Intersections are critical points in urban traffic networks, yet they account for a significant share of traffic accidents, congestion, and inefficiencies. Despite advancements in traffic control systems, traditional approaches struggle to adapt to dynamic road conditions, leading to safety risks, especially for vulnerable road users such as pedestrians and cyclists. The Digital Twins for Intelligent Intersections (DT4II) project aims to transform intersection management using real-time digital twins to address this challenge. By integrating advanced sensing technologies, predictive analytics, and AI-driven decision-making, DT4II enables intersections to analyze, forecast, and mitigate potential traffic hazards dynamically. The system continuously learns from real-world data, allowing for adaptive traffic control strategies that enhance both safety and efficiency. This research will contribute to smart infrastructure development, laying the foundation for a new paradigm in urban mobility and intersection management.