Abstract:Accurate air traffic prediction in the terminal airspace (TA) is pivotal for proactive air traffic management (ATM). However, existing data-driven approaches predominantly rely on time series-based forecasting paradigms, which inherently overlook critical aircraft state information, such as real-time kinematics and proximity to airspace boundaries. To address this limitation, we propose \textit{AeroSense}, a direct state-to-flow modeling framework for air traffic prediction. Unlike classical time series-based methods that first aggregate aircraft trajectories into macroscopic flow sequences before modeling, AeroSense explicitly represents the real-time airspace situation as \textit{a dynamic set of aircraft states}, enabling the direct processing of a variable number of aircraft instead of time series as inputs. Specifically, we introduce a situation-aware state representation that enables AeroSense to sense the instantaneous terminal airspace situation directly from microscopic aircraft states. Furthermore, we design a model architecture that incorporates masked self-attention to capture inter-aircraft interactions, together with two decoupled prediction heads to model heterogeneous flow dynamics across two key functional areas of the TA. Extensive experiments on a large-scale real-world airport dataset demonstrate that AeroSense consistently achieves state-of-the-art performance, validating that direct modeling of microscopic aircraft states yields substantially higher predictive fidelity than time series-based baselines. Moreover, the proposed framework exhibits superior robustness during peak traffic periods, achieves Pareto-optimal performance under dayparting multi-object evaluation, and provides meaningful interpretability through attention-based visualizations.
| Comments: | There are issues with the authors of the paper I submitted, as well as problems with the content of the article, so it needs to be withdrawn. Thank you for your understanding |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.11198 [cs.LG] |
| (or arXiv:2604.11198v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.11198 arXiv-issued DOI via DataCite |
From: Anqi Liu [view email]
[v1]
Mon, 13 Apr 2026 08:54:19 UTC (2,030 KB)
[v2]
Tue, 14 Apr 2026 15:07:49 UTC (1 KB) (withdrawn)
[v3]
Thu, 16 Apr 2026 03:22:24 UTC (2,030 KB)
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