关键词: 城市交通;非线性预测;时空论;灰色Verhulst模型, 神经网络

Abstract: With consideration of the economy development tendency in China, a civil vehicle population prediction model is developed based on the space-time theory. First, with the domestic and international experience on vehicle development, the gray Verhulst model is used to describe the vehicle population development tendency and predict the vehicle population in the next 20 years. Second, several social and economical indexes related to vehicle population are selected by comparing the correlation coefficient value, and then the principal component method is used to reduce dimension of the selected indexes and obtain some principal indexes. Based on the econometrics theory, a forecasting model is formulated to predict the vehicle population in the next 20 years. Integrating these two forecasting models, a non-liner combination forecasting model is developed based on the BP neural network. The reliability and accuracy of the linear combination forecasting model are tested by the vehicle data from 2003 to 2007. Finally, the civil vehicle population of China in the next 20 years is predicted again based on the linear combination forecasting model.

Key words: urban traffic, non-liner prediction, space-time theory, grey Verhulst model, neural network