Game-theoretic Decentralized Coordination for Airspace Sector Overload Mitigation

2025-11-14

AI Summary

In decentralized air traffic management systems, sector-level autonomous decision-making leads to airspace overload. Method: This paper proposes a distributed coordination mechanism based on potential games: it models sector self-interested behavior and an adjustable cooperation factor, transforming flight departure time optimization into pure-strategy Nash equilibrium computation; theoretically proving the system constitutes a potential game whose overload-free solutions correspond to global minima of the potential function. A best-response dynamics algorithm enables fully distributed implementation without central coordination. Results: Simulations on 24-hour real European flight data demonstrate that even under low cooperation levels, the approach significantly reduces overload rates, exhibits strong scalability, and matches centralized optimizers in performance. The core contribution lies in the first formulation of sector coordination under bounded cooperation as a provably convergent potential game, coupled with an efficient distributed realization.

Technology Category

• Game Theory and Economic Paradigms: Coordination and Collaboration
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Modeling self-interested sector behaviors to mitigate airspace overload
• Achieving Nash equilibrium without shared decision-making between sectors
• Reducing overload through tunable cooperation using real flight data

Innovation

• Game-theoretic decentralized coordination for airspace overload
• Best response dynamics model self-interested sector behaviors
• Tunable cooperativeness factor enables Nash equilibrium convergence