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.

翻译：本文提出了面向城市交通管理的数字孪生开发方法与实际应用。当前多数数字孪生研究聚焦于其“眼睛”——即新兴的感知与识别技术，如目标检测与跟踪。然而，数字孪生区别于传统仿真器的核心在于其“大脑”——即从已观测与感知信息中提取规律、进行预测并作出智能决策的能力。为提升城市交通管理的实际价值，数字孪生需以人工智能为驱动核心，并结合低延迟高带宽的传感与网络技术，即信息物理系统。本文旨在为研究人员与实践者指明数字孪生发展面临的挑战与机遇；搭建跨学科对话的桥梁；并为挖掘数字孪生在多元城市交通应用中的潜力提供技术路线图。