  Models for predicting aircraft motion are an important component of modern
aeronautical systems. These models help aircraft plan collision avoidance
maneuvers and help conduct offline performance and safety analyses. In this
article, we develop a method for learning a probabilistic generative model of
aircraft motion in terminal airspace, the controlled airspace surrounding a
given airport. The method fits the model based on a historical dataset of
radar-based position measurements of aircraft landings and takeoffs at that
airport. We find that the model generates realistic trajectories, provides
accurate predictions, and captures the statistical properties of aircraft
trajectories. Furthermore, the model trains quickly, is compact, and allows for
efficient real-time inference.
