SMART URBAN PLANNING &TRAFFIC CONGESTION PREDICTION WITH RAG

The present study proposes an intelligent, AI- driven urban traffic management framework that leverages the latest in Retrieval-Augmented Generation (RAG), hybrid deep- learning architectures, and multi-source IoT data streams to predict and mitigate traffic congestion in real time. Such a system integrates GRU–LSTM temporal modeling with a RAG-enhanced knowledge retrieval pipeline, hence allowing the model to integrate Pattern- based forecasting with contextual reasoning derived from historical traffic logs, incident reports, and environmental metadata. A modular data pipeline ingests heterogeneous sources such as GPS traces, sensor networks, and camera feeds, while a preprocessing layer normalizes, filters, and fuses said data for high-fidelity predictive modeling The platform represents an interactive web- based visualization dashboard that enables dynamic congestion heatmaps, predictive flow analysis, and decision-support metrics for urban planners. Experimental evaluations show highly effective predictions, accuracy, low latency, and strong operational stability under real-world traffic loads. By integrating RAG-based interpretability with deep learning and IoT-enabled sensing, the current research provides a scalable paradigm for next-generation smart mobility infrastructure and offers significant new advances in urban planning, adaptive transport control, and also in sustainable mobility systems. Deep learning, GRU–LSTM hybrid model, IoT data pipeline, real-time traffic analytics, retrieval-augmented generation, smart city infrastructure, smart urban mobility, traffic congestion prediction, and vector database are the