Hybrid GA based online support vector machine model for short-term traffic flow forecasting

Authors:
Haowei Su;Shu Yu
Affiliations:
College of Computer Science and Engineering, South China University of Technology, Guangzhou, P.R. China;College of Computer Science and Engineering, South China University of Technology, Guangzhou, P.R. China
Venue:
APPT'07 Proceedings of the 7th international conference on Advanced parallel processing technologies
Year:
2007

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Abstract

In this paper, a hybrid genetic algorithm (GA) based online support vector machine (OSVM) prediction model for short-term traffic flow forecasting is proposed, according to the data collected sequentially by the probe vehicle or the loop detectors, which can update the forecasting function in real time via online learning way, and the parameters used in the OSVM were optimized by GA. As a result, it is fitter for the real engineering application. The GA based OSVM model was tested by using the I-880 database, the result shows that this model is superior to the back-propagation neural network (BPNN) model.