Discussion on complexity and TCAS indicators for coherent safety net transitions

Transition between Separation Management in ATM and Collision Avoidance constitutes a source of potential risks due to non-coherent detection and resolution clearances between them. To explore an operational integration between these two safety nets, a complexity metric tailored for both Separation Management and Collision Avoidance, based on the intrinsic complexity, is proposed. To establish the framework to compare the complexity metric with current Collision Avoidance detection metrics, a basic pair-wise encounter model has been considered. Then, main indicators for horizontal detection of TCAS, i.e. tau and taumod, have been contrasted with the complexity metric. A simple method for determining the range locus for specific TCAS tau values, depending on relative speeds and encounter angles, was defined. In addition, range values when detection thresholds were infringed have been found to be similar, as well as its sensitivity to relative angles. Further work should be conducted for establishing a framework for the evaluation and validation of this complexity metric. This paper defines basic principles for an extended evaluation, including multi-encounter scenarios and longer look ahead times

From Single Aircraft to Communities: A Neutral Interpretation of Air Traffic Complexity Dynamics

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.Comment: 21 pages, 30 figures, 2 tables, submitted to Research Transportation Part

Discussion on density-based clustering methods applied for automated identification of airspace flows

Air Traffic Management systems generate a huge amount of track data daily. Flight trajectories can be clustered to extract main air traffic flows by means of unsupervised machine learning techniques. A well-known methodology for unsupervised extraction of air traffic flows conducts a two-step process. The first step reduces the dimensionality of the track data, whereas the second step clusters the data based on a density-based algorithm, DBSCAN. This paper explores advancements in density-based clustering such as OPTICS or HDBSCAN*. This assessment is based on quantitative and qualitative evaluations of the clustering solutions offered by these algorithms. In addition, the paper proposes a hierarchical clustering algorithm for handling noise in this methodology. This algorithm is based on a recursive application of DBSCAN* (RDBSCAN*). The paper demonstrates the sensitivity of these algorithms to different hyper-parameters, recommending a specific setting for the main one, which is common for all methods. RDBSCAN* outperforms the other algorithms in terms of the density-based internal validity metric. Finally, the outcome of the clustering shows that the algorithm extracts main clusters of the dataset effectively, connecting outliers to these main clusters

Identification of spatiotemporal interdependencies and complexity evolution in a multiple aircraft environment

To support future automated transitions among the ATM safety nets, this study elaborates identification of the complex traffic scenarios based on the concept of aerial ecosystems. As an extension of the TCAS operational domain and evolving from the separation management towards collision avoidance layer, the concept has been developed as a stepwise algorithm for identification of cooperative aircraft involved in the safety event – detected conflict, and negotiating their resolution trajectories before the ecosystem deadlock event occurs, in which at least one aircraft stays out of a conflict-free resolution. As a response to this threshold, the paper examines generation of both acceptable and candidate resolution trajectories, with respect to the original aircraft trajectories. The candidate trajectories are generated from a set of tactical waypoints and a return waypoint to the original trajectory. Described methodology has been practically implemented to one ecosystem scenario, characterizing its evolution in terms of the intrinsic complexity. By introducing the heading maneuver changes and delay in the resolution process, the results have shown how the scenario complexity is increasing, especially affected by the states of two aircraft in the initial conflict. Furthermore, it has been demonstrated an evolution in the amount of the acceptable and candidate trajectory solutions, for which the minimum complexity value is satisfied. A goal of the study was to explore the lateral resolutions capacity at certain moments and its timely decrement

A machine learning approach to air traffic interdependency modelling and its application to trajectory prediction

Air Traffic Management is evolving towards a Trajectory-Based Operations paradigm. Trajectory prediction will hold a key role supporting its deployment, but it is limited by a lack of understanding of air traffic associated uncertainties, specifically contextual factors. Trajectory predictors are usually based on modelling aircraft dynamics based on intrinsic aircraft features. These aircraft operate within a known air route structure and under given meteorological conditions. However, actual aircraft trajectories are modified by the air traffic control depending on potential conflicts with other traffics. This paper introduces surrounding air traffic as a feature for ground-based trajectory prediction. The introduction of air traffic as a contextual factor is addressed by identifying aircraft which are likely to lose the horizontal separation. For doing so, this paper develops a probabilistic horizontal interdependency measure between aircraft supported by machine learning algorithms, addressing time separations at crossing points. Then, vertical profiles of flight trajectories are characterised depending on this factor and other intrinsic features. The paper has focused on the descent phase of the trajectories, using datasets corresponding to an en-route Spanish airspace volume. The proposed interdependency measure demonstrates to identify in advance conflicting situations between pairs of aircraft for this use case. This is validated by identifying associated air traffic control actions upon them and their impact on the vertical profile of the trajectories. Finally, a trajectory predictor for the vertical profile of the trajectory is developed, considering the interdependency measure and other operational factors. The paper concludes that the air traffic can be included as a factor for the trajectory prediction, impacting on the location of the top of descent for the specific case which has been studied

Assessment of Potential Conflict Detection by the ATCo

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