|Table of Contents|

Compression method of traffic flow data based on compressed sensing(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2012年03期
Page:
113-119
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Compression method of traffic flow data based on compressed sensing
Author(s):
LI Qing-quan1; 2;  ZHOU Yao2; 3;  YUE Yang1; 2;  YEH Anthony Gar-On4
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
Keywords:
intelligent transportation system;  compressed sensing;  data compression;  redundant dictionary;  Gauss projection;  L1-synthesis algorithm
PACS:
U491.112
DOI:
-
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. 5 tabs, 8 figs, 18 refs.

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Last Update: 2012-06-30