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

Content Provider	IEEE Xplore Digital Library
Author	Ganglong Duan Peng Liu Peng Chen Qiao Jiang Ni Li
Copyright Year	2011
Description	Author affiliation: School of Economics and Management, Xi'an University of Technology, China, 710054 (Ganglong Duan; Peng Liu; Peng Chen; Qiao Jiang; Ni Li)
Abstract	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.
Starting Page	1526
Ending Page	1530
File Size	928648
Page Count	5
File Format	PDF
ISBN	9781612841809
e-ISBN	9781612841816
DOI	10.1109/FSKD.2011.6019790
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Data mining Introduction RS Accuracy short-term traffic flow prediction Predictive models SVM Mathematical model Kernel Meteorology
Content Type	Text
Resource Type	Article

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