A Survey on Real-Time Traffic Signal Optimization using Deep Learning and Vehicle Tracking

This comprehensive survey investigates how the convergence of deep learning and vehicle tracking technologies is transforming real-time traffic signal optimization. The objective is to systematically evaluate emerging approaches that address urban congestion challenges through intelligent traffic control. Our methodology includes analysis of state-of-the-art implementations across various urban environments, comparative assessment of deep learning architectures with emphasis on reinforcement learning techniques, and critical examination of vehicle detection systems. Key findings demonstrate 15-30% reductions in traffic delays, 20-25% decreases in emissions, and significant improvements in system responsiveness compared to traditional methods. The analysis encompasses both theoretical frameworks and real-world deployments, validating these systems’ operational scalability while addressing privacy, security, and cost-effectiveness concerns. These findings underscore the vital role of AI-driven traffic management in shaping the future of urban mobility and provide a foundation for next-generation intelligent transportation systems.