基于相似性的短时交通流预测
[Abstract]:In order to improve the prediction accuracy of short-term traffic flow and carry out traffic flow planning and management more accurately, a short-term traffic flow forecasting method based on similarity is introduced. This method is used to study the time scale similarity of a single point of traffic flow on the California Expressway in the United States. It is found that the similarity to the traffic flow on the day of the week is greater than the similarity of the traffic flow in the adjacent days. On the basis of this, the wavelet neural network model is established. The traffic flow data of four days and the traffic flow data of adjacent 4 days are formed into a group, and more than 200 sets of data are used to train the wavelet neural network respectively. Then the traffic flow on the same day is predicted. It is found that the MRE, MSPE value of the former is lower than that of the latter, and the EC value of the former is higher than that of the latter. The prediction accuracy of the former is higher than that of the latter, and the effectiveness of the proposed method is verified.
【作者单位】: 南京信息工程大学信息与控制学院;
【分类号】:U491.112
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