For improving traffic flow forecasting precision, a forecasting method that combines nonlinear regression Support Vector Machines (SVM) with Principal Component Analysis (PCA) was proposed. PCA was used to extract features from forecasting variables and produce fewer principal components. These principal components were input to nonlinear regress SVM for traffic flow forecasting. The kernel parameters of the SVM were determined with Bayesian inference. The efficiency of the method was illustrated through analyzing Jinan urban traffic flow data. Analysis results show that the traffic flow forecasting method that combines nonlinear regression SVM with PCA can not only improve forecasting precision but reduce computation cost, which can improve the real-time performance of forecasting. The forecasting precision of the proposed method is higher than that of commonly used traffic flow forecasting methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic Flow Forecasting by Combination of SVM with PCA


    Beteiligte:
    Sun, Zhanquan (Autor:in) / Pan, Jingshan (Autor:in) / Zhang, Zhanjun (Autor:in) / Zhang, Lidong (Autor:in) / Ding, Qingyan (Autor:in)


    Erscheinungsdatum :

    2010-06-15


    Format / Umfang :

    52010-01-01 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    New Bayesian combination method for short-term traffic flow forecasting

    Wang, Jian / Deng, Wei / Guo, Yuntao | Elsevier | 2014



    Traffic flow forecasting

    Kerner, B.S. / Herrtwich, R.G. | Tema Archiv | 2001