Urban traffic congestion leads to daily delays, driven by outdated, rigid control systems.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.

This paper introduces Visible Light Communication (VLC) to enhance traffic signal efficiency and vehicle trajectory management at urban intersections. A multi-intersection traffic control system is proposed, integrating VLC localization services with learning-based traffic signal control. VLC facilitates communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. By leveraging vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) interactions, joint transmission and data collection are achieved via mobile optical receivers. The system aims to reduce waiting times for pedestrians and vehicles while improving overall traffic safety. It is designed to be flexible and adaptive, accommodating diverse traffic movements during multiple signal phases. VLC cooperative mechanisms, transmission range, relative pose concepts, and queue/request/response interactions help balance traffic flow between intersections, enhancing the overall road network performance.

Inefficient urban traffic management remains a critical challenge, as conventional signal controllers—built on fixed timing plans—cannot cope with the dynamic nature of modern city traffic. This study addresses this limitation by developing a decentralized MARL-based framework capable of coordinating five interconnected intersections as a unified traffic cell. Central to the proposed solution is the Strategic Anti-Blocking Phase Adjustment (SAPA) module, which enables intersections to autonomously modify phase durations in response to real-time traffic conditions. The framework is designed to handle heterogeneous demand patterns, with particular emphasis on arterial corridors connecting urban centers to peripheral zones. Integration of a Visible Light Communication (VLC) network allows continuous monitoring of key variables, including vehicle kinematics and pedestrian activity, feeding the agents with rich environmental feedback. Experimental evaluation confirms the effectiveness of the approach: the SAPA-augmented DQN achieves roughly 33% shorter vehicle queues and a ~70% reduction in pedestrian waiting counts relative to a standard DQN baseline. Remarkably, these gains bring the value-based method to a performance level comparable to MAPPO, a considerably more complex multi-agent policy optimization algorithm, establishing SAPA as an efficient and scalable enhancement for intelligent urban traffic control.

Urban traffic management is increasingly challenged by rising vehicle and pedestrian flows, resulting in congestion, delays, and safety riskW. This article proposes an innovative traffic signal control framework that integrates Deep Reinforcement Learning (DRL) with Visible Light Communication (VLC) to optimize operations at intersections, which are critical bottlenecks in urban networks. A decentralized DRL agent is deployed at each intersection and trained on local traffic states, enabling real-time decision-making for both vehicular and pedestrian movements. VLC is used to support low-latency, infrastructure-to-user communication, providing accurate data on positions, speeds, queue lengths, and stop durations. The system employs a queue/request/response mechanism to adapt signal phases dynamically, resolve conflicts, and prioritize urgent flows. The proposed approach is validated through simulations and real-world trials, demonstrating superior performance over centralized and traditional agent-based methods by substantially reducing waiting and travel times while enhancing safety. The solution is scalable and adaptable to a wide range of intersection configurations, with SUMO-based experiments confirming its potential for more efficient and intelligent urban traffic control.

With the modernization of vehicles, new opportunities have emerged when it comes to automotive innovation. Due to the technological evolution of today's new vehicles, it is possible for vehicles to be connected (CV) to each other, exchanging traffic and safety information. This exchange of information, together with the sensor technology applied, makes it possible for autonomous vehicles (AV) to travel from one place to another safely without human intervention. Combining these two technologies it is possible to achieve connected and autonomous vehicles (CAVs). New communication technologies between vehicles and/or infrastructures can increase safety, comfort, accessibility, and data privacy while traveling. With a connected traffic environment, it is expected to obtain important information about the vehicular and pedestrian traffic flow in order to optimise it. So visible light communication (VLC) is then proposed as an integrated approach to optimize traffic signals and vehicle trajectory at urban intersections. Using the LED technology implemented in road infrastructures like streetlamps and traffic lights and in vehicles on headlights, it is possible to establish various types of communications. With this amount of information collected via VLC it is necessary to have a system that intelligently controls in real time all traffic and responds to all incoming vehicle’s manoeuvring requests. A two connected four-arm intersection scenario with two lanes in both directions is considered. It is assumed that communication between infrastructure and vehicles exists, resulting in a cooperative environment of information sharing. The simulation process of the referred system involves using the SUMO urban mobility simulator to create the traffic scenario and generate traffic flows. A reinforcement learning system, with a Deep Q-Learning algorithm is used to control both intersections, optimising traffic flows and minimise congestion during peak hours. A comparative analysis of the simulation results highlights the benefits of the proposed approach in terms of throughput, reducing delays and minimising vehicle stops, revealing improved patterns for optimising signals and trajectories. Evaluation on separate training and test sets guarantees the model's reliability and effectiveness.

This paper introduces the use of Visible Light Communication (VLC) to enhance traffic signal efficiency and vehicle trajectory management at urban intersections. By integrating VLC localization with learning-based traffic signal control, a multi-intersection traffic system is proposed. VLC enables communication between connected vehicles and infrastructure through mobile optical receivers. The primary objectives are to reduce waiting times and improve overall traffic safety by accommodating diverse traffic movements during multiple signal phases. Cooperative mechanisms and queue/response interactions balance traffic flow between intersections, enhancing road network performance. A reinforcement learning scheme optimally schedules traffic signals, with agents at each intersection using VLC-enabled vehicle communication to improve traffic flow and overall system optimization. Evaluated using the SUMO urban mobility simulator, the system demonstrates reduced waiting and travel times. The decentralized and scalable nature of this approach highlights its potential applicability in real-world traffic scenarios.

