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@article{200404,
        author = {KASIREDDY VANAJA and GANTA SUPRIYA and ERINENI LAKSHMI ROJA and Mrs.N.Radha},
        title = {INTELLIGENT TRAFFIC CONTROL SYSTEM USING REINFORCEMENT LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {4116-4124},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200404},
        abstract = {This project presents an Intelligent Traffic Control System using Reinforcement Learning (RL) to optimize traffic signal timing and improve traffic flow at intersections. The system analyses traffic conditions and dynamically controls signal phases to reduce congestion and waiting time. The model is implemented using Python, TensorFlow, and RL frameworks like OpenAI Gym and Stable Baselines3. Vehicle detection and traffic density estimation are performed using OpenCV, while traffic scenarios are simulated using SUMO. The RL agent learns optimal signal control strategies through interaction with the simulated environment. This approach helps in reducing traffic congestion and improving urban traffic management.},
        keywords = {Reinforcement Learning, Intelligent Traffic Control, Traffic Optimization, Machine Learning, Urban Mobility, Smart City Automation.},
        month = {May},
        }