Research on Signal Control Method of Single Intersection Based on Reinforcement Learning

With growth of urbanization, urban traffic becomes more and more congested. As an important node of traffic flow convergence and diversion, the intersection efficiency will affect the operation efficiency of the urban transportation system to a large extent. The existing intersection signal control mostly uses the empirical fixed signal timing. The empirical fixed signal timing seriously affects the traffic efficiency of the intersection. This paper starts with the signal control method of a single intersection in the city, and improves the operation efficiency of the intersection by optimizing the signal control method of the intersection. This paper proposes the intersection signal control method based on reinforcement learning, and adopts the Q-learning reward and punishment signal design method, implementing optimization of intersection signal control. Finally, the simulation experiment of the intersection is carried out by PTV-Vissim9.0 software to verify the feasibility of the theory.

• Record URL:
  • https://doi.org/10.1061/9780784483053.015
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784483053
• Supplemental Notes:
  • © 2020 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Ren, Yilong
  • Zhang, Le
  • Jiang, Han
  • Liu, Chengsheng
• Conference:
  • 20th COTA International Conference of Transportation Professionals
  • Location: Xi’an , China
  • Date: 2020-8-14 to 2020-8-16
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 173-184
• Monograph Title: CICTP 2020: Transportation Evolution Impacting Future Mobility

Subject/Index Terms

• TRT Terms: Machine learning; Signalized intersections; Simulation; Traffic signal control systems; Urban areas
• Identifier Terms: VISSIM (Computer model)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01767314
• Record Type: Publication
• ISBN: 9780784483053
• Files: TRIS, ASCE
• Created Date: Mar 22 2021 10:34AM