Bayesian Travel Time Reliability Models

Travel time reliability is a stochastic process affected by multiple factors, with traffic volume being the most important one. This study built up and advanced the multi-state models by proposing regressions on the proportions and distribution parameters for underlying traffic states. The Bayesian analysis provides valid credible intervals for each parameter without asymptotic assumption. Two alternative approaches were proposed and evaluated. The first approach is a Bayesian multi-state travel time regression model which provides a regression for key model parameters to traffic volume; the second approach is a hidden Markov regression which not only provides a link between key model parameters and traffic volume, but also incorporates the dependency structure among traffic volume in adjacent time windows. Both approaches provide advanced methodology for modeling traffic time reliability under complex stochastic scenarios.

• Record URL:
  http://www.mautc.psu.edu/docs/VT-2012-02.pdf
• 
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Virginia Tech Transportation Institute

  Blacksburg, Virginia  United States 

  Mid-Atlantic Universities Transportation Center

  Pennsylvania State University
  201 Transportation Research Building
  University Park, PA  United States  16802-4710

  Research and Innovative Technology Administration

  University Transportation Centers Program
  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Guo, Feng
  • Zhang, Dengfeng
  • Rakha, Hesham
• Publication Date: 2015-6-30

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; References; Tables;
• Pagination: 66p

Subject/Index Terms

• TRT Terms: Bayes' theorem; Methodology; Regression analysis; Reliability (Statistics); Traffic simulation; Traffic volume; Travel time
• Subject Areas: Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01613779
• Record Type: Publication
• Files: UTC, TRIS, RITA, ATRI, USDOT
• Created Date: Oct 21 2016 4:34PM