Hierarchical ordered logistic regression analysis of urban rail transit driver fatigue determinants: impact of emotion regulation and sleep patterns

Urban rail transit (URT) drivers play a crucial role in ensuring operational safety; however, existing literature lacks in-depth research on the underlying mechanisms influencing driver fatigue in URT systems. To fill this gap, this study investigates the associations between emotion regulation, sleep patterns, and mental fatigue among rail transit drivers. Drawing on a survey of 185 drivers from the Lanzhou urban rail transit system, a dataset of 3,515 samples was analyzed using a hierarchical ordered logistic regression model. This approach accounts for the multi-level influences of multidimensional emotional intelligence and sleep characteristics on fatigue levels across different track segments. The findings reveal that both emotion regulation and sleep status significantly impact driver fatigue, with heterogeneity analysis identifying distinct fatigue determinants for drivers with varying levels of experience. This research deepens the understanding of the factors influencing driver fatigue in urban rail transit and promotes targeted interventions to enhance operational safety.