Unlocking air traffic flow prediction through microscopic aircraft-state modeling
2026-05-11 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed AeroSense, a new method to predict air traffic flow by looking at the exact positions and movements of individual aircraft at a given moment, instead of using past aggregated traffic data. This approach keeps the detailed information about each plane and adapts well to changes in traffic density without needing historical data. Tests using real-world data showed AeroSense predicts better, especially when the airspace is busy. This suggests that looking at the current state of all planes can improve air traffic predictions compared to conventional methods.
air traffic flowterminal airspaceADS-B trajectoriesaircraft statestime series forecastingtraffic densityair traffic managementmicroscopic modelingstate-to-flow modeling
Authors
Bin Wang, Anqi Liu, Jiangtao Zhao, Yanyong Huang, Peilan He, Guiyuan Jiang, Feng Hong, Yanwei Yu, Tianrui Li
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.