arXiv AI September 7, 2026 By Yongjie Fu, Mehmet K. Turkcan, Mahshid Ghasemi, Zhaobin Mo, Chengbo Zang, Abhishek Adhikari, Zoran Kostic, Gil Zussman, Xuan Di

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

Read the original on arXiv AI →

The paper outlines methods and applications for creating AI‑powered digital twins (DTs) tailored to urban traffic management. It emphasizes that while most DT research focuses on sensing and perception, the true differentiator lies in the DT’s predictive and decision‑making "brain" that extracts patterns and informs actions. By integrating artificial intelligence with low‑latency, high‑bandwidth cyber‑physical systems, the authors propose a framework that can guide researchers and practitioners in addressing challenges, fostering interdisciplinary dialogue, and unlocking diverse urban transportation applications.

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