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Smart city article details
| Title | Enhancing Traffic Flow With Visible Light Communication: A Deep Reinforcement Learning Approach |
|---|---|
| ID_Doc | 24038 |
| Authors | Vieira M.A.; Galvão G.; Vieira M.; Véstias M.; Louro P.; Jardim-Goncalves R. |
| Year | 2025 |
| Published | Proceedings of SPIE - The International Society for Optical Engineering, 13374 |
| DOI | http://dx.doi.org/10.1117/12.3039148 |
| Abstract | Visible Light Communication (VLC) offers an innovative solution for vehicular communication, combining illumination and data transmission seamlessly within existing infrastructure. This paper presents a novel traffic management system that integrates VLC with Artificial Intelligence (AI) to enhance safety and reduce delays for pedestrians and vehicles. By leveraging reinforcement learning (RL), the system adapts traffic signals dynamically based on real-time data, achieving significant improvements in traffic flow and efficiency. The proposed approach employs Deep Reinforcement Learning (DRL), with AI agents managing individual intersections or entire networks. VLC’s rapid data exchange capabilities enable real-time queue/request/response interactions, optimizing signal operations. Simulations conducted in the SUMO traffic simulator confirm the system’s effectiveness, demonstrating reduced waiting times and improved travel experiences compared to traditional methods. A major challenge addressed is the scalability of traffic signal coordination across multiple intersections. The distributed RL approach facilitates semi-independent yet cooperative signal control, accommodating both pedestrian and vehicle dynamics for optimal traffic management. This solution exhibits adaptability across various scenarios, positioning it as a robust framework for urban mobility. Aligned with CyberSecPro's mission, this research emphasizes the integration of cybersecurity in smart city infrastructures. The VLC-based system not only optimizes traffic management but also ensures secure, privacy-respecting data exchanges, advancing safe and efficient innovations in urban traffic systems. © 2025 SPIE. |
| Author Keywords | Connected Vehicles (CV); Cybersecurity; Deep Reinforcement Learning (DRL); Multi-Agent Systems; Queue/Request/Response Methodology; Traffic Flow Simulation; Traffic Signal Optimization; Urban Traffic Management; Visible Light Communication (VLC) |
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