This study introduces an innovative approach to facilitate navigation within crowded multi-terminal airports using Visible Light Communication (VLC) technology. Existing luminaires serve as transmission points, conveying encoded messages through modulated light signals to provide location-specific guidance to users. Equipped with tetrachromatic LEDs and VLC capabilities, these luminaires efficiently transmit data, demonstrated through coverage map analysis and the implementation of the OOK modulation scheme. A novel mesh cellular hybrid structure is proposed, eliminating traditional gateways and enhancing system flexibility. Integrating VLC into an Edge/Fog architecture leverages VLC's wireless connectivity and secure line-of-sight communication while utilizing existing lighting infrastructure. This integration enables distributed data processing, storage, and communication at the network edge, thereby improving system performance and responsiveness. The paper presents a detailed airport model generation and analyzes two user categories: pedestrians and luggage/passenger carriers. Users are equipped with PINPIN optical sensors to receive and interpret modulated light signals, facilitating localization and positioning calculations. A communication protocol tailored to VLC specifications is discussed, alongside coding and decoding techniques to ensure reliable transmission. This approach demonstrates the potential of VLC to enhance navigation and user experience in high-density environments like airports.

This paper introduces a novel approach, Visible Light Communication (VLC), to optimize urban intersections by integrating VLC localization services with learning-based traffic signal control. The system enhances communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. Through Vehicle-to-Vehicle (V2V) and Infrastructure-to-Vehicle (I2V) interactions, joint data transmission and collection occur via mobile optical receivers. The goal is to reduce waiting times for pedestrians and vehicles, enhancing overall traffic safety by employing flexible and adaptive measures accommodating diverse traffic movements. VLC cooperative mechanisms, transmission range, relative pose concepts, and queue/request/response interactions help balance traffic flow and improve road network performance. Evaluation in the SUMO urban mobility simulator demonstrates advantages, reducing waiting and travel times for both vehicles and pedestrians. The system employs a reinforcement learning scheme for effective traffic signal scheduling, utilizing VLC-ready vehicles to communicate positions, destinations, and routes. Agents at intersections calculate optimal strategies, communicating to optimize overall traffic flow. The proposed decentralized and scalable approach, especially suitable for multi-intersection scenarios, showcases the feasibility of applying reinforcement learning in real-world traffic scenarios.

Urban traffic management faces growing challenges due to increasing volumes of vehicles and pedestrians, resulting in congestion, delays, and safety concerns. This study introduces an innovative traffic signal control framework that integrates Multi-Agent Reinforcement Learning (MARL) with Visible Light Communication (VLC) and a Self-Adaptive Phase Adjustment (SAPA) module to enhance coordination across urban intersections. In the proposed architecture, each intersection is governed by an independent Deep Reinforcement Learning (DRL) agent capable of making real-time, decentralized decisions for both vehicular and pedestrian flows. Cooperative behavior emerges through the MARL framework, allowing agents to account for the dynamic states of neighboring intersections and improve network-wide efficiency. VLC provides high-resolution, low-latency data exchange between vehicles, pedestrians, and infrastructure, enabling precise sensing of position, speed, queue length, and stop duration. The introduction of the SAPA module further enhances adaptability by dynamically adjusting phase durations based on real-time queue/request/response patterns, resolving conflicts and prioritizing urgent demands. Extensive simulations and field tests demonstrate that the MARL–VLC–SAPA system significantly outperforms centralized and conventional agent-based approaches, reducing waiting and travel times while improving overall safety and responsiveness in complex urban environments.

The rapid growth of urban vehicle and pedestrian flows has intensified congestion, delays, and safety concerns, underscoring the need for sustainable and intelligent traffic management in modern cities. Traditional centralized traffic signal control systems often face challenges of scalability, heterogeneity of traffic patterns, and limited real-time adaptability. To address these limitations, this study proposes a decentralized Multi-Agent Reinforcement Learning (MARL) framework for adaptive traffic signal control, where Deep Reinforcement Learning (DRL) agents are deployed at each intersection and trained on local conditions to enable real-time decision-making for both vehicles and pedestrians. A key innovation lies in the integration of Visible Light Communication (VLC), which leverages existing LED-based infrastructure in traffic lights, streetlights, and vehicles to provide high-capacity, low-latency, and energy-efficient data exchange, thereby enhancing each agent’s situational awareness while promoting infrastructure sustainability. The framework introduces a queue–request–response mechanism that dynamically adjusts signal phases, resolves conflicts between flows, and prioritizes urgent or emergency movements, ensuring equitable and safer mobility for all users. Validation through microscopic simulations in SUMO and preliminary real-world experiments demonstrates reductions in average waiting time, travel time, and queue lengths, along with improvements in pedestrian safety and energy efficiency. These results highlight the potential of MARL–VLC integration as a sustainable, resilient, and human-centered solution for next-generation urban traffic management.