Self-reorganized supporting tools for conflict resolution in high-density airspace volumes

Present research on Air Traffic Management (ATM) is tending to improve airspace capacity, accessibility and the efficiency of operations in high-density areas, while maintaining or improving the safety performance indicators. Tactical interventions from the Air Traffic Control (ATC) system to preserve safety distances between aircraft have some inherent shortages when scalability problems arise, that could lead to a well-known capacity saturation. An increased number of detected conflicts in dense traffic volumes can affect not only the ATC procedures but also the full safety net, since the present Traffic alert and Collision Avoidance System (TCAS) has been designed only for low dense areas. To overcome these shortages at tactical level without appealing to the strategic airspace restrictions, this paper presents an innovative automation-based concept in future design of the ATM system supporting an irruptive shift from the centrally controlled ATM system to a distributed system, in which a set of aircraft constitutes a dynamic ecosystem, with self-governed capabilities, to find the optimal conflict-free resolution trajectories. The concept has been developed within the methodological approach "hotspot-cluster-ecosystem" which provides a smooth transition from trajectory management (TM), separation management (SM) to the collision avoidance (CA) layer, seeking for an advanced time horizon in which the airspace users would timely negotiate resolutions before an ATC directive is issued. A dynamic demand-capacity balance (DCB) approach is illustrated by identifying clusters and analyzing ecosystems considering deviations of pairwise conflicting aircraft to the surrounding traffic (ST). The ecosystem is described by its membership size and spatially temporal interdependencies (STIs), i.e. potential 4D positions of the members driven by defined maneuverability checks, and generated conflict intervals between each pair of members. Finally, computed interdependencies provide an insight of the ecosystem complexity through the ratio of a total number of feasible resolutions over the ecosystem time.