Urban cold-chain logistics demand predicting model based on improved neural network model

• Main Authors: Chen Ying, Wu Qiuming, Shao Lijin
• Format: Article
• Language: English
• Published: EDP Sciences 2020-01-01
• Series: International Journal of Metrology and Quality Engineering
• Subjects: back-propagation neural network; principal component analysis; cold chain logistics; demand prediction
• Online Access: https://www.metrology-journal.org/articles/ijmqe/full_html/2020/01/ijmqe190033/ijmqe190033.html

With the popularity of the Internet and mobile terminals, the development of e-commerce has become hotter. Therefore, e-commerce research starts to focus on the statistics and prediction of the cargo volume of logistics. This study briefly introduced the back-propagation (BP) neural network model and principal component analysis (PCA) method and combined them to obtain an improved PCA-BP neural network model. Then the traditional BP neural network model and the improved PCA-BP neural network model were used to perform the empirical analysis of the cold chain logistics demand of fruits and vegetables in city A from 2010 to 2018. The results showed that the main factors that affected the local cold chain logistics demand were the growth rate of GDP, the added value of primary industry, the planting area of fruits and vegetables, and the consumption price index of fruits and vegetables; both kinds of neural networks model could effectively predict the cold chain logistics demand, but the predicted value of the PCA-BP neural network model was more fitted with the actual value. The prediction error of the BP neural network model was larger, and the fluctuation was obvious within the prediction interval. Moreover, the time required for the prediction by the PCA-BP neural network model was less than that by the BP neural network model. In summary, the improved PCA-BP neural network model is faster and more accurate than the traditional BP model in predicting the cold chain logistics demand.