Unlocking air traffic flow prediction through microscopic aircraft-state modeling

Abstract:Short-term air traffic flow prediction in terminal airspace is essential for proactive air traffic management. Existing approaches predominantly model traffic flow as aggregated time series. However, traffic dynamics are governed by aircraft states and their interactions in continuous airspace. Such aggregation obscures fine-grained information, including aircraft kinematics, boundary interactions, and control-intent cues. Here we present AeroSense, a state-to-flow modeling paradigm that predicts future traffic flow directly from instantaneous airspace situations represented as dynamic sets of aircraft states derived from ADS-B trajectories. By establishing an end-to-end mapping from microscopic aircraft states to future regional traffic flow, AeroSense preserves aircraft-level dynamics while naturally accommodating varying traffic density, and avoids reliance on historical look-back windows. Experiments on a large-scale real-world dataset show that AeroSense exhibits strong predictive accuracy and robustness compared with time series-based forecasting approaches, without requiring exhaustive hyperparameter tuning. These findings suggest that aircraft-state situation modeling provides a promising alternative to conventional time-series forecasting in air traffic flow management.

Machine Learning (cs.LG)

arXiv:2605.10083 [cs.LG]

(or arXiv:2605.10083v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.10083 Focus to learn more arXiv-issued DOI via DataCite

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