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

Gonçalo Galvão,M. Vieira,M. Vieira,Mário Véstias,P. Louro

Published 2025 in Italian National Conference on Sensors

ABSTRACT

Highlights Demonstrates adaptive traffic control using Deep Q-Learning in a 5-intersection network. Introduces the SAPA module for dynamic, predictive signal phase adjustments. Integrates VLC-enabled connected vehicles and pedestrian flows for realistic scenarios. SAPA reduces congestion and queue lengths, enhancing overall traffic efficiency. Supports safer, more responsive urban mobility and smarter traffic management. What are the main findings? Demonstrates the potential of adaptive deep reinforcement learning strategies to improve urban traffic flow in complex multi-intersection scenarios. Highlights how integrating SAPA can further enhance efficiency, reducing congestion and delays for both vehicles and pedestrians. What are the implications of the main findings? Adaptive deep reinforcement learning with SAPA can improve urban traffic flow, reducing congestion and delays. Cities can achieve more efficient, adaptive traffic management, enhancing mobility and pedestrian safety. Abstract Urban traffic congestion leads to daily delays, driven by outdated, rigid control systems. As vehicle numbers grow, fixed-phase signals struggle to adapt to real-time conditions. This work presents a decentralized Multi-Agent Reinforcement Learning (MARL) system to manage a traffic cell composed of five intersections, introducing the novel Strategic Anti-Blocking Phase Adjustment (SAPA) module, developed to enable dynamic phase time adjustments. The goal is to optimize arterial traffic flow by adapting strategies to different traffic generation patterns, simulating priority movements along circular or radial arterials, such as inbound or outbound city flows. The system aims to manage diverse scenarios within a cell, with the long-term goal of scaling to city-wide networks. A Visible Light Communication (VLC) infrastructure is integrated to support real-time data exchange between vehicles and infrastructure, capturing vehicle position, speed, and pedestrian presence at intersections. The system is evaluated through multiple performance metrics, showing promising results: reduced vehicle queues and waiting times, increased average speeds, and improved pedestrian safety and overall flow management. These outcomes demonstrate the system’s potential to deliver adaptive, intelligent traffic control for complex urban environments.

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