Vehicle queue length has been recognized as an important metric of level of service at intersections. However, there are some problems in the traditional methods of calculating the queue length. In this paper, we propose a scheme about queue length detection based on the analysis of cumulative input-output model, which is addressed by the method of estimating lane-by-lane arrival and departure profiles in real-time using two low angle cameras. First, the vehicle heads and tails are detected under the hypothesis generation and hypothesis verification framework. Then, with respect to the projection distortion, the stable features are used for classifying the position of each vehicle with the specified traffic lanes. After the vehicles crossing the pre-defined detection lines is estimated, real-time estimation of the arrival and departure profiles can be constructed. Experimental results show that this method can be carried out accurately under challenging congestion conditions during the rush hour.


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    Titel :

    Real Time Estimation of Lane-by-Lane Arrival and Departure Profiles at Signalized Intersections with Two Low-Angle Cameras


    Beteiligte:
    Xin, Le (Autor:in) / Chen, Yangzhou (Autor:in) / Hu, Jiangbi (Autor:in)

    Kongress:

    18th COTA International Conference of Transportation Professionals ; 2018 ; Beijing, China


    Erschienen in:

    CICTP 2018 ; 1545-1559


    Erscheinungsdatum :

    02.07.2018




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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