IDEAS home Printed from https://ideas.repec.org/a/hin/jjmath/8877865.html
   My bibliography  Save this article

Container Throughput Forecasting of Tianjin-Hebei Port Group Based on Grey Combination Model

Author

Listed:
  • Chen He
  • Huipo Wang
  • Antonio Di Crescenzo

Abstract

Container throughput forecasting plays an important role in port capacity planning and management. Regarding the issue of container throughput of Tianjin-Hebei Port Group, considering the container throughput is an incomplete grey information system affected by various factors, the effect is often unsatisfactory by adopting a single forecasting model. Therefore, this paper studies the issue by combining fractional GM (1, 1) and BP neural network. The comparison results show that the combination model performs better than other single models separately and has a higher level of forecasting accuracy. Furthermore, the combination model is adopted to forecast the container throughput of Tianjin-Hebei Port Group from 2021 to 2025, which would be a data reference for the future development optimization for the container operation of Tianjin-Hebei Port Group.

Suggested Citation

  • Chen He & Huipo Wang & Antonio Di Crescenzo, 2021. "Container Throughput Forecasting of Tianjin-Hebei Port Group Based on Grey Combination Model," Journal of Mathematics, Hindawi, vol. 2021, pages 1-9, August.
    • Handle: RePEc:hin:jjmath:8877865
    DOI: 10.1155/2021/8877865
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/jmath/2021/8877865.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/jmath/2021/8877865.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/8877865?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yi Xiao & Minghu Xie & Yi Hu & Ming Yi, 2023. "Effective multi‐step ahead container throughput forecasting under the complex context," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1823-1843, November.
    2. Anqiang Huang & Xinjun Liu & Changrui Rao & Yi Zhang & Yifan He, 2022. "A New Container Throughput Forecasting Paradigm under COVID-19," Sustainability, MDPI, vol. 14(5), pages 1-20, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jjmath:8877865. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.