A Joint Estimation of Network Origin-Destination Traffic Flow and Route Choice Model Parameters Using the Bayesian Approach

This study proposes a statistical model to estimate link traffic flows in a congested network. In this study, it is assumed that traffic flows conform to the stochastic user equilibrium (SUE) principle. In the case of SUE, as the authors know, the network traffic flow pattern is obviously affected by the dispersion parameter adopted in the logit formula. Hence, they make some efforts to estimate this dispersion parameter in this paper. In this study, the Bayesian theorem is applied to derive the probability density function of the conditional distribution. While the calculation of Bayesian posterior will typically be intractable, a Markov chain Monte Carlo (MCMC) algorithm is used to estimate the traffic flow. The performance of the proposed method is tested on the Nguyen-Dupuis network through a numerical example.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 1449-1460
  • Monograph Title: CICTP 2016: Green and Multimodal Transportation and Logistics

Subject/Index Terms

Filing Info

  • Accession Number: 01606960
  • Record Type: Publication
  • ISBN: 9780784479896
  • Files: TRIS, ASCE
  • Created Date: Jun 29 2016 3:06PM