Abstract Predicting the traffic flow is an important thing in intelligent traffic management system. Traffic can be caused due to many reasons. The reasons vary from minor road damage or blockade to major accident. The data we receive from the road is of many different structures. To improve the traffic flow prediction accuracy a combined traffic flow prediction model based on Deep learning graph convolutional neural network and long-term memory network extracts the feature of topology structure and time structure in traffic data. The residual network optimizer for all model and reduces the gradient disappearance of currents is explosion in network degradation which finally leads to traffic flow prediction. The experimental results of the combined traffic flow prediction model are more accurate than the traditional convolutional neural network.


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    Titel :

    TIME AND TOPOLOGY STRUCTURE BASED TRAFFIC FLOW PREDICTION WITH ARTIFICIAL INTELLIGENCE AND NEURAL NETWORK


    Beteiligte:
    E S MADHAN (Autor:in) / A ARUN (Autor:in) / P SHOBHA RANI (Autor:in) / T RAMESH (Autor:in) / GUPTA MEENAKSHI (Autor:in) / SAVITA (Autor:in)

    Erscheinungsdatum :

    2021-12-09


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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    Sadek, Adel W. / Smith, Brian L. / Demetsky, Michael J. | Transportation Research Record | 1998


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    Guozhen Tan, / Wenjiang Yuan, / Hao Ding, | IEEE | 2004