Title :
Online support vector regression model for short-term traffic forecasting
Author :
Su, Haowei ; Zhang, Ling ; Yu, Shu
Author_Institution :
South China Univ. of Technol., Guangzhou
Abstract :
In this paper, a new short-term traffic flow prediction model and method based on online support vector regression (OSVR) 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. As a result, it is fitter for a real engineering application. The OSVR model was tested by using 1-880 database, the result shows that this model is superior to the back-propagation neural network (BPNN) model.
Keywords :
regression analysis; support vector machines; traffic engineering computing; back-propagation neural network model; loop detectors; online support vector regression model; probe vehicle; short-term traffic flow prediction model; short-term traffic forecasting; Automotive engineering; Data flow computing; Intelligent transportation systems; Mathematical model; Neural networks; Predictive models; Support vector machines; Technology forecasting; Traffic control; Uncertainty;
Conference_Titel :
Computer and Computational Sciences, 2007. IMSCCS 2007. Second International Multi-Symposiums on
Conference_Location :
Iowa City, IA
Print_ISBN :
978-0-7695-3039-0
DOI :
10.1109/IMSCCS.2007.58
