Short-term traffic flow forecasting model based on support vector machine regression

作者：Fu, Gui^*; Han, Guo-Qiang; Lu, Feng; Xu, Zi-Xin

来源：Journal of South China University of Technology(Natural Science Edition), 2013, 41(9): 71-76.

DOI：10.3969/j.issn.1000-565X.2013.09.012

• Forecasting error
• Forecasting modeling
• Linear regression problems
• Prediction methods
• Short-term traffic flow
• Short-term traffic flow forecasting
• Support vector machine regressions
• Traffic flow detections

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 linear regression 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.

• 出版日期2013-9
• 单位华南理工大学