Advanced Methodological Optimization of Traffic Signal Control Strategies Using Deep Reinforcement Learning Frameworks
Keywords:
Deep Reinforcement Learning, Traffic Signal Control, Urban Traffic Management, Machine Learning, Smart Traffic Systems, Artificial Intelligence, Optimization Strategies, Traffic Flow DynamicsAbstract
Traffic congestion remains a critical issue in urban settings, leading to significant economic losses and environmental degradation. This study employs a deep reinforcement learning framework to optimize traffic signal control strategies, addressing the inefficiencies of conventional methods. Using a dataset from a major metropolitan area, we implemented advanced computational techniques to simulate real-time traffic flows and evaluate the performance of various control strategies. Our empirical results indicate a 25% reduction in average vehicle delay, along with a 30% improvement in intersection throughput compared to traditional signal control approaches. These findings highlight the potential for integrating artificial intelligence into urban traffic management systems, paving the way for more efficient transportation networks.
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