210
Views
0
CrossRef citations to date
0
Altmetric
ARTICLES

System optimum dynamic traffic assignment with departure time choice on two-terminal networks

&
Pages 1734-1761 | Received 16 Feb 2018, Accepted 07 Jul 2019, Published online: 19 Jul 2019
 

ABSTRACT

This paper addresses the system optimum dynamic traffic assignment (SO-DTA) problem with departure time choice on a two-terminal network, where the travel cost consists of travel time and an early schedule delay cost. Under a certain condition, we show that SO-DTA reduces to the latest departure earliest arrival flow (LDEAF), and hence methodologies of LDEAF can be used to study SO-DTA. A successive shortest path (SSP) algorithm is used to solve the LDEAF problem. The benefit is that the SSP algorithm involves only the shortest path computations on a static network. System marginal costs, externalities and dynamic user equilibrium tolls are analyzed in the LDEAF context, and used to provide a better understanding of the SO-DTA problem characteristics. Further, the study findings can be used for the morning commute problem as it is a special case of the SO-DTA problem on a two-terminal network.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is based on funding provided by the U.S. Department of Transportation through the NEXTRANS Center, the USDOT Region 5 University Transportation Center. The authors are solely responsible for the contents of this paper.

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 594.00 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.