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.
The Improved Forecasting Model for Short-Term Traffic Flow Based on Phase Space Reconstruction
Second International Conference on Transportation Engineering ; 2009 ; Southwest Jiaotong University, Chengdu, China
29.07.2009
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
The Improved Forecasting Model for Short-Term Traffic Flow Based on Phase Space Reconstruction
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