Traffic prediction by combining macroscopic models and Gaussian processes - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2023

Traffic prediction by combining macroscopic models and Gaussian processes

Résumé

We propose a physics informed statistical framework for traffic travel time prediction. On one side, the discrepancy of the considered mathematical model is represented by a Gaussian process. On the other side, the traffic simulator is fed with boundary data predicted by a Gaussian process, forced to satisfy the mathematical equations at virtual points, resulting in a multi-objective optimization problem. This combined approach has the merit to address the shortcomings of the purely model-driven or data-driven approaches, while leveraging their respective advantages. Indeed, models are based on physical laws, but cannot capture all the complexity of real phenomena. On the other hand, pure statistical outputs can violate basic characteristic dynamics. We validate our approach on both synthetic and real world data, showing that it delivers more reliable results compared to other methods. “This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.”
Fichier principal
Vignette du fichier
WBGpredictionHAL.pdf (1.59 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04345140 , version 1 (14-12-2023)

Identifiants

  • HAL Id : hal-04345140 , version 1

Citer

Alexandra Würth, Mickael Binois, Paola Goatin. Traffic prediction by combining macroscopic models and Gaussian processes. 2023. ⟨hal-04345140⟩
87 Consultations
35 Téléchargements

Partager

Gmail Facebook X LinkedIn More