A Prediction of Vehicle Possession in Hunan Province Based on Principal Component and BP Neural Network

摘要

Prediction of car ownership has direct reference significance for the development of urban transportation and construction of urban roads. By analyzing the impact factors of urban auto possession, this paper first analyzes 8 indicators such as urban population, GDP, road passenger traffic and so on determined by some references, then establish BP neural network model to predict the vehicles possession in Hunan Province from 2006 to 2008. The figures of prediction is 989,300, 1,221.800 and 1,370,300 respectively in 2006, 2007 and 2008, which is very close to the real ownership of 946,400,1,217,200 and 1,426,700 respectively. It shows the prediction is very accurate. This suggests that the BP neural network has very strong learning and generalization abilitv and can be employed in prediction of vehicle possession effectively.