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Development and evaluation of a computer-aided system for analyzing human error in railway operations

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  • Kim, Dong San
  • Baek, Dong Hyun
  • Yoon, Wan Chul

Abstract

As human error has been recognized as one of the major contributors to accidents in safety-critical systems, there has been a strong need for techniques that can analyze human error effectively. Although many techniques have been developed so far, much room for improvement remains. As human error analysis is a cognitively demanding and time-consuming task, it is particularly necessary to develop a computerized system supporting this task. This paper presents a computer-aided system for analyzing human error in railway operations, called Computer-Aided System for Human Error Analysis and Reduction (CAS-HEAR). It supports analysts to find multiple levels of error causes and their causal relations by using predefined links between contextual factors and causal factors as well as links between causal factors. In addition, it is based on a complete accident model; hence, it helps analysts to conduct a thorough analysis without missing any important part of human error analysis. A prototype of CAS-HEAR was evaluated by nine field investigators from six railway organizations in Korea. Its overall usefulness in human error analysis was confirmed, although development of its simplified version and some modification of the contextual factors and causal factors are required in order to ensure its practical use.

Suggested Citation

  • Kim, Dong San & Baek, Dong Hyun & Yoon, Wan Chul, 2010. "Development and evaluation of a computer-aided system for analyzing human error in railway operations," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 87-98.
    • Handle: RePEc:eee:reensy:v:95:y:2010:i:2:p:87-98
    DOI: 10.1016/j.ress.2009.08.005
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    References listed on IDEAS

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    1. Ola Svenson, 1991. "The Accident Evolution and Barrier Function (AEB) Model Applied to Incident Analysis in the Processing Industries," Risk Analysis, John Wiley & Sons, vol. 11(3), pages 499-507, September.
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    Cited by:

    1. Bing Wu & Xinping Yan & Yang Wang & C. Guedes Soares, 2017. "An Evidential Reasoning‐Based CREAM to Human Reliability Analysis in Maritime Accident Process," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 1936-1957, October.
    2. Xu, Bin & Wu, Qi & Xi, Chen & He, Ren, 2020. "Recognition of the fatigue status of pilots using BF–PSO optimized multi-class GP classification with sEMG signals," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    3. Felipe Aguirre & Mohamed Sallak & Walter Schön & Fabien Belmonte, 2013. "Application of evidential networks in quantitative analysis of railway accidents," Journal of Risk and Reliability, , vol. 227(4), pages 368-384, August.
    4. Zhou, Jian-Lan & Tu, Ren-Fang & Xiao, Hai, 2022. "Large-scale group decision-making to facilitate inter-rater reliability of human-factors analysis for the railway system," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    5. Huang, Cheng-Hao & Huang, Ding-Hsiang & Lin, Yi-Kuei, 2023. "Network reliability prediction for random capacitated-flow networks via an artificial neural network," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    6. Al-Baraa Abdulrahman Al-Mekhlafi & Ahmad Shahrul Nizam Isha & Nicholas Chileshe & Mohammed Abdulrab & Ahmed Farouk Kineber & Muhammad Ajmal, 2021. "Impact of Safety Culture Implementation on Driving Performance among Oil and Gas Tanker Drivers: A Partial Least Squares Structural Equation Modelling (PLS-SEM) Approach," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    7. Chen, Yuanjiang & Feng, Wei & Jiang, Zhiqiang & Duan, Lingling & Cheng, Shuangyi, 2021. "An accident causation model based on safety information cognition and its application," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    8. Liu, Yanyan & Li, Keping & Yan, Dongyang, 2024. "Quantification analysis of potential risk in railway accidents: A new random walk based approach," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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