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<p>Since human error continues to be a significant cause of operational accidents and service interruptions, human reliability is a crucial component of railway safety. Operator fatigue and drowsiness greatly reduce situational awareness and reaction time, which raises the risk of risky behavior. An eye-tracking-based framework for real-time operator vigilance monitoring and human reliability evaluation in railroad operations is presented in this study. In order to continuously analyze blink duration, frequency, variability, and eye closure behavior as physiological markers of fatigue, an eye detection system was created. A sample of twenty people under various sleep conditions participated in experimental trials using a railway driving simulator. The findings demonstrate that inadequate sleep is linked to a higher estimated Human Error Probability (HEP = 0.74) than adequate sleep conditions (HEP = 0.39), suggesting a significant rise in error likelihood when tired. Human reliability was also impacted by individual factors; higher HEP values were found for gender-related differences (HEP = 0.64) and age-related effects (HEP = 0.65). When combined with the Human Error Assessment and Reduction Technique (HEART), these results show how sensitive blink-based fatigue indicators are to changes in operator performance. The study offers quantitative proof of the viability of integrating eye-tracking data with Human Reliability Analysis for fatigue assessment, despite sample size limitations. The suggested framework emphasizes how vision-based monitoring technologies can help with proactive fatigue management and improve railway operations’ safety.</p>

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Integration of eye tracking technology for human reliability in railway engineering

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • A. F. Yusop,
  • M. E. M. Aliza,
  • M. A. M. Nor,
  • M. A. Hamidi,
  • R. Goyal

摘要

Since human error continues to be a significant cause of operational accidents and service interruptions, human reliability is a crucial component of railway safety. Operator fatigue and drowsiness greatly reduce situational awareness and reaction time, which raises the risk of risky behavior. An eye-tracking-based framework for real-time operator vigilance monitoring and human reliability evaluation in railroad operations is presented in this study. In order to continuously analyze blink duration, frequency, variability, and eye closure behavior as physiological markers of fatigue, an eye detection system was created. A sample of twenty people under various sleep conditions participated in experimental trials using a railway driving simulator. The findings demonstrate that inadequate sleep is linked to a higher estimated Human Error Probability (HEP = 0.74) than adequate sleep conditions (HEP = 0.39), suggesting a significant rise in error likelihood when tired. Human reliability was also impacted by individual factors; higher HEP values were found for gender-related differences (HEP = 0.64) and age-related effects (HEP = 0.65). When combined with the Human Error Assessment and Reduction Technique (HEART), these results show how sensitive blink-based fatigue indicators are to changes in operator performance. The study offers quantitative proof of the viability of integrating eye-tracking data with Human Reliability Analysis for fatigue assessment, despite sample size limitations. The suggested framework emphasizes how vision-based monitoring technologies can help with proactive fatigue management and improve railway operations’ safety.