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<p>With the rapid development of maritime autonomous surface ships (MASS), human factors remain critical in shaping accident causation. This study develops a multi-layered causal analysis model by integrating the Human Factors Analysis and Classification System (HFACS) with Bayesian networks (BN), aiming to identify key contributing factors and their transmission pathways. Utilizing data primarily drawn from 9 publicly available MASS accident investigation reports (published between 2010 and 2025) and 45 simulation-based human reliability studies reported in the literature, the model captures the probabilistic dependencies across management, supervisory, precondition, and operational behavior levels. The results show that a weak organizational climate, inadequate operational planning, and impaired operator condition together form the core causal chain of maritime accidents. The most influential behavioral error pathway, linking operational errors (R1) through operator condition (P2) and task and authority allocation (S2) to safety culture and policies (M2), exerts the strongest effect on accident probability. Sensitivity analysis indicates that upper-level management factors indirectly shape behavioral risk through supervisory and precondition layers, while multi-level interactions amplify the impact of operator conditions on unsafe acts. These findings validate the HFACS-BN framework for quantitative human factor analysis and reveal that remote control and human–automation interaction amplify multi-level risk propagation, offering theoretical and preliminary empirical insights for improving intervention strategies and intelligent maritime safety management.</p>

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A HFACS-BN model-based investigation of human factors in MASS accidents

  • 期刊论文

  • 发表日期：

    2026年5月5日

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  • Li Liu,
  • Shixiong Sheng,
  • Wenhui Xu,
  • Xiaoyang Gao,
  • Jiansi Li

摘要

With the rapid development of maritime autonomous surface ships (MASS), human factors remain critical in shaping accident causation. This study develops a multi-layered causal analysis model by integrating the Human Factors Analysis and Classification System (HFACS) with Bayesian networks (BN), aiming to identify key contributing factors and their transmission pathways. Utilizing data primarily drawn from 9 publicly available MASS accident investigation reports (published between 2010 and 2025) and 45 simulation-based human reliability studies reported in the literature, the model captures the probabilistic dependencies across management, supervisory, precondition, and operational behavior levels. The results show that a weak organizational climate, inadequate operational planning, and impaired operator condition together form the core causal chain of maritime accidents. The most influential behavioral error pathway, linking operational errors (R1) through operator condition (P2) and task and authority allocation (S2) to safety culture and policies (M2), exerts the strongest effect on accident probability. Sensitivity analysis indicates that upper-level management factors indirectly shape behavioral risk through supervisory and precondition layers, while multi-level interactions amplify the impact of operator conditions on unsafe acts. These findings validate the HFACS-BN framework for quantitative human factor analysis and reveal that remote control and human–automation interaction amplify multi-level risk propagation, offering theoretical and preliminary empirical insights for improving intervention strategies and intelligent maritime safety management.