Adaptive Traffic Signal System Utilizing YOLOv11 and Fuzzy Logic for Congestion Mitigation - Dio Damas Permadi; Muchtar Ali Setyo Yudono; Abdul Haris Kuspranoto; Ardin Rozandi; Marina Artiyasa; Alvin Mubarok; Dwi Septiani (Universitas Islam Indragiri, 2026)
The increasing number of vehicles in urban and suburban areas has led to traffic congestion, resulting in longer travel times, higher exhaust emissions, and an increased risk of accidents. Conventional fixed-time traffic signal systems often fail to respond dynamically to changing traffic conditions, leading to inefficient vehicle queues. This study proposes the development of an adaptive traffic signal system that utilizes YOLOv11 and fuzzy logic to detect vehicle volume and adjust green light durations in real time. YOLOv11 is employed to detect vehicles in each lane, while fuzzy logic is used to regulate green signal durations based on the detected vehicle counts. Experimental results demonstrate a detection accuracy of 0.92 and a recall of 0.93. The green light duration varies from 80 seconds for low traffic volumes to 100 seconds for high traffic volumes. The traffic signal cycle is dynamically adjusted according to vehicle density, with a maximum total cycle time of 100 seconds. Overall, the proposed system is proven effective in reducing congestion and improving traffic management efficiency at intersections with high vehicle volumes.