Traffic State Estimation Based on Eulerian and Lagrangian Observations in a Mesoscopic Modeling Framework - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Traffic State Estimation Based on Eulerian and Lagrangian Observations in a Mesoscopic Modeling Framework

Résumé

The paper proposes a model-based framework for estimating traffic states from Eulerian (loop) and/or Lagrangian (probe) data. Lagrangian-Space formulation of the LWR model adopted as the underlying traffic model provides suitable properties for receiving both Eulerian and Lagrangian external information. Three independent methods are proposed to address Eulerian data, Lagrangian data and the combination of both, respectively. These methods are defined in a consistent framework so as to be implemented simultaneously. The proposed framework has been verified on the synthetic data derived from the same underlying traffic flow model. Strength and weakness of both data sources are discussed. Next, the proposed framework has been applied to a freeway corridor. The validity and performance have been tested using the data from a microscopic simulator.
Fichier principal
Vignette du fichier
doc00028533.pdf (1.8 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01717642 , version 1 (16-06-2021)

Identifiants

  • HAL Id : hal-01717642 , version 1

Citer

Aurélien Duret, Yufei Yuan. Traffic State Estimation Based on Eulerian and Lagrangian Observations in a Mesoscopic Modeling Framework. TRB 2017, Transportation research board annual meeting, Jan 2017, Washington DC, United States. 23p. ⟨hal-01717642⟩
32 Consultations
19 Téléchargements

Partager

Gmail Facebook X LinkedIn More