The ship AIS trajectory data is a record sequence of the ship's position and time. It contains a wealth of vessel navigation information, which helps to statistically analyze and predict ship traffic in a specific water area on a small time scale. At the same time, the ship AIS trajectory data is susceptible to noise and data loss in the process of collection, transmission, and analysis, resulting in a decrease in acquisition quality. In this regard, this paper determines whether the massive AIS data is abnormal and removes it, completes the noise reduction work, and uses the cubic spline interpolation to make the lost data be reconstructed. On the basis of obtaining clean data, a discriminant function is constructed to count the regularity of arrival of the ship on the observation surface, and then a time series method is used to model the ship traffic flow through the observation section at different time periods on a certain day. The simulation experiment confirms the rationality of the forecast result through comprehensive comparison with the RBF neural network model, and provides a reference for the maritime department to implement refined management.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Ship traffic flow prediction based on AIS data mining


    Contributors:
    Li, Jiadong (author) / Li, Xueqi (author) / Yu, Lijuan (author)


    Publication date :

    2018-05-01


    Size :

    485184 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Ship traffic flow prediction method and system based on target monitoring data

    WANG LIN / DI PENG / PAN WEI et al. | European Patent Office | 2023

    Free access

    Ship Traffic Flow Prediction Using EEMD-edRVFL-RF

    Wang, Weifeng / Zheng, Qianqian / Li, Ran et al. | IEEE | 2024


    Regional ship traffic flow prediction method and system

    CHEN XINQIANG / HAN BING / WU ZHONGDAI et al. | European Patent Office | 2023

    Free access

    Traffic flow prediction method based on mixed feature mining

    HUANG QIAN / JI WEI / SONG XIAOFENG et al. | European Patent Office | 2020

    Free access