Unlocking air traffic flow prediction through microscopic aircraft-state modeling

TL;DR

AeroSense introduces a microscopic aircraft-state modeling framework that predicts air traffic flow directly from real-time aircraft positions, outperforming traditional aggregated time series methods especially in dense traffic conditions.

Contribution

The paper presents AeroSense, a novel end-to-end model that leverages instantaneous aircraft states for accurate, fine-grained air traffic flow prediction without relying on historical data windows.

Findings

AeroSense improves prediction accuracy over traditional methods.

The approach effectively handles high-density traffic scenarios.

Real-world experiments validate the model's effectiveness.

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.