Due to the complexity of traffic flow characteristics, the traditional statistical regression models have been unsuitable for the traffic flow prediction. And thereby the paper proposes the fuzzy time series method to predict shortterm traffic flow. First, we proposes an improved fuzzy time series prediction model, i.e. , ratio-median lengths of intervals two-factors high-order fuzzy time series. The prediction model simultaneously considers impact of many factors on the traffic flow formulation. For achieving higher prediction accuracy, the ratio-median lengths of intervals method is adopted to adaptively partition the universe of discourse of linguistic variable. Then it is used to predict the raw traffic flow data which are collected at Zizhu Bridge in Beijing. The experiment result verifies that the improved fuzzy time series prediction model can achieve high prediction accuracy.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Short-term traffic flow prediction based on ratio-median lengths of intervals two-factors high-order fuzzy time series


    Beteiligte:
    Liang Zhao, (Autor:in) / Fei-Yue Wang, (Autor:in)


    Erscheinungsdatum :

    2007-12-01


    Format / Umfang :

    170100 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Short-term traffic flow prediction based on fuzzy time series

    Gao,H. / Li,L. / Chen,L. et al. | Kraftfahrwesen | 2002


    Short-Term Traffic Flow Prediction Based on Fuzzy Time Series

    Gao, Haijun / Li, Lingxi / Chen, Long et al. | SAE Technical Papers | 2002




    Short-term traffic flow prediction method based on time series decomposition

    WANG WEI / ZHOU WEI / HUA XUEDONG | Europäisches Patentamt | 2020

    Freier Zugriff