Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity
This work addresses a critical challenge in air traffic management by providing a more reliable and interpretable tool for analyzing complexity, which is incremental as it builds on existing GNN methods for a specific domain.
The paper tackled the problem of real-time assessment of Air Traffic Controller task demand by introducing an interpretable Graph Neural Network framework that predicts upcoming clearances from static traffic scenarios, significantly outperforming existing heuristics and baselines.
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.