Stochastic Link Flow Model for signalized traffic networks with uncertainty in demand

S. Lin, T. L. Pan, W. H.K. Lam, R. X. Zhong, B. De Schutter

Research output: Contribution to journalConference articleScientificpeer-review

4 Citations (Scopus)
31 Downloads (Pure)

Abstract

In order to investigate the stochastic features in urban traffic dynamics, we propose a Stochastic Link Flow Model (SLFM) for signalized traffic networks with demand uncertainties. In the proposed model, the link traffic state is described using four different link state modes, and the probability for each link state mode is determined based on the stochastic link states. The SLFM model is expressed as a finite mixture approximation of the link state probabilities and the dynamic link flow models for all the four link state modes. Using data from microscopic traffic simulator SUMO, we illustrate that the proposed model can provide a reliable estimation of the link traffic states, and as well as good estimations on the link state uncertainties propagating within a signalized traffic network.

Original languageEnglish
Pages (from-to)458-463
JournalIFAC-PapersOnLine
Volume51
DOIs
Publication statusPublished - 2018
Event15th IFAC Symposium on Control in Transportation Systems - Savona, Italy
Duration: 6 Jun 20188 Jun 2018
Conference number: 15
http://www.cts2018.unige.it/

Keywords

  • Stochastic traffic model
  • Traffic signals
  • Urban traffic network

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