Spatio-temporal similarities, one of the characteristics to describe the relativity of traffic phenomenon, can be utilized to predict short-term traffic flow. These similarities not always appear at spatial adjacent road links because of complexity of road network. In this paper, we adopt Cross-Correlation Function to depict similarities between different traffic flow series according to the observed flow data. The process characteristic generalizes the evolvement rules of traffic flow which are essentials need to be tackled by a prediction model. After choosing the most correlative road links and their time delay instead of the upstream or downstream ones, a Hybrid Process Neural Network is constructed to predict shortterm traffic flow, which uses various scales to catch traffic features such as daily-periodicity, weekly-periodicity and spatiotemporal process, since a simple model is not good enough to depict all these rules. Application of the proposed method is demonstrated, and the experimental results show that our method outperforms other compared methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Hybrid Process Neural Network based on Spatio-Temporal Similarities for Short-Term Traffic Flow Prediction


    Beteiligte:
    Hu, Cheng (Autor:in) / Xie, Kunqing (Autor:in) / Song, Guojie (Autor:in) / Wu, Tianshu (Autor:in)


    Erscheinungsdatum :

    01.10.2008


    Format / Umfang :

    632316 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Short-term traffic flow prediction method based on spatio-temporal correlation

    QI YONG / XIONG TING / ZHANG WEIBIN et al. | Europäisches Patentamt | 2020

    Freier Zugriff

    A Unified Spatio-Temporal Model for Short-Term Traffic Flow Prediction

    Duan, Peibo / Mao, Guoqiang / Liang, Weifa et al. | IEEE | 2019



    Short-Term Traffic Flow Prediction on Highways Based on Self-Supervised Spatio-Temporal Transformer

    Guo, Xingping / Song, Jingni / Du, Kai et al. | Springer Verlag | 2025


    Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning

    Zhang, Weibin / Yu, Yinghao / Qi, Yong et al. | Taylor & Francis Verlag | 2019