By Topic

Short-term traffic flow prediction based on rough set and support vector machine

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
$31 $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

5 Author(s)
GangLong Duan ; Sch. of Econ. & Manage., Xi'an Univ. of Technol., Xi'an, China ; Peng Liu ; Peng Chen ; Qiao Jiang
more authors

According to the highly complexity, nonlinearity and uncertainty of traffic flow, a single prediction model is difficult to ensure the prediction accuracy and efficiency. To overcome the lack of the single prediction method, this paper uses a prediction method that combining rough set with support vector machine, called RS-SVM, by exploiting complementary advantages of both approaches. Firstly, this method uses the rough set theory for data reduction pretreatment, and then constructs the traffic flow prediction model based on support vector machine according to the information structure. The results of the model are better than the BP Neural network and single support vector machine model. Besides, the combined prediction model not only has fault tolerant and anti-jamming capability, but also can shorten the operation time and improve the speed of the system and also forecast accuracy. Hence, it can be used to forecast real-time traffic flow.

Published in:

Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on  (Volume:3 )

Date of Conference:

26-28 July 2011