Long-term 4D trajectory prediction using generative adversarial networks

Four-dimensional trajectory prediction is one of the key technologies of air traffic management (ATM) and plays a considerably significant role in enhancing air traffic safety, accelerating air traffic flow and improving ATM efficiency. In this work, we propose a novel long-term 4D trajectory prediction model based on generative adversarial network (GAN). First, trajectory data is preprocessed. Then, three deep generation models for trajectory prediction are designed based on one-dimensional convolution neural network (Conv1D-GAN), two-dimensional convolution neural network (Conv2D-GAN), and long short-term memory neural network (LSTM-GAN). Finally, the models are trained and tested using historical 4D trajectory data from Beijing to Chengdu, China. The results demonstrate that the Conv1D-GAN is the most suitable generative adversarial network for long-term aircraft trajectory prediction.