Deep Learning for Vessel Trajectory Prediction Using Clustered AIS Data

Cheng-Hong Yang, Guan-Cheng Lin, Chih-Hsien Wu, Yen-Hsien Liu (), Yi-Chuan Wang () and Kuo-Chang Chen ()
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Cheng-Hong Yang: Department of Information Management, Tainan University of Technology, Tainan 71002, Taiwan
Guan-Cheng Lin: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Chih-Hsien Wu: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Yen-Hsien Liu: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Yi-Chuan Wang: Department of Business Administration, CTBC Business School, Tainan 709, Taiwan
Kuo-Chang Chen: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan

Mathematics, 2022, vol. 10, issue 16, 1-19

Abstract: Accurate vessel track prediction is key for maritime traffic control and management. Accurate prediction results can enable collision avoidance, in addition to being suitable for planning routes in advance, shortening the sailing distance, and improving navigation efficiency. Vessel track prediction using automatic identification system (AIS) data has attracted extensive attention in the maritime traffic community. In this study, a combining density-based spatial clustering of applications with noise (DBSCAN)-based long short-term memory (LSTM) model (denoted as DLSTM) was developed for vessel prediction. DBSCAN was used to cluster vessel tracks, and LSTM was then used for training and prediction. The performance of the DLSTM model was compared with that of support vector regression, recurrent neural network, and conventional LSTM models. The results revealed that the proposed DLSTM model outperformed these models by approximately 2–8%. The proposed model is able to provide a better prediction performance of vessel tracks, which can subsequently improve the efficiency and safety of maritime traffic control.

Keywords: automatic identification system; density-based spatial clustering of applications with noise; long short-term memory (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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