A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

  • Hongzhuan Zhao Key Laboratory of Cyber Physical Social Dependable Service Computation, Chongqing University; School of Automation of Chongqing University
  • Dihua Sun Key Laboratory of Cyber Physical Social Dependable Service Computation, Chongqing University; School of Automation of Chongqing University
  • Min Zhao Key Laboratory of Cyber Physical Social Dependable Service Computation; College of Computer of Chongqing University
  • Senlin Cheng Key Laboratory of Cyber Physical Social Dependable Service Computation, Chongqing University; School of Automation of Chongqing University
Keywords: Cyber-physical system (CPS), information fusion, Support Vector Machine (SVM), multi-classification, Intelligent Transport System (ITS), traffic parameters forecasting,

Abstract

With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS). Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM) classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.

Author Biographies

Hongzhuan Zhao, Key Laboratory of Cyber Physical Social Dependable Service Computation, Chongqing University; School of Automation of Chongqing University
College of Automation of Chongqing University, Ph.D
Dihua Sun, Key Laboratory of Cyber Physical Social Dependable Service Computation, Chongqing University; School of Automation of Chongqing University
School of Automation of Chongqing University, Professor of Intelligent transportation system
Min Zhao, Key Laboratory of Cyber Physical Social Dependable Service Computation; College of Computer of Chongqing University
College of computer of Chongqing University
Senlin Cheng, Key Laboratory of Cyber Physical Social Dependable Service Computation, Chongqing University; School of Automation of Chongqing University
School of Automation of Chongqing University

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Published
2016-04-25
How to Cite
1.
Zhao H, Sun D, Zhao M, Cheng S. A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting. PROMET [Internet]. 2016Apr.25 [cited 2019Dec.6];28(2):117-24. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/1643
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Articles