In recent days, Marine activities are rapidly increasing in various sectors such as migration, logistics etc. and it is required to developa robust system for assisting the helmsman/driver in navigation. Therefore, it is essential to track the location of ships to avoid collision and in assisting the ships which diverts to different ship lanes. Hence, predicting location of ships is crucial to encounter dangerous and risky situations by developing ship trajectory prediction model. This paper illustrates a novel approach to predict ships’ trajectory patterns by combining of different Deep Learning (DL) techniques. Initially, the cohort is collected from Automatic Identification System (AIS) data and then firstly, Principal Component Analysis (PCA) is utilized for filtration and then scaling and interpolation is incorporated. Further, for trajectory pattern recognition Graph convolutional networks (GCNs) model is employed and finally a hybrid model using two networks Graph Attention Networks (GAT) with Bi-directional Long Short Term Memory Networks (Bi-LSTM) namely GAT-Bi-LSTM is proposed for the prediction of different marine traffic patterns. Therefore, the performance of the proposed model is determined in terms of Mean Absolute Error (MAE), Root Mean Square Error(RMSE), Average Displacement Error (ADE), and Final Displacement Error (FDE).


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Marine Traffic Pattern Prediction System using Graph Attention Network and Bi-directional Long Short-Term Memory Network


    Beteiligte:
    Tharcis, P. (Autor:in) / Husain, Saif O. (Autor:in) / Buvaneswari, B. (Autor:in) / Poornima, Ediga (Autor:in) / Sundaram, N. Kalyana (Autor:in)


    Erscheinungsdatum :

    18.10.2024


    Format / Umfang :

    313798 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






    Asymmetric Long-Term Graph Multi-Attention Network for Traffic Speed Prediction

    Hwang, Jiyoung / Noh, Byeongjoon / Jin, Zhixiong et al. | IEEE | 2022


    Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model

    Baghbani, Asiye / Bouguila, Nizar / Patterson, Zachary | Transportation Research Record | 2022

    Freier Zugriff