The forecasting of traffic flow is an important thesis of the research of intelligent transportation systems (ITS). A good method of traffic flow forecasting can play a very important role in traffic control and transportation programming. This paper first analyzed the Kalman filter theory in detail considering the following features of the Kalman filter method: choose the forecast factors flexibly, forecast accurately, and calculate expediently. Then a short-term traffic volume forecast model based on the Kalman filter theory was established. At last, the paper simulated one segment of the Jing-Jin-Tang freeway using the traffic flow data from a historical monitoring database in order to verify the availability of the advanced model. This research may provide a basis for traffic data analysis and a support for traffic management and traffic control.


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    Titel :

    Research of Short-Term Traffic Flow Forecast Method Based on the Kalman Filter


    Beteiligte:
    Chen, Feng (Autor:in) / Jia, Yuanhua (Autor:in) / An, Wenjuan (Autor:in) / Zhang, Na (Autor:in) / Niu, Zhonghai (Autor:in)

    Kongress:

    11th International Conference of Chinese Transportation Professionals (ICCTP) ; 2011 ; Nanjing, China


    Erschienen in:

    ICCTP 2011 ; 960-968


    Erscheinungsdatum :

    2011-07-26




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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