This paper focuses on terminal air traffic complexity indicators. By thorough analysis of previous research, the benefits and limitations of the existing terminal complexity models are identified. According to these findings, a new approach for determining terminal air traffic complexity indicators is proposed which assumes that terminal complexity could be determined based on approach air traffic controller (ATCO) tasks. The comprehensive list of general approach ATCO tasks was defined using a literature review and observation of training exercises, forming the basis for subsequent expert group workshops which enabled the acquisition of ATCOs’ knowledge data. Through these workshops, new approach ATCO tasks were additionally identified, and terminal complexity indicators were defined with airspace and traffic parameters. These new tasks and indicators present a novelty in this field of research since they incorporate ATCOs’ knowledge as the data input and consider various traffic scenarios, all types of traffic, weather conditions, and off-nominal situations.

Designing and developing artificial intelligence (AI)-based systems that can be trusted justifiably is one of the main issues aviation must face in the coming years. European Union Aviation Safety Agency (EASA) has developed a user guide that could be potentially transformed as means of compliance for future AI-based regulation. Designers and developers must understand how the learning assurance process of any machine learning (ML) model impacts trust. ML is a narrow branch of AI that uses statistical models to perform predictions. This work deals with the learning assurance process for ML-based systems in the field of air traffic control. A conflict detection tool has been developed to identify separation infringements among aircraft pairs, and the ML algorithm used for classification and regression was extreme gradient boosting. This paper analyses the validity and adaptability of EASA W-shaped methodology for ML-based systems. The results have identified the lack of the EASA W-shaped methodology in time-dependent analysis, by showing how time can impact ML algorithms designed in the case where no time requirements are considered. Another meaningful conclusion is, for systems that depend highly on when the prediction is made, classification and regression metrics cannot be one-size-fits-all because they vary over time.

This article presents a methodology for analyzing probabilistic air traffic complexity by integrating prediction uncertainties in convective weather scenarios. With the Performance Review Unit (PRU) model as a base, this method modifies the original framework by incorporating a weather-related complexity indicator. The approach was tested in Austrian airspace using ensemble weather forecasts and historical flight plan data. The results demonstrated that a probabilistic model effectively assesses traffic complexity and captures trends in complexity over time, providing greater reliability in high-complexity sectors. Validation revealed a strong alignment between simulator complexity values and probabilistic complexity, especially in sectors characterized by dense data distributions. In contrast, sectors with more elongated distributions tended to overestimate complexity. Quantitative analysis indicated that the error between the probabilistic mean complexity and the simulator complexity values ranged from 12% to 23%, with higher errors in sectors with lower complexity. This validation confirmed the model’s ability to predict complexity trends, thereby assisting flow manager positions (FMPs) in traffic flow and airspace management. Overall, this study demonstrated that probabilistic complexity assessment provides a deeper understanding of traffic behaviour, facilitating more effective air traffic flow management in uncertain and dynamic conditions.

Instrument flight procedures are essential and critical components of the global aviation system. They are designed for all phases of flight, i.e. the standard instrument departures, standard instrument arrivals, instrument approaches and the en-route phase of flight. Instrument flight procedures are designed from various aeronautical data, information, dimensions, etc., which are named instrument flight procedure elements according to this paper. Development of air navigation systems affect design of instrument flight procedures and flexible use of airspace. The design process is carried out within a framework defined by international and national standards, organizational norms and economic aspects. Instrument flight procedure elements are a fundamental part of the process. Deviations of these elements from full compliance with international regulations can significantly and negatively affect air traffic safety. The objective of this paper was to investigate the basic prerequisites for statistical analysis of the design quality of instrument flight procedures, which have not been explored before. Six prerequisites were proposed for acquiring the data and preparing them for further statistical use.

Current methods to reduce air traffic complexity during convective weather conditions are sometimes not sufficient to meet the traffic demand in such situations. The most common strategy deployed by flow management personnel for complexity reduction during convective weather is to apply a limit to the number of aircraft entering the affected or surrounding airspace. Such complexity reduction measures globally affect the airspace capacity and can be inefficient. A better outcome in complexity reduction would be a complexity reduction measure that either restructures the airspace to rebalance complexity or a measure that cherry-picks flights with the highest impact on complexity. This paper proposes a method for complexity reduction in convective weather, which applies short-term air traffic flow management measures (STAMs) such as airspace restructuring and individual aircraft trajectory changes. Complexity reduction measures are applied to the most complex sector, thus reducing the global effect of a measure. The results of this paper showed that STAMs can reduce air traffic complexity by up to 19.63%. The benefit gained from STAMs comes with diminishing returns when more than four STAMs are applied to the most complex sector. When comparing traffic scenarios without convective weather to those with convective weather, it was shown that convective weather has a variable impact on air traffic complexity, ranging from 91% to 184% of the nominal complexity value.

Artificial intelligence (AI) is a cutting-edge technology that can replicate knowledge, operation and, at some point, understanding at a human-like level. The AWARE project aims to develop an AI assistant application (ASA) designed to support air traffic control (ATC) operations by building a platform based on enhanced artificial situational awareness. One of the pillars of the ASA system is to develop a set of functionalities that mimic the behavior of human actions based on the development of technical tools. Regarding safety issues, conflict detection and resolution (CD&R) is the pillar to identify conflicts and avoid mid-air collisions. The goal is to build a CD&R that can be embedded into the ASA system and generate outputs that can be usable and valuable for ATC. CD&R tool is based on two subsystems: The CD component identifies potential separation minima infringements, while the CR module produces explainable resolution maneuvers with standardized syntax for seamless ATCO integration. CD uses a deterministic algorithmic approach grounded in trajectory prediction models, while CR implements a hierarchical decision-making architecture that emulates expert ATCO cognitive processes within a client-service paradigm where pilots serve as end-users.