are selected as the datasets for analyzing. Fishbone diagram is applied to obtain the factors which cause the accident from the perspective of human-equipment-environment-management system theory. Then, the Bayesian network method was selected to establish a railway operation safety accident prediction model, and the sensitivity analysis method was used to obtain the sensitivity of each variable factor to the accident level. The results show that season, location, trouble maker and job function have a significant impact on railway safety, and their sensitivity was 0.4577, 0.4116, 0.3478 and 0.3192, respectively. Research helps the railway sector to understand the fundamental causes of accidents, and provides an effective reference for accident prevention, which is conducive to the long-term development of railway transportation.
Keywords
Bayesian network; causes of accident; fishbone diagram; prediction model; railway safety
Hrčak ID:
272606
URI
Publication date:
15.4.2022.
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