An Expost Information Altering Strategy to Improve Day-to-Day Network Traffic Evolution

Travel time information can influence people’s travel choices but there are many types of travel information and they can be disseminated to travelers in different ways. This paper studies the travel information provision problem using expost rather than predicted travel time information, that is, in each day, a traveler is told of the experienced travel time of her current route from yesterday. Taking advantage of the findings of behavioral science that humans have bounded memory and perception capabilities, the expost information is not released as is but altered before it is released, with the degree of altering falling within travelers’ perception/memory error band so as not to cause traveler distrust. This problem is mathematically formulated as a dynamical programming problem with a nonlinear pairwise swapping process to describe the day-to-day network traffic evolution under altered expost travel time information. This nonlinear problem was solved using a direct search algorithm, and a numerical example is given to analyze the present information altering strategy and to examine the performance of the current algorithm under different initial conditions.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ADB20 Standing Committee on Effects of Information and Communication Technologies (ICT) on Travel Choices. Alternate title: Ex Post Information Tailoring Strategy to Regulate Day-to-Day Network Traffic Evolution
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Zhang, Wenyi
  • Zhang, H Michael
  • Guan, Wei
  • Yan, Xuedong
  • Huang, Ailing
• Conference:
  • Transportation Research Board 95th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2016-1-10 to 2016-1-14
• Date: 2016

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 17p
• Monograph Title: TRB 95th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Mathematical models; Memory; Perception; Traffic data; Traffic forecasting; Travel time
• Subject Areas: Data and Information Technology; Highways; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01592084
• Record Type: Publication
• Report/Paper Numbers: 16-2345
• Files: TRIS, TRB, ATRI
• Created Date: Feb 29 2016 4:56PM