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Urban infrastructure and public safety are severely hampered by traffic congestion and accidents. This research suggests an enhanced traffic light control system that makes use of deep learning methods and IoT integration to address these problems. The system computes vehicle density in real-time and indicates smart light conditions at traffic signals by combining the object identification algorithm of YOLOv3 with Internet of Things sensors. The secret to the system is the use of YOLOv3 for precise vehicle identification and categorization in video feeds recorded by security cameras. The technology can effectively identify cars, collisions, and violent incidents on the road by utilising YOLOv3. In order to enable quick emergency response, the system sends notification alerts via GSM connection to neighbouring police stations and hospitals upon detection. In addition, the suggested system uses Internet of Things devices to calculate the number of vehicles at crossings. Traffic signal timings can be dynamically adjusted thanks to this real-time data on vehicle flow, which improves traffic flow and lessens congestion. IoT integration also makes it possible for traffic signals to have smart light indication, which adjusts signal timings in response to current traffic circumstances to improve efficiency and safety.
Rahul et al. (Fri,) studied this question.