Traffic flow time series prediction based on statistics learning theory
AiLing Ding
XiangMo Zhao
LiCheng Jiao
Nat. Key Lab of Radar Signal Process., Xidian Univ., Xi'an, China;
This paper appears in: Intelligent Transportation Systems, 2002. Proceedings. The IEEE 5th International Conference on
Publication Date: 2002
On page(s): 727- 730
ISSN:
ISBN: 0-7803-7389-8
INSPEC Accession Number: 7503495
Digital Object Identifier: 10.1109/ITSC.2002.1041308
Posted online: 2003-07-09 09:44:59.0
Abstract
For intelligent transportation systems, a new traffic flow time series prognostication is proposed in this paper. Compared with classical methods, support vector machine has a good generalize ability for limited training samples, which has a characteristic of rapid convergence and avoiding the local minimum. At the end of this paper, the simulation experiment for the traffic flow of one practice crossing proves the validity and efficiency and high application value in traffic flow prediction.
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