From Single Aircraft to Communities: A Neutral Interpretation of Air Traffic Complexity Dynamics
This work addresses air traffic management fragmentation for controllers and users by providing a neutral, explainable tool, though it is incremental as it builds on existing complexity metrics.
The paper tackles the problem of fragmented air traffic management by introducing a neutral metric for single aircraft complexity and extending it to identify complex communities of interdependent aircraft, demonstrating through use-cases that the algorithm can formalize and guide controller decisions while increasing transparency and fairness.
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.