Collaborative Decision-Making and Optimal Utilization of Pathfinding Flights during Convective Weather

TL;DR

This paper develops a unified analytical framework for pathfinder operations under convective weather by coupling weather-driven state transitions with multi-agent decision-making. It introduces a four-state Markov chain to model fix closures and reopenings, and utility-based models for flights, ATC, and airline dispatchers, enabling worst-case analyses of collective rejection. The work further formulates and solves sequencing optimizations for ATC- and dispatcher-initiated pathfinder offers, validated via discrete-event simulation using JFK/JFK-like data. Key findings show distinct optimal sequences under ATC versus dispatcher objectives, evidence that early offers provide the bulk of system benefit, and that modest flight selflessness can substantially improve resilience under uncertainty. The framework offers a practical, data-driven basis for decision-support tools in weather-impacted air traffic operations and highlights the value of endogenous pathfinder decision-making in improving terminal-area flow and reliability.

Abstract

Air traffic operations are strongly influenced by convective weather, and one common response is pathfinder operations, in which a designated aircraft tests the viability of weather-impacted airspace and routes. Despite relatively routine use in practice, how pathfinder operations evolve under uncertainty and how the pathfinder decision-making process unfolds are largely treated as exogenous. Addressing this gap requires jointly modeling weather-driven system accessibility, flight responses to pathfinder offers, and the sequencing of those offers to improve outcomes. We develop a unified analytical framework that connects weather-driven system state transitions, flight acceptance decisions, and the sequencing of pathfinder offers. We first construct a four-state Markov chain to model stochastic closure and reopening of exit points, or fixes, out of the terminal departure airspace surrounding a major airport, pathfinder selection, and pathfinding execution, and analyze its steady-state behavior to characterize long-term capacity and delay implications. We introduce utility-based decision models for flights, air traffic control (ATC), and dispatchers, and analyze worst-case collective rejection to quantify system vulnerability under selfless behavior and uncertainty. Finally, we formulate optimization problems that model ATC-initiated and dispatcher-initated pathfinder offers, with the goal of optimizing the sequence of pathfinder offers. Using a discrete event simulation for a major US airport, we show that ATC- and dispatcher-driven objectives lead to distinct, near real-time sequencing strategies, providing the first formal decision models for pathfinder operations under weather uncertainty.

Figures (13)

• Figure 1: Example of a pathfinder operation in the New York terminal area (N90). Orange circles denote departure fixes, purple circles denote major airports, and the red trajectory corresponds to the designated pathfinder flight (DAL569).
• Figure 2: Markov chain representation of the aircraft pathfinding process. Transition probabilities are governed by $P_{\mathrm{good}}$, $P_{\mathrm{accept}}$, and $P_{\mathrm{success}}$, capturing weather observations, acceptance of pathfinder offers, and pathfinding success, respectively.
• Figure 3: Stakeholders involved in the pathfinder selection. The figure shows the three primary stakeholders: ATC, airline dispatchers, and individual flights.
• Figure 4: $P_{\mathrm{accept},\,i}$ as a function of utility $U_i$ for different sensitivity values $\beta_i$. Higher $\beta_i$ yields more deterministic decisions, while lower $\beta_i$ leads to more randomized acceptance.
• Figure 5: Simulation flow for runway sequencing, fix updates, and pathfinder evaluation.