The real-time, accurate and credible prediction information of traffic flow is the promise and the basic of realizing dynamic route guidance system, but the traditional prediction methods and neutral network don't work well with their inherent defects. Support Vector Machine (SVM) is a new learning machine with preferable prediction for the small samples. In this paper, a new prediction model of short-term traffic flow based on Lagrange Support Vector Regression (LSVR) was presented. Experimental results showed that compared to other forecasting method, LSVR could make better effects.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Short-Term Traffic Flow Prediction Based on Lagrange Support Vector Regression


    Beteiligte:
    Liu, Yanzhong (Autor:in) / Li, Xuhong (Autor:in) / Shao, Xiaojian (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




    Short-Term Traffic Flow Prediction Based on Lagrange Support Vector Regression

    Liu, Y. / Li, X. / Shao, X. et al. | British Library Conference Proceedings | 2007


    Short-Term Traffic Flow Prediction Algorithm for Expressway Based on Long Short-Term Memory and Support Vector Regression

    Guo, Lan Ying / Chang, Hui / Cheng, Xin et al. | British Library Conference Proceedings | 2020


    Short-Term Traffic Flow Prediction Algorithm for Expressway Based on Long Short-Term Memory and Support Vector Regression

    Wang, Hong Fei / Chang, Hui / Zhou, Jing Mei et al. | SAE Technical Papers | 2020


    Prediction Model for Urban Expressway Short-Term Traffic Flow Based on the Support Vector Regression

    Rong, C.-L. / Chun, W.-Q. / American Society of Civil Engineers | British Library Conference Proceedings | 2010