DocumentCode :
3508613
Title :
The Application of Nonparametric Regressive Algorithm for Short-Term Traffic Flow Forecast
Author :
Wang Xinying ; Juan Zhicai ; Liu Miao ; Yuan, Song
Author_Institution :
Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun
Volume :
3
fYear :
2009
fDate :
7-8 March 2009
Firstpage :
767
Lastpage :
770
Abstract :
Short-term traffic flow forecast is an important topic in the research field of intelligent transportation systems. The article analyses the preliminary results in the short-term traffic flow forecast, takes full advantage of the characteristics of K-neatest neighbor (KNN) classifiers, and builds a model based on nonparametric regressive algorithm.The historical and metrical data is classified by KNN,and the state vector is constructed by utilizing the output of KNN classifier. Traffic flow forecasting for the next period is entirely based on the state vectors.The experimental results show that the model was verified more accurate.
Keywords :
forecasting theory; pattern classification; regression analysis; traffic engineering computing; transportation; K-neatest neighbor classifiers; intelligent transportation systems; nonparametric regressive algorithm; short-term traffic flow forecast; Computer science; Educational technology; Intelligent transportation systems; Mathematical model; Predictive models; Roads; Sun; Technology forecasting; Telecommunication traffic; Traffic control; KNN; Short-term traffic flow predictions; nonparametric regressive algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Education Technology and Computer Science, 2009. ETCS '09. First International Workshop on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-1-4244-3581-4
Type :
conf
DOI :
10.1109/ETCS.2009.707
Filename :
4959424
Link To Document :
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