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.
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9781467365956
-
Supplemental Notes:
- Abstract reprinted with permission of IEEE.
-
Corporate Authors:
Institute of Electrical and Electronics Engineers (IEEE)
3 Park Avenue, 17th Floor
New York, NY United States 10016-5997 -
Authors:
- Tchrakian, Tigran T
- Zhuk, Sergiy
- Nogueira, Alberto Costa
-
Conference:
- 18th International IEEE Conference on Intelligent Transportation Systems (ITSC)
- Location: Canary Islands , Spain
- Date: 2015-9-15 to 2015-9-18
- Publication Date: 2015
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
- TRT Terms: Algorithms; Macroscopic traffic flow; Merging area; Traffic density; Traffic estimation; Traffic models
- Identifier Terms: Galerkin Method
- Uncontrolled Terms: Data assimilation; Diverging area
- Subject Areas: Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01602726
- Record Type: Publication
- ISBN: 9781467365956
- Files: TRIS
- Created Date: May 2 2016 3:25PM