Smart Traffic Management System Using Machine Learning

This paper presented the design of a low-cost, camera-based Smart Traffic Management System that detects and counts vehicles using YOLO, identifies emergency vehicles using an AlexNet-based classifier, adjusts green light durations according to lane congestion, and gives an automatic clear path to emergency vehicles before restoring normal operation. By relying only on camera video and open-source software, it avoids the cost of in-road sensors and vehicle-mounted hardware. Although the current implementation is a software simulation with the limitations noted above, it provides a foundation for deployment with real signal controllers and for extensions such as siren detection, multi-intersection coordination and reinforcement-learning-based timing

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