Four-Dimensional Aircraft Trajectory Prediction Based on Generative Deep Learning

Abstract

Aircraft four-dimensional (4D) trajectory prediction (TP) is among the critical techniques of current existing automation systems in air traffic management (ATM), which is also the basis of future airspace operation models. This paper proposes a 4D TP method based on a conditional tabular generative adversarial network (CTGAN). The model is trained and tested using historical 4D trajectory data from the Hong Kong region. It is then compared with the most extensively researched conditional generative adversarial network (CGAN) and LSTM methods in the field of TP. The results show that Jensen–Shannon (JS) divergence, maximum mean discrepancy (MMD), and mean Euclidean distance (MED) of the model evaluation metrics for the selected route are 0.0539, 0.0150, and 0.0085, respectively. This confirms that the model for predicting 4D trajectory can accurately learn complex airspace’s trajectory distribution characteristics. Compared with the CTGAN algorithms shown in previous studies, the enhanced CTGAN approach developed in our research can reduce around 10% running time and 20% MED. Compared to CGAN, it results in an 81.55% reduction in MED. Compared to LSTM, when predicting with a 10-minute time span, the average mean absolute error for parameters latitude (Lat), longitude (Lon), geometric height (GH), and ground speed (GS) decreases by 7.76, 84.48, 81.98, and 36.51%, respectively.