Citation

Huang Y, Li L, Liu Z, Zhu H, Zhu L. Transp. Res. Rec. 2019; 2673(11): 891-900.

Copyright

(Copyright © 2019, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198119826071

PMID

unavailable

Abstract

This paper describes an experiment conducted to establish a workload model by employing physiological methods to measure driver workload and fatigue under real working conditions. Experienced healthy metro drivers were selected as subjects; they performed normal schedules during which simultaneous electrocardiogram (ECG) recording was used to assess their levels of fatigue. Then, subjective workload assessment and reaction time tests were conducted during each break interval to monitor the drivers' physiological and psychological performance. Based on task analysis, driving workload models with time weight parameters of four types of tasks were established and the workload real-time changes during different shifts were evaluated. The results demonstrate that workload tends to increase over time and it is significantly higher during manual driving mode than autonomous mode (p = 0.015 < 0.05). Driving fatigue occurs earlier in the night shift than in the day shift according to ECG spectrum analysis results. Although the results of reaction time tests show no significance (p = 0.917 > 0.05), the increase in the number of reaction errors after fatigue driving indicates a reduction in drivers' cognitive ability. Regression analysis shows a significant regression relationship with a mutual incentive effect between workload and fatigue in three shifts (R2 > 0.4). These will be used as a future reference for fatigue research and to help develop reasonable schedules to ensure operational safety.

Language: en