Optimizing Railway Safety by Analyzing Human Reliability Techniques - A review

Human reliability analysis (HRA) is a critical component in ensuring the safety and efficiency of railway engineering. As railway systems grow more complex, the methodologies used to assess and improve human reliability must also advance. This review provides a comprehensive analysis of the evolutio...

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Veröffentlicht in:Journal of physics. Conference series 2025-01, Vol.2933 (1), p.12014
Hauptverfasser: Aliza, M. E. M., Yusop, A. F., Hamidi, M. A., Nor, M. A. M.
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Sprache:eng
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Zusammenfassung:Human reliability analysis (HRA) is a critical component in ensuring the safety and efficiency of railway engineering. As railway systems grow more complex, the methodologies used to assess and improve human reliability must also advance. This review provides a comprehensive analysis of the evolution of HRA, from the first-generation techniques to the third-generation approaches currently in use. Through a broad survey of the literature, comparative analysis, and detailed case studies, this review traces the development of HRA methods, showing the evolution from traditional techniques to modern hybrid approaches. The review also emphasizes the significance of hybrid Human Error Assessment and Reduction Technique (HEART) methods, which integrate multiple HRA approaches to provide a more comprehensive and accurate assessment of human reliability. The hybrid technique offers a more accurate estimation than standard methods, as evidenced by the determined Pearson coefficient of 0.9990 between the simulation findings and the HEP values of HEART-related methodologies. It also explores the integration of human factors into railway safety systems, underscoring the importance of considering human-machine interactions and the cognitive and behavioural aspects of railway operations. Key findings indicate that while traditional HRA methods laid the groundwork, there is a growing need for continuous innovation to address the increasing complexity of railway systems. This includes the development of hybrid models that combine insights from various HRA techniques and the incorporation of advanced human-machine interaction paradigms to further minimize human error rates. The objective of this review is to offer recommendations for future research in the field of HRA for railway engineering. It advocates for the development of advanced hybrid models with the use of cutting-edge technology like machine learning and artificial intelligence. By combining historical insights with modern technological advancements, the goal is to create more robust and reliable HRA methods that can better support the safety and efficiency of railway operations.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2933/1/012014