Traffic flow prediction is one of the point research contents of transportation engineering. Accurate ship traffic flow prediction is the value to ensure the safety of ship navigation and smooth channel. In order to predict the port ship traffic flow more accurately, aiming at the shortcomings of the traditional prediction model, a port ship traffic flow prediction model based on long-term and short-term memory network (LSTM) is proposed. Finally, Qingdao port is taken as an example to predict and compare with ARIMA model. The results show that, compared with ARIMA model, the mean absolute percentage error (MAPE) of LSTM model is as low as 3.476%, which indicates that the prediction accuracy of LSTM model is higher and it can be well applied to the field of ship traffic flow prediction. According to the prediction results, it can provide basic basis for channel planning and design and ship navigation management, maximize the navigation capacity of the channel, and optimize the allocation of port resources.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Ship traffic flow forecast of Qingdao port based on LSTM


    Contributors:
    Ji, Zhe (author) / Wang, Le (author) / Zhang, Xiaobo (author) / Wang, Fengwu (author)

    Conference:

    Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021) ; 2021 ; Chongqing,China


    Published in:

    Proc. SPIE ; 12081


    Publication date :

    2022-02-07





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Ship traffic flow forecast of Qingdao port based on LSTM

    Ji, Zhe / Wang, Le / Zhang, Xiaobo et al. | British Library Conference Proceedings | 2022


    Research on ship traffic flow prediction based on GTO-CNN-LSTM

    Ding, Runzhen / Xie, Haibo / Dai, Cheng et al. | SPIE | 2024


    BP neural network based on Qingdao City air logistics demand forecast

    Chen, Wenbo / Cao, Yunchun | British Library Conference Proceedings | 2023


    Factor Cluster Analysis of Qingdao Port Logistics Competitiveness

    Wei Xu / Xiaohan Gong | DOAJ | 2020

    Free access

    Prediction of ship traffic flow based on RF-bidirectional LSTM neural network

    Sun, Xiaocong / Yu, Chen / Fu, Yuhui et al. | SPIE | 2022