Enhancing Insight into Air Traffic Controller Fatigue: A Dynamic Quantitative Examination through Biological Rhythms

Abstract

To scientifically and effectively predict fatigue risk among air traffic controllers, the authors developed a dynamic evaluation model tailored to the routine activities of traffic controllers. By considering biorhythms and workload, we identified causes of fatigue and quantitatively analyzed their impact. Our study involved 24-hour sleep deprivation experiments, collecting electroencephalogram (EEG) data to track fatigue over time. Expert scoring determined workload coefficients for different periods and positions. Using experimental data, we established and validated a mathematical model for dynamic fatigue risk assessment during various work periods. Results align with controllers' actual fatigue levels and self-assessment scores, indicating the proposed method's effectiveness in early fatigue detection and ensuring aviation safety.