Javier Alberto Perez Castan

Air Traffic Control Officers (ATCOs) are responsible for controlling air traffic and ensuring the safety of the aircraft. Capacity, understood as the maximum number of aircraft that can be safely managed for one hour, is calculated based on the workload of ATCOs. This calculation normally is based on a manual and tedious data collection process that demands a high consumption of human resources. To improve and relieve human re-sources, automation tools that automatically generate a preliminary annotation of Air Traffic Control (ATC) activity have been developed. This paper focuses on the feasibility of employing data-driven approaches using neural networks to classify ATC events, as well as if it is possible to improve the performance of these ATC-activity tools. Particularly, this approach seeks to infer ATC intent for separation actions, which are the most critical in terms of ATC workload. A modular methodology has been developed to include information from different sources: flight plans, radar trajectories, trajectory prediction, conflict detection and rule-based knowledge. Different experiments are evaluated based on the different input’s combination, as well as three neural networks (Multilayer Perceptron, Convolutional Neural Network and TabNet). Results show that TabNet is the best neural network option, reaching a similar performance in task classification than current ATC tools and improving classification metrics around 4% by employing the outputs of ATC tool metrics as inputs.

One of today’s major challenges in air transport is accommodating future growth in traffic demand, which requires addressing capacity limitations. Since separation minima influence airspace capacity, technological progress enables exploring innovative approaches. This paper presents the Ad Hoc Separation concept, which involves applying different separation minima between aircraft pairs based on aircraft type, weight, encounter geometry, flight level, or wind. As a novel approach requiring operational changes to the current ATM system, further research is justified only if tangible benefits are demonstrated. Fast-time simulations in European en-route sectors, both conventional and Free Route Airspace, are performed to assess the benefits. The results show a capacity gain of about one aircraft per hour, along with positive environmental and cost-efficiency benefits.

Artificial intelligence (AI) is one of the most promising technologies that are being implemented in different areas. In particular, air traffic control (ATC) is undergoing a breakthrough with the introduction of AI for automation purposes. However, the use of AI in safety-critical systems, which is the control of air traffic, presents several uncertainties that must be solved before its implementation. Risk assessment and hazard identification are crucial for safe integration of any system, and AI-based systems are no exception. The goal of this work is to provide insights on the risk assessment developed for a specific AI-based system for ATC and how the lessons learned can be applied for further research. AISA (Artificial Intelligence Situational Awareness) Foundation for Advanced Automation is a European project that proposes building a foundation for automating some monitoring tasks and constitutes a team with the ATC operators. Risk assessment performs a safety analysis by identifying hazards, analysing them and their risks (based on probability and severity) and providing mitigation measures. The primary risks of distributed human-machine SA are identified, and many proposed mitigation measures are related to the implementation of safety requirements during the design phase of the system; 74 hazards were identified, and more than 150 mitigation measures were proposed. The main contribution of the developed risk library is that it expands the knowledge of AI hazards for their introduction into the ATC system.

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.

ICAO at global level and the EU at regional level require that instrument flight (IFR) trajectories remain within volumes of controlled airspace. To this end, a volume of specific dimensions is defined around the aerodrome, typically named Control Zone (CTR). Once outside this volume, the aircraft must integrate into other control areas, which are defined beyond the boundaries of the CTR. In many countries, in the absence of clear criteria, very restrictive aircraft climb/descent gradients are used to define the lateral boundaries of CTRs, which results in airspace volumes that are excessively large for the intended purpose. Defining volumes larger than necessary may constrain the operations of other types of aircraft, such as General Aviation conducting visual flights (VFR) in adjacent airspace or of new users like Unmanned Aircraft System (UAS). This article proposes criteria for defining the lateral boundaries of CTRs based on ADS-B trajectories data from actual operations at several airports. The new criteria are based on the actual climb and descent gradients experienced by aircraft, and in particular, on the distance from the aerodrome reference point (ARP) at which the aircraft reaches a specific altitude (target height). The 99.9th percentile of flight paths were selected to define these new criteria. The results of applying the proposed criteria are compared with currently published dimensions for these zones and show that the volumes defined for the CTRs could be considerably reduced. The value of the reduction will depend on the particular aerodrome considered.

The increasing demand for air travel has intensified the need for more efficient air traffic management (ATM) solutions. One of the key challenges in this domain is the optimal sectorization of airspace to ensure balanced controller workload and operational efficiency. Traditional airspace sectors, typically static and based on historical flow patterns, often fail to adapt to evolving traffic complexity, resulting in imbalanced workload distribution and reduced system performance. This study introduces a novel methodology for optimizing ATC sector geometries based on air traffic complexity indicators, aiming to enhance the balance of operational workload across sectors. The proposed optimization is formulated in the horizontal plane using a two-dimensional cell-based airspace representation. A graph-partitioning optimization model with spatial and operational constraints is applied, along with a refinement step using adjacent-cell pairs to improve geometric coherence. Tested on real data from Madrid North ACC, the model achieved significant complexity balancing while preserving sector shapes in a real-world case study based on a Spanish ACC. This work provides a methodological basis to support static and dynamic airspace design and has the potential to enhance ATC efficiency through data-driven optimization.

