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Deep Learning for Vessel Trajectory Prediction Using Clustered AIS Data

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  • Cheng-Hong Yang

    (Department of Information Management, Tainan University of Technology, Tainan 71002, Taiwan
    Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
    Ph.D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
    School of Dentistry, Kaohsiung Medical University, Kaohsiung 80708, 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)

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.

Suggested Citation

  • Cheng-Hong Yang & Guan-Cheng Lin & Chih-Hsien Wu & Yen-Hsien Liu & Yi-Chuan Wang & Kuo-Chang Chen, 2022. "Deep Learning for Vessel Trajectory Prediction Using Clustered AIS Data," Mathematics, MDPI, vol. 10(16), pages 1-19, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2936-:d:888455
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    Citations

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    Cited by:

    1. Lixiang Zhang & Yian Zhu & Jiang Su & Wei Lu & Jiayu Li & Ye Yao, 2022. "A Hybrid Prediction Model Based on KNN-LSTM for Vessel Trajectory," Mathematics, MDPI, vol. 10(23), pages 1-20, November.
    2. Li, Huanhuan & Xing, Wenbin & Jiao, Hang & Yang, Zaili & Li, Yan, 2024. "Deep bi-directional information-empowered ship trajectory prediction for maritime autonomous surface ships," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).

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