For intelligent transportation systems, a new traffic flow time series prognostication is proposed in this paper. Compared with classical methods, support vector machine has a good generalize ability for limited training samples, which has a characteristic of rapid convergence and avoiding the local minimum. At the end of this paper, the simulation experiment for the traffic flow of one practice crossing proves the validity and efficiency and high application value in traffic flow prediction.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic flow time series prediction based on statistics learning theory


    Beteiligte:
    AiLing Ding, (Autor:in) / XiangMo Zhao, (Autor:in) / LiCheng Jiao, (Autor:in)


    Erscheinungsdatum :

    01.01.2002


    Format / Umfang :

    213179 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    TRAFFIC FLOW TIME SERIES PREDICTION BASED ON STATISTICS LEARNING THEORY

    Ding, A. / Zhao, X. / Jiao, L. et al. | British Library Conference Proceedings | 2002


    Short-Term Traffic Flow Prediction Based on Fuzzy Time Series

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


    Traffic flow time series prediction method and device

    DUAN HONGYUN / PENG CHEN / WANG WEI | Europäisches Patentamt | 2020

    Freier Zugriff

    Short-term traffic flow prediction based on fuzzy time series

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


    DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction

    Chen, Yuanfang / Chen, Falin / Ren, Yizhi et al. | ArXiv | 2017

    Freier Zugriff