A Methodology to Perform Air Traffic Complexity Analysis Based on Spatio-Temporal Regions Constructed Around Aircraft Conflicts

Abstract

One of the main missions of air traffic management is to guarantee en route safety. This safety is quantified through some minimum separation distance between pairs of flying aircraft. Current systems are human-based, i.e., have human air traffic controllers assuring minimum separation is maintained and in cases a loss of separation is predicted, they take actions to prevent the occurrence of such events. The constant and rapid increment of the air traffic demand is pushing current air traffic control systems to their limits. Development of automatic decision support systems, which can be used to automate, or support aircraft conflict detection and resolution, is considered a possible solution. However, the combinatorial nature of the problem poses several challenges for such a task. Metrics or various analytical procedures to produce information about the complexity of given scenarios can be used to guide the solution search process. In this paper, we present a complexity analysis based on the spatio-temporal interdependencies identified by the use of spatio-temporal regions constructed around the aircraft conflicts. INDEX TERMS Aircraft conflict, complexity metrics, continuous space-time regions.

Spatiotemporal Graph Indicators for Air Traffic Complexity Analysis

“…First of all, there have been previous works that model air traffic as a graph. Koca et al [18] effectively transform traffic to a graph representation by identifying relevant aircraft to a conflict by means of spatiotemporal regions. However, their work is applicable only to conflict resolution.…”

“…Their approach bears some similarities to this paper; however, as we will explain, there are some key differences. First of all, they consider two aircraft interdependent only if there is a potential conflict between them, while conflicts affect the complexity of a situation, they are not the only source of it [4,7,8,18]. Furthermore, they do not consider the severity of interdependencies.…”

Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning

“…However, multiple pairwise conflicts can have varying spatial and temporal boundaries, i.e., their overlap in space and time. Koca et al [43], introduce the concept of a compound ecosystem, with an ecosystem being the set of aircraft affected by the occurrence of a conflict. They propose that multiple ecosystems can be considered together if they have at least one common member and the conflicts overlap in time more than 10% of their duration.…”

On the underlying dynamics of traffic conflicts related to stochastic behaviour

“…A metric to characterise the airspace, complexity is investigated by Koca. 62 Trajectories are defined as a series of 4D waypoints. Based on these, a hierarchical method is outlined to define clusters by identifying relevant aircraft according to spatio-temporal boxes.…”