Author(s): Vieira, Manuela ; Galvão, Gonçalo ; Vieira, Manuel Augusto ; Véstias, Mário ; Vieira, Pedro ; Louro, Paula
Date: 2024
Persistent ID: http://hdl.handle.net/10400.21/18121
Origin: Repositório Científico do Instituto Politécnico de Lisboa
Subject(s): visible light communication (VLC); deep reinforcement learning (DRL); traffic signal optimization; connected vehicles (CVs); multi-agent systems; urban traffic management; queue/request/ response methodology; traffic flow simulation
This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to enhance traffic signal control, reduce congestion, and improve safety, through real-time monitoring and dynamic traffic management. Leveraging VLC technology, the system uses existing road infrastructure to transmit live data on vehicle and pedestrian positions, speeds, and queues. AI agents, employing Deep Reinforcement Learning (DRL), process this data to manage traffic flows dynamically, applying anti-bottleneck and rerouting techniques to balance pedestrian and vehicle waiting times. A centralized global agent coordinates the local agents controlling each intersection,enabling indirect communication and data sharing to train a unified DRL model. This model makes real-time adjustments to traffic light phases, utilizing a queue/request/response system for adaptive intersection management. Tested using simulations and real-world trials involving standard and rerouting scenarios, the approach demonstrates significantly better performance in regard to the rerouting configuration, reducing congestion and enhancing traffic flow and pedestrian safety.Scalable and adaptable to various intersection types, including four-way, T-intersections, and roundabouts, the system’s efficacy is validated using the SUMO urban mobility simulator, resulting in notable reductions to travel and waiting times for both vehicles and pedestrians.
