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

arXiv: https://arxiv.org/abs/2507.13423 · HTML: https://mlsanity.com/paper/2507.13423/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification](https://mlsanity.com/paper/2011.09588/) (2020)
- [CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships](https://mlsanity.com/paper/2207.03586/) (2022)
- [Agent Prioritization for Autonomous Navigation](https://mlsanity.com/paper/1909.08792/) (2019)
- [Regularization Strategies for Quantile Regression](https://mlsanity.com/paper/2102.05135/) (2021)

## Built on by (most cited first, 2 of 2)

- [A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace](https://mlsanity.com/paper/2601.03120/) (2026)
- [Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions](https://mlsanity.com/paper/2605.23696/) (2026)

## Abstract (arXiv)

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.