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Research on human error analysis of high-speed railway traffic dispatchers based on an improved weighted BN-CREAM model.

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Title: Research on human error analysis of high-speed railway traffic dispatchers based on an improved weighted BN-CREAM model.
Authors: Liu, C.1, Chang, D.1, Gong, D.1 dqgong@bjtu.edu.cn
Source: Advances in Transportation Studies. Nov2025, Vol. 67, p85-100. 16p.
Subjects: Human error, High speed trains, Safety, Bayesian analysis, Transport workers
Abstract: High-speed railway traffic dispatchers occupy a pivotal role in ensuring the safe and efficient operation of high-speed railway systems. Errors committed by these dispatchers can precipitate severe operational consequences, underscoring the necessity for robust methodologies to mitigate human-induced failures. This study introduces an advanced approach for analyzing human errors among high-speed railway traffic dispatchers, leveraging an enhanced weighted Bayesian Network (BN) integrated with the Cognitive Reliability and Error Analysis Method (CREAM), termed the improved weighted BN-CREAM. The proposed methodology synthesizes the strengths of the CREAM model and Bayesian Networks, augmented by an innovative weighted algorithm, to construct a comprehensive human error analysis framework tailored for high-speed railway traffic dispatchers. The efficacy of the model is empirically validated through a detailed case study, demonstrating its capability to identify and quantify human error probabilities with high precision. Based on the findings, targeted preventive measures are proposed to enhance the reliability and safety of high-speed railway operations. This research contributes to the field by providing a systematic and quantifiable tool for human error analysis, thereby supporting the development of more resilient railway dispatching systems. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Human+error%22">Human error</searchLink><br /><searchLink fieldCode="DE" term="%22High+speed+trains%22">High speed trains</searchLink><br /><searchLink fieldCode="DE" term="%22Safety%22">Safety</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Transport+workers%22">Transport workers</searchLink>
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  Data: High-speed railway traffic dispatchers occupy a pivotal role in ensuring the safe and efficient operation of high-speed railway systems. Errors committed by these dispatchers can precipitate severe operational consequences, underscoring the necessity for robust methodologies to mitigate human-induced failures. This study introduces an advanced approach for analyzing human errors among high-speed railway traffic dispatchers, leveraging an enhanced weighted Bayesian Network (BN) integrated with the Cognitive Reliability and Error Analysis Method (CREAM), termed the improved weighted BN-CREAM. The proposed methodology synthesizes the strengths of the CREAM model and Bayesian Networks, augmented by an innovative weighted algorithm, to construct a comprehensive human error analysis framework tailored for high-speed railway traffic dispatchers. The efficacy of the model is empirically validated through a detailed case study, demonstrating its capability to identify and quantify human error probabilities with high precision. Based on the findings, targeted preventive measures are proposed to enhance the reliability and safety of high-speed railway operations. This research contributes to the field by providing a systematic and quantifiable tool for human error analysis, thereby supporting the development of more resilient railway dispatching systems. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.53136/97912218219496
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        Text: English
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        PageCount: 16
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      – SubjectFull: Human error
        Type: general
      – SubjectFull: High speed trains
        Type: general
      – SubjectFull: Safety
        Type: general
      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Transport workers
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              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
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