• L Sherin Beevi
• Dr. Joe Prathap, P. M.
• Nandyala Gowtham
• Nallapaneni Deepak
• Ravilla Abhishek

The traditional traffic light system that we see on roads today works on a fixed time schedule, where each light cycle is set to a predetermined duration. However, this approach can lead to inefficiencies, particularly during peak traffic hours. A smart AI-based traffic light system can overcome these challenges by using real-time data and machine learning algorithms to optimize traffic flow. The system can collect data from various sources, such as sensors, cameras, and GPS devices, to get a real-time picture of traffic conditions on the road. Using this data, the system can dynamically adjust the timing and sequence of traffic lights to optimize traffic flow, reduce congestion, and improve safety. For example, during peak hours, the system can give more green light time to the direction with the most traffic, while also dynamically adjusting the cycle duration based on real-time traffic conditions. Moreover, the system can learn from historical traffic data to optimize traffic light schedules during different times of the day and week. The AI algorithms can identify patterns in traffic flow, such as rush hour traffic, and adjust the traffic light cycles accordingly to improve traffic flow and reduce congestion. Overall, a smart AI-based traffic light system can help reduce traffic congestion, improve safety on the roads, and make our cities more efficient and livable

CNN, Deep learning, Traffic optimization

"Efficient Traffic Flow Management System Using Ai-Powered Traffic Lights in DEEP Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.e514-e521, November-2023, Available :http://www.jetir.org/papers/JETIR2311467.pdf

"Efficient Traffic Flow Management System Using Ai-Powered Traffic Lights in DEEP Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppe514-e521, November-2023, Available at : http://www.jetir.org/papers/JETIR2311467.pdf