ArticleEntropy (Basel, Switzerland)2021
Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Research on risk factors identification and optimization strategies for railway tunnel construction in ecologically sensitive areas.Scientific reports · 2025Article
- A quantitative analysis method based on network evolution for risk factors of safety production in chemical enterprises.Scientific reports · 2025Article
- A HAZOP-based hazard identification model for urban gas accidents: Development and empirical validation.PloS one · 2025Article
- Identifying Key Factors of Hazardous Materials Transportation Accidents Based on Higher-Order and Multilayer Networks.Entropy (Basel, Switzerland) · 2023Article
- Special Issue "Complex Dynamic System Modelling, Identification and Control".Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This study investigates a critical hazard identification method for railway accident prevention. A new accident causation network is proposed to model the interaction between hazards and accidents. To realize consistency between the most likely and shortest causation paths in terms of hazards to accidents, a method for measuring the length between adjacent nodes is proposed, and the most-likely causation path problem is first transformed to the shortest causation path problem. To identify critical hazard factors that should be alleviated for accident prevention, a novel critical hazard identification model is proposed based on a controllability analysis of hazards. Five critical hazard identification methods are proposed to select critical hazard nodes in an accident causality network. A comparison of results shows that the combination of an integer programming-based critical hazard identification method and the proposed weighted direction accident causality network considering length has the best performance in terms of accident prevention.
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Registered trials
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