Aircraft Trajectory Prediction for Terminal Airspace Employing Social Spatiotemporal Graph Convolutional Network

Abstract

Four-dimensional (4-D) trajectory prediction is the core of air traffic management technologies such as flow management, conflict detection and resolution, arrival and departure sequencing, and aircraft abnormal behavior monitoring. The airport terminal airspace has a complex airspace structure, and the flight status of aircraft is changeable, which poses a challenge to trajectory prediction. To this end, aiming at the problem of track prediction in airport terminal area, we propose a 4-D trajectory prediction model of social spatiotemporal graph convolutional neural network (S-STGCNN) based on pattern matching. For each type of flight pattern, an S-STGCNN is trained to improve the robustness. Taking each aircraft as a node of a graph, the spatiotemporal graph convolution is used to extract features of the graph so as to simultaneously characterize the time dependence of trajectories and the interaction between aircraft. The time extrapolation convolutional neural network is used to generate the predicted trajectory. Experimental results show that the trajectory prediction method proposed in this paper has a higher prediction accuracy and a generalization ability than other models.