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On September 7, 2026, arXiv cs.AI published a research paper titled “AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications”. This study proposes a framework for urban transportation digital twins that combines artificial intelligence with Cloud-edge Collaboration (CPS) technologies. The system aims to enhance the perception of vulnerable users, such as elderly people and people with disabilities, and to achieve real-time data collection, processing, and decision-making feedback through the CPS architecture, thereby optimizing the urban transportation environment and improving the travel safety and convenience for specific groups.
本文提出用于城市交通管理数字 twin（DT）的方法与应用。虽然现有研究多关注 DT 的“眼睛”即感知技术，但区分 DT 与传统模拟器的关键在于其“大脑”，即从感知数据中提取模式并进行预测决策的能力。为提升城市交通管理水平，DT 需由人工智能驱动，并辅以低延迟高带宽的传感与网络技术，即信息物理系统（CPS）。本文旨在帮助研究人员和从业者识别 DT 发展的挑战与机遇，促进跨学科交流，并为利用 DT 于多样化城市交通应用提供路线图。