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

Aurélien Duret and Yufei Yuan

Transportation Research Part B: Methodological, 2017, vol. 101, issue C, 51-71

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%.

Keywords: Traffic state estimation; Data assimilation; LWR model; Mesoscopic model; Eulerian observation; Loop data; Lagrangian observation; Probe data; Traffic monitoring; Traffic forecasting (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.trb.2017.02.008

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