Correlation of Work Fatigue and Mental Workload in Train Drivers: A Cross-sectional Study

Hassanzadeh-Rangi, Narmin; Jalilian, Hamed; Farshad, Ali-Asghar; Khosravi, Yahya · 2023 · Crossref

DOI: 10.34172/jrhs.2023.135

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Summary

This cross-sectional study investigates the correlation between work fatigue and mental workload among train drivers in the Tehran Metro, addressing a gap in research regarding subway operations where human-machine interaction is complex. The study was motivated by evidence that train drivers experience high levels of fatigue and mental demand, factors known to contribute to human error and accidents in railway systems. The primary objective was to assess overall, physical, and mental fatigue levels and determine their relationship with mental workload dimensions throughout a work shift. The research involved 863 train drivers from five metro lines who completed validated Persian versions of the Samn-Perelli Fatigue Scale and the Fatigue Assessment Scale (FAS) at the beginning and end of their shifts. Mental workload was measured using the National Aeronautics and Space Administration Task Load Index (NASA-TLX) at the mid-point and end of the shifts. Statistical analyses, including Spearman and Pearson correlation tests and multiple linear regression, were employed to evaluate the relationships between fatigue, workload, and potential confounding factors such as age, sleep duration, and work experience. The results indicated that overall, physical, and mental fatigue levels increased significantly from the start to the end of the shift (P < 0.001). Similarly, mental workload and its dimensions, including mental demand, time pressure, and frustration, were significantly higher at the end of the shift compared to the middle. The strongest correlation was found between overall workload and time pressure (R = 0.68, P < 0.001). Regression analysis confirmed that overall workload significantly predicted overall, physical, and mental fatigue. Additionally, work experience, overtime, and weekly work hours were significant predictors of fatigue, while factors such as night shift work and educational level showed no significant correlation with fatigue levels in this model. The study concludes that mental workload is a significant contributor to work fatigue in train drivers, with mental demand and time pressure being the most critical dimensions. The findings suggest that fatigue management strategies should prioritize reducing mental workload and addressing time pressure to mitigate the risk of human error and accidents. The authors recommend further dynamic and qualitative studies to develop targeted intervention programs for metro operators, emphasizing that sustained attention and vigilance tasks are central causes of the stress leading to fatigue.

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Topics

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• truck driver fatigue
• workload measurement
• mental demand
• time on task
• shift work driving
• stress driving

Information type

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• Empirical Findings: physiological data, self report data
• Theoretical Contribution: theory or model