Optimizing Traffic Signal Control Systems Using Machine Learning to Minimize Urban Congestion: A Case Study in Metropolis City
Keywords:
Traffic Signal Control, Machine Learning, Urban Congestion, Reinforcement Learning, Transportation Engineering, Smart Cities, Real-Time Data AnalysisAbstract
Urban traffic congestion remains a critical challenge for transportation engineers, particularly as urban populations continue to grow. This study investigates the application of machine learning algorithms in optimizing traffic signal control systems. We applied a combination of reinforcement learning and deep neural networks to real-time traffic data collected from major intersections in Metropolis City over a six-month period. The results indicated a significant reduction in average vehicle waiting times by 35%, and a 20% overall improvement in traffic flow efficiency was achieved compared to traditional timing schedules. This research provides empirical evidence for the benefits of integrating advanced computational techniques within existing urban infrastructures, showcasing a pathway for smarter city planning and management.
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