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By: Saranya M, Monica Sree A, Chandra Sudiksha S, Archana P, and C Hemalatha
Abstract– Traffic lights at intersections automatically adjust based on the flow of traffic. Reinforcement learning enables continuous learning and optimization for more effective traffic control. Traffic lights are dynamically adjusted thanks to sensors that collect real-time data on the number of cars. By predicting the condition of the roads, predictive analytics helps avoid traffic jams. reduces stop-and-go traffic, which in turn reduces fuel consumption and pollution. A scalable system designed to boost travel efficiency.the additional feature of rising of brick or throne on the road when the signal is red. The system is designed to be scalable, making it suitable for use in multiple intersections and adaptable as traffic demands increase. This helps enhance travel efficiency and supports smoother, safer road networks. A notable safety feature includes the use of physical barriers, such as retractable bricks or roadblocks, which rise when the traffic signal turns red. This mechanism prevents vehicles from crossing the intersection during restricted periods, improving safety for both drivers and pedestrians. By combining intelligent signal adjustments, real-time monitoring, and innovative safety interventions, this system offers a comprehensive approach to modern traffic management, contributing to more sustainable, efficient, and safer urban transportation.
Keywords: Traffic lights ,Reinforcement learning, Optimization, Traffic control, Sensors,Predictive analytics,Traffic jam,Stop-and-go traffic,Fuel consumption,Pollution reduction,Scalable system,Travel efficiency.
Citation:
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