The prediction of vessel berthing trajectory can provide reference for the supervision of vessel traffic services, and has high application value in the early warning of vessel collision, grounding and other accidents. Aiming at the problem that it is difficult to predict the movement trend of vessels in the crowded port water, this paper establishes a vessel berthing trajectory prediction model based on bidirectional Gated Recurrent Unit (Bi-GRU). By learning the AIS data of Tianjin port, the vessel trajectories are predicted and compared with other recurrent neural network models such as LSTM and GRU. The experimental results show that the prediction method based on Bi-GRU model has higher accuracy and smaller error.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vessel trajectory prediction based on AIS data and bidirectional GRU


    Contributors:
    Wang, Chang (author) / Ren, Hongxiang (author) / Li, Haijiang (author)


    Publication date :

    2020-07-01


    Size :

    481103 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Bidirectional Data-Driven Trajectory Prediction for Intelligent Maritime Traffic

    Xiao, Ye / Li, Xingchen / Yao, Wen et al. | IEEE | 2023


    Vessel Trajectory Prediction Based on Context-Assisted Information

    Wang, Jianing / Jiao, Lianmeng / Pan, Quan | IEEE | 2024



    Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model

    Xinyun WU / Jiafei CHEN / Caiquan XIONG et al. | DOAJ | 2024

    Free access

    Optimizing Maritime Vessel Trajectory Prediction Using Space-Based AIS Data and PSO-BiGRU

    Rahayu, Dicka Ariptian / Widyawan / Ardiyanto, Igi et al. | IEEE | 2024