Traffic state estimation is an important part of the traffic control process and aims to creates an accurate understanding of the current situation in traffic system. Bayesian Filtering is a statistical modelling framework that is useful in representing traffic state update as well as the relation between traffic state and detection data. This study develops a hybrid approach and uses non-parametric Gaussian Process (GP) to model the state-space transition of traffic system. Through representing the system models as either fully data-driven GP or as a hybrid model using a parametric mean function fusing the conventional principle of traffic flow with the data-driven approach, the requirement of an analytical model can be removed or relaxed. The computational results show that the proposed approach for lane based TSE can capture both short-term fluctuations and larger demand changes. In particular, the Bayesian nature of the GP models offer relative ease in quantifying the model uncertainties in combination with a conventional traffic flow model.


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    Title :

    A Hybrid Modelling Approach for Traffic State Estimation at Signalized Intersections


    Contributors:


    Publication date :

    2021-09-19


    Size :

    808571 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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