GREENWAVE SYNCHRONIZATION SYSTEMS WITH DEEP REINFORCEMENT LEARNING (DRL) FOR TRAFFIC NETWORKS

Abstract

The increasing complexity of urban traffic networks in metropolitan areas demands innovative solutions for efficient traffic management. Greenwave synchronization, which aims to optimize traffic signal coordination to reduce stops and delays, has shown promise in improving traffic flow and reducing environmental impacts. However, existing solutions often fail to scale effectively in dense and large-scale networks. This paper proposes a scalable Deep Reinforcement Learning (DRL) framework designed to synchronize traffic signals across extensive urban traffic networks. By leveraging multi-agent DRL architectures and advanced spatio-temporal data integration, the system adapts dynamically to fluctuating traffic conditions while maintaining computational efficiency. The proposed framework demonstrates its scalability by managing thousands of intersections, achieving significant reductions in travel times, vehicle stops, and emissions. This study provides a foundation for implementing scalable greenwave synchronization systems, addressing the challenges posed by dense urban traffic and paving the way for sustainable metropolitan mobility.

Keywords

• Machine Learning
• Deep Learning
• Associative Classification
• Deep Reinforcement Learning (DRL)
• Scalable Urban Traffic Optimization
• Greenwave Coordination
• Sustainable Mobility

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