Traffic state estimation (TSE) is a crucial component for efficient traffic control and management. In the literature, given limited and potentially noisy traffic observations, a variety of model-driven and data-driven TSE approaches are developed to reconstruct traffic state variables, such as vehicle density and velocity. In practice, the confidence levels of the reconstruction output also carry valuable insights for transportation practitioners and engineers to develop traffic control measures. In this work, we propose the adoption of the physics-informed Bayesian deep learning (PIBDL) neural network for traffic state estimation and uncertainty quantification (TSE-UQ). Equipped with the knowledge of flow conservation laws, the physics-informed neural network has the advantage of accurately estimating the traffic states, and the component of Bayesian inference in PIBDL enables UQ of the TSE output. We demonstrate the effectiveness of the proposed approach by designing a case study with a synthetic vehicle density dataset, and comparing the PIBDL performance to a baseline deep learning (DL) neural network in TSE-UQ. The results from the case study demonstrate the capability of PIBDL in TSE-UQ applications.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Physics-Informed Bayesian Deep Learning for Traffic State Estimation and Uncertainty Quantification


    Contributors:


    Publication date :

    2024-09-24


    Size :

    961258 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Physics Informed Deep Learning for Traffic State Estimation

    Huang, Jiheng / Agarwal, Shaurya | IEEE | 2020



    Physics-Informed Deep Learning for Modeling & Simulation Under Uncertainty

    Patrick Leser / Geoffrey Bomarito / James Warner et al. | NTRS


    Physics-informed deep learning with Kalman filter mixture for traffic state prediction

    Niharika Deshpande / Hyoshin (John) Park | DOAJ | 2025

    Free access

    Physics-informed deep learning with Kalman filter mixture for traffic state prediction

    Deshpande, Niharika / Park, Hyoshin (John) | Elsevier | 2025

    Free access