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

Hastiya Annisa Fitri; Ludfi Pratiwi Bowo; Siti Hidayanti Mutiara Kurnia; Indra Kurniawan; Mutharuddin; Subaryata; Ridwan Aji Budi Prasetyo; Ari Widyanti · 2025 · Crossref

DOI: 10.5109/7402639

archive: archived pipeline: cataloged verified

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Summary

This study investigates the mental workload of professional truck drivers under varying environmental and temporal conditions, addressing the critical safety risks associated with cognitive overload and fatigue in freight transport. Motivated by the high incidence of severe accidents involving heavy trucks in Indonesia and the lack of comprehensive mental workload assessments for this demographic, the research aims to quantify how rural versus urban road conditions and daytime versus nighttime driving influence driver cognition. The study seeks to bridge the gap between traditional physical endurance metrics and modern cognitive demand assessments, providing evidence-based insights for optimizing driver safety and performance management. The experimental design involved 50 professional male truck drivers who participated in a video-based simulation across four scenarios: rural daytime, rural nighttime, urban daytime, and urban nighttime. Each 15-minute scenario required participants to detect motorcyclists appearing on screen, with performance measured via response time, hit rate, and error rate. Mental workload was assessed using two complementary methods: the NASA Task Load Index (NASA-TLX) for subjective evaluation across six dimensions (mental, physical, temporal demand, effort, frustration, and performance) and Heart Rate Variability (HRV) metrics for objective physiological measurement. HRV indices, including absolute and normalized low/high frequency powers and heart rate, were recorded using a Polar H10 monitor and analyzed with Kubios HRV software. Statistical analysis employed one-way and two-way repeated measures ANOVA to determine significant differences across scenarios and factors. The results indicate a progressive increase in subjective mental workload from rural daytime to urban nighttime conditions. Specifically, Mental Demand and Temporal Demand were significantly higher in urban nighttime scenarios compared to rural daytime ($p = 0.025$ and $p = 0.047$, respectively). Physiological data corroborated these findings, showing significantly increased heart rate and reduced normalized Low Frequency (nLF) power during urban nighttime driving ($p < 0.03$), indicative of elevated cognitive stress. Two-way ANOVA revealed that road condition significantly affected HRV indices, while driving time significantly elevated Mental Demand and Frustration. Performance metrics showed that response times were slowest in rural nighttime scenarios, hit rates were higher at night, and error rates peaked in urban nighttime driving. These findings suggest that reduced visibility and high traffic density in urban night settings impose the greatest cognitive burden, whereas rural night driving leads to slower reactions likely due to monotony and reduced alertness. The study concludes that both environmental complexity and time of day distinctly contribute to truck driver mental workload, with urban nighttime conditions posing the highest risk for cognitive overload and errors. By integrating subjective and objective measures, the research provides a robust framework for assessing driver workload, highlighting the need for targeted interventions in transportation safety policies. The findings imply that fleet management and regulatory bodies should consider scenario-specific workload risks, particularly in developing countries like Indonesia, to mitigate fatigue-related accidents and optimize driver scheduling and training protocols.

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Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• workload measurement
• mental demand
• stress driving
• truck driver fatigue
• shift work driving
• road complexity

Information type

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• Empirical Findings: physiological data, self report data
• Theoretical Contribution: theory or model