Smart Traffic Control System for Dubai: A Simulation Study Using YOLO Algorithms

Abstract

Dubai's growing population and public transportation have led to an increase in vehicular traffic and associated challenges. To tackle these issues, there is a rising interest in using machine learning (ML)techniques to improve the city's traffic control system. This study aims to explore the potential of ML in enhancing traffic management and creating a sustainable urban environment. A novel approach to traffic management in Dubai that combines AI and ML algorithms has been proposed, with the potential to significantly improve traffic flow and safety. A simulation study on a smart traffic control system that utilizes YOLO algorithms for real-time vehicle detection and counting is presented. The system optimizes traffic light timings and balances traffic load among different roads to reduce congestion and improve traffic flow. The simulation results demonstrate that the system is highly effective in adapting to changing traffic conditions and reducing congestion. The study concludes that the use of ML algorithms such as YOLO has the potential to revolutionize traffic management in urban areas, leading to a more efficient and sustainable transportation system. Further research and development in this area could bring significant benefits to both motorists and the environment.