Given the ongoing interest in the application of Machine Learning (ML) techniques, the development of new Air Traffic Control (ATC) tools is paramount for the improvement of the management of the air transport system. This article develops an ATC tool based on ML techniques for conflict detection. The methodology develops a data-driven approach that predicts separation infringements between aircraft within airspace. The methodology exploits two different ML algorithms: classification and regression. Classification algorithms denote aircraft pairs as a Situation of Interest (SI), i.e., when two aircraft are predicted to cross with a separation lower than 10 Nautical Miles (NM) and 1000 feet. Regression algorithms predict the minimum separation expected between an aircraft pair. This data-driven approach extracts ADS-B trajectories from the OpenSky Network. In addition, the historical ADS-B trajectories work as 4D trajectory predictions to be used as inputs for the database. Conflict and SI are simulated by performing temporary modifications to ensure that the aircraft pierces into the airspace in the same time period. The methodology is applied to Switzerland’s airspace. The results show that the ML algorithms could perform conflict prediction with high-accuracy metrics: 99% for SI classification and 1.5 NM for RMSE.

The expected growth of air traffic in the following decades demands the implementation of new operational concepts to avoid current limitations of the air traffic management system. This paper focuses on the strategic conflict management for four-dimensional trajectories (4DT) in free-route airspace. 4DT has been proposed as the future operational concept to manage air traffic. Thus, aircraft must fulfil temporary restrictions at specific waypoints in the airspace based on time windows. Based on the temporary restrictions, a strategic conflict management method is proposed to calculate the conflict probability of an aircraft pair (that intersects in the air) and to calculate temporary-blocking windows that quantify the time span at which an aircraft cannot depart because one conflict could occur. This methodology was applied in a case-study for an aircraft pair, including the uncertainty associated with 4DT. Moreover, a sensitivity analysis was performed to characterise the impact of wind conditions and speed control on the temporary-blocking windows. The results concluded that it is feasible to propose 4DT strategic de-confliction based on temporary-blocking windows. Although, uncertainty variables such as wind and speed control impact on the conflict probability and the size of the temporary-blocking windows.

The demand for air mobility services will depend on the safety of these operations but also on the transportation time savings in congested urban areas. An adequate air space structure is therefore essential to achieve both objectives. Corridors, the most extended solution proposed nowadays, can meet the safety requirements necessary for air taxi operations, but they are rigid (point-to-point solutions) and would increase delays. As an alternative, this paper presents the airspace structure proposed in the SESAR AMU-LED Project, based on layers to assure both safety and efficiency of air taxi operations. In this proposal, small UAS will fly in the bottom part, called the Very Low Level, whereas air taxis will fly in the upper part. The paper applies a collision risk model to determine the minimum required safety buffer between both layers to assure the necessary safety levels. The results obtained show that a buffer of 10 m between them would meet the required safety levels for air taxi operations.

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.

The expected growth of air traffic in the coming decades demands an increase in airspace capacity, which is already close to saturation in many scenarios. One of the limiting factors of this capacity is the separation minima. At present, the separation standards that apply in a given volume of airspace are fixed, and their values were determined decades ago. Therefore, in order to increase airspace capacity, this is an area in which improvement is sought, namely through the implementation of new operational concepts, which include the redefinition of separation minima and the way they are applied. A key issue in this redefinition of separation minima is the question of the possibility of reducing the current standards. However, a reduction in the separation to a fixed value may not be a valid solution, as not all aircraft and ways of operation are the same. In this paper, the authors propose a new operational concept, the Ad Hoc or Variable separation minima. Ad Hoc separation refers to the application of different separation minima values in the same volume of airspace, depending on a set of factors, e.g., aircraft model and encounter geometry, among others. In this research, the factors that define these Ad Hoc separation minima and their relationships are discussed. A model for their determination is presented. Simulations are performed to analyze the operational feasibility of the Ad Hoc separation minima. The results show that the application of this concept is operationally feasible.

The required minimum separation distance between aircraft is believed to be one of the limiting factors on airspace capacity. In recent decades, aircraft separation rules have been modified by progressively shortening the required minimum separation distance. Following this trend in the coming years, a further reduction in the minimum separation distance would be expected. Still, a thorough assessment of the impact of this action on air traffic management performance should be carried out before investing in a reduction of separation minima. A Monte Carlo analysis of the en-route Spanish airspace shows that it is worth reducing the en-route minimum separation distance from 5 NM to 3 NM. This paper shows that a separation minima reduction will bring significant fuel savings, flight delay reduction, air traffic controller workload drop, and overall improvement of safety.