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Intelligent Traffic Control Strategies for VLC-Connected Vehicles and Pedestrian Flow Management.

Gonçalo Galvão1,2, Manuela Vieira1,2,3, Manuel Augusto Vieira1,3

  • 1Electronics Telecommunication and Computer Department, Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, 1949-014 Lisboa, Portugal.

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Summary
This summary is machine-generated.

This study introduces a Multi-Agent Reinforcement Learning (MARL) system with Strategic Anti-Blocking Phase Adjustment (SAPA) to dynamically manage urban traffic signals. The adaptive system reduces congestion, improves traffic flow, and enhances safety in complex city environments.

Keywords:
autonomous vehiclesdeep reinforcement learning (DRL)multi-agent systemstraffic management and efficiencyurban traffic managementvisible light communication (VLC)

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Area of Science:

  • Intelligent Transportation Systems
  • Artificial Intelligence in Urban Planning
  • Traffic Engineering

Background:

  • Urban traffic congestion is a persistent issue caused by rigid, outdated traffic signal control systems.
  • Increasing vehicle numbers exacerbate delays as fixed-phase signals fail to adapt to real-time traffic conditions.
  • Existing systems lack the flexibility to optimize traffic flow for diverse urban mobility patterns.

Purpose of the Study:

  • To develop and evaluate a decentralized Multi-Agent Reinforcement Learning (MARL) system for adaptive traffic signal control.
  • To introduce and test the Strategic Anti-Blocking Phase Adjustment (SAPA) module for dynamic phase time adjustments.
  • To optimize arterial traffic flow and manage diverse traffic scenarios within urban traffic cells, with potential for city-wide scalability.

Main Methods:

  • Implementation of a decentralized Multi-Agent Reinforcement Learning (MARL) system managing five interconnected intersections.
  • Integration of a novel Strategic Anti-Blocking Phase Adjustment (SAPA) module for dynamic traffic signal phase optimization.
  • Utilizing Visible Light Communication (VLC) infrastructure for real-time vehicle and pedestrian data exchange.

Main Results:

  • Significant reduction in vehicle queues and waiting times.
  • Demonstrated increase in average vehicle speeds and overall traffic flow efficiency.
  • Improved pedestrian safety and enhanced management of complex traffic scenarios.

Conclusions:

  • The developed MARL system with SAPA offers a promising solution for adaptive and intelligent urban traffic control.
  • The system effectively optimizes traffic flow by dynamically adjusting signal phases based on real-time conditions.
  • The approach shows potential for significant improvements in urban mobility and safety, scalable to city-wide networks.