Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity

TL;DR

This paper presents an interpretable Graph Neural Network framework that predicts air traffic controller task demand and identifies key aircraft contributing to airspace complexity, outperforming existing metrics.

Contribution

Introduces a novel, interpretable GNN model for real-time air traffic demand prediction that highlights specific aircraft influencing complexity.

Findings

Model outperforms heuristic and baseline metrics

Provides per-aircraft task demand scores

Enhances understanding of airspace complexity drivers

Abstract

Real-time assessment of near-term Air Traffic Controller (ATCO) task demand is a critical challenge in an increasingly crowded airspace, as existing complexity metrics often fail to capture nuanced operational drivers beyond simple aircraft counts. This work introduces an interpretable Graph Neural Network (GNN) framework to address this gap. Our attention-based model predicts the number of upcoming clearances, the instructions issued to aircraft by ATCOs, from interactions within static traffic scenarios. Crucially, we derive an interpretable, per-aircraft task demand score by systematically ablating aircraft and measuring the impact on the model's predictions. Our framework significantly outperforms an ATCO-inspired heuristic and is a more reliable estimator of scenario complexity than established baselines. The resulting tool can attribute task demand to specific aircraft, offering a new way to analyse and understand the drivers of complexity for applications in controller training and airspace redesign.