Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach

TL;DR

This paper introduces a deep generative convolutional recurrent neural network for accurate 4D aircraft trajectory prediction, integrating meteorological data and advanced inference techniques to improve aviation safety and efficiency.

Contribution

It presents a novel end-to-end model combining convolutional and LSTM networks with a tree-based weather feature mapping and advanced inference methods for trajectory prediction.

Findings

High prediction accuracy demonstrated on real flight data

Effective integration of weather features improves model robustness

Trajectory generation benefits from beam search and Kalman filtering

Abstract

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.