IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i9p7435-d1137422.html
   My bibliography  Save this article

Prediction of Ship Trajectory in Nearby Port Waters Based on Attention Mechanism Model

Author

Listed:
  • Junhao Jiang

    (Navigation College, Dalian Maritime University, Dalian 116026, China)

  • Yi Zuo

    (Navigation College, Dalian Maritime University, Dalian 116026, China
    Maritime Big Data & Artificial Intelligent Application Centre, Dalian Maritime University, Dalian 116026, China)

Abstract

In recent years, the prediction of ship trajectory based on automatic identification system (AIS) data has become an important area of research. Among the existing studies, most focus on a single ship to extract features and train models for trajectory prediction. However, in a real situation, AIS contains a variety of ships and trajectories that need a general model to serve various cases. Therefore, in this paper, we include an attentional mechanism to train a multi-trajectory prediction model. There are three major processes in our model. Firstly, we improve the traditional density-based spatial clustering of applications with noise (DBSCAN) algorithm and apply it to trajectory clustering. According to the clustering process, ship trajectories can be automatically separated by groups. Secondly, we propose a feature extraction method based on a hierarchical clustering method for a trajectory group. According to the extraction process, typical trajectories can be obtained for individual groups. Thirdly, we propose a multi-trajectory prediction model based on an attentional mechanism. The proposed model was trained using typical trajectories and tested using original trajectories. In the experiments, we chose nearby port waters as the target, which contain various ships and trajectories, to validate our model. The experimental results show that the mean absolute errors (MAEs) of the model in longitude (°) and latitude (°) compared with the baseline methods were reduced by 8.69% and 6.12%.

Suggested Citation

  • Junhao Jiang & Yi Zuo, 2023. "Prediction of Ship Trajectory in Nearby Port Waters Based on Attention Mechanism Model," Sustainability, MDPI, vol. 15(9), pages 1-31, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7435-:d:1137422
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/9/7435/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/9/7435/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pan Sheng & Jingbo Yin, 2018. "Extracting Shipping Route Patterns by Trajectory Clustering Model Based on Automatic Identification System Data," Sustainability, MDPI, vol. 10(7), pages 1-13, July.
    2. Truong Ngoc Cuong & Sam-Sang You & Le Ngoc Bao Long & Hwan-Seong Kim, 2022. "Seaport Resilience Analysis and Throughput Forecast Using a Deep Learning Approach: A Case Study of Busan Port," Sustainability, MDPI, vol. 14(21), pages 1-25, October.
    3. Xinqiang Chen & Jun Ling & Yongsheng Yang & Hailin Zheng & Pengwen Xiong & Octavian Postolache & Yong Xiong, 2020. "Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xuyang Han & Costas Armenakis & Mojgan Jadidi, 2021. "Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
    2. Fuentes, Gabriel, 2021. "Generating bunkering statistics from AIS data: A machine learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    3. Andreas Komninos & Charalampos Kostopoulos & John Garofalakis, 2022. "Automatic generation of sailing holiday itineraries using vessel density data and semantic technologies," Information Technology & Tourism, Springer, vol. 24(2), pages 265-298, June.
    4. Li, Huanhuan & Jiao, Hang & Yang, Zaili, 2023. "AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7435-:d:1137422. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.