Dynamic evolution of physiological load in train drivers traversing long railway tunnel sections

OBJECTIVE: Long railway tunnels expose train drivers to abrupt luminance transitions, spatial confinement, and monotonous visual environments that may elevate physiological load and compromise operational safety. This study aims to characterize the dynamic evolution of train drivers' physiological load across distinct sections of a long railway tunnel and to establish a quantitative multi-indicator evaluation framework to support tunnel safety design and driver-state monitoring. METHODS: Naturalistic driving experiments were conducted in the Heishan Railway Tunnel (16 km) in western China, with drivers traversing the tunnel at a constant speed of 155 km/h. Drivers' oculomotor signals (pupil diameter, mean fixation duration, standard deviation of fixation-point distribution), electrodermal activity (EDA), and heart rate variability (SDNN) were synchronously recorded using a Tobii Pro Glasses 3 eye tracker and a BIOPAC MP160 system. The evaluation path (17 km) was divided into entrance, transition, middle, and exit sections. A fuzzy comprehensive evaluation (FCE) model integrated with the entropy weight method (EWM) was developed for quantitative load assessment. RESULTS: All five section-discriminative indicators showed statistically significant between-section differences with medium-to-large effect sizes. The entrance section yielded the highest composite physiological load score (76.74), reflecting an overt visual-sympathetic dual-activation stress response. The exit section ranked second (74.08), corresponding to a reactivation response. The middle section produced the lowest composite score (69.41), but multi-indicator joint analysis revealed that this low score reflects reduced stress-response intensity rather than reduced risk, corresponding to a covert hypovigilant state rather than a true safety zone. The transition section showed intermediate values (72.43). CONCLUSIONS: Train drivers in long railway tunnels exhibit a distinctive three-zone risk structure-overt high-load at the entrance, covert hypovigilance in the middle, and reactivation at the exit-differing qualitatively from patterns reported for highway tunnels. The EWM-FCE framework established here provides a quantitative basis for railway tunnel safety design and driver-state monitoring.