Maritime situational awareness requires real-time traffic prediction over a large area based on the Automatic Identification System (AIS). The second requirement is allowing input from all the traffic. We propose Random Forests (RF) for ship movement prediction and demonstrate how it can be adapted to varying zone shapes and anomaly detection tasks. We also apply it to the clustering of vessels to regularly and irregularly moving ships. Our research area is the Baltic Sea and the recording period of data is 26 July 2022 ... 12 August 2022. Results from the class of regularly behaving ships (499 ships out of 634) show 0.2 ... 2.1 km mean absolute error (MAE) over 15 min ... 2 h which reaches the same accuracy as many published cases with more expensive computational models. The prediction for all supported time intervals can be updated every 10 min, which makes the implementation practical for large-scale situational awareness systems.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Wide-Area Ship Movement Prediction Using Random Forests


    Weitere Titelangaben:

    Communic.Comp.Inf.Science


    Beteiligte:
    Razminia, Abolhassan (Herausgeber:in) / Nguyen, Dinh Hoa (Herausgeber:in) / Vähämäki, Tanja (Autor:in) / Farahnakian, Farshad (Autor:in) / Nevalainen, Paavo (Autor:in) / Heikkonen, Jukka (Autor:in)

    Kongress:

    International Symposium on Intelligent Technology for Future Transportation ; 2024 ; Helsinki, Finland October 20, 2024 - October 22, 2024



    Erscheinungsdatum :

    12.03.2025


    Format / Umfang :

    26 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Ship movement prediction

    VARTULIA BOGDAN / DOLARI TOBIAS | Europäisches Patentamt | 2025

    Freier Zugriff

    Travel Time Reliability Prediction Using Random Forests

    Zhao, Mo / Zhang, Xiaoxiao / Appiah, Justice et al. | Transportation Research Record | 2023



    AIS Method for Prediction of Ship Movement Time in Harbor Area using AIS Data

    KIM KWANG IL | Europäisches Patentamt | 2022

    Freier Zugriff

    MULTIFIDELITY AERODYNAMIC FLOW FIELD PREDICTION USING RANDOM FORESTS

    Nagawkar, Jethro R. / Brittain, Marc W. / Leifsson, Leifur T. | TIBKAT | 2021