## 1 Introduction  
Urban traffic management presents a significant challenge for modern cities, as the growing volume of vehicles and pedestrians contributes to congestion, delays, and safety risks [1]. Given the spatial constraints in urban areas, expanding road infrastructure is no longer a viable solution. Instead, optimizing traffic flow at intersections has become critical, with adaptive traffic signal control emerging as one of the most effective strategies. Adaptive systems can significantly reduce congestion and improve intersection efficiency by using real-time data from traffic networks, such as traffic flow, waiting times, and vehicle queues. Visible Light Communication (VLC) offers a versatile and efficient solution for modern traffic management, seamlessly integrating into existing infrastructure, such as vehicles, streetlights, and traffic signals [2; 3]. Beyond illumination and communication, VLC enables dynamic and adaptive traffic signal control [4; 5], particularly when combined with AI. This paper introduces a novel approach to traffic management based on DRL that emphasizes the design and impact of reward functions tailored for standard and rerouting scenarios, enhancing both vehicular efficiency and pedestrian safety. Intersections are key bottlenecks in road networks, making intelligent signal control crucial for improving traffic flow. Recent advances in Deep Reinforcement Learning (DRL) have shown promise in dynamically managing traffic signals for both vehicles and pedestrians [6; 7]. However, optimizing traffic flow across multiple intersections is challenging due to varying traffic conditions and the need for information sharing [8]. The rise of connected vehicles (CVs) adds further potential to traffic management. Through advanced communication technologies, connected vehicles (CVs) can exchange real-time traffic and safety information with each other and the infrastructure, improving road safety, comfort, and efficiency. This connected traffic environment enables better optimization of both vehicular and pedestrian flows. VLC is an innovative solution that can complement CV technologies to optimize traffic management. By leveraging LED technology in both road infrastructure and vehicles, VLC paves the way for smarter, more efficient traffic management. This work investigates the integration of VLC and AI, particularly DRL, to design reward functions that optimize traffic signal control in both standard and rerouting scenarios. This research focuses on applying DRL in vehicular communication to enhance traffic flow, improve intersection efficiency, and balance the needs of vehicles and pedestrians in real-world urban environments. By leveraging emerging technologies, such as VLC and connected vehicles (CVs), the study aims to demonstrate their potential to address modern traffic management challenges effectively. The core contributions are as follows:  
* Demonstrating the feasibility of VLC in outdoor environments as a complementary technology to existing vehicular communication systems. * Proposing an integrated framework where VLC is utilized to collect real-time traffic data, which are then processed and analyzed using DRL models. * Showcasing how the proposed approach can effectively optimize vehicle flow and traffic signal control in urban traffic scenarios, enhancing intersection efficiency and addressing modern traffic management challenges. The paper is organized as follows. The introduction outlines the challenges in urban traffic management and explores the potential of integrating connected vehicles (CVs) with Visible Light Communication (VLC) to optimize traffic signal control and rerouting. Section 2 discusses the advantages of using VLC for communication in traffic systems, highlights key issues, such as congestion and safety, examines VLC's role in real-time traffic management, and introduces Deep Reinforcement Learning (DRL) for traffic signal optimization.