*2.3. Details of the Simulation Campaign*

To ensure safe and efficient traffic flow, ATCOs must predict future flight paths based on their perception and interpretation of multiple data on the radar display [27]. In this study, it was considered from the outset that one of the cornerstones of the experiment should be data collected in a especially designed simulation campaign. In this way, in addition to the numerical data, it is also possible to access all the information concerning the radar display, as well as the actions taken by ATCOs.

The exercises simulated in this research reproduce realistic en route scenarios where aircraft are established at certain flight levels. En route ATCOs are responsible for monitoring, controlling, and managing aircraft and traffic flows in the ATC sectors they have been assigned [28]. In the exercises simulated in the experiment described in this paper, the sectors of responsibility varied throughout the exercises, though sectors within Madrid Area Control Centre (ACC) airspace were always used.

In the simulations, each participant simulated four exercises. Each exercise lasted 45 min and was designed to be simulated in parallel in two sectors. The taskload value for the first exercise was 141.40, and this value continued increasing until Exercise 4, which had a value of 228.55. Table 1 presents a comparison of the designed taskload values for each of the exercises.


**Table 1.** Comparison of the designed values of the four exercises in the simulation program.

As mentioned above, to introduce subjective assessment of the workload by participants, the ISA method was used. This method was implemented on the platform through a Python program that was run in parallel to the simulations.

A window asking the participants to evaluate their perceived level of workload appeared every two and a half minutes in a fixed place on the radar display. To do so, they had to press one of the five buttons available under the question, with '1' being the lowest value and '5' the highest value. They had 20 s to select one of the options before the window automatically closed.

The data presented in this paper relate to six participants. All participants were ATCO students with an average age of 21 years and previous knowledge in the field of air traffic management. The ATCO students who participated in the study were selected on the basis of their performance in other practical tests developed during their training.

Throughout their training, participants were trained in concepts related to airspace management, conflict resolution strategies, and the operation of a control position. Although they had previously performed different exercises and simulations with other simulation platforms, this experiment was their first contact with the SkySim platform. For this reason, prior to the test exercises, they underwent specific training on the platform.

The test simulations for each participant took place on two days, with an interval of one week between them. Each day the participant performed simulations, they would simulate two exercises with a one-hour break between. Before starting the simulations, the participants were informed of the aim of the project, and all agreed to their data being analysed as part of the research.

#### *2.4. Data Registered after the Simulations*

During the course of the simulations, a multitude of data were recorded. Specifically, for the purposes of this work, two categories of data were of interest:


In the study of human factors, the exclusive use of subjective measures of workload assessment has certain limitations. On the contrary, some of its main advantages are the relatively low effort required to acquire data and high user acceptance [29]. These advantages were considered decisive for the implementation of subjective measures in this study.

As mentioned above, neurophysiological data were also recorded during the simulations and will be studied in later stages of this line of research. Physiological measures have been shown to be sensitive to differences in taskload and task demand in a variety of domains [30]. For this reason, they are of interest in the study. However, to be able to compare the variation in these variables against the taskload, the first step is to have a good baseline for that taskload.

The use of subjective workload values is a preliminary step. It is assumed that the most complex traffic situations and the most difficult combinations of existing events will lead participants to evaluate these traffic situations with the highest workload values. The idea is to use these values to define the best baseline taskload profile for future use in determining other workload indicators.

From the data related to the subjective assessment of workload, two variables are of interest:


These two variables will be used as an intermediary step in the establishment of a methodology that can obtain the best reference taskload profile. The results of this analysis and its implications are presented in the following section.

### **3. Results**

In the safety-critical area of ATC, workload remains a dominant consideration when seeking to improve the performance of ATC systems [31]. As mentioned above, subjective workload data will be used as an indicator to establish the best event-based taskload baseline.

The starting point is to try to assess the suitability of the design profile as a baseline. This would be the simplest situation, since this profile is available from the beginning of

the creation of the exercises. In the following two subsections, the results obtained from the combined representation of this design profile and reaction time data and workload values will be presented.
