The steady development of China’s economy has led to the rapid development of the logistics industry. Nowadays, the logistics efficiency in the world has been at a high position, but compared with advanced developed countries, logistics costs are still higher. Establishing an effective logistics demand forecasting model is of great significance to reduce logistics costs and optimize the layout. This paper establishes a combined model based on the research of grey GM (1, 1) and BP neural network considering the scope of application and error. Using the combined model to fit the freight volume data of China in the past 20 years, the results show that the combined model has less error and higher precision in the forecasting of logistics demand.
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