Game-theoretic Decentralized Coordination for Airspace Sector Overload Mitigation

TL;DR

This paper introduces a game-theoretic decentralized mechanism for airspace sector overload mitigation, enabling independent sectors to coordinate effectively without central control, ensuring convergence to stable solutions and reducing congestion.

Contribution

It models sector interactions as a potential game with best response dynamics, providing convergence guarantees and demonstrating effectiveness with real flight data.

Findings

Reduces airspace overload significantly in simulations

Ensures convergence to stable equilibrium states

Maintains solution quality comparable to centralized methods

Abstract

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 mechanism 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 any shared decision making. A tunable cooperativeness factor models the degree to which each sector is willing to reduce overload in other sectors. We prove that the proposed mechanism satisfies a potential game structure, ensuring that best response dynamics converge to a pure Nash equilibrium, under a mild 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 matching the solution quality of centralized solvers.