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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
06.02.2025
DOI
https://doi.org/10.7307/ptt.v37i1.659
ISSUE
2025 (Vol 37), Issue 1
LICENSE
Copyright (c) 2025 Ping WAN, Wei-Lun YANG, Jie-Wen LUO, Xiao-Feng MA
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Importance Analysis of Causative Nodes for Accident Chains of Railway Locomotive Operation Based on STPA-PageRank Method

Authors:Ping WAN, Wei-Lun YANG, Jie-Wen LUO, Xiao-Feng MA

Abstract

Nowadays, in terms of complex and random incidents for locomotive operation, the prevention and control for every tiny and possible influencing factor is not only costly, but also brings great psychological burden to locomotive drivers. Firstly, 68 sets of data of railway locomotive operation accidents happened in recent two years were collected and compiled. Secondly, the system theory process analysis (STPA) method was adopted to extract 68 accident chains based on those data. Then, the complex network theory and PageRank algorithm were utilised to calculate the importance of every node in directed-weighted network formed by those accident chains. The results showed that the importance of human factors is significantly higher than other layers including environment, facility and management. Especially, no effective control behaviour (H7) and false control behaviour (H10) are the top two important causative nodes among all human factors. Besides, being forced to stop (D39) and overrunning of signal (D42) are the top two important causative nodes among unsafe events. For those nodes with high value of PageRank, some targeted security measures should be adopted, so as to save risk management investment and improve the overall safety level of the locomotive operation system.

Keywords:railway locomotive operation, risk management, complex network, STPA, PageRank

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