Digital Twins for Intelligent Intersections: A Literature Review

Intelligent intersections play a pivotal role in urban mobility, demanding innovative solutions such as digital twins to enhance safety and efficiency. This literature review investigates the integration and application of digital twins for intelligent intersections, a critical area within smart urban traffic systems. The review systematically categorizes existing research into five key thematic areas: (i) Digital Twin Architectures and Frameworks; (ii) Data Processing and Simulation Techniques; (iii) Artificial Intelligence and Machine Learning for Adaptive Traffic Control; (iv) Safety and Protection of Vulnerable Road Users; and (v) Scaling from Localized Intersections to Citywide Traffic Networks. Each theme is explored comprehensively, highlighting significant advancements, current challenges, and critical insights. The findings reveal that multi-layered digital twin architectures incorporating real-time data fusion and AI-driven decision-making enhances traffic efficiency and safety. Advanced simulation techniques combined with sophisticated AI/ML algorithms demonstrate notable improvements in real-time responsiveness and predictive accuracy for traffic management. Additionally, the integration of digital twins has shown substantial promise in safeguarding vulnerable road users through proactive and adaptive safety strategies. Despite these advancements, key challenges persist, including interoperability of diverse data sources, scalability of digital twins for extensive traffic networks, and managing uncertainty within dynamic urban environments. Addressing these challenges will be essential for the future development and deployment of intelligent, adaptive, and sustainable intersection management systems.