Sensitivity Analysis for Stochastic User Equilibrium Network Flows—A Dual Approach
Jiang Qian Ying and
Toshihiko Miyagi
Additional contact information
Jiang Qian Ying: Faculty of Regional Studies, Gifu University, Yanagido, Gifu, 501, Japan
Toshihiko Miyagi: Faculty of Regional Studies, Gifu University, Yanagido, Gifu, 501, Japan
Transportation Science, 2001, vol. 35, issue 2, 124-133
Abstract:
Recently, extensive studies have been conducted on the computational methods of sensitivity analysis for the Wardropian equilibrium modeling of traffic networks and their applications. But the same problems in the context of the stochastic user equilibrium modeling seem not to have been addressed. In this paper, we present a method for sensitivity analysis for network flows at stochastic user equilibrium. Our method is developed from a dual formulation of the stochastic user equilibrium analysis. By adopting Dial's algorithm for stochastic traffic assignment, we are able to formulate a computationally efficient link-based algorithm for the sensitivity analysis. Since the Wardropian equilibrium in a traffic network is an extreme case of stochastic user equilibrium with θ → ∞, θ being a dispersion parameter in the expected utility function for stochastic route choice, the method presented here can also be used for sensitivity analysis of the Wardropian equilibrium by setting θ large enough.
Date: 2001
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)
Downloads: (external link)
http://dx.doi.org/10.1287/trsc.35.2.124.10137 (application/pdf)
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:inm:ortrsc:v:35:y:2001:i:2:p:124-133
Access Statistics for this article
More articles in Transportation Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().