Case study evaluating physiological workload differences among air traffic controllers, highlighting implications for safety and capacity management.
Controller workload remains the principal functional constraint on air traffic management system capacity, yet its measurement in operational settings is complicated by substantial inter-individual variability that aggregate traffic-based models fail to capture. This case study investigates whether physiological workload differs significantly between certified air traffic controllers (ATCs) exposed to identical operational conditions, a question with direct implications for both safety management and dynamic capacity planning. Three licensed tower (TWR) controllers were monitored during a standardised 50-min heavy-load simulation exercise. During the experiment, physiological stress indicators were monitored, specifically LF/HF, SDNN, and mean RR, in 5-min intervals measured by a single-lead ECG monitor with a sampling frequency of 1000 Hz, supplemented by a 3D accelerator for actigraphy. Simultaneously, photoplethysmographic (PPG) recording was performed in synchronization with a second single-lead ECG for control purposes. Despite identical traffic scenarios, statistically significant inter-individual differences were confirmed for all three parameters ( p ≤ 0.002). Median LF/HF values differed by up to 74% between controllers, SDNN by up to 45%, and mean RR by 5.83%. These differences exceed the magnitudes typically reported between low- and high-workload conditions in within individual aviation studies, demonstrating that individual physiological reactivity, rather than traffic complexity alone, is a primary determinant of operational workload. The findings challenge the current practice of treating airport capacity as a fixed threshold and support the concept of dynamic, real-time capacity management informed by continuous individual physiological monitoring. Practically, the approach enables identification of stress-susceptible controllers, supports personalised shift scheduling, and provides a foundation for deploying operationally compatible wearable ECG monitoring during actual ATC operations.
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Hoika et al. (2026) studied this question.
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