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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Prediction of Vehicle Motion Based on Markov Model


    Contributors:
    Zhao, Dan (author) / Gao, Yangshui (author) / Zhang, Zhilong (author) / Zhang, Yi (author) / Luo, Tao (author)


    Publication date :

    2017-12-01


    Size :

    4446512 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Motion Prediction of Tugboats Using Hidden Markov Model

    Zhang, Zijian / Zhao, Jie / Wang, Tengfei et al. | IEEE | 2023


    Markov-based failure prediction for human motion analysis

    Dockstader, / Imennov, / Tekalp, | IEEE | 2003


    Markov-Based Failure Prediction for Human Motion Analysis

    Dockstader, S. / Imennov, N. / Tekalp, A. et al. | British Library Conference Proceedings | 2003


    Modeling and Prediction of Vehicle Routes Based on Hidden Markov Model

    Akabane, Ademar T. / Pazzi, Richard W. / Madeira, Edmundo R. M. et al. | IEEE | 2017