From Time Series to State: Situation-Aware Modeling for Air Traffic Flow Prediction

• URL: http://arxiv.org/abs/2604.11198v3
• Date: Thu, 16 Apr 2026 03:22:24 GMT
• Title: From Time Series to State: Situation-Aware Modeling for Air Traffic Flow Prediction
• Abstract summary: We propose textitAeroSense, a direct state-to-flow modeling framework for air traffic prediction.<n>AeroSense represents the real-time airspace situation as textita dynamic set of aircraft states, enabling the direct processing of a variable number of aircraft.<n>Experiments on a large-scale real-world airport dataset demonstrate that AeroSense consistently achieves state-of-the-art performance.
• Score: 28.381858327505828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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