  Trajectory prediction (TP) plays an important role in supporting the
decision-making of Air Traffic Controllers (ATCOs). Traditional TP methods are
deterministic and physics-based, with parameters that are calibrated using
aircraft surveillance data harvested across the world. These models are,
therefore, agnostic to the intentions of the pilots and ATCOs, which can have a
significant effect on the observed trajectory, particularly in the lateral
plane. This work proposes a generative method for lateral TP, using
probabilistic machine learning to model the effect of the epistemic uncertainty
arising from the unknown effect of pilot behaviour and ATCO intentions. The
models are trained to be specific to a particular sector, allowing local
procedures such as coordinated entry and exit points to be modelled. A dataset
comprising a week's worth of aircraft surveillance data, passing through a busy
sector of the United Kingdom's upper airspace, was used to train and test the
models. Specifically, a piecewise linear model was used as a functional,
low-dimensional representation of the ground tracks, with its control points
determined by a generative model conditioned on partial context. It was found
that, of the investigated models, a Bayesian Neural Network using the Laplace
approximation was able to generate the most plausible trajectories in order to
emulate the flow of traffic through the sector.
