用图神经网络量化空管任务负荷，可解释地识别复杂度来源。
Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity
- 基于图神经网络建模飞机间交互，预测空管指令数量。
- 相比传统方法，预测准确率显著提升，能更可靠评估空域复杂度。
- 可逐个分析每架飞机对任务负荷的贡献，适合空管培训与空域设计。
实时评估近期内空中交通管制员（ATCO）的任务负荷是日益拥挤空域中的关键挑战，现有复杂度指标常无法捕捉超出简单飞机数量的细微操作驱动因素。本文提出一种可解释的图神经网络（GNN）框架，通过静态交通场景中飞机间的交互关系，预测即将发布的航路许可数量。关键在于，我们通过系统性地移除单架飞机并测量模型预测结果的变化，推导出每架飞机的可解释任务负荷评分。该框架显著优于基于人工经验的启发式方法，并在场景复杂度估计上超越现有基准。所得工具可将任务负荷归因于特定飞机，为理解复杂度驱动因素提供了新途径，适用于管制员训练与空域重构。
原文摘要 · Abstract (English)
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.
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