Traffic flow prediction plays an important role in Intelligent Transportation Systems(ITS). To improve the accuracy of traffic flow prediction, this paper proposes a multi-location based on Trend-Seasonal Decomposition and GCN Traffic Flow Forecasting Models for the task of multi-location traffic flow prediction. In this paper, the proposed model mainly consists of two functions: First, the Trend-Seasonal component decomposes the temporal data of traffic flow into a more predictable trend part and a seasonal or periodic part. Second, GCN is used to obtain spatial information between different observation points and improve the accuracy of multi-position prediction. Finally, the experiments for the PeMS04 and PeMS08 data sets are carried out to verify the effectiveness of proposed model.


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    Titel :

    A Model Based on Trend-Seasonal Decomposition and GCN for Traffic Flow Prediction


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jia, Yingmin (Herausgeber:in) / Zhang, Weicun (Herausgeber:in) / Fu, Yongling (Herausgeber:in) / Wang, Jiqiang (Herausgeber:in) / Wang, Jiajun (Autor:in) / Li, Yong (Autor:in) / Zhang, Jiahao (Autor:in)

    Kongress:

    Chinese Intelligent Systems Conference ; 2023 ; Ningbo, China October 14, 2023 - October 15, 2023



    Erscheinungsdatum :

    08.10.2023


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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