Learning Probabilistic Airspace Demand from Microscopic Aircraft Interaction Graphs

Air traffic flow prediction is essential for proactive air traffic management, yet existing approaches predominantly forecast future regional flows from aggregated time-series histories. Such aggregation can obscure the states and interactions of individual aircraft that directly shape near-term traffic. We investigate whether the current airspace snapshot can instead support probabilistic demand forecasting without historical flow sequences. We introduce AEROGRAPH, which represents the variable aircraft population as a dynamic multi-relational graph with physically typed interactions and regional task nodes, and predicts discrete distributions over 15-minute aircraft counts. Across two real-world airports, microscopic snapshot-based models consistently outperform time-series models. Ablation studies identify kinematic interactions and regional task structure as the most consistent contributors. Under bidirectional cross-airport zero-shot transfer, AEROGRAPH retains its advantage under spatial-temporal domain shift. These results show that airspace snapshot modeling provides a strong alternative to aggregated time-series histories for near-term airspace demand forecasting.