  Reliable 4D aircraft trajectory prediction, whether in a real-time setting or
for analysis of counterfactuals, is important to the efficiency of the aviation
system. Toward this end, we first propose a highly generalizable efficient
tree-based matching algorithm to construct image-like feature maps from
high-fidelity meteorological datasets - wind, temperature and convective
weather. We then model the track points on trajectories as conditional Gaussian
mixtures with parameters to be learned from our proposed deep generative model,
which is an end-to-end convolutional recurrent neural network that consists of
a long short-term memory (LSTM) encoder network and a mixture density LSTM
decoder network. The encoder network embeds last-filed flight plan information
into fixed-size hidden state variables and feeds the decoder network, which
further learns the spatiotemporal correlations from the historical flight
tracks and outputs the parameters of Gaussian mixtures. Convolutional layers
are integrated into the pipeline to learn representations from the
high-dimension weather features. During the inference process, beam search,
adaptive Kalman filter, and Rauch-Tung-Striebel smoother algorithms are used to
prune the variance of generated trajectories.
