A TDV attention-based BiGRU network for AIS-based vessel trajectory prediction

Jin Chen; Jixin Zhang; Hao Chen; Yong Zhao; Hongdong Wang

doi:10.1016/j.isci.2023.106383

A TDV attention-based BiGRU network for AIS-based vessel trajectory prediction

iScience. 2023 Mar 13;26(4):106383. doi: 10.1016/j.isci.2023.106383. eCollection 2023 Apr 21.

Authors

Jin Chen¹, Jixin Zhang², Hao Chen¹, Yong Zhao³, Hongdong Wang⁴

Affiliations

¹ Hunan University, College of Computer Science and Electronic Engineering, Hunan, China.
² Hubei University of Technology, School of Computer Science, Hubei, China.
³ National University of Defense Technology, College of Aerospace Science and Engineering, Hunan, China.
⁴ Shanghai Jiao Tong University, School of Naval Architecture Ocean and Civil Engineering, Shanghai, China.

Abstract

Automatic identification system (AIS) is a vessel-based system for the automatic broadcast and reception of vessel information, and it also supports data for trajectory prediction. Since the vessel's sailing route is flexible and changeable and the AIS broadcast is unconfirmed, the trajectory varies greatly and the original AIS data contains some noisy trajectory, which leads to low prediction accuracy and stability. Therefore, to solve the above problem, this paper proposes a trajectory prediction method based on bidirectional gate recurrent unit (BiGRU) and trajectory direction vector (TDV) with attention mechanism. This paper firstly proposes a TDV to associate latitude and longitude with the course and speed. Then the paper proposes an attention mechanism to self-adaptively update weight to the TDV in different stages to eliminate unreasonable predicted trajectory points. Finally, this paper combines the TDV attention mechanism and the BiGRU network to train a vessel trajectory prediction model.

Keywords: Artificial intelligence; Computer science; Machine learning.