As an important hub port along the southeast coast of China, Xiamen Port provides strong support for the development of regional trade, and the sea-rail intermodal transport has strong potential in port consolidation and hinterland expansion. This paper uses the grey GM (1, 1) model and Markov model to build a prediction model for the sea-rail intermodal throughput of Xiamen port, and conducts statistical analysis on the sea-rail intermodal throughput data of Xiamen port from 2014 to 2021. The results show that the average relative error of the improved model is reduced from 15.66% to 6.25%, which greatly improves the accuracy of the improved model and increases the credibility of the model of sea-rail intermodal throughput of Xiamen Port. This paper predicts the development trend of the sea-rail intermodal throughput of Xiamen port in the next three years, providing a certain basis for the construction of the sea-rail intermodal transport market in Xiamen.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Forecast of sea-rail throughput of Xiamen Port based on improved grey prediction model


    Contributors:
    Tang, Huiyi (author) / Shi, Jingbin (author) / Liu, Xiaojia (author)

    Conference:

    Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022) ; 2022 ; Guangzhou,China


    Published in:

    Proc. SPIE ; 12302


    Publication date :

    2022-11-23





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Forecast of sea-rail throughput of Xiamen Port based on improved grey prediction model

    Tang, Huiyi / Shi, Jingbin / Liu, Xiaojia | British Library Conference Proceedings | 2022


    Predict Port Throughput Based on Probabilistic Forecast Model

    Chen, Yihan / Jin, Zhonghua / Liu, Xuejun | Springer Verlag | 2017


    Analysis of Container Throughput at Jintang Dapukou Port Based on Grey Prediction Method

    Yi-Xuan, H. E. / Wan-Zheng, A. I. / Hong-Gang, Zhang et al. | Springer Verlag | 2025


    Forecast of port freight volume based on grey RBF neural network combination model

    Jiao, Yang / Li, Lianbo / Zhu, Zhenyu et al. | British Library Conference Proceedings | 2022


    Container hub-port vulnerability: Hong Kong, Kaohsiung and Xiamen

    Su, Dong-Taur / Hsieh, Cheng-Hsien / Tai, Hui-Huang | Taylor & Francis Verlag | 2016