Maritime traffic prediction is critical for ocean transportation safety management. In this paper, we propose a novel knowledge assisted methodology for maritime traffic forecasting based on a vessel's waterway pattern and motion behavior. The vessel's waterway pattern is extracted through a proposed lattice-based DBSCAN algorithm that significantly reduces the problem scale, and its motion behavior is quantitatively modeled for the first time using kernel density estimation. The proposed methodology facilitates the knowledge extraction, storage, and retrieval, allowing for seamless knowledge transfer to support maritime traffic forecasting. By incorporating both the vessel's waterway pattern and motion behavior knowledge, our solution suggests a set of probable coordinates with the corresponding probability as the forecasting output. The proposed forecasting algorithm is capable of accurately predicting maritime traffic 5, 30, and 60 min ahead, while its computation can be efficiently completed in milliseconds for single vessel prediction. Owing to such a high computational efficiency, an extensive predictive analysis of hundreds of vessels has been reported for the first time in this paper. A web-based prototype platform is implemented for Singapore waters to demonstrate the solution's feasibility in a real-world maritime operation system. The proposed approaches can be generalized for other marine waters around the world.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Maritime Traffic Probabilistic Forecasting Based on Vessels' Waterway Patterns and Motion Behaviors




    Erscheinungsdatum :

    2017




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    55.84 / 55.24 / 55.84 Straßenverkehr / 55.24 Fahrzeugführung, Fahrtechnik



    Maritime Traffic Probabilistic Forecasting Based on Vessels’ Waterway Patterns and Motion Behaviors

    Xiao, Zhe / Ponnambalam, Loganathan / Fu, Xiuju et al. | IEEE | 2017


    Forecasting inland waterway grain traffic

    Babcock, Michael W. | Online Contents | 2002


    Waterway Systems Traffic Analysis

    C. J. Khisty / S. George | NTIS | 1996


    Maritime Autonomous Vessels

    Xu, Haitong ;Moreira, Lúcia ;Guedes Soares, Carlos | GWLB - Gottfried Wilhelm Leibniz Bibliothek | 2023

    Freier Zugriff

    Maritime Autonomous Vessels

    Xu, Haitong ;Moreira, Lúcia ;Guedes Soares, Carlos | TIBKAT | 2023

    Freier Zugriff