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

Wenjie Li (), Jialing Dai, Yi Xiao, Shengfa Yang and Chenpeng Song
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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

Maritime Economics & Logistics, 2021, vol. 23, issue 3, No 6, 495-521

Abstract: 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.

Keywords: Three Gorges ship lock; Yangtze River; Freight demand modeling; Forecasting; Backward propagation; Genetic algorithm; Neural networks; China (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1057/s41278-020-00169-0

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