Denoising Algorithm of Express Way Floating Car Data Based on Wavelet Threshold

被引：0
|
作者：
Wang H.-Y. [1 ,2 ]
Lang Y. [2 ]
Han H.-H. [3 ]
Wang X.-G. [2 ]
Mei W.-B. [1 ]
机构：
[1] School of Information and Electronics, Beijing Institute of Technology (BIT), Beijing
[2] China Transport Telecommunications & Information Center (CTTIC), Beijing
[3] Yanching Institute of Technology, College of Information Science and Technology, Langfang, 065201, Hebei
来源：
Mei, Wen-Bo (wbmei@bit.edu.cn) | 1600年 / Beijing Institute of Technology卷 / 37期
关键词：
Floating car data; Mean square deviation; Noise-signal ratio; Wavelet denoising;
D O I：
10.15918/j.tbit1001-0645.2017.07.011
中图分类号：
学科分类号：
摘要：
Data denoising is a foundation work for extracting traffic information from floating car data. Wavelet analysis method shows excellent advantages in data denoising of doping noise signals. In this paper, a wavelet threshold denoising algorithm was proposed to suit data denoising of the original floating car data. According to noise-signal ratio and mean square deviation, the data denoising effect of floating car data was analyzed based on the constructed new threshold value and threshold function to determine the wavelet basis function as well as decomposition level of wavelet, so as to develop an effective algorithm for the wavelet threshold denoising of the floating car data. Results show that, the constructed wavelet threshold algorithm can improve the correlation of denoised floating car data to RTMS data by more than 10%. It means the floating car data can be effectively denoised. © 2017, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
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页码：717 / 720and770
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