The Effects of Driving Environment on the Mental Workload of Train Drivers

Abd Rahman, Nurul Izzah; Dawal, Siti Zawiah Md.; Bahreininejad, Ardeshir · 2013 · Crossref

DOI: 10.4028/www.scientific.net/aef.10.93

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Summary

This study investigates how varying driving environments affect the mental workload of train drivers, addressing the critical safety concern that approximately 75% of train accidents are attributed to human error. The research is motivated by the need to understand how environmental factors influence driver vigilance and cognitive load, as both excessive and insufficient mental workload can degrade performance. While Electroencephalography (EEG) is a established method for measuring mental workload in road drivers, its application to train drivers remains limited. Consequently, this study aims to quantify the impact of specific weather and lighting conditions on train drivers’ brain activity to inform ergonomic improvements and work schedule management. The experimental design involved fifteen male train drivers, aged 24 to 48, with an average of 14 years of experience. Participants performed monotonous train driving tasks in a computer-based simulator under three distinct environmental conditions: clear sunny day, rainy day, and rainy night. Each session lasted 20 minutes, with a five-minute break between conditions. Brain activity was recorded using an EEG BIOPAC MP150 System, capturing signals from the Fz (intentional and motivational centers) and Pz (perception and differentiation) electrodes. Electrooculogram (EOG) data were collected to remove eye-blink artifacts. The analysis focused on the mean alpha power (8–13 Hz), calculated over three-minute intervals using Fast Fourier Transform, with the first 120 seconds of each session excluded to eliminate drift. Statistical significance was assessed using the Friedman Test. The results demonstrated that driving conditions significantly impacted EEG mean alpha power ($\chi^2 (2) = 6.333, P = 0.042$). Under clear sunny conditions, alpha power initially decreased, indicating focused engagement, before increasing dramatically toward the end of the session, suggesting relaxation. In contrast, rainy night driving produced a distinct pattern: mean alpha power decreased after the second time interval (approximately six minutes into the drive). This decrease signifies an increase in mental workload during the early phase, followed by a trend indicative of reduced vigilance. Notably, there was a 37% difference in mean alpha power between daytime and rainy night conditions during the early driving periods. The Pz channel data specifically highlighted increased sleepiness and lower alertness levels during rainy night driving compared to other conditions. The study concludes that rainy night driving imposes a unique cognitive burden on train drivers, characterized by initial high workload followed by significant sleepiness and reduced vigilance. These findings underscore the danger of fatigue-related errors in adverse weather and low-light conditions. The authors recommend that railway management implement carefully structured work schedules and duty rosters to ensure drivers are adequately rested before night shifts. By aligning operational practices with physiological data, organizations can mitigate the risk of accidents caused by inappropriate mental workload, thereby enhancing overall railway safety and driver well-being.

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Topics

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• mental demand
• workload measurement
• neuro workload indices

Information type

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• Empirical Findings: physiological data
• Theoretical Contribution: theory or model