How to measure the mental workload. Assessment of the workload to the train drivers.
DOI: 10.5100/jje.29.377
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the critical challenge of assessing and managing the mental workload and fatigue of train drivers to ensure safe and efficient railway operations. The author, Hiroshi Ugajin, highlights that while train safety relies on complex infrastructure, the driver’s role in executing precise acceleration, deceleration, and emergency responses is paramount. Consequently, maintaining optimal driver conditions is essential. The paper reviews historical approaches to workload management, noting that earlier methods based on the product of continuous duty distance and time (LT value) failed to achieve practical utility despite their theoretical basis in physiological data like heart rate and flicker fusion frequency. With the advent of high-speed rail and improved track conditions, the relationship between speed and workload has evolved, necessitating more sophisticated evaluation models. The primary methodological focus is the "Cumulative Workload" (CW) model developed by Ikeda (1988). This model estimates fatigue accumulation and recovery over time by dividing a driver’s daily life into four categories: work, rest during duty, non-duty waking hours, and sleep. It assigns relative workload coefficients ($\alpha_i$) to specific actions based on subjective ratings from 116 experienced drivers, ranging from -1 (recovery) to 1 (fatigue accumulation). The model calculates CW continuously, assuming linear changes in fatigue within each activity segment. The validity of this model was verified through two methods: first, by correlating the model’s estimated fatigue increase with drivers’ subjective ratings of 155 duty schedules, yielding a high correlation coefficient ($r = .849$); second, by comparing the model’s continuous fatigue estimates with self-reported fatigue levels from 36 drivers over 18 days, resulting in a satisfactory correlation ($r = .750$). The model has since been implemented in software for schedule creation in various railway companies. Regarding short-term workload impacts, the paper examines the effects of increased train speeds. Physiological indicators alone are insufficient for predicting workload due to variables like vehicle performance and driver adaptation. However, eye-tracking studies reveal that higher speeds increase the dispersion of gaze duration, indicating heightened monitoring demands as drivers frequently switch attention targets and wait for signals. Subjective evaluations identify three main functional areas affected by speed: signal confirmation, speed regulation, and disturbance/instrument monitoring. While backup systems and information support devices can alleviate burdens related to signal confirmation and speed regulation, the monitoring of external disturbances, particularly at level crossings, remains a significant stressor and safety concern. The paper concludes by noting ongoing research using dual-task methods, such as melody memory tests, to further quantify workload changes during speed regulation events, emphasizing the need for continued ergonomic refinement in high-speed rail environments.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | failed | — | — | — | 2 | 2026-08-23 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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- Empirical Findings: physiological data
- Methodological Resource: metric or index
- Theoretical Contribution: theory or model