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Using artificial neural networks to predict container flows between the major ports of Asia

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  • Feng-Ming Tsai
  • Linda J.W. Huang

Abstract

Container flow information is a critical issue for port operators and liners to support their strategic planning and decision-making. This study uses artificial neural networks (ANNs) to predict container flows by considering GDP, interest rates, the value of export and import trade, the numbers of export and import containers and the number of quay cranes. ANNs are developed for data mining purposes, and the developed model can simultaneously predict container flows between the major ports of Asia. The forecasting results indicate that the prediction errors are relatively small in most selected ports, and thus shipping companies can use the container flow prediction model to make decisions concerning operations. The results can be further applied to the trend analysis of container flows among the major ports of Asia, and a community analysis of the containers was conducted for the purpose of supply chain management.

Suggested Citation

  • Feng-Ming Tsai & Linda J.W. Huang, 2017. "Using artificial neural networks to predict container flows between the major ports of Asia," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5001-5010, September.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:17:p:5001-5010
    DOI: 10.1080/00207543.2015.1112046
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    References listed on IDEAS

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    Cited by:

    1. Christoph Martius & Lutz Kretschmann & Miriam Zacharias & Carlos Jahn & Ole John, 2022. "Forecasting worldwide empty container availability with machine learning techniques," Journal of Shipping and Trade, Springer, vol. 7(1), pages 1-24, December.
    2. 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.
    3. Feng, Hongxiang & Grifoll, Manel & Zheng, Pengjun, 2019. "From a feeder port to a hub port: The evolution pathways, dynamics and perspectives of Ningbo-Zhoushan port (China)," Transport Policy, Elsevier, vol. 76(C), pages 21-35.
    4. Dohee Kim & Eunju Lee & Imam Mustafa Kamal & Hyerim Bae, 2025. "Long‐term forecasting of maritime economics index using time‐series decomposition and two‐stage attention," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 153-172, January.
    5. Huang, Dong & Grifoll, Manel & Sanchez-Espigares, Jose A. & Zheng, Pengjun & Feng, Hongxiang, 2022. "Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic," Transport Policy, Elsevier, vol. 128(C), pages 1-12.
    6. Jin, Jiahuan & Ma, Mingyu & Jin, Huan & Cui, Tianxiang & Bai, Ruibin, 2023. "Container terminal daily gate in and gate out forecasting using machine learning methods," Transport Policy, Elsevier, vol. 132(C), pages 163-174.

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