Yangtze River is probably the world's busiest inland waterway. Ships need to be guided when passing through a controlled waterway based on their long-term speed prediction. Inaccurate ship speed prediction leads to nonoptimal traffic signaling, which may cause a significant traffic jam. For the existing intelligent traffic signaling system, the ship speed is assumed to be constant, which has caused many problems and issues. This paper proposes a novel algorithm to construct an improved multilayer perceptron (MLP) network for accurate long-term ship speed prediction, in which the hidden neurons of the MLP are optimized by the particle swarm optimization method. The effectiveness and efficiency of the method are guaranteed by using the orthogonal least squares method, which is the fast approach for the construction of the MLP network in a stepwise forward procedure. The model is driven by easily acquired dynamic data of the ships, including the speed and the position. The effectiveness of the proposed method is further confirmed by comparing with several traditional modeling techniques. To the best of our knowledge, this is the first time that a ship speed model is built for long-term prediction. The experimental results show that the developed model is in good agreement with the real-life data, with more than 97% accuracy. It will help to generate the optimal traffic commands for Yangtze River in an intelligent traffic signaling system.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Long-Term Ship Speed Prediction for Intelligent Traffic Signaling


    Beteiligte:
    Gan, Shaojun (Autor:in) / Liang, Shan (Autor:in) / Li, Kang (Autor:in) / Deng, Jing (Autor:in) / Cheng, Tingli (Autor:in)


    Erscheinungsdatum :

    2017-01-01


    Format / Umfang :

    1695274 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    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


    Long-Term Urban Traffic Speed Prediction With Deep Learning on Graphs

    Yu, James J. Q. / Markos, Christos / Zhang, Shiyao | IEEE | 2022


    Road section traffic speed short-term prediction method in view of long-term equilibrium relationship

    XIA JINGXIN / NIE QINGHUI / QIAN ZHENDONG et al. | Europäisches Patentamt | 2015

    Freier Zugriff

    Intelligent traffic signaling system

    Lakshmi, Ch. Jaya / Kalpana, S. | IEEE | 2017