Runway throughput at high-density European airports is governed not only by the aircraft interarrival separation on final approach but also by runway occupancy time (ROT). As reduction of Separation Minima (SM) becomes operationally and technically feasible and as Air Traffic Control Officers (ATCOs) improve delivery accuracy, ROT is expected to play a progressively larger role as the capacity-limiting mechanism. This study provides the first quantitative evidence of this effect through an analysis of a saturated traffic scenario operating under the RECAT-EU wake turbulence separation scheme and reduced surveillance minima of 2.5 nautical miles, based on ADS-B surveillance data. For each RECAT-EU leader–following sequence, ROT distributions were compared against the actual approach-to-approach times and the applicable SM to derive the probability that ROT becomes the binding constraint. Results show that, when arrival separation is governed by the reduced surveillance-based separation rather than by wake turbulence constraints, ROT is significantly more likely to become the limiting factor for runway capacity. This evidence provides a quantitative basis to guide future research, which should focus on ROT reduction strategies tailored to aircraft performance, including flexible exit taxiway concepts and adaptive exit allocation, prioritising those aircraft sequences identified by this study as most ROT sensitive.

Separation minima are the minimum required distances between aircraft in vertical or horizontal dimensions, established to ensure safety levels in controlled airspace. Nevertheless, current separation standards are fixed and have remained unchanged for decades, limiting airspace capacity. As a result, separation management is an area where improvement is sought. This research proposes an innovative concept of operation: Ad Hoc (or variable) separation, which involves redefining both the separation values and the way they are applied (separation mode). This concept refers to the application of different separation minima values between aircraft pairs in the same airspace volume based on factors such as aircraft model and mass, encounter geometry, and flight level, among others. A novel collision risk model is developed to compute these Ad Hoc separation minima values from Monte Carlo and Fast Time Simulations. The methodology was applied to a Spanish en-route sector (LECMZMU) to determine safe horizontal Ad Hoc separations. The best results show reductions from 5 NM (current horizontal separation minima value) to 4 NM in over 90% of the evaluated situations, and from 5 NM to 3 NM in more than 80% of encounters for low intersection angles (around 15°). In contrast, for intersection angles around 45°, reductions from 5 NM to 4 NM are possible in 68% of encounters, and from 5 NM to 3 NM in only 11%. These findings confirm the feasibility of Ad Hoc separation and its potential to enhance airspace capacity while maintaining safety levels, supporting its integration into future operational concept.

The expected number of drone operations in the coming decades, together with the fact that most of them will take place in very-low-level airspace, will lead to a density of drone flights much greater than that of conventional manned aviation. In this context, the number of conflicts (i.e., 4D convergence of drone trajectories below the safe separation minima) will be much more frequent than in manned aviation and, therefore, conventional air traffic management methods or even the specific proposed mechanisms for drone traffic management are unlikely to be able to solve them safely. This paper considers a set of simulated drone trajectories in a high-density urban environment to analyze the applicability of machine learning regression and classification techniques to detect conflicts among such trajectory times in advance of their occurrence in order to provide new methods to manage the expected drone traffic density safely and efficiently. This would not be possible with current drone traffic management solutions. The obtained results suggest that the Random Forest, Artificial Neural Networks and Logistic Regression algorithms could detect nearly all near-collisions up to 10 s before they occur, and the first two algorithms could also detect a significant number of near-collisions more than 60 s earlier.

The International Civil Aviation Organization (ICAO) specifies that in the design phase of instrument flight procedures, an additional clearance may be added to an obstacle when flights are over mountainous terrain. This clearance increase can be up to 100 per cent of the minimum obstacle clearance (MOC). Airspace and instrument flight procedure designers usually face the problem of determining what value should be applied, since setting the maximum value of 100% often implies operational penalties, but there are no standardized criteria to determine lower values. The ICAO PANS-OPS indicates that the additional clearance over obstacles in mountainous areas is caused by two effects, both related to orography and wind speed. The first effect is due to the altimeter indication error. The second one is related to the loss of altitude when an aircraft is exposed to turbulence produced by mountain waves. This paper presents a methodology for determining the additional clearance to be applied over obstacles when, in the flight procedure design phase, the overflight of mountainous terrain is expected. Through this methodology, results have been achieved for the proposal of an appropriate additional clearance. The development of graphs and tables allows us to identify which additional value should be considered in each case.