Short-term Traffic Flow Forecasting Based on Local Prediction Method in Chaotic Time Series
Liao Rong-hu · Computer Technology and Development · 2015
To improve the accuracy of urban short-term traffic flowforecasting,the chaotic time series analysis is applied to urban shortterm traffic flowdata,study the two local chaotic time series prediction,including adding- weight zero- rank local- region method and adding-weight one-rank local-region method. Euclidean distance method and vector angle method used in selecting neighbor points in local prediction method are being researched,and these two methods can not reflect the overall characteristics of the neighbor points,in viewof this problem,an improved neighboring phase point selection method which integrated relative degree of similarity and distance to select neighbor phase points is presented. Then the old methods and the improved method are used in the Beijing short-term traffic flowprediction. The results showthat local prediction method in chaotic time series can be used in short-term traffic flowforecasting,and the improved method has higher accuracy in prediction than the old methods.