An online dual estimation algorithm is developed to jointly estimate in real-time traffic quantities such as queue lengths, occupancies and flows, as well as the parameters of a macroscopic model of a signalized junction. These parameters include turning ratios and saturation flows, together with model uncertainties. The proposed novel methodology is based on the Expectation-Maximization algorithm, modified for real-time estimation, with a Kalman filter implementing the expectation step and a multivariate gradient-based approach for the maximisation step. The algorithm is validated by simulating the typical signalized 3-arm and 4-arm junctions. This work is aimed to form a part of the adaptive control loops for traffic light systems that are able to autonomously adjust with changing traffic conditions, so as to ensure efficient vehicle flows.


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    Title :

    Real-Time Parametric Modeling and Estimation of Urban Traffic Junctions


    Contributors:


    Publication date :

    2019-12-01


    Size :

    2574691 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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