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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Control Oriented Freeway Traffic Modelling by Physics-Regularized Machine Learning


    Contributors:
    Binjaku, K. (author) / Mece, E.K. (author) / Pasquale, C. (author) / Sacone, S. (author)


    Publication date :

    2023-09-24


    Size :

    641635 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Implementation-Oriented Freeway Traffic Control Strategies

    Ferrara, Antonella / Sacone, Simona / Siri, Silvia | Springer Verlag | 2018


    Freeway traffic modelling and control

    Ferrara, Antonella / Sacone, Simona / Siri, Silvia | TIBKAT | 2018


    Traffic Flow Modeling With Gradual Physics Regularized Learning

    Yuan, Yun / Wang, Qinzheng / Yang, Xianfeng Terry | IEEE | 2022