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Smart city article details

Title	Enabling Smart Cities: Ai-Powered Prediction Models For Urban Traffic Optimization
ID_Doc	23044
Authors	Revathy G.; Thangavel M.; Senthilvadivu S.; Savithri M.C.
Year	2025
Published	4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025 - Proceedings
DOI	http://dx.doi.org/10.1109/ICSADL65848.2025.10933292
Abstract	Efficient traffic flow is essential for sustainable growth, environmental health, and enhanced quality of life in contemporary urban settings. The intricacy and dynamic character of urban traffic can make traditional traffic control techniques inadequate. In order to optimize urban traffic, this study suggests an AI-powered prediction model that combines real-time data from many IoT-enabled sources, including sensors, GPS data, and mobile devices, with machine learning algorithms. The suggested model predicts traffic patterns and makes adaptive recommendations to maximize traffic flow, lessen congestion, and cut down on delays by utilizing deep learning and reinforcement learning. When compared to conventional techniques, simulations employing real-world information from many urban locations show notable reductions in traffic and travel time. The model is a great tool for smart city efforts because of its scalability and adaptability, which provide traffic authorities and city planners the capacity to make decisions in real time. This study advances the infrastructure of smart cities and applies AI to address the problems of urban transportation. © 2025 IEEE.
Author Keywords	AI-Powered Prediction Models; IoT-Driven Traffic Solutions; Machine Learning in Transportation; Real-Time Traffic Management; Smart Cities; Urban Traffic Optimization

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