Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918096931913728 |
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| author | Henderson, Edward Gould, Dewi Everson, Richard De Ath, George Pepper, Nick |
| author_facet | Henderson, Edward Gould, Dewi Everson, Richard De Ath, George Pepper, Nick |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13423 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity Henderson, Edward Gould, Dewi Everson, Richard De Ath, George Pepper, Nick Machine Learning Artificial Intelligence 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. |
| title | Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.13423 |