The posterior probability distribution of traffic flow: A new scheme for the assignment of stochastic traffic flow

Chong Wei, Yasuo Asakura, Takamasa Iryo

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This study proposes a new scheme for assigning traffic flows that aims to capture the stochastic nature of route traffic flows. We consider the route traffic flows to be random variables. The distribution of these random variables is formulated as a conditional probability distribution for a given assumption: the traffic network is in stochastic user equilibrium. From a Bayesian perspective, we treat the conditional distribution as a posterior distribution of route traffic flows, which is obtained using Bayes' theorem. We develop a basic Metropolis-Hastings (M-H) sampling scheme, as well as a M-H within Gibbs sampling scheme, to draw samples from the posterior distribution. We estimate characteristics such as the means and variances of route traffic flows from simulated samples. The proposed model can directly output the route traffic flows, and has a highly flexible computation process.

Original languageEnglish
Pages (from-to)753-771
Number of pages19
JournalTransportmetrica A: Transport Science
Volume9
Issue number8
DOIs
Publication statusPublished - 2013 Sep 1
Externally publishedYes

Keywords

  • Bayes' theorem
  • contemporaneous model
  • Markov chain Monte Carlo
  • posterior distribution
  • stochastic traffic assignment

ASJC Scopus subject areas

  • Transportation
  • Engineering(all)

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