Game-theoretic Regulated Decentralized Coordination for Airspace Sector Overload Mitigation

Decentralized air traffic management systems offer a scalable alternative to centralized control, but often assume high levels of cooperation.

In practice, such assumptions frequently break down since airspace sectors operate independently and prioritize local objectives.

We address the problem of sector overload in decentralized air traffic management by proposing a regulated decentralized protocol that models self-interested behaviors based on best response dynamics.

Each sector adjusts the departure times of flights under its control to reduce its own congestion, without requiring centralized joint optimization.

A tunable cooperativeness factor models the degree to which each sector accounts for overload in other sectors, while a minimal admissibility rule prevents local updates from creating new overloads.

We prove that the proposed protocol satisfies a potential game structure, ensuring that best response dynamics converge to a pure Nash equilibrium under this restriction.

In addition, we identify a sufficient condition under which an overload-free solution corresponds to a global minimizer of the potential function.

Numerical experiments using 24 hours of European flight data demonstrate that the proposed algorithm substantially reduces overload even with only minimal cooperation between sectors, while maintaining scalability and achieving solution quality comparable to the centralized benchmark.