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Communication Dans Un Congrès Année : 2023

A PINN approach for traffic state estimation and model calibration based on loop detector flow data

Résumé

We analyze the performances of a Physics Informed Neural Network (PINN) strategy applied to traffic state estimation and model parameter identification in realistic situations. The traffic dynamics is modeled by a first order macroscopic traffic flow model involving two physical parameters and an auxiliary one. Besides, observations consist of (averaged) density and flow synthetic data computed at fixed space locations, simulating real loop detector measurements. We show that the proposed approach is able to give a good approximation of the underlying dynamics even with poorer information. Moreover, the precision generally improves as the number of measurement locations increases.
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Dates et versions

hal-04206224 , version 1 (13-09-2023)

Identifiants

  • HAL Id : hal-04206224 , version 1

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Paola Goatin, Daniel Inzunza. A PINN approach for traffic state estimation and model calibration based on loop detector flow data. MT-ITS 2023 - 8th International Conference on Models and Technologies for Intelligent Transportation Systems, Jun 2023, Saint-Laurent-Du-Var, France. ⟨hal-04206224⟩
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