By Topic

Comparative study on feature extraction of mass traffic data using multiple methods

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

3 Author(s)
Yin Wang ; Department of Automation and TNList, Tsinghua University, Beijing, China 100084 ; Jianming Hu ; Zuo Zhang

This paper aims at extracting the typical and significant features of the traffic network by using variant feature extraction methods. Combined with the intrinsic tempo-spatial characteristics of traffic flow data, data mining technique is introduced to extract the main features of the temporal and spatial relationship and the typical patterns of the traffic network. We introduce three methods in feature extraction: principal component analysis (PCA), robust PCA and kernel PCA. By selecting the eigenvalues according to decreasing magnitude of eigenvalues, we design a transform matrix to reduce the dimensionality of the original matrix, as well as obtain the features of the traffic network. By comparing the results of feature extraction of different methods, we find a better way to extract the typical features in urban traffic data and attempt to explain some the features.

Published in:

Intelligent Vehicles Symposium, 2009 IEEE

Date of Conference:

3-5 June 2009