Aircraft Trajectory

Aircraft trajectory research focuses on accurately predicting, analyzing, and optimizing flight paths for various applications, from air traffic management to autonomous vehicle navigation. Current research employs diverse machine learning models, including neural networks (e.g., seq2seq, transformers, graph attention networks), and probabilistic methods (e.g., Gaussian mixture models, Bayesian neural networks) to improve trajectory prediction accuracy and efficiency, often incorporating contextual information like weather and air traffic density. These advancements have significant implications for enhancing safety, efficiency, and automation in air traffic control, search and rescue operations, and the development of autonomous flight systems.