Prediction of Waterway Cargo Transportation Volume to Support Maritime Transportation Systems Based on GA-BP Neural Network Optimization

Guangying Jin (), Wei Feng and Qingpu Meng
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Guangying Jin: School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
Wei Feng: School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
Qingpu Meng: School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China

Sustainability, 2022, vol. 14, issue 21, 1-24

Abstract: Water transportation is an important part of comprehensive transportation and plays a critical role in a country’s economic development. The world’s cargo transportation is dominated by waterway transportation, and maritime transportation Systems (MTS) are the main part of the waterway transportation system. The flow of goods plays a key role in the economic development of the ports along the route. The sustainable development of maritime transportation, the maritime transportation economy and the environment have great practical significance. In this paper, the principle of the BP (back propagation) neural network is used to predict the freight transportation volume of China’s waterways, and the genetic algorithm (GA) is used to optimize the BP neural network, so as to construct the GA-BPNN (back propagation neural network) prediction model. By collecting and processing the data of China’s water cargo transport volume, the experimental results show that prediction accuracy is significantly improved, which proves the reliability of the method. The experimental methods and results can provide certain reference information for the optimization, upgrade, and more scientific management of sustainable MTS in China and internationally, provide key information for port cargo handling plans, help optimize port layout, and improve transportation capacity and efficiency.

Keywords: water transport cargo volume; MTS; BP neural network; GA algorithm; GA-BPNN model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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