A combined method based on of Principal Component Analysis (PCA), Subtraction Fuzzy Clustering (SFC), and Non-Parametric Regression (NPR) is proposed in view of nonlinearity and uncertainty for short-term forecasting of real-time traffic flow. A highly efficient case database is created from the original traffic volumes after the operations of PCA and SFC. The data-driven method of K-nearest neighbours NPR is used to make the forecasting. An emulation experiment is designed to test the validity of the method. The example results show it is better than normal NPR and meets real-time requirement.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    The Combined Short-Term Forecasting Approach to Traffic Flow Based on Non-Parametric Regression


    Beteiligte:
    Zhang, Xiao-li (Autor:in) / He, Guo-guang (Autor:in)

    Kongress:

    First International Conference on Transportation Engineering ; 2007 ; Southwest Jiaotong University, Chengdu, China



    Erscheinungsdatum :

    2007-07-09




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    The Combined Short-Term Forecasting Approach to Traffic Flow Based on Non-Parametric Regression

    Zhang, X.-l. / He, G.-g. / China Communications and Transportation Association; Transportation & Development Institute (American Society of Civil Engineers) | British Library Conference Proceedings | 2007


    Short Term Traffic Flow Forecasting Based on Artificial Neural Network Combined Predictor

    Nie, P. / Yu, Z. / He, Z. et al. | British Library Conference Proceedings | 2007




    Use of Local Linear Regression Model for Short-Term Traffic Forecasting

    Sun, Hongyu / Liu, Henry X. / Xiao, Heng et al. | Transportation Research Record | 2003