Enhancing Traffic Flow Efficiency Through Adaptive Signal Control: A Real-Time Data-Driven Approach
Keywords:
adaptive signal control, traffic management, real-time data, urban congestion, machine learning, traffic optimization, smart transportation, empirical analysisAbstract
Traffic congestion remains a critical challenge in urban transportation, leading to increased travel times and economic losses. This study employs a real-time data-driven adaptive signal control system designed to optimize traffic flow at critical intersections. Utilizing advanced machine learning algorithms, we analyzed a rich dataset collected from smart city sensors over six months. By integrating vehicle count, speed, and delay data, we developed a predictive model that dynamically adjusts signal timings based on real-time traffic conditions. Our results indicate a significant reduction in average vehicle delay by 35% and an increase in intersection throughput by 25% compared to conventional fixed-time signal strategies. This paper contributes to the growing body of knowledge on smart transportation systems, highlighting the practical applications of adaptive signal control in mitigating urban congestion.
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