计算机技术与发展Issue(1)：1-5,5.DOI:10.3969/j.issn.1673-629X.2015.01.001
基于混沌时间序列局域法的短时交通流预测
Short-term Traffic Flow Forecasting Based on Local Prediction Method in Chaotic Time Series
摘要
Abstract
To improve the accuracy of urban short-term traffic flow forecasting,the chaotic time series analysis is applied to urban short-term traffic flow data,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 view of 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 flow prediction. The results show that local prediction method in chaotic time series can be used in short-term traffic flow forecasting,and the improved method has higher accuracy in prediction than the old methods.关键词
交通流预测/混沌时间序列/邻近点/加权零阶局域法/加权一阶局域法Key words
traffic flow forecasting/chaotic time series/neighbor point/adding-weight zero-rank local-region method/adding-weight one-rank local-region method分类
信息技术与安全科学引用本文复制引用
廖荣华,兰时勇,刘正熙..基于混沌时间序列局域法的短时交通流预测[J].计算机技术与发展,2015,(1):1-5,5.基金项目
国家“863”高技术发展计划项目(2012AA011804) （2012AA011804）
