Resumen
At present, decision-making in ATM is fragmented between different stakeholders who have different objectives. This fragmentation, in unison with competing KPAs, leads to complex interdependencies between performance indicators, which results in an imbalance, with some of these indicators being penalized to the apparent benefit of others. Therefore, it is necessary to support ATM stakeholders in systematically uncovering hidden trade-offs between KPAs. Existing literature confirms this claim, but how to solve it has not been fully addressed. In this paper, we envision air traffic complexity to be the framework through which a common understanding among stakeholders is enhanced. 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. Through use-cases based on synthetic and real historical traffic, we first show that the algorithm can serve to formalize and improve decision-making. Further, we illustrates how the provided information can be used to increase transparency of the decision makers towards different airspace users. In order to showcase the methodology, we develop a tool that visualizes different outputs of the algorithm. Lastly, we conduct sensitivity analysis in order to systematically analyse how each input affects the methodology.