Hierarchical ordered logistic regression analysis of urban rail transit driver fatigue determinants: impact of emotion regulation and sleep patterns
DOI: 10.1038/s41598-026-44865-y
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Summary
This study investigates the determinants of psychological fatigue among urban rail transit (URT) drivers, addressing a gap in literature that has historically prioritized physical fatigue over latent psychological states. The research focuses on the synergistic effects of emotion regulation and sleep patterns on driver fatigue, motivated by the critical need to enhance operational safety in high-frequency transit systems. By integrating these factors into a unified analytical framework, the authors aim to clarify the mechanisms underlying fatigue formation and provide empirical evidence for targeted interventions. The methodology involved a survey of 185 professional URT drivers from the Lanzhou system, generating a dataset of 3,515 samples. Each driver evaluated their fatigue levels across 19 distinct track segments, creating a nested data structure with repeated measures. To account for within-driver correlations and multi-level influences, the study employed a hierarchical ordered logistic regression model. This approach partitioned variance into segment-specific environmental factors (Level 1), such as curve radius and slope gradient, and driver-specific attributes (Level 2), including emotional intelligence dimensions and sleep characteristics. The dependent variable, fatigue, was measured on a seven-point Likert scale, while independent variables were coded to reflect proficiency and quality levels. Results indicate that both emotion regulation and sleep status significantly impact fatigue levels. Specifically, Emotional Management Proficiency Level (EMPL) and Sleep Quality (SQ) exerted the strongest influences, with one-level improvements in each reducing the probability of moderate-to-severe fatigue by approximately 7.7–7.8%. Emotional Recognition Proficiency Level (ERPL) and Sleep Initiation (SI) also showed significant effects. Heterogeneity analysis revealed distinct patterns based on driving experience: emotion regulation had a substantially stronger impact on less-experienced drivers, likely due to higher cognitive loads and adaptation challenges. Conversely, sleep patterns were more critical for experienced drivers, who rely more on physiological recovery than active emotional coping strategies. The hierarchical model demonstrated superior fit compared to standard models, reducing AIC and BIC metrics by nearly 9%. The findings underscore the necessity for differentiated fatigue mitigation strategies. For novice drivers, interventions should prioritize emotional intelligence training and mentorship to manage psychological stress. For experienced drivers, operators should focus on optimizing sleep hygiene, shift rosters, and rest facilities to ensure physiological recovery. These insights provide practical guidance for URT management to implement targeted safety measures, thereby improving operational reliability. The study acknowledges limitations regarding its single-city sample and reliance on self-reported data, suggesting future research incorporate objective physiological measures and broader geographic datasets.
Key finding
Emotion regulation and sleep patterns significantly reduce urban rail transit driver fatigue, with emotion regulation being more impactful for less-experienced drivers and sleep patterns having a stronger effect on experienced drivers.
Methodology
survey
Sample size: 185
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-06-01 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-06-01 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: physiological data
- Theoretical Contribution: theory or model