基于小波变换的ITS数据双侧最优化集成方法与应用

A Double-sided Optimization of ITS Data Aggregation Via Wavelet Transformation

通过对ITS数据的频率特性详细分析,近年来迅速发展的基于小波变换的数据集成方法能够高效地满足不同交通用途对数据集成的需求.但现有的集成方法仅通过ITS数据序列之间的共同特性来反映数据的本质特征,忽略了数据集成过程的信息损失问题.本文通过引入信息损失指标,建立双侧最优化的集成方法,提升了数据集成的准确性.在此基础上,开发了基于MATLAB平台的数据集成软件,以北京市三环的交通流数据为例进行了实例分析,提出实际工作中应用数据集成方法的建议.

Based on aggregation technique analysis of the frequency characteristics of traffic flow data, the waveletcould meet the demands of various transportation purposes on the aggregation efficiently. However, the method reflects the essential characteristics of data only by analyzing the similarities among ITS data series, which neglects the information loss during aggregation. This Paper develops a double-sided optimization method through introducing Information-Loss Index in order to improve the accuracy during data aggregation. Based on the method, this paper develops the software implementing the algorithm based on MatLab platform, and uses the traffic flow data from the third-ring express road in Beijing as a case study, The conclusions from the paper provideds suggestions for the real applications of the data aggregation.