EconPapers    
Economics at your fingertips  
 

Stochastic Congestion and Pricing Model with Endogenous Departure Time Selection and Heterogeneous Travelers

Wuping Xin and David Levinson

Mathematical Population Studies, 2015, vol. 22, issue 1, 37-52

Abstract: In a stochastic roadway congestion and pricing model, one scheme (omniscient pricing) relies on the full knowledge of each individual journey cost and of early and late penalties of the traveler. A second scheme (observable pricing) is based on observed queuing delays only. Travelers are characterized by late-acceptance levels. The effects of various late-acceptance levels on congestion patterns with and without pricing are compared through simulations. The omniscient pricing scheme is most effective in suppressing the congestion at peak hours and in distributing travel demands over a longer time horizon. Heterogeneity of travelers reduces congestion when pricing is imposed, and congestion pricing becomes more effective when cost structures are diversified rather than identical. Omniscient pricing better reduces the expected total social cost; however, more travelers improve welfare individually with observable pricing. The benefits of a pricing scheme depend on travelers' cost structures and on the proportion of late-tolerant, late-averse, and late-neutral travelers in the population.

Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1080/08898480.2013.836423 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Stochastic congestion and pricing model with endogenous departure time selection and heterogeneous travelers (2006) Downloads
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:taf:mpopst:v:22:y:2015:i:1:p:37-52

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GMPS20

DOI: 10.1080/08898480.2013.836423

Access Statistics for this article

Mathematical Population Studies is currently edited by Prof. Noel Bonneuil, Annick Lesne, Tomasz Zadlo, Malay Ghosh and Ezio Venturino

More articles in Mathematical Population Studies from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-22
Handle: RePEc:taf:mpopst:v:22:y:2015:i:1:p:37-52