A Real-Time Traffic Information Prediction Model Based on AOSVR and On-Line Learning

Author

Zhao, Mo ; Cao, Kai ; Yu, Shao-wei

Author_Institution

Sch. of Traffic & Vehicle Eng., Univ. of Shandong Univ. of Technol., Shangdong

fYear

2006

fDate

17-20 Sept. 2006

Firstpage

487

Lastpage

492

Abstract

Acquiring the real-time information about traffic flow is one of the important steps toward the realization of ITS. In this paper, we propose a real-time traffic prediction model with warm start by integrating an accurate on-line support vector regression (AOSVR) with a corrected on-line learning algorithm that is used for improving computational rate. The forecasting implementation has showed that the proposed model is faster and more exact than AOSVR with both cold and warm start when it is applied to an actual real-time forecasting scheme

Keywords

learning (artificial intelligence); real-time systems; regression analysis; support vector machines; traffic information systems; online learning; online support vector regression; real-time traffic information prediction model; Adaptive control; Demand forecasting; Economic forecasting; Predictive models; Programmable control; Real time systems; Risk management; Support vector machines; Traffic control; Vehicle dynamics;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE

Conference_Location

Toronto, Ont.

Print_ISBN

1-4244-0093-7

Electronic_ISBN

1-4244-0094-5

Type

conf

DOI

10.1109/ITSC.2006.1706788

Filename

1706788