**1. Introduction**

Aviation combines great technological development with key activities performed by humans. Human factors are dedicated to better understanding how humans can safely and efficiently integrate with technology in aviation [1]. At the tactical level, air traffic controllers (ATCOs) are at the core of today's air traffic management (ATM) [2].

As their work has a direct influence on air traffic safety, ATCOs must be highly trained and skilled to provide air traffic control (ATC) services [3]. Due to the great responsibility associated with the activities performed by these professionals, identifying which situations cause greater difficulty in their decision-making process so as to be able to model these situations to limit their workload is of great interest. In particular, the study of air traffic controllers' workload is a topic of significant relevance within the air traffic industry.

The environment in which ATCOs work is inherently dynamic and requires them not only to perform their tasks safely and efficiently, but also to interact with a multitude of systems and coordinate with other people. Exploration of digitalisation and the possibility of automating some of these tasks is increasingly prevalent in current research. As an example, ref. [4] presents an experiment conducted with six ATCOs to analyse the possibilities and challenges of automation in terms of teamwork in a realistic ATC en route phase scenario. The authors in [5] presented a methodology for predicting if and when

**Citation:** Zamarreño Suárez, M.; Arnaldo Valdés, R.M.; Pérez Moreno, F.; Delgado-Aguilera Jurado, R.; López de Frutos, P.M.; Gómez Comendador, V.F. Methodology for Determining the Event-Based Taskload of an Air Traffic Controller Using Real-Time Simulations. *Aerospace* **2023**, *10*, 97. https:// doi.org/10.3390/aerospace10020097

Academic Editors: Jordi Pons-Prats and Michael Schultz

Received: 9 November 2022 Revised: 16 January 2023 Accepted: 16 January 2023 Published: 18 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

ATCOs would react to the presence of conflicts, which was developed through the use of deep learning techniques.

The use of simulations in the research and development stages has many advantages. One of them is the ability to create highly realistic exercises with controlled ATC events in order to understand their influence on the decision-making processes of air traffic controllers when resolving such events. In addition, it also allows for the testing and validation of new functionalities prior to large-scale implementation. These simulations also allow for the recreation of unusual or emergency situations in a controlled manner. In summary, current needs and future trends make simulators invaluable tools for both ATCO training and ATM system development [6].
