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Estimating waterway freight demand at Three Gorges ship lock on Yangtze River by backpropagation neural network modeling

Author

Listed:
  • Wenjie Li

    (Chongqing Jiaotong University)

  • Jialing Dai

    (Chongqing Jiaotong University)

  • Yi Xiao

    (Chongqing Jiaotong University)

  • Shengfa Yang

    (Chongqing Jiaotong University)

  • Chenpeng Song

    (Chongqing Jiaotong University)

Abstract

The waterway freight volume through the Three Gorges ship lock (TGL) has increased significantly since it started operation in 2003 and exceeded the designed capacity of 100 million tons in 2011, 19 years earlier than expected. This made the TGL a bottleneck for the Yangtze River waterway transport development. Based on the historical freight volumes and types through the TGL, a combination of qualitative and quantitative analyses are conducted here to identify the key factors affecting freight demand. A genetic algorithm and backpropagation (GA–BP) forecasting model (optimized backpropagation neural network model using a genetic algorithm) was developed to estimate freight demand at the TGL over the period 2020–2040. We predict that freight demand will continue to increase, reaching 260.2 million tons in the basic scenario by 2040 (or 224.7 million tons in the conservative scenario and 276.7 million tons in the optimistic scenario). However, the growth rate will gradually decline. The freight composition will tend to be more stable and homogeneous, with over 64% of the freight related to investment and construction. From the perspective of freight volume evolution, the necessity of the Three Gorges New Locks Project is justified and necessary.

Suggested Citation

  • Wenjie Li & Jialing Dai & Yi Xiao & Shengfa Yang & Chenpeng Song, 2021. "Estimating waterway freight demand at Three Gorges ship lock on Yangtze River by backpropagation neural network modeling," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(3), pages 495-521, September.
    • Handle: RePEc:pal:marecl:v:23:y:2021:i:3:d:10.1057_s41278-020-00169-0
    DOI: 10.1057/s41278-020-00169-0
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