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A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of ports

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  • Rashed, Yasmine
  • Meersman, Hilde
  • Sys, Christa
  • Van de Voorde, Eddy
  • Vanelslander, Thierry

Abstract

The decision to provide new or additional capacities in the ports is challenging since it should be supported by a growing demand, which require modelling and forecasting the demand of container throughput. This paper develops an instrument which assists the ports’ infrastructure investment-decision-making. A three-step approach is developed by combining the autoregressive distributed lag model with economic scenarios to capture the potential impact of specific risks. The empirical analysis is based on an annual time series (1995–2017) for the total container throughput measured in twenty-foot equivalent units for the main ports within the Hamburg-Le Havre (H-LH) range and a number of economic indices. The study shows that there exists a long-run relationship between the trade indices of EU19 and the total container throughput. The empirical model estimates that the elasticity of the container throughput in the H-LH range to trade indices is about 1.4 on average, and it shows that the moment at which congestion emerges depends strongly on the specific scenario considered.

Suggested Citation

  • Rashed, Yasmine & Meersman, Hilde & Sys, Christa & Van de Voorde, Eddy & Vanelslander, Thierry, 2018. "A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of ports," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 127-141.
    • Handle: RePEc:eee:transa:v:117:y:2018:i:c:p:127-141
    DOI: 10.1016/j.tra.2018.08.010
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    Cited by:

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    2. 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.
    3. Li, Hongtao & Bai, Juncheng & Li, Yongwu, 2019. "A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    4. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    5. Zheng, Shiyuan & Wang, Kun & Fu, Xiaowen & Zhang, Anming & Ge, Ying-En, 2022. "The effects of information publicity and government subsidy on port climate change adaptation: Strategy and social welfare analysis," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 284-312.
    6. Saeed, Naima & Nguyen, Su & Cullinane, Kevin & Gekara, Victor & Chhetri, Prem, 2023. "Forecasting container freight rates using the Prophet forecasting method," Transport Policy, Elsevier, vol. 133(C), pages 86-107.
    7. Taolei Guo & Junjie Chen & Pei Liu, 2022. "Impact of Emerging Transport Technologies on Freight Economic and Environmental Performance: A System Dynamics View," IJERPH, MDPI, vol. 19(22), pages 1-17, November.
    8. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    9. Guo, Taolei & Liu, Pei & Wang, Chao & Xie, Jingci & Du, Jianbang & Lim, Ming K., 2023. "Toward sustainable port-hinterland transportation: A holistic approach to design modal shift policy mixes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    10. Zhaoxia Guo & Weiwei Le & Youkai Wu & Wei Wang, 2019. "A Multi-Step Approach Framework for Freight Forecasting of River-Sea Direct Transport without Direct Historical Data," Sustainability, MDPI, vol. 11(15), pages 1-15, August.

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