The challenge of traffic state estimation (TSE) lies in the sparsity of observed traffic data and the sensor noise present in the data. This paper presents a new approach – physics informed deep learning (PIDL) method – to tackle this problem. PIDL equips a deep learning neural network with the strength of the physical law governing traffic flow to better estimate traffic conditions. A case study is conducted where the accuracy and convergence-time of the algorithm are tested for varying levels of scarcely observed traffic density data – both in Lagrangian and Eulerian frames. The estimation results are encouraging and demonstrate the capability of PIDL in making accurate and prompt estimation of traffic states.


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    Titel :

    Physics Informed Deep Learning for Traffic State Estimation


    Beteiligte:
    Huang, Jiheng (Autor:in) / Agarwal, Shaurya (Autor:in)


    Erscheinungsdatum :

    20.09.2020


    Format / Umfang :

    698355 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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