Optimal Control for Traffic Flow Networks

M. Gugat, M. Herty, A. Klar and G. Leugering
Additional contact information
M. Gugat: Institutfür Angewandte Mathematik, FAU Erlangen-Nürnberg
M. Herty: TU Darmstadt
A. Klar: TU Darmstadt
G. Leugering: FAU Erlangen-Nürnberg

Journal of Optimization Theory and Applications, 2005, vol. 126, issue 3, No 6, 589-616

Abstract: Abstract We consider traffic flow models for road networks where the flow is controlled at the nodes of the network. For the analytical and numerical optimization of the control, the knowledge of the gradient of the objective functional is useful. The adjoint calculus introduced below determines the gradient in two ways. We derive the adjoint equations for the continuous traffic flow network model and derive also the adjoint equations for a discretized model. Numerical examples for the solution of problems of optimal control for traffic flow networks are presented.

Keywords: Traffic flows; networks; hyperbolic systems; adjoint systems. (search for similar items in EconPapers)
Date: 2005
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10957-005-5499-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:126:y:2005:i:3:d:10.1007_s10957-005-5499-z

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-005-5499-z

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().