Multi-Agent Adaptive Traffic Signal Control Based on Q-Learning and Speed Transition Matrices

Advancements in technology and the emergence of vehicle-to-everything communication encourage new research approaches. Continuously sharing data through the onboard unit, connected vehicles (CVs) have proven to be a valuable source of real-time microscopic traffic data. Utilizing CVs as mobile sensors is a key driver for traffic safety improvement and increasing the effective operative road capacity. Data obtained from CVs can be effectively processed using speed transition matrices (STMs) while preserving spatial and temporal characteristics. This research proposes a new approach to adaptive traffic signal control utilizing STMs and a cooperative multi-agent learning system for the environment of CVs. To confirm its effectiveness, the concept is tested in a simulated environment of an intersection network, comparing different CVs’ penetration rates and cooperation coefficients between agents.

• Publication year

  2025

• Venue

  Italian National Conference on Sensors

• Publication date

  2025-12-01

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.3390/s25237327PMID 41374701PMCID 12694223
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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