• DocumentCode
    116336
  • Title

    Infinitesimal perturbation analysis of stochastic hybrid systems: Application to congestion management in traffic-light intersections

  • Author

    Wardi, Y. ; Seatzu, C.

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
  • fYear
    2014
  • fDate
    15-17 Dec. 2014
  • Firstpage
    6752
  • Lastpage
    6757
  • Abstract
    This paper presents a new approach to congestion management at traffic-light intersections. The approach is based on controlling the relative lengths of red/green cycles in order to have the congestion level track a given reference. It uses an integral control with adaptive gains, designed to provide fast tracking and wide stability margins. The gains are inverse-proportional to the derivative of the plant-function with respect to the control parameter, and are computed by infinitesimal perturbation analysis. Convergence of this technique is shown to be robust with respect to modeling uncertainties, computing errors, and other random effects. The framework is presented in the setting of stochastic hybrid systems, and applied to a particular traffic-light model. This is but an initial study and hence the latter model is simple, but it captures some of the salient features of traffic-light processes. The paper concludes with comments on possible extensions of the proposed approach to traffic-light grids with realistic flow models.
  • Keywords
    continuous systems; convergence; discrete systems; perturbation techniques; road traffic control; stability; stochastic systems; adaptive gains; congestion management; convergence; infinitesimal perturbation analysis; integral control; plant-function derivative; stability margins; stochastic hybrid systems; traffic-light grids; traffic-light intersections; Computational modeling; Convergence; Equations; Mathematical model; Optimization; Real-time systems; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-1-4799-7746-8
  • Type

    conf

  • DOI
    10.1109/CDC.2014.7040449
  • Filename
    7040449