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Copyright (c) 2024 Global Journal of Artificial Intelligence and Technology Development
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Traffic control is an important part of urban planning, safety, and efficiency. In this research we look at how AI-based traffic control is implemented using computer vision and compare it to older approaches. We investigated the potential benefits of artificial intelligence systems in optimizing traffic flow, improving safety, and decreasing congestion. The study compares the performance of a computer vision-based AI traffic controller to traditional traffic management approaches. Traffic congestion is a widespread problem in cities, resulting in lost time, higher fuel usage, and increased pollution. To solve these issues, there has been a surge in interest in the use of artificial intelligence (AI) and computer vision technologies for traffic management. We look at the creation and assessment of an AI-based traffic control system, as well as how it compares to existing techniques. A new method of traffic control that makes use of the notion of object counting is applied. We present a system that can intelligently control traffic based on real-time item counting data using computer vision and artificial intelligence. We evaluated the two traffic control methods based on several critical performance metrics, including the accuracy of pedestrian detection and vehicle counts in individual lanes. Compared to the conventional approach, the AI-powered traffic control system demonstrated noteworthy advantages. It achieved a 15% reduction in travel time, a 10% decrease in fuel consumption, and an impressive 25% enhancement in traffic flow efficiency. Notably, the AI system also registered a remarkable 30% reduction in traffic accidents, highlighting its potential to significantly enhance safety on the roadways.