TY - JOUR
T1 - Air traffic controllers communication analysis as a proxy of task demand and mental workload
T2 - Using voice recording markers for safety critical task
AU - Muñoz-de-Escalona, Enrique
AU - Leva, Maria Chiara
AU - Gianini, Gabriele
AU - de Frutos, Patricia Lopez
AU - Jadronova, Martina
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/11
Y1 - 2025/11
N2 - The anticipated increase in air traffic over the coming decades will lead to a more congested airspace, thereby heightening the mental workload of air traffic controllers, who are responsible for maintaining the safety, reliability, and efficiency of air travel. Sustained overload is recognized in the literature as a significant factor influencing performance. Developing computational models for real-time monitoring of air traffic controllers’ mental workload is critical for devising effective task support strategies to prevent the detrimental effects of declines in human performance. This research stems from consideration related to simple mental workload model for air traffic controllers based on behavioural markers that can be unobtrusively collected in real time from their voice recordings. These markers may reflect the task load and complexity experienced by a human operator, which in turn can be used in this study as a proxy indicator of mental workload. In this study over 80 h of voice recordings from real air traffic controllers were analysed to examine changes in behaviours and strategies that they might employ in response to variations in task demands while performing their tasks. Results indicate that traffic density levels can be predicted through behavioural measures derived from their voice communications. This suggests that variations in task demands lead to changes in their behaviours and strategies to effectively manage the situation and, thereby, reflect their mental workload. This early-stage model will be cross-validated and refined in a subsequent research stage using well-established psychophysiological and subjective measurements.
AB - The anticipated increase in air traffic over the coming decades will lead to a more congested airspace, thereby heightening the mental workload of air traffic controllers, who are responsible for maintaining the safety, reliability, and efficiency of air travel. Sustained overload is recognized in the literature as a significant factor influencing performance. Developing computational models for real-time monitoring of air traffic controllers’ mental workload is critical for devising effective task support strategies to prevent the detrimental effects of declines in human performance. This research stems from consideration related to simple mental workload model for air traffic controllers based on behavioural markers that can be unobtrusively collected in real time from their voice recordings. These markers may reflect the task load and complexity experienced by a human operator, which in turn can be used in this study as a proxy indicator of mental workload. In this study over 80 h of voice recordings from real air traffic controllers were analysed to examine changes in behaviours and strategies that they might employ in response to variations in task demands while performing their tasks. Results indicate that traffic density levels can be predicted through behavioural measures derived from their voice communications. This suggests that variations in task demands lead to changes in their behaviours and strategies to effectively manage the situation and, thereby, reflect their mental workload. This early-stage model will be cross-validated and refined in a subsequent research stage using well-established psychophysiological and subjective measurements.
KW - Air Traffic Management
KW - Behavioural Markers
KW - Classification models
KW - High Mental workload Detection
KW - Human Performance & Reliability
KW - Mental Workload
KW - Prediction models
KW - Risk Management
KW - Speech-Based Workload Estimation
UR - https://www.scopus.com/pages/publications/105008343652
U2 - 10.1016/j.ssci.2025.106928
DO - 10.1016/j.ssci.2025.106928
M3 - Article
AN - SCOPUS:105008343652
SN - 0925-7535
VL - 191
JO - Safety Science
JF - Safety Science
M1 - 106928
ER -