Big data driven vessel trajectory prediction based on sparse multi-graph convolutional hybrid network with spatio-temporal awareness

Abstract

The revolutionary progress of deep learning and data mining technology has greatly contributed to the rapid development of maritime Internet of Things. In order to advance intelligent maritime traffic services, it is necessary to accurately predict the future trajectories of vessels, which is conducive to collision avoidance, maritime surveillance and abnormal behavior detection. In this work, a new method of vessel trajectory prediction based on sparse multi-graph convolutional hybrid network with spatio-temporal awareness is proposed, namely SMCHN model, which uses sparse spatial graph to capture the adaptive interactions between vessels and remove redundant interactions, and sparse temporal graph can be used to simulate the movement trends of vessels, bringing the movement behavior of vessels closer and closer to reality. In addition, the hybrid network is designed to fuse information from different sources by adjusting weights. Finally, the temporal convolutional network with gating mechanism is used to predict the future trajectories of vessels. Experimental results show that the SMCHN model improves 31%, 66% and 54% in short-term, medium-term and long-term vessel trajectory prediction compared with the optimal baseline model.