Analysis and prediction of factors influencing private car ownership in China based on XGBoost-neural network

Abstract

Accurate prediction of future car ownership is of great importance for road traffic planning, automobile industry development planning and the formulation of related policies. Therefore, this paper constructs a machine learning-based analysis and prediction model of the factors influencing private car ownership. First, the XGBoost method is used to identify the factors affecting private car ownership based on the data published by the National Bureau of Statistics. Then, comparing the prediction effects of three methods, XGBoost, random forest and neutral network, we found that neural network has better prediction accuracy in the private car ownership prediction model. Finally, based on the neural network method, the future private car ownership in China is predicted. The results of the study showed that GDP per capita and urbanization rate are the two most important factors affecting private car ownership; by 2030, China's private car ownership is expected to reach 438.3 million, 452.56 million and 469.42 million under the low, medium and high development scenarios, respectively.