The study of human factors in aviation makes an important contribution to safety. Within this discipline, real-time simulations (RTS) are a very powerful tool. The use of simulators allows for exercises with controlled air traffic control (ATC) events to be designed so that their influence on the performance of air traffic controllers (ATCOs) can be studied. The CRITERIA (atC event-dRiven capacITy modEls foR aIr nAvigation) project aims to establish capacity models and determine the influence of a series of ATC events on the workload of ATCOs. To establish a correlation between these ATC events and neurophysiological variables, a previous step is needed: a methodology for defining the taskload faced by the ATCO during the development of each simulation. This paper presents the development of this methodology and a series of recommendations for extrapolating the lessons learnt from this line of research to similar experiments. This methodology starts from a taskload design, and after RTS and through the use of data related to the subjective evaluation of workload as an intermediate tool it allows the taskload profile experienced by the ATCO in each simulation to be defined. Six ATCO students participated in this experiment. They performed four exercises using the SkySim simulator. As an example, a case study of the analysis of one of the participants is presented.

It has always been a topic of great interest in air transport management to be able to estimate controller workload. So far, research has not had the opportunity to make use of real data on the controller’s actions. We have enough data to be able to use machine learning methods. The aim of this work is to predict the controller’s actions to know his workload. Several machine learning models were tested to try different combinations of features and the selected algorithms and two models were finally chosen. The predictions provided by the models are good enough to be used when a first approximation of the workload in a sector is to be obtained. Finally, explainability techniques were employed to discover the patterns found by the AI in the machine learning models. Thanks to these techniques, we can build a profile of the critical flights that increase the workload the most.

Automated alert systems for air traffic controllers (ATCOs) often generate many nuisance visual warnings that ATCOs routinely ignore, potentially compromising safety. To explore an alternative modality, we tested vibrotactile alerts to warn ATCOs of aircraft entering their sector. In two experiments with 118 novice participants, Experiment 1 compared a visual color-change alert to a simple vibrotactile pulse. Using a high-difficulty scenario with nine handoffs, simple vibrotactile alerts elicited significantly faster response times than visual alerts without increasing mental workload or degrading performance. In Experiment 2 we added gradual and continuous vibrotactile alerts, but no further advantage emerged from their implementation. Nevertheless, all vibrotactile alerts reliably captured attention without adverse effects, demonstrating the feasibility of non-invasive tactile devices to augment or replace visual alerts and help ATCOs allocate their cognitive resources more effectively. However, this is an exploratory study at a pre-operational stage without direct practical implications for ATC planning and control.

This paper presents the development and implementation of a psychological model aimed at predicting the mental states of Air Traffic Controllers (ATCOs) within an Exploratory research project, entitled CODA (The Controller Adaptative Digital Systems Assistant), within the SESAR 3 Joint Undertaking and European Union’s Horizon Europe research and innovation programme. The proposed model aims to advance human–machine collaboration in air traffic management by enabling the precise prediction of critical operator cognitive and affective states, including mental workload, fatigue, stress, and attentional engagement. By formally integrating core cognitive processes—namely perception, comprehension, and decision-making—within its architecture, the model provides a principled framework for the continuous monitoring and real-time adaptation of support systems. Such adaptive capabilities are intended to optimize the allocation of assistance provided by artificial agents, thereby strengthening human–system coordination and contributing to enhanced operational safety and efficiency within the complex and highly dynamic environment of air traffic control. To estimate the parameters of the model, several air traffic simulations were conducted with expert controllers. In these simulations changes to traffic situations were introduced. Those changes could affect the controllers' mental states. The results of these changes were observed in the measured verbal and psychophysiological dependent variables. This paper presents results that partially validate the initial parameters of the models. These results will contribute to a future improvement of the model by refining the parameters of the proposed formulas for calculating mental workload, fatigue, stress, and vigilance in the air traffic control task.