关键词: 混沌特性, 卡尔曼滤波, 相空间重构, 短时交通流, 预测模型

Abstract: In order to improve the prediction accuracy of city traffic flow and overcome the shortcomings that a single prediction model can’t well reflect the essential characteristics of traffic flow, based on the chaotic characteristic of traffic flow, the coupled Kalman filtering theory and the principles of phase space reconstruction phase method is proposed, a Kalman filtering traffic flow forecasting model based on phase space reconstruction is established. This model is based on phase points of the phase space reconstruction as state vector phase point description, using the Kalman filtering theory in real time prediction and correction phase point of future evolution, the simulation based on the traffic flow data of a section of Jiaozuo city is carried out. By comparing the related performance index analysis, results show that Kalman filtering prediction index model based on phase space reconstruction is better than the single model unimproved, the prediction accuracy is improved by 16.75%.

Key words: chaotic characteristics, Kalman filtering, phase space reconstruction, short-term traffic flow, prediction model