Abstract
This paper intends to propose a quantitative relationship model in the layout between express service network and other life service facilities. Through big data mining technology, we collected GPS location of express service points, residential areas, grocery stores and transportation stations in Beijing. Then, using Multiple linear regression, SVM and random forest machine learning model the quantitative relationship between the number of express service points and other data in urban area, as well as the approach of model stacking get an integration machine learning model. Finally, the model and research ideas of this paper provide decision-making basis for the rational construction and planning of logistics service networks in first-tier cities in China.
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