A Sequential Planning Framework for the Operational Reality of Interacting Air Traffic Flow Regulations and Traffic Flow Programs

TL;DR

This paper introduces RegulationZero, a sequential planning framework using hierarchical Monte Carlo Tree Search to optimize air traffic flow regulations, effectively managing cascading effects and improving sector reliability.

Contribution

It presents a novel sequential planning approach that operates in regulation space, integrating MCTS with local regulation proposals for better air traffic management.

Findings

Effective mitigation of regulation cascading effects.

Improved sector reliability and traffic flow management.

Compatibility with existing slot-allocation systems.

Abstract

Air Traffic Flow Management (ATFM) traffic regulations are being increasingly used as rising demand meets persistent workforce shortages. This operational strain has amplified a critical phenomenon that we call \emph{regulation cascading}: the compounding, non-linear interactions that occur when multiple regulations influence one another in unpredictable ways. As the number and complexity of regulations grow, cascading effects become more pronounced, undermining the network operator's ability to protect sectors reliably. To address this challenge, we introduce RegulationZero, a sequential planning framework that natively operates in the regulation space, optimizing over ordered sequences of flow-level regulations that remain fully compatible with existing slot-allocation systems such as CASA and RBS++. At its core, the method employs a hierarchical Monte Carlo Tree Search (MCTS) that first samples congestion hotspots and then selects candidate regulations synthesized by a local proposal engine. Each proposal is evaluated by a fast First-Planned-First-Served (FPFS) allocator to estimate its reward, with these feedbacks guiding the subsequent MCTS exploration.