Reinforcement learning-based multi-agent system for network traffic signal control

A challenging application of artificial intelligence systems involves the scheduling of traffic signals in multi-intersection vehicular networks. This paper introduces a novel use of a multi-agent system and reinforcement learning (RL) framework to obtain an efficient traffic signal control policy. The latter is aimed at minimising the average delay, congestion and likelihood of intersection cross-blocking. A five-intersection traffic network has been studied in which each intersection is governed by an autonomous intelligent agent. Two types of agents, a central agent and an outbound agent, were employed. The outbound agents schedule traffic signals by following the longest-queue-first (LQF) algorithm, which has been proved to guarantee stability and fairness, and collaborate with the central agent by providing it local traffic statistics. The central agent learns a value function driven by its local and neighbours' traffic conditions. The novel methodology proposed here utilises the Q-Learning algorithm with a feedforward neural network for value function approximation. Experimental results clearly demonstrate the advantages of multi-agent RL-based control over LQF governed isolated single-intersection control, thus paving the way for efficient distributed traffic signal control in complex settings.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
• Authors:
  • AREL, I
  • LIU, C
  • Urbanik, T
  • KOHLS, A G
• Publication Date: 2010

Language

• English

Media Info

• Pagination: 128-135
• Serial:
  • IET Intelligent Transport Systems
  • Volume: 4
  • Issue Number: 2
  • Publisher: Institution of Engineering and Technology (IET)
  • ISSN: 1751-956X
  • EISSN: 1751-9578
  • Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Intelligent transportation systems; Intersections; Stiffness; Traffic control; Traffic delays; Traffic signals
• Uncontrolled Terms: Optimum
• ITRD Terms: 9010: Delay; 5931: Flexibility; 5931: Flexible; 8735: Intelligent transport system; 455: Junction; 5931: Rigid; 5931: Rigidity; 5931: Stiff; 654: Traffic control; 565: Traffic signal
• Subject Areas: Highways; Operations and Traffic Management; I73: Traffic Control;

Filing Info

• Accession Number: 01336510
• Record Type: Publication
• Source Agency: TRL
• Files: ITRD
• Created Date: Apr 15 2011 3:49PM