Chapter
Apr 26, 2012
The Improved Forecasting Model for Short-Term Traffic Flow Based on Phase Space Reconstruction
Authors: Hui Zhuo [email protected], Limin Jia [email protected], Guoqiang Cai, and Dongmei LiuAuthor Affiliations
Publication: International Conference on Transportation Engineering 2009
Abstract
Based on characteristic analysis of traffic flow time sequences, the paper focuses on research of the State Space Reconstruction method of chaotic time series dynamic systems. In order to improve the consistency of changing trend between proximal point and center point, the additional constraint is imposed that nearest neighbors have temporal separation greater than the mean period of the time series when confirming neighbor Phase Points, and the error correction method is put forward to make full use of the one-step forecasting result. Thus the Improved Adding-weighted One-rank Local-region Prediction model (IAOL) is set up. The results of the experiments on actual traffic flow data from the Beijing traffic system show that the IAOL model has high prediction accuracy and credibility.
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© 2009 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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National ITS Research Center, Research Institute of Highway, Ministry of Transport, No. 8 Xi Tu Cheng Road, Haidian District, Beijing, China. Post Code: 100088. E-mail: [email protected]
Key Laboratory of Rail Traffic Control and Safety of Beijing Jiaotong University, No. 3 of Shangyuan Residence, Haidian District, Beijing, China, Post Code: 100044. E-mail: [email protected]
Guoqiang Cai
Key Laboratory of Rail Traffic Control and Safety of Beijing Jiaotong University, No. 3 of Shangyuan Residence, Haidian District, Beijing, China, Post Code: 100044
Dongmei Liu
National ITS Research Center, Research Institute of Highway, Ministry of Transport, No. 8 Xi Tu Cheng Road, Haidian District, Beijing, China. Post Code: 100088
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