Aircraft Trajectory Prediction with Inverted Transformer

Aircraft trajectory prediction plays a crucial role in air traffic management and significantly enhances operational safety and efficiency. In this work, we propose an aircraft trajectory prediction model that utilizes an Inverted Transformer, which is a state-of-the-art model for time-series prediction. In a vanilla Transformer, temporal tokens containing the multivariate features are embedded for each time step, but in the Inverted Transformer, each series is embedded independently into variate tokens. The Inverted Transformer captures multivariate correlations in a trajectory sequence, resulting in improved prediction accuracy. The proposed model was validated with actual air traffic surveillance data from the terminal airspace of Incheon International Airport, South Korea. The experimental results demonstrate that the prediction accuracy from the proposed model is superior to those of other deep learning-based prediction models.