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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 154703, 9 pages
Research Article

Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model

1College of Transportation, Jilin University, Changchun 130025, China
2State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130025, China
3Jilin Province Key Laboratory of Road Traffic, Jilin University, Changchun 130025, China

Received 30 May 2015; Accepted 3 August 2015

Academic Editor: Michael Small

Copyright © 2015 Qichun Bing et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Short-term traffic flow prediction is one of the most important issues in the field of adaptive traffic control system and dynamic traffic guidance system. In order to improve the accuracy of short-term traffic flow prediction, a short-term traffic flow local prediction method based on combined kernel function relevance vector machine (CKF-RVM) model is put forward. The C-C method is used to calculate delay time and embedding dimension. The number of neighboring points is determined by use of Hannan-Quinn criteria, and the CKF-RVM model is built based on genetic algorithm. Finally, case validation is carried out using inductive loop data measured from the north–south viaduct in Shanghai. The experimental results demonstrate that the CKF-RVM model is 31.1% and 52.7% higher than GKF-RVM model and GKF-SVM model in the aspect of MAPE. Moreover, it is also superior to the other two models in the aspect of EC.