Please use this identifier to cite or link to this item:
|Title:||Models and algorithms for the traffic assignment problem with link capacity constraints|
Capacitated traffic assignment
|Source:||Nie, Y., Zhang, H.M., Lee, D.-H. (2004-05). Models and algorithms for the traffic assignment problem with link capacity constraints. Transportation Research Part B: Methodological 38 (4) : 285-312. ScholarBank@NUS Repository. https://doi.org/10.1016/S0191-2615(03)00010-9|
|Abstract:||This paper explores the models as well as solution techniques for the link capacitated traffic assignment problem (CTAP) that is capable of offering more realistic traffic assignment results. CTAP can be approximated by the uncapacitated TAP using different dual/penalty strategies. Two important and distinctive approaches in this category are studied and implemented efficiently. The inner penalty function (IPF) approach establishes a barrier on the boundary of the feasible set so that constraints are not violated in the solution process, and the augmented Lagrangian multiplier (ALM) approach combines the exterior penalty with primal-dual and Lagrangian multipliers concepts. In both implementations, a gradient projection (GP) algorithm was adopted as the uniform subproblem solver for its excellent convergence property and reoptimization capability. Numerous numerical results demonstrated through efficient implementations of either the IPF or the ALM approach that CTAP is computationally tractable even for large-scale problems. Moreover, the relative efficiency of IPF and ALM was explored and their sensitivity to different algorithmic issues was investigated. © 2003 Elsevier Ltd. All rights reserved.|
|Source Title:||Transportation Research Part B: Methodological|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 23, 2018
WEB OF SCIENCETM
checked on Jan 8, 2018
checked on Jan 22, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.