Traffic Flow Forecasting based on PCA and Wavelet Neural Network

摘要：

Accurate short-term traffic flow forecasting has become a crucial step in the overall goal of better road network management A combination approach based on Principal Component Analysis (PCA) and Wavelet Neural Network (WNN) is presented for short-term traffic flow forecasting. The historical data of the forecasted traffic volume and interrelated volumes have been processed by PCA first, and then the results of PCA form the input data for WNN. The proposed method is applied to predict the real traffic flow in Yanta cross, Xian city, China. The forecast results show that this proposed method is better than the typical Back-Propagation neural network (BP NN) method with the same data.

关键词： forecasting principal component analysis Wavelet neural network

作者: Gao Guorong Liu Yanping

作者单位: College of Science, Northwest Agriculture & Forest University College of Science, Northwest A&F University Shanxi Yangling, China

会议类型: 国际会议

会议名称: 2010 International Conference of Informationa Science and Management Engineering(2010年信息科学与管理工程国际学术会议 ISME 2010)

会议地点: 西安

会议语种:英文

页码: 158-161

在线出版日期: 2010-08-07（万方平台首次上网日期，不代表论文的发表时间）