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@article{171118,
        author = {AKSHAY C S and JWALA JOSE and SRITHA S},
        title = {TRAFFIC PREDICTION SYSTEM USING ARTIFICIAL INTELLIGENCE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {7},
        pages = {4066-4069},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171118},
        abstract = {Traffic congestion is a pervasive issue in modern cities, leading to increased travel times, higher fuel consumption, and environmental degradation. With the growing urban population and the corresponding rise in vehicular traffic, efficient traffic management is becoming more challenging. This paper presents a traffic prediction system (TPS) that utilizes Artificial Intelligence (AI) and machine learning (ML) techniques to predict future traffic conditions and suggest optimized routes. The system uses historical traffic data, real-time sensor data, weather conditions, and public event data to forecast traffic volumes and congestion. The system also integrates dynamic routing recommendations to minimize delays. This paper discusses the key components of such a system, the methodologies used, challenges faced, and the impact of this system on urban traffic management.},
        keywords = {Traffic Prediction, Artificial Intelligence, Machine Learning, Real-Time Data, Dynamic Routing, Smart Cities, Traffic Management, Congestion Control.},
        month = {January},
        }