DYNAMIC TRAFFIC SIGNAL CONTROL USING A SELF-LEARNING FUZZY-NEURAL INTELLIGENT SYSTEM

This paper introduces an adaptive and self-learning intelligent traffic signal control system for networks or corridor applications. It is aimed at meeting the requirements of dynamic traffic optimal control. The system is developed based on intelligent fuzzy modeling, fuzzy-neural algorithms and traffic delay minimization algorithms. It consists of two major parts: off-line and on-line. The mathematical model used in this system is novel and is used to quantify the fuzzy traffic conditions. Results obtained from computer simulations and the lab tests with the prototype using field traffic data have shown very promising achievements in reducing traffic delays and overall performance improvements.

• Corporate Authors:

  Transportation Association of Canada (TAC)

  Ottawa, Ontario  Canada 
• Authors:
  • Wu, Jingxian
  • Zhou, W-W
  • Miska, E
• Conference:
  • 1995 TAC Annual Conference Proceedings. Volume 5 - Transportation Technology, Urban Transportation and Intelligent Transportation Systems (ITS) Sessions
  • Location: Victoria, British Columbia, Canada
  • Date: 1995-10-22 to 1995-10-25
• Publication Date: 1995

Language

• English

Media Info

• Features: Figures; References; Tables;
• Pagination: v.p.

Subject/Index Terms

• TRT Terms: Algorithms; Fuzzy sets; Learning; Networks; Traffic signal controllers; Transportation corridors
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Terminals and Facilities; I73: Traffic Control;

Filing Info

• Accession Number: 00720520
• Record Type: Publication
• Files: TRIS, ATRI
• Created Date: Apr 16 1996 12:00AM