DocumentCode :
2917088
Title :
Real-time Traffic Flow Forecasting Based on MW-AOSVR
Author :
Wang, Fan ; Fang, Yu ; Tan, Guozhen
Author_Institution :
Dept. of Comput. Sci. & Eng., Dalian Univ. of Technol., Dalian, China
Volume :
3
fYear :
2009
fDate :
21-22 Nov. 2009
Firstpage :
323
Lastpage :
326
Abstract :
Accurate traffic flow forecasting is the key to the development of intelligent transportation systems (ITS). However, the classical forecasting method using the support vector regression (SVR) based on RBF kernel does not support online learning and has the problems of information loss, long processing time, low robustness and so on. An effective Marr Wavelet kernel which we combine the wavelet theory with AOSVR (MW-AOSVR) to construct for traffic flow forecasting is presented in this paper. The forecasting performance of MW-AOSVR is evaluated by real-time traffic flow data of southbound US 101 Freeway, in Los Angeles, USA and a variety of experiments are carried out. The experimental results demonstrate that the proposed approach with Marr Wavelet kernel provides more optimal performance than that with radial basis function (RBF) kernel and has much more precise forecasting rate and higher efficiency, especially for boundary approximation.
Keywords :
radial basis function networks; regression analysis; support vector machines; traffic engineering computing; wavelet transforms; Marr wavelet kernel; boundary approximation; intelligent transportation systems; radial basis function kernel; realtime traffic flow forecasting; support vector regression; wavelet theory; Application software; Demand forecasting; Information technology; Intelligent transportation systems; Kernel; Machine learning; Space technology; Technology forecasting; Training data; Wavelet domain; #NAME?;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on
Conference_Location :
Nanchang
Print_ISBN :
978-0-7695-3859-4
Type :
conf
DOI :
10.1109/IITA.2009.423
Filename :
5369419
Link To Document :
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