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

Duret, Aurélien; Yuan, Y.

doi:10.1016/j.trb.2017.02.008

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

Title

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

Author

Duret, Aurélien (Université de Lyon)
Yuan, Y. (TU Delft Transport and Planning)

Date

2017-07-01

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%.

Subject

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