Purpose The purpose of this paper is to set out a methodology for characterising the complexity of air traffic control (ATC) sectors based on individual operations. This machine learning methodology also learns from the data on which the model is based. Design/methodology/approach The methodology comprises three steps. Firstly, a statistical analysis of individual operations is carried out using elementary or initial variables, and these are combined using machine learning. Secondly, based on the initial statistical analysis and using machine learning techniques, the impact of air traffic flows on an ATC sector are determined. The last step is to calculate the complexity of the ATC sector based on the impact of its air traffic flows. Findings The results obtained are logical from an operational point of view and are easy to interpret. The classification of ATC sectors based on complexity is quite accurate. Research limitations/implications The methodology is in its preliminary phase and has been tested with very little data. Further refinement is required. Originality/value The methodology can be of significant value to ATC in that when applied to real cases, ATC will be able to anticipate the complexity of the airspace and optimise its resources accordingly.

Today, aircraft demand is exceeding the capacity of the Air Traffic Control (ATC) system. As a result, airspace is becoming a very complex environment to control. The complexity of airspace is thus closely related to the workload of controllers and is a topic of great interest. The major concern is that variables that are related to complexity are currently recognised, but there is still a debate about how to define complexity. This paper attempts to define which variables determine airspace complexity. To do so, a novel methodology based on the use of machine learning models is used. In this way, it tries to overcome one of the main disadvantages of the current complexity models: the subjectivity of the models based on expert opinion. This study has determined that the main indicator that defines complexity is the number of aircraft in the sector, together with the occupancy of the traffic flows and the vertical distribution of aircraft. This research can help numerous studies on both air traffic complexity assessment and Air Traffic Controller (ATCO) workload studies. This model can also help to study the behaviour of air traffic and to verify that there is symmetry in structure and the origin of the complexity in the different ATC sectors. This would have a great benefit on ATM, as it would allow progress to be made in solving the existing capacity problem.

The main goal of this article is to analyse the probability of detecting potential conflicts by the Air Traffic Controller (ATCo). The ATCo ensures the safety of aircraft and one of its main functions is collision avoidance. Collision avoidance is known as separation provision and this term means assuring the safe distance between each aircraft by sides, vertical and longitudinal minimums of separation. The air traffic controller must ensure a high level of airspace capacity. The work performance is related to high demands on individual characteristics, knowledge, skills and, of course, air traffic characteristics. In addition to analysing the probability of detecting potential conflicts, the study of the most influential factors on this safety event is considered of special relevance since the ATCo represents the last executive section of the air traffic control system and failure to detect potential conflicts could lead to a possible infringement of the minimum separation distances between aircraft or even a collision. In order to carry out this approach, Bayesian Networks will be used due to their high predictive capacity. In addition, a dual approach based on knowledge and real operational data provided by an ANSP will be used. These data are one of the great advantages of this study compared to those included in the current literature.

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.

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