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.

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 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.

A Mid-Air Collision (MAC) is a fatal event with tragic consequences. To reduce the risk of a MAC, it is imperative to understand the precursors that trigger it. A primary precursor to a MAC is a loss of separation (LOS) or a separation infringement. This study develops a model to identify the factors contributing to a LOS between aircraft pairs. A Bayesian Network (BN) model is used to estimate the conditional dependencies of the factors affecting criticality, that is, how close the LOS has come to becoming a collision. This probabilistic model is built using GeNIe software from data (based on a database created from incident analysis) and expert judgment. The results of the model allow identification of how factors related to the scenario, the human factor (ATC and flight crew) or the technical systems, affect the criticality of the LOS. Based on this information, it is possible to exclude irrelevant elements that do not contribute or whose influence could be neglected, and to prioritize work on the most important ones, in order to increase ATM safety.

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.