The prediction of vehicle motion plays a key role in many ways. Such as the road congestion identification, the references for the drivers, the construction of the city road network, and the design of the vehicular network routing. We propose a vehicle motion model considering the real vehicle movement scene in this paper. First, we analyze the motion of the vehicle with the data from May 1 to June 10 in shanghai. By sampling the data, we compute the cumulative distribution function of the entropy of the vehicle. Then, according to the characteristics of the entropy of each order, we select the second order Markov to establish the corresponding motion model. Finally we introduce the holidays and the flow of traffic in different periods in a day to the model. The performance of the vehicle motion prediction model we propose is more accuracy than that of the traditional second-order Markov.


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    Titel :

    Prediction of Vehicle Motion Based on Markov Model


    Beteiligte:
    Zhao, Dan (Autor:in) / Gao, Yangshui (Autor:in) / Zhang, Zhilong (Autor:in) / Zhang, Yi (Autor:in) / Luo, Tao (Autor:in)


    Erscheinungsdatum :

    01.12.2017


    Format / Umfang :

    4446512 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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