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    Learning-based Traffic State Reconstruction using Probe Vehicles

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    Type
    Preprint
    Authors
    Liu, John
    Barreau, Matthieu
    Johansson, Karl H.
    KAUST Grant Number
    OSR-2019-CRG8-4033
    Date
    2020-11-10
    Permanent link to this record
    http://hdl.handle.net/10754/666000
    
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    Abstract
    This article investigates the use of a model-based neural-network for the traffic reconstruction problem using noisy measurements coming from probe vehicles. The traffic state is assumed to be the density only, modeled by a partial differential equation. There exist various methods for reconstructing the density in that case. However, none of them perform well with noise and very few deal with lagrangian measurements. This paper introduces a method that can reduce the processes of identification, reconstruction, prediction, and noise rejection into a single optimization problem. Numerical simulations, based either on a macroscopic or a microscopic model, show good performance for a moderate computational burden.
    Sponsors
    This research is partially funded by the KAUST Office of Sponsored Research under Award No. OSR-2019-CRG8-4033, the Swedish Foundation for Strategic Research and Knut and Alice Wallenberg Foundation. The authors are affiliated with the Wallenberg AI, Autonomous Systems and Software Program (WASP).
    Publisher
    arXiv
    arXiv
    2011.05031
    Additional Links
    https://arxiv.org/pdf/2011.05031
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