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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 418303, 8 pages
Research Article

Accurate Multisteps Traffic Flow Prediction Based on SVM

1School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
2School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
3School of Navigation, Dalian Maritime University, Dalian 116026, China
4Department of Mechanical and Manufacturing Engineering, Aalborg University, 169220 Aalborg, Denmark

Received 29 August 2013; Accepted 12 September 2013

Academic Editor: Rui Mu

Copyright © 2013 Zhang Mingheng 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.


Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM) are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.