Title :
Short-term Traffic Flow Prediction Based on Incremental Support Vector Regression
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 incremental support vector regression (ISVR) is proposed, according to the data collected sequentially by the probe vehicle or loop detectors, which can update the prediction function in real time via incremental learning way. As a result, it is fitter for the real engineering application. The ISVR model was tested by using the 1-880 database, and the result shows that this model is superior to the back-propagation neural network (BPNN) model.
Keywords :
learning (artificial intelligence); road traffic; support vector machines; incremental learning; incremental support vector regression; short-term traffic flow prediction; Computer science; Educational institutions; Least squares methods; Mathematical model; Neural networks; Predictive models; Support vector machines; Telecommunication traffic; Traffic control; Vehicle detection;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.661
