In order to give full play to the overall efficiency of freeway networks and raise the level of operation and service, freeway management is faced with higher requirements and greater challenges than ever before. This paper proposes a general method of traffic state estimation, one key step of management, which is significant and fundamental. Through the study on the stochastic nonlinear second-order macroscopic traffic flow model and Extended Kalman Filter (EKF), the system model presented in this paper performs well in traffic state estimation which can provide scientific bases and technical strategies for the control of freeway network. Also, the detailed performance evaluation on the model is one of the main achievements. The evaluation includes statistical indices, distances, time intervals, initial values and so on.


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    Title :

    Freeway Traffic State Estimation Based on Extended Kalman Filter


    Contributors:
    Dong, Changyin (author) / Wang, Hao (author) / Yang, Wanbo (author) / Pan, Yiwei (author) / Li, Ye (author)

    Conference:

    15th COTA International Conference of Transportation Professionals ; 2015 ; Beijing, China


    Published in:

    CICTP 2015 ; 420-430


    Publication date :

    2015-07-13




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English