Abstract
Container throughput forecasting plays an important role in port capacity planning and management. Regarding the issue of container throughput of Tianjin-Hebei Port Group, considering the container throughput is an incomplete grey information system affected by various factors, the effect is often unsatisfactory by adopting a single forecasting model. Therefore, this paper studies the issue by combining fractional GM (1, 1) and BP neural network. The comparison results show that the combination model performs better than other single models separately and has a higher level of forecasting accuracy. Furthermore, the combination model is adopted to forecast the container throughput of Tianjin-Hebei Port Group from 2021 to 2025, which would be a data reference for the future development optimization for the container operation of Tianjin-Hebei Port Group.
Highlights
Transportation by sea is the most important pattern of transportation in the international trade. 80%∼90% of the total import and export goods in China are conducted by sea [1] in which container transport has most proportion. is is due to the development of container transportation, which makes the operation develop in the direction of aggregation and rationalization and saves the packaging materials and miscellaneous costs, guarantees the cargo integrity, shortens the transport time, and reduces the transport cost
In order to achieve better performance and more accurate results, considering the various factors entangling and the relevant research studies studied, we have decided to establish a combination model for the container throughput issue consisting of the fractional grey model (GM) (1, 1) model and BP neural network model
Gao et al [26] used the BP neural network to study the forecast of the short-term rainstorm; Deshwal et al [27] has established a language recognition system using the BP neural network model; Duddu et al [28] used the BP neural network model to predict visibility at the road connectivity level; Liu et al [29] used fractional GM (1, 1) and BP neural network for power load forecasting and so on
Summary
Transportation by sea is the most important pattern of transportation in the international trade. 80%∼90% of the total import and export goods in China are conducted by sea [1] in which container transport has most proportion. is is due to the development of container transportation, which makes the operation develop in the direction of aggregation and rationalization and saves the packaging materials and miscellaneous costs, guarantees the cargo integrity, shortens the transport time, and reduces the transport cost. Transportation by sea is the most important pattern of transportation in the international trade. 80%∼90% of the total import and export goods in China are conducted by sea [1] in which container transport has most proportion. Tianjin-Hebei Port Group is located on the west bank of China’s Bohai Economic Rim as, which is one of the shipping hubs in northern China, mainly including Tianjin Port, Tangshan Port, Qinhuangdao Port, and Huanghua Port. In the first quarter of 2021, Tianjin Port completed a container throughput of 4.469 million TEU, increasing 20.4% year on year, setting the record highest in the same period [2]. E structural arrangement of this paper is as follows: the second part reviews the relevant literature, the third part introduces the research methods, the fourth part gives the forecasting results and discusses the results, and the fifth part gives the research conclusions of this paper
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