Abstract The sustained increase in air traffic during the last decades represents a challenge to the air traffic management system in general. Thus, it is of utmost importance to develop strategies that can safely increase air traffic controller's handling capacity without increasing task related strain. This research proposes and validates a predictive model of air traffic controller's mental workload. Our model is based on COMETA, a model that considers the effect of the most relevant air traffic events in the cognitive complexity of the task. In the version of COMETA used in this study we include the online effects of the controllers' actions on the state of the airspace. To validate the model, a laboratory experiment was conducted using a simulator to precisely control the task workload factors. We used traffic density and airspace complexity as experimental factors because they are the most commonly acknowledged sources of mental workload in air traffic control literature. The measured dependent variables were selected because they have been found to correlate with mental workload in ATC tasks, namely, ISA and NASA indexes, electrodermal activity, heart rate, and different performance measures. The results demonstrate that our model can successfully predict air traffic controllers' mental workload across a wide range of task workload conditions. In addition, our results provide a clear portrait of the complex interactions between the different sources of task workload and their effects on mental workload. In the conclusion we consider the limitations and opportunities for the application of this model to improve policies.

Highlights • A closed-loop model of air traffic controllers' mental workload is proposed. • A simulator experimental study controlling airspace and traffic task workload was performed. • Subjective, physiological, and performance measures are used to validate the model. • Multivariate analysis is used to shed light into the complex relationships between mental and task workload.