Smart Urban Planning and Traffic Congestion Prediction

Managing congestion in city road networks has become increasingly difficult,leading to increased commute times, environmental pollution, and inefficiencies in infrastructure planning. Traditional traffic management systems often rely on outdated reports and manual assessments, making it difficult for city planners to make real- time, data-driven decisions. To address this issue, this project proposes an AI-powered Smart Urban Planning & Traffic Management System leveraging Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to enhance real-time decision-making for traffic optimization and urban development.