By Topic

Analysis on Urban Traffic Network States Evolution Based on Grid Clustering and Wavelet De-noising

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Zuo Zhang ; Member, IEEE, Department of Automation, Tsinghua University and Tsinghua National Laboratory of Information Science and Technology, Beijing, China. Tel: +86-10-62772343; Fax: +86-10-62786911. ; Pingxin Zhang ; Yaomin Yin ; Lin Hou

Traffic state and evolution are important for better knowledge of urban traffic properties, as well as for the better traffic control and management. Therefore, it has attracted much attention recently. Model-based and data-driven are two kinds of methods in handling with such issues. With the wide deployment of ITS, large volume traffic data are available and data-driven methods such as clustering analysis have found their applications in ITS. According to physical characteristics of urban traffic flow, the paper follows the data-driven analysis and develops a grid-based clustering method for traffic state extraction and state evolution analysis. It also designs a wavelet transformation as a filter to decrease the noise in raw traffic data. Results on de-noised signals show more definite trends for traffic state evolutions.

Published in:

2008 11th International IEEE Conference on Intelligent Transportation Systems

Date of Conference:

12-15 Oct. 2008