Robust Traffic Density Estimation Using Discontinuous Galerkin Formulation of a Macroscopic Model

In this paper, the authors develop a data-assimilation algorithm for a macroscopic model of traffic flow. The algorithm is based on the Discontinuous Galerkin Method and Minimax Estimation, and is applied to a macroscopic model based on a scalar conservation law. The authors present numerical results which demonstrate the shock-capturing capability of the algorithm under high uncertainty in the initial traffic condition, using only sparse measurements, and under time-dependent boundary conditions. The latter makes it possible for estimation to be performed on merge/diverge sections, allowing the possibility of the deployment of the algorithm to road networks.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 2147-2152
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01602726
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:25PM