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Title	A More Efficient Way To Control Traffic Lights Through Ai-Led Smart City Management
ID_Doc	2745
Authors	Choudhary S.; Ali S.S.; Babu N.R.; Sharma H.; Kaliraman B.; Dhankhar Y.
Year	2023
Published	Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023
DOI	http://dx.doi.org/10.1109/ICTACS59847.2023.10390336
Abstract	Urban regions are nevertheless plagued by traffic congestion, which increases travel times, fuel use, and greenhouse gas emissions. To lessen traffic congestion and enhance the performance of traffic systems, effective traffic control is crucial. The ideal flow of traffic is not achieved by traditional traffic light control technologies, which are based on planned signal plans or fixed timings that do not adjust to real-time traffic circumstances. By dynamically changing signal timing based on real-time traffic data, artificial intelligence (AI) has emerged as a viable method to optimize traffic signal regulation. This paper suggests a real-time traffic signal timing optimization system that is powered by AI and leverages machine learning methods. This suggested work is a traffic light control system that uses artificial intelligence and offers a number of advantages.For instance, a shorter trip distance, improved fuel economy, and lower pollutants. Additionally, the system is easily upgradeable with new data sources or algorithms and may be incorporated with current traffic management systems. It suggested using an artificial intelligence-based traffic light control system (TLCS) to greatly enhance traffic flow and relieve congestion in metropolitan areas. Real-time data and machine learning algorithms combined to enable dynamic and adaptive signal control, resulting in more effective traffic management and improved quality of life for city dwellers. This clarifies their potential to enhance urban transportation and lessen congestion. © 2023 IEEE.
Author Keywords	Fog; Haze; OpenCv; radars; Sensors; YOLOv3

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