Short-term traffic flow prediction is an important basis for traffic state discrimination and traffic congestion prediction. The ARIMA-SVM combined prediction model is used to forecast the urban short-term traffic flow. Firstly, the ARIMA model and SVM model are used to forecast the urban short-term traffic flow. Then, the ARIMA-SVM combined prediction model was obtained by using the updated dynamic weight weighted fusion method to forecast the urban traffic flow, and the results were compared with the separate ARIMA model and SVM model. The empirical results show that the ARIMA-SVM combined model can more accurately predict the city's short-term traffic flow.


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    Titel :

    Short-Term Traffic Flow Forecast Based on ARIMA-SVM Combined Model


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wang, Wuhong (Herausgeber:in) / Wu, Jianping (Herausgeber:in) / Jiang, Xiaobei (Herausgeber:in) / Li, Ruimin (Herausgeber:in) / Zhang, Haodong (Herausgeber:in) / Peng, Jiaxin (Autor:in) / Xu, Yongneng (Autor:in) / Wu, Menghui (Autor:in)

    Kongress:

    International Conference on Green Intelligent Transportation System and Safety ; 2021 November 19, 2021 - November 21, 2021



    Erscheinungsdatum :

    28.10.2022


    Format / Umfang :

    14 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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