A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation

S. Y. Liu, S. Lin, Y. B. Wang, B. De Schutter, W. H.K. Lam

Research output: Contribution to journalConference articleScientificpeer-review

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In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks.

Original languageEnglish
Pages (from-to)15029-15034
Volume53 (2020)
Issue number2
Publication statusPublished - 2021
Event21st IFAC World Congress 2020 - Berlin, Germany
Duration: 12 Jul 202017 Jul 2020


  • Bayesian
  • Markov traffic model
  • Traffic signals
  • Urban traffic network


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