AI RESEARCH
Towards Situation-aware State Modeling for Air Traffic Flow Prediction
arXiv CS.LG
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ArXi:2604.11198v1 Announce Type: new 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.