Joint Air Traffic Flow and Capacity Management via Answer Set Programming

2026-06-22

AI Summary

This study addresses the common practice in air traffic flow and capacity management of treating airspace trajectory flow management (ATFM) and dynamic airspace configuration (DAC) as separate problems, which limits system-wide efficiency. To overcome this limitation, the paper presents the first unified optimization framework that jointly models ATFM and DAC, integrating trajectory replanning, flight delay adjustments, and dynamic sector reconfiguration into a single formulation. The problem is efficiently solved using Answer Set Programming (ASP). Experimental instances derived from OpenSky historical data demonstrate that the ASP approach outperforms mixed-integer programming (MIP) in computational performance and matches the CASA heuristic in small-scale scenarios. Notably, dynamic sector configuration contributes most significantly to performance gains, though its inclusion requires careful constraints to prevent search space oscillations.

Abstract

Operational Air Traffic Flow and Capacity Management (ATFCM) balances flight demand with available sector capacity, to ensure safe and efficient operations. Mathematical models enhance operational ATFCM performance by framing demand-capacity balancing as an optimization problem, maximizing efficiency while adhering to safety constraints. However, SOTA research optimizes the aircraft trajectories (called ATFM) or the sector configuration (called DAC) separately. This leaves a research gap of whether joint optimization of ATFM and DAC can bring benefits. We partially address this limitation by introducing a joint ATFCM model with an encoding in Answer Set Programming (ASP). The ASP implementation is evaluated against two baselines applied to our joint model: a SOTA Mixed Integer Programming (MIP) model and an iterative CASA-based heuristic. Computational experiments utilize an instance generator fitted to historical OpenSky Network flight data. Our results indicate that the ASP model outperforms the MIP model, while ASP remains competitive against heuristics on small instances. Furthermore, while DAC has the largest improvement on solving performance compared to rerouting and delaying, unrestricted variants of DAC or rerouting lead to search space thrashing.

Problem

• Air Traffic Flow and Capacity Management
• Joint Optimization
• ATFM
• DAC
• Demand-Capacity Balancing

Innovation

• Answer Set Programming
• Joint Optimization
• Air Traffic Flow Management
• Dynamic Airspace Configuration
• ATFCM