Traffic state estimation based on Eulerian and Lagrangian observations in a mesoscopic modeling framework

Aurélien Duret, Yufei Yuan

Research output: Contribution to journalArticleScientificpeer-review

13 Citations (Scopus)
77 Downloads (Pure)

Abstract

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 has been tested using the data from a microscopic simulator, and the performance is satisfactory even for low rate of probe vehicles around 5%.

Original languageEnglish
Pages (from-to)51-71
Number of pages21
JournalTransportation Research. Part B: Methodological
Volume101
DOIs
Publication statusPublished - 1 Jul 2017

Keywords

  • Data assimilation
  • Eulerian observation
  • Lagrangian observation
  • Loop data
  • LWR model
  • Mesoscopic model
  • Probe data
  • Traffic forecasting
  • Traffic monitoring
  • Traffic state estimation

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