Publication Cover
Transportation Letters
The International Journal of Transportation Research
Volume 9, 2017 - Issue 2
212
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

Social welfare maximization by optimal toll design for congestion management: models and comprehensive numerical results

Pages 81-89 | Received 11 Mar 2015, Accepted 03 Apr 2016, Published online: 02 Jun 2016
 

Abstract

The purpose of this paper is to present a bi-level-based optimization model and develop a genetic algorithms (GA)-based method to solve the optimal toll design with elastic demand problem for congestion management and to determine the second-best linked-based optimal toll locations and toll levels simultaneously. The upper-level subprogram is to maximize the total social welfare given certain toll level constraints. The lower level subprogram is a traditional user equilibrium problem with elastic demand. The proposed GA model is applied to the Sioux Falls network, which has 76 links and 24 OD-pairs, assuming homogeneous users. Comprehensive numerical results including solutions achieved under continuous tolling and discrete tolling schemes, tolling on optimized links and tolling on heuristically selected most congested links are carefully presented and compared. The impact of value of time and the elastic demand sensitivity are also comprehensively investigated.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 273.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.