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

Thinh Hoang, Daniel Delahaye

TL;DR

This paper tackles regulation-space planning for Air Traffic Flow Management to curb regulation cascading by modeling sequential regulation design as a finite-horizon decision process. It introduces RegulationZero, a hierarchical Monte-Carlo Tree Search framework that uses a Regulation Proposal Engine and an FPFS-based allocator to iteratively build and evaluate sequences of flow-level regulations, maintaining compatibility with existing slot-allocation systems such as CASA and RBS++. The method leverages hotspot targeting, flight footprints, and community-detection-based grouping to propose high-leverage regulations, with PUCT-driven selection and KL-regularized exploration guiding the search. Empirical results on large-scale European data show RegulationZero delivering robust, scalable improvements over a greedy policy and outperforming a trajectory-space simulated annealing baseline at longer planning horizons, while delaying only a minority of affected flights. The work demonstrates the potential of regulation-space planning to reduce RC-induced volatility and highlights practical considerations for realism, expert knowledge integration, and future deployment in operational ATFM contexts.

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.

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

TL;DR

This paper tackles regulation-space planning for Air Traffic Flow Management to curb regulation cascading by modeling sequential regulation design as a finite-horizon decision process. It introduces RegulationZero, a hierarchical Monte-Carlo Tree Search framework that uses a Regulation Proposal Engine and an FPFS-based allocator to iteratively build and evaluate sequences of flow-level regulations, maintaining compatibility with existing slot-allocation systems such as CASA and RBS++. The method leverages hotspot targeting, flight footprints, and community-detection-based grouping to propose high-leverage regulations, with PUCT-driven selection and KL-regularized exploration guiding the search. Empirical results on large-scale European data show RegulationZero delivering robust, scalable improvements over a greedy policy and outperforming a trajectory-space simulated annealing baseline at longer planning horizons, while delaying only a minority of affected flights. The work demonstrates the potential of regulation-space planning to reduce RC-induced volatility and highlights practical considerations for realism, expert knowledge integration, and future deployment in operational ATFM contexts.

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.

Paper Structure

This paper contains 57 sections, 1 theorem, 84 equations, 18 figures, 11 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. The lagging of current Traffic Flow Management measures behind operational realities.
  3. The Challenges of Regulation Planning & Regulation Cascading
  4. The Operation of Delay Programs
  5. The Complex Interactions between Regulations
  6. Our Contributions
  7. Paper Organization
  8. Literature Review
  9. Trajectory Space Slot Optimization Methods
  10. Stochastic and MILP formulations under capacity and uncertainty
  11. Market mechanisms and meta-heuristics
  12. Network-wide, multi-objective fairness, connectivity, and environmental goals
  13. Why Regulations still Persist after Decades of Slot Optimization Research
  14. Regulation Space Optimization Methods
  15. Positioning of our Paper
  16. ...and 42 more sections

Key Result

Proposition 1

For each $h$, let $q_h(\cdot) = q(s,h,\cdot)$ and $\lambda_h = \lambda_R(s,h)$. The inner maximizer $\bar{\pi}_R(\cdot|h,s)$ and its attained value $U_h(s)$ solve With inclusive inner values $U_h(s)$, Thus $\bar{\pi}$ is the nested solution

Figures (18)

  • Figure 1: The general Flow Regulation Workflow in the European Context.
  • Figure 2:
  • Figure 3: Regulations (EU) and Flow Programs (US) that will serve as input for slot allocation algorithms.
  • Figure 4: The ordering of regulation affects the flows available for the next step. "xN" means the flow contains N flights.
  • Figure 5: RegulationZero Key Components
  • ...and 13 more figures

Theorems & Definitions (2)

  • Proposition 1
  • proof