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Mental workload assessment and prediction for train operators
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- Author(s) / Creator(s)
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Train protection and control systems are crucial for improving railway safety by reducing operator-related accidents. However, they shift operators from manual control to monitoring, presenting both advantages and challenges. This change requires rapid assimilation of vast information, risking mental overload and performance degradation. Therefore, assessment and prediction of the workload associated with train systems is essential. This paper examines mental workload studies in train operator settings, categorizing them by their approaches (subjective-objective, analytical-empirical), methods, metrics, and the types of train cab systems studied. It also analyzes how train technology affects operator workload, emphasizing the importance of addressing workload during system design for safe and efficient railway operations. Our analysis highlighted a preference for the subjective-empirical approach for analyzing train operators' workload, often applied after system prototypes and simulator experiments are available. Early workload analysis is recommended for user-centred design, preventing operator errors and costly redesigns. Furthermore, the literature presented diverse findings on the effects of in-cab systems and automation on train operators' workload. These disparities may arise from system characteristics, individual differences, environmental factors, operational conditions, and infrastructure variations. Additionally, differences in the stages of information processing studied can contribute to varying workload outcomes for the same system.
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- Date created
- 2024-04-01
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- Subjects / Keywords
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- Type of Item
- Conference/Workshop Presentation