<![CDATA[Traffic congestion at signalized intersections remains a persistent challenge due to the dominance of fixed-time traffic lights that cannot respond to short-term variations in vehicle demand. Although recent advances in computer vision and Internet of Things (IoT) technologies enable adaptive traffic control, many existing solutions depend on costly hardware platforms or simulation-based validation, limiting their applicability in resource-constrained contexts. This study proposes and evaluates a low-cost AIoT-based automatic traffic light prototype that integrates visual sensing, real-time vehicle detection, and adaptive signal control within an end-to-end operational framework. The system utilizes an ESP32-CAM as a vision sensor, a server-side You Only Look Once (YOLO) model for vehicle detection, and an ESP32 microcontroller for traffic light actuation, with communication implemented via WiFi using the HTTP protocol. Experimental validation is conducted under controlled prototype scenarios with traffic densities ranging from zero to three vehicles per lane to examine feasibility under hardware and network constraints. The results indicate reliable vehicle counting performance, achieving 100% accuracy for low to moderate densities and 93.3% accuracy at higher prototype density levels. Compared with a fixed-time strategy, the adaptive mechanism dynamically adjusts green light durations, reducing idle green time under low demand and increasing service time as vehicle density rises. The findings provide empirical insights into the feasibility and performance limits of low-cost vision-based adaptive traffic control systems.]]>
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