Distributed Particle Filter for Urban Traffic Networks Using a Platoon-Based Model

Raw measurement data are too noisy to directly obtain queue and traffic flow estimates usable for feedback control of urban traffic. In this paper, the authors propose a recursive filter to estimate traffic state by combining the real-time measurements with a reduced model of expected traffic behavior. The latter is based on platoons rather than individual vehicles in order to achieve faster implementations. This new model is used as a predictor for real-time traffic estimation using the particle filtering framework. As it becomes infeasible to let a truly large traffic network be managed by one central computer, with which all the local units would have to communicate, the authors also propose a distributed version of the particle filter (PF) where the local estimators exchange information on flows at their common boundaries. The quality of the platoon-based PFs, both centralized and distributed, is assessed by comparing their queue-size estimates with the true queue sizes in simulated data.


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  • Accession Number: 01527838
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: May 5 2014 11:57AM