Paper Info
Title
Short-term traffic flow prediction based on rough set and support vector machine
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.
Year
DOI
Venue
2011
10.1109/FSKD.2011.6019790
FSKD
Keywords
Field
DocType
rough set theory,rs,traffic flow prediction model,information structure,rs-svm,antijamming capability,data mining introduction,fault tolerant capability,svm,short-term traffic flow prediction,support vector machine,forecast accuracy,traffic information systems,data reduction pretreatment,support vector machines,prediction model,mathematical model,accuracy,rough set,predictive models,meteorology,kernel,traffic flow,data mining
Data mining,Computer science,Artificial intelligence,Artificial neural network,Kernel (linear algebra),Traffic flow,Pattern recognition,Support vector machine,Rough set,Fault tolerance,Relevance vector machine,Machine learning,Data reduction
Conference
Volume
Issue
ISBN
3
null
978-1-61284-180-9
Citations 
PageRank 
References 
2
0.44
0
Authors
5
Name
Order
Citations
PageRank
Ganglong Duan192.35
Peng Liu231.13
Peng Chen3142.88
Qiao Jiang420.44
Ni Li522.13