Traffic Signal Optimization and Simulation Using Fuzzy Dynamic Programming

This research proposes a new methodology and develops its implementation algorithm that utilizes Fuzzy Dynamic Programming (FDP) to provide adaptive signal control of two closely-spaced intersections in response to real-time traffic fluctuations. A characteristic feature of this new approach is that the uncertainties and imprecision in vehicle detection, forecasted traffic arrivals, discharge and resulting delay and other performance measures can be taken into account by the fuzzy set theory. It makes the approach superior to the conventional deterministic method and more efficient than stochastic methods. Consequently, the algorithm would be appropriate for real-time implementation. The problem is formulated in a decision network to find the optimal phase sequencing and phase duration that minimizes a pre-specified performance measure (such as delay or queue length) over a finite horizon. The optimization process is based on advanced vehicle information obtained from loop detectors set back a certain distance from the stop-line. Vehicle trajectories -- from their detection till their arrival at the stop-line -- are modeled at the microscopic level and described in fuzzy sets. The simulation results have exhibited that the DP algorithm is superior to PASSER III and TRANSYT-7F in handling demand fluctuations for medium- to high-flow scenarios when the field demand is increased from the one used in off-line optimization.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 2140-2151
  • Monograph Title: CICTP 2014: Safe, Smart, and Sustainable Multimodal Transportation Systems

Subject/Index Terms

Filing Info

  • Accession Number: 01531819
  • Record Type: Publication
  • ISBN: 9780784413623
  • Files: TRIS, ASCE
  • Created Date: Jul 2 2014 3:03PM