AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications

This research introduces methods and applications for developing AI-powered, cyber-physical systems (CPS) enabled digital twins (DTs) for urban transportation management. The core distinction of these DTs lies not just in sensing ('eyes') but in their advanced predictive and decision-making capabilities ('brain'), leveraging AI and high-speed networking to enhance urban transportation efficiency and safety, particularly for vulnerable users. The paper emphasizes the integration of artificial intelligence with real-time sensing and networking technologies.

The development of AI-powered cyber-physical digital twins for urban transportation represents a significant advancement in infrastructure management. It enables more sophisticated predictive analytics and real-time decision-making, which can optimize traffic flow, reduce congestion, and enhance safety across urban environments, particularly benefiting vulnerable populations.

Digital twins for urban transportation management are characterized by their predictive and decision-making 'brain', beyond just sensing capabilities.

Forward consideration, not a verified fact.