Understanding the Formation Mechanisms of Fatigued Driving among Heavy-Duty Truck Drivers: A Mixed-Methods Study from a Human Factors Perspective

Abstract

Heavy truck drivers operate under sustained high workload and tight delivery deadlines, making fatigue-related driving a persistent road-safety risk. Prior studies have emphasized crash outcomes or fatigue-detection technologies, yet provide limited explanation of why drivers continue to drive while fatigued and how multiple pressures jointly shape such behavior. This study models the mechanism of fatigued driving among heavy truck drivers and derives implications for human-centered intelligent cockpit interventions using a two-stage mixed-methods design. First, 30 semi-structured interviews were analyzed thematically to identify key determinants and construct a qualitative model. Second, a survey of 110 Chinese heavy truck drivers was conducted to test the model using structural equation modeling, with variables including time pressure, economic pressure, inadequate in-cab facilities, trip demands, self-perception bias, and fatigued driving behavior. Results show that time pressure is the strongest positive predictor of fatigued driving, substantially increasing the likelihood of continuing to operate a vehicle while fatigued. Self-perception bias also positively predicts fatigued driving, indicating that underestimating fatigue risk and overestimating one’s capability are important psychological drivers. Transport distance exhibits a negative association, suggesting potential self-regulation or experiential adaptation on longer trips. No significant moderating effects were observed, but the overall model supports a multi-factor pathway shaped by task stressors and subjective cognition. Based on these findings, we propose two cockpit directions for fatigue management: mitigating cognitive bias through driver-state monitoring with timely feedback, and alleviating time-pressure structures via schedule support and information assistance. This work provides empirical evidence on behavioral mechanisms underlying fatigued driving and informs intelligent cockpit design and broader transport safety interventions.

Keywords: Fatigued Driving, Heavy-duty Truck Drivers, Time Pressure, Cognitive Biases

DOI: 10.54941/ahfe1007877