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, despite traffic dynamics being governed by aircraft states and interactions in continuous airspace. Such aggregation obscures fine-grained information including aircraft kinematics, boundary interactions, and control intent. Here we present AeroSense, a state-to-flow modeling framework 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 without relying on historical look-back windows. Experiments on a large-scale real-world dataset show that AeroSense consistently improves predictive accuracy over aggregation-based forecasting approaches, particularly during high-density traffic periods. These findings suggest that instantaneous airspace situations provide an effective alternative to conventional time-series-based traffic forecasting paradigms.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.10083 [cs.LG] |
| (or arXiv:2605.10083v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.10083 arXiv-issued DOI via DataCite (pending registration) |
From: Anqi Liu [view email]
[v1]
Mon, 11 May 2026 07:04:55 UTC (2,411 KB)
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