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A Multi-Step Approach Framework for Freight Forecasting of River-Sea Direct Transport without Direct Historical Data

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  • Zhaoxia Guo

    (Business School, Sichuan University, Chengdu 610065, China)

  • Weiwei Le

    (Business School, Sichuan University, Chengdu 610065, China)

  • Youkai Wu

    (Business School, Sichuan University, Chengdu 610065, China)

  • Wei Wang

    (College of Harbor, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China)

Abstract

The freight forecasting of river-sea direct transport (RSDT) is crucial for the policy making of river-sea transportation facilities and the decision-making of relevant port and shipping companies. This paper develops a multi-step approach framework for freight volume forecasting of RSDT in the case that direct historical data are not available. First, we collect publicly available shipping data, including ship traffic flow, speed limit of each navigation channel, free-flow running time, channel length, channel capacity, etc. The origin–destination (O–D) matrix estimation method is then used to obtain the matrix of historical freight volumes among all O–D pairs based on these data. Next, the future total freight volumes among these O–D pairs are forecasted by using the gray prediction model, and the sharing rate of RSDT is estimated by using the logit model. The freight volume of RSDT is thus determined. The effectiveness of the proposed approach is validated by forecasting the RSDT freight volume on a shipping route of China.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4252-:d:255263
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    References listed on IDEAS

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    Cited by:

    1. 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.

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