Journal of South China University of Technology (Natural Science Edition) ›› 2013, Vol. 41 ›› Issue (9): 71-76.doi: 10.3969/j.issn.1000-565X.2013.09.012

• Computer Science & Technology • Previous Articles     Next Articles

Short- Term Traffic Flow Forecasting Model Based on Support Vector Machine Regression

Fu Gui1 Han Guo- qiang1 Lu Feng2 Xu Zi- xin1   

  1. 1.School of Computer Science and Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China;2.Guangzhou Research Institute of Traffic Management Science,Guangzhou 510640,Guangdong,China
  • Received:2013-02-05 Revised:2013-05-19 Online:2013-09-25 Published:2013-08-01
  • Contact: 傅贵(1975-)，男，在职博士生，高级工程师，主要从事智能交通系统技术研究． E-mail:longman@188.com
  • About author:傅贵(1975-)，男，在职博士生，高级工程师，主要从事智能交通系统技术研究．
  • Supported by:

    NSFC- 广东省政府联合基金资助项目(U1035004);国家自然科学基金青年科学基金资助项目(61003270);广州市科技计划重点支撑项目(11A11080267);广东省计算科学重点实验室开放基金资助项目(201206005)

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Abstract:

As the short- term traffic flow forecasting theories and approaches help to improve the ability of traffic control systems to automatically adapt to traffic flow changes,this paper proposes a short- term traffic flow forecasting model based on the support vector machine regression by using a kernel function to transform the issues into a linearregression problem in Hilbert Space.Then,the corresponding experiments are conducted based on the data from the traffic flow detection systems in Guangzhou.It is found that the forecasted results accord well with the actual data,and that the forecasting error of the proposed model is less than those of the prediction methods based on Kalman filtering.Thus,the feasibility and effectiveness of the proposed model are verified.

Key words: traffic control, short- term traffic flow, forecasting model, machine learning, support vector machine regression