Compression method of traffic flow data based on compressed sensing

LI Qing-quan; ZHOU Yao; LE Yang; YE Jia-an

doi:10.19818/j.cnki.1671-1637.2012.03.017

Volume 12 Issue 3

Turn off MathJax

Article Contents

Article Navigation > Journal of Traffic and Transportation Engineering > 2012 > 12(3): 113-119

Next Previous

LI Qing-quan, ZHOU Yao, LE Yang, YE Jia-an. Compression method of traffic flow data based on compressed sensing[J]. Journal of Traffic and Transportation Engineering, 2012, 12(3): 113-119. doi: 10.19818/j.cnki.1671-1637.2012.03.017

Citation:

LI Qing-quan, ZHOU Yao, LE Yang, YE Jia-an. Compression method of traffic flow data based on compressed sensing[J]. Journal of Traffic and Transportation Engineering, 2012, 12(3): 113-119. doi: 10.19818/j.cnki.1671-1637.2012.03.017

Citation:

PDF( 673 KB)

Compression method of traffic flow data based on compressed sensing

doi: 10.19818/j.cnki.1671-1637.2012.03.017

LI Qing-quan^{1, 2
,},
ZHOU Yao^{2, 3},
LE Yang^{1, 2},
YE Jia-an⁴

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, Hubei, China
2.
Engineering Research Center for Spatio-Temporal Data Smart Acquisition and Application of Ministry of Education, Wuhan University, Wuhan 430079, Hubei, China
3.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, Hubei, China
4.
Centre of Urban Studies and Urban Planning, The University of Hong Kong, Hong Kong, China

More Information

Author Bio:
LI Qing-quan (1965-), male, professor, PhD, +86-27-68778222, qqli@whu.edu.cn
Received Date: 2012-01-17
Publish Date: 2012-06-25

Abstract

Abstract

In order to obtain transformation matrix accurately, a new compression method of traffic flow data based on compressed sensing was introduced.The original data were projected into the low-dimension space directly by Gauss projection regardless of transformation matrix selection at the data compression side.Firstly, traffic flow data were proved to have sparse representation under the K-SVD trained dictionary.Secondly, original high-dimension data were projected into low-dimension space at the data compression side by using the random matrix with restricted isometry property, which made efficient and rapid data compression possible.Finally, after data transmission, data decompression were accomplished by convex algorithm at the data processing side.The traffic flow data obtained from the coil sensors located on a certain highway of America were used to validated the new method.The experimental result shows that the data compression method is fast and efficient.When the compression ratio is 4∶1, the relative error of data decompression is only 0.060 8.
- intelligent transportation system,
- compressed sensing,
- data compression,
- redundant dictionary,
- Gauss projection,
- L1-synthesis algorithm

FullText(HTML)

References(18)

References

[1]	XIAO Yang, LU Ling-yun, GAO Shuang, et al. Traffic data denoising compression for intelligent traffic systems based on2-D discrete wavelet transformation[J]. Journal of Beijing Jiaotong University, 2004, 28(5): 1-5. (in Chinese). doi: 10.3969/j.issn.1673-0291.2004.05.001
[2]	LIAN Qiu-sheng, WANG Cheng-ru, KONG Ling-fu. The choice of wavelet in electrocardiogram compression algorithm[J]. Computer Engineering and Applications, 2002, 38(18): 6-8. (in Chinese). doi: 10.3321/j.issn:1002-8331.2002.18.003
[3]	COIFMAN R R, WICKERHAUSER M V. Entropy-based algorithms for best basis selection[J]. IEEE Transactions on Information Theory, 1992, 38(2): 713-718. doi: 10.1109/18.119732
[4]	CAI Dun-hu. The research on the performance of manifold wavelet basis in image denoising[D]. Wuhan: Wuahan University, 2003. (in Chinese).
[5]	FEI Ming-wei, YUE Quan-ming, ZHANG Pei-chao, et al. Wavelets selection of compression and reconstruction algorithm based on digital recorded data from a faulted power system[J]. Automation of Electric Power Systems, 2005, 29(17): 64-67, 97. (in Chinese). doi: 10.3321/j.issn:1000-1026.2005.17.012
[6]	WEI Yu-fen, MENG Yan-jun. The choice of orthogonal wavelets base in image compression[J]. Equipment Manufac-turing Technology, 2009(4): 49-50, 61. (in Chinese). doi: 10.3969/j.issn.1672-545X.2009.04.020
[7]	ZHAO Zhi-qiang, ZHANG Yi, HU Jian-ming, et al. Compara-tive study of PCA and ICA based traffic flow compression[J]. Journal of Highway and Transportation Research and Devel-opment, 2008, 25(11): 109-118. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200811022.htm
[8]	QU Li, HU Jian-ming, ZHANG Yi. A flow volumes data compression approach for traffic network based on principal component analysis[C]∥IEEE. Proceedings of the2007IEEE Intelligent Transportation Systems Conference. Seattle: IEEE, 2007: 125-130.
[9]	GENG Yan-bin, YU Lei, WU Xu, et al. Signal-processing-based ITS data compression techniques and applications[J]. China Civil Engineering Journal, 2006, 39(11): 107-113. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TMGC200611017.htm
[10]	CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509. doi: 10.1109/TIT.2005.862083
[11]	DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/TIT.2006.871582
[12]	CANDES E J, WAKIN M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 21-30. doi: 10.1109/MSP.2007.914731
[13]	JIN Jian, GU Yuan-tao, MEI Shun-liang. An introduction to compressive sampling and its applications[J]. Journal of Electronics and Information Technology, 2010, 32(2): 470-475. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201002041.htm
[14]	RAUHUT H, SCHNASS K, VAN DERGHEYNST P. Compressed sensing and redundant dictionaries[J]. IEEE Transactions on Information Theory, 2008, 54(5): 2210-2219. https://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4494699&contentType=Journals+%26+Magazines
[15]	CANDES E J, ELDAR Y C, NEEDELL D, et al. Com-pressed sensing with coherent and redundant dictionaries[J]. Applied and Computational Harmonic Analysis, 2011, 31(1): 59-73. https://www.sciencedirect.com/science/article/pii/S1063520310001156
[16]	AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322. https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.89.4935&rank=5&q=sparse%20radon%20transform&osm=&ossid=
[17]	XU Peng. Study on sparse decomposition of signal and its application in EEG processing[D]. Chengdu: University of Electronic Science and Technology of China, 2006. (in Chinese).
[18]	TROPP J A, GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666. https://authors.library.caltech.edu/27144

Relative Articles

Supplements(0)

Cited By

Get Citation

PDF

XML

Article Metrics

Article views (869) PDF downloads(1183)

Compression method of traffic flow data based on compressed sensing

doi: 10.19818/j.cnki.1671-1637.2012.03.017

Abstract

References

Catalog

Article Metrics

Related

Compression method of traffic flow data based on compressed sensing

doi: 10.19818/j.cnki.1671-1637.2012.03.017

Abstract

References

Catalog

Article Metrics

Related

Export File

Citation

Format

Content