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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    Title :

    Physics Informed Deep Learning for Traffic State Estimation


    Contributors:


    Publication date :

    2020-09-20


    Size :

    698355 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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