We present methods and applications for the development of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its ``eyes," which is the emerging sensing and perception like object detection and tracking, what really distinguishes the DT from a traditional simulator lies in its ``brain," the prediction and decision making capabilities of extracting patterns and making informed decisions from what has been seen and perceived. In order to add value to urban transportation management, DTs need to be powered by artificial intelligence and complement with low-latency high-bandwidth sensing and networking technologies, in other words, cyberphysical systems. This paper can be a pointer to help researchers and practitioners identify challenges and opportunities for the development of DTs; a bridge to initiate conversations across disciplines; and a road map to exploiting potentials of DTs for diverse urban transportation applications.
@article{arxiv.2501.10396,
title = {AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications},
author = {Yongjie Fu and Mehmet K. Turkcan and Mahshid Ghasemi and Zhaobin Mo and Chengbo Zang and Abhishek Adhikari and Zoran Kostic and Gil Zussman and Xuan Di},
journal= {arXiv preprint arXiv:2501.10396},
year = {2026}
}