Christian Eduardo Verdonk Gallego

Air traffic management (ATM) is at a turning point. The goal is to increase capacity while keeping safety and improving sustainability. At the centre of this change is trajectory-based operations (TBO), a long-planned shift from reactive control to managing flights through shared, precise four-dimensional trajectories and air-ground synchronisation. These two pillars reduce uncertainty and give more options for addressing ATM challenges. TBO depends on digitalisation, which allows trajectory sharing and synchronisation between air and ground. This digital foundation supports higher levels of automation in ATM. Automation here is not only about executing tasks, but deciding the best time, place, and actor (human or system) for each action, based on the best available information. Following the EASA AI roadmap, automation is moving from the role of a simple assistant to that of a teammate, capable of collaborating with human controllers in a shared operational environment. This shift has to be addressed from a Human Systems Integration (HSI) perspective, raising design, procedure, and training challenges. The result is two big changes happening together. Firstly, TBO opens a larger solution space for managing traffic strategically and tactically. Secondly, automation transforms the role of the controller, from tactical operator to strategic orchestrator. This chapter first explains the TBO paradigm, its evolution, and its digital foundations. It then addresses the challenge of integrating automation, comparing approaches and presenting the official European roadmap. A theoretical lens is introduced, using key HSI principles from leading experts. These ideas are applied to two SESAR case studies: a “Digital Assistant” and an “ATC Teammate”. They illustrate the move from human-centred assistance to full human-artificial-agent teamwork. The chapter ends compiling lessons learnt and posing the challenge for the future ahead.

This paper investigates the impact of trajectory predictor performance on the encounter probability generated by an adaptive conflict detection tool and examines the flexibility of the tool dependent on its adjustable thresholds, using historical radar track data. To achieve these objectives, two figures of merit were proposed: System Dynamic Range and System Tuning Envelope. To examine the conflict detection’s performance variability under different uncertainty levels and predictor types, simple multi-horizon trajectory predictors trained with two machine learning techniques of different characteristics are assessed at various look-ahead times: extreme gradient boosting with a discrete nature and a multi-layer perceptron regressor with a continuous nature. The results highlight the interdependence between the performances of the trajectory predictor and the conflict detector, and the quantification of this relationship can be represented through a sigmoid function. In addition, the two proposed figures of merit are effective for selecting suitable operating points in an adaptive conflict detector, based on dynamic thresholds and the performance requirements necessary for the trajectory predictors to achieve the expected detection performance at different look-ahead time.

At present, decision-making in ATM is fragmented between different stakeholders who have different objectives. This fragmentation, in unison with competing KPAs, leads to complex interdependencies between performance indicators, which results in an imbalance, with some of these indicators being penalized to the apparent benefit of others. Therefore, it is necessary to support ATM stakeholders in systematically uncovering hidden trade-offs between KPAs. Existing literature confirms this claim, but how to solve it has not been fully addressed. In this paper, we envision air traffic complexity to be the framework through which a common understanding among stakeholders is enhanced. We introduce the concept of single aircraft complexity to determine the contribution of each aircraft to the overall complexity of air traffic. Furthermore, we describe a methodology extending this concept to define complex communities, which are groups of interdependent aircraft that contribute the majority of the complexity in a certain airspace. Through use-cases based on synthetic and real historical traffic, we first show that the algorithm can serve to formalize and improve decision-making. Further, we illustrates how the provided information can be used to increase transparency of the decision makers towards different airspace users. In order to showcase the methodology, we develop a tool that visualizes different outputs of the algorithm. Lastly, we conduct sensitivity analysis in order to systematically analyse how each input affects the methodology.

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.

Present air traffic complexity metrics are defined considering the interests of different management layers of ATM. These layers have different objectives which in practice compete to maximize their own goals, which leads to fragmented decision making. This fragmentation together with competing KPAs requires transparent and neutral air traffic information to pave the way for an explainable set of actions. In this paper, we introduce the concept of single aircraft complexity, to determine the contribution of each aircraft to the overall complexity of air traffic. Furthermore, we describe a methodology extending this concept to define complex communities, which are groups of interdependent aircraft that contribute the majority of the complexity in a certain airspace. In order to showcase the methodology, a tool that visualizes different outputs of the algorithm is developed. Through use-cases based on synthetic and real historical traffic, we first show that the algorithm can serve to formalize controller decisions as well as guide controllers to better decisions. Further, we investigate how the provided information can be used to increase transparency of the decision makers towards different airspace users, which serves also to increase fairness and equity. Lastly, a sensitivity analysis is conducted in order to systematically analyse how each input affects the methodology.

Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the losses of separation (Los) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports, with the limitations that the considered categories need to be manually annotated by experts and that general taxonomies are seldom exploited. Methods: To address the current gaps, authors propose to perform exploratory data analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering and then to develop an algorithm able to extract the Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on syntactic analysis. TOKAI is a tool for investigation developed by EUROCONTROL and its taxonomy is intended to become a standard and harmonised approach to future investigations. Results: Leveraging on the LoS events reported in the public databases of the Comisión de Estudio y Análisis lisis de Notificaciones de Incidentes de Tránsito Aéreo and the United Kingdom Airprox Board, authors show how their proposal is able to automatically extract meaningful and actionable information from safety reports, other than to classify their content according to the TOKAI taxonomy. The quality of the approach is also indirectly validated by checking the connection between the identified factors and the main contributor of the incidents. Conclusions: Authors’ results are a promising first step toward the full automation of a general analysis of LoS reports supported by results on real-world data coming from two different sources. In the future, authors’ proposal could be extended to other taxonomies or tailored to identify factors to be included in the safety taxonomies.

Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the loss of separation (LoS) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports, with the limitations that the considered categories need to be manually annotated by experts and that general taxonomies are seldom exploited. Methods: To address the current gaps, authors propose to perform exploratory data analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering and then to develop an algorithm able to extract the Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on syntactic analysis. TOKAI is a general taxonomy developed by EUROCONTROL and intended to become a standard and harmonised approach to future investigations. Results: Leveraging on the LoS events reported in the public databases of the Comisión de Estudio y Análisis lisis de Notificaciones de Incidentes de Tránsito Aéreo and the United Kingdom Airprox Board, authors show how their proposal is able to automatically extract meaningful and actionable information from safety reports and to classify them according to the TOKAI taxonomy. The quality of the approach is also indirectly validated by checking the connection between the identified factors and the main contributor of the incidents. Conclusions: Authors’ results are a promising first step toward the full automation of a general analysis of LoS reports supported by results on real world data coming from two different sources. In the future, authors’ proposal could be extended to other taxonomies or tailored to identify factors to be included in the safety taxonomies.