A modelling framework for freeway traffic systems specifically oriented to the definition of suitable control schemes is developed in this paper. The traffic system includes different vehicle classes (passengers and freight vehicles) whose behaviour is explicitly modelled. Vehicles can enter the free-way arriving from the mainstream or through on-ramps on which traffic volumes may be regulated with ramp metering techniques. Moreover, Artificial Intelligence (AI) techniques and, specifically a Machine Learning (ML) approach, are the modelling methodology proposed in this work. More in detail, a hybrid model is defined in which a physics-based component, provided by a multi-class version of METANET model, is added to a machine learning model as a regularization component and the derived model is trained to reproduce the behavior of different classes of users. The effectiveness of the hybrid model is shown by training and testing it with real traffic data.


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    Titel :

    Control Oriented Freeway Traffic Modelling by Physics-Regularized Machine Learning


    Beteiligte:
    Binjaku, K. (Autor:in) / Mece, E.K. (Autor:in) / Pasquale, C. (Autor:in) / Sacone, S. (Autor:in)


    Erscheinungsdatum :

    2023-09-24


    Format / Umfang :

    641635 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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