Sustainable Urban Traffic Management: AI and Renewable Energy Integration

Traffic congestion and environmental pollution are critical challenges in urban areas, stemming from outdated and inefficient traffic management systems. This paper introduces an AI-driven smart traffic management system powered by renewable energy to tackle these issues effectively. Leveraging advanced AI algorithms and IoT devices like sensors and cameras, the system dynamically analyzes real-time traffic conditions to optimize flow, reduce delays, and prioritize emergency vehicles. By incorporating renewable energy sources, including photovoltaic or solar power, the system minimizes reliance on fossil fuels and reduces greenhouse gas emissions by a large margin. This is aligned with the United Nations’ 7th Sustainable Development Goal, Affordable and Clean Energy, as it encourages the adoption of renewable energy, and the 11th Sustainable Development Goal, Sustainable Cities and Communities, through sustainable urban infrastructure. This will be a methodology that involves system design, technology selection, data integration, and phased implementation for scalable and adaptable implementation for cities of any size. In addressing both the environmental and technological aspects, this project can potentially revolutionize the way urban transportation is carried out, offering an eco-friendly, cost-effective, and sustainable solution for smart city development.