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Mental Workload in Truck Driving: A NASA-TLX and HRV-Based Comparison Across Day-Night and Rural-Urban Conditions

Year: 2025

Type: article

Abstract: Truck-involved accidents often result in severe consequences due to the vehicle's size and mass, making driver fatigue and cognitive overload critical risk factors.Mental workload reflects real-time cognitive demands during driving, where excessive levels accelerate fatigue, impair response time, and increase accident risk.This research investigates the effects of four driving scenarios (rural daytime, rural nighttime, urban daytime, urban nighttime) on the mental workload of 50 professional male truck drivers, using NASA Task Load Index (NASA-TLX) assessment and Heart Rate Variability (HRV) metrics (aLF, aHF, ln-aLF, ln-aHF, nLF, nHF, and HR in bpm).Performance was measured through response time, hit rate, and error rate.The NASA-TLX results indicate a proportional increase in mental workload from rural daytime to urban nighttime.Mental Demand (p = 0.025) and Temporal Demand (p = 0.047) are significantly higher in urban nighttime compared to rural daytime, while other dimensions, though not significant, show a general pattern of increasing mean scores, with Performance showing the opposite pattern.Heart rate (bpm) significantly increases, and nLF power shows a significant reduction during urban nighttime scenario compared to rural daytime (p < 0.03), indicating increased mental workload.Two-way ANOVA further revealed that road condition (urban vs. rural) significantly affected HRV indices (nLF, nHF, and HR), while driving time (nighttime vs. daytime) significantly elevated Mental Demand and Frustration, highlighting the distinct contribution of environmental and temporal factors to driver workload.Significantly, response times were slowest in rural nighttime scenarios, hit rates were higher at night than during the day, and error rate peaked in urban nighttime driving, suggesting that reduced visibility and traffic density contribute to increased mental workload.These findings provide a more structured and advanced methods for measuring truck drivers' mental workload, benefiting company management in optimizing driver performance and safety.

Source: Evergreen

DOI: 10.5109/7402639