A transportation digital twin represents a digital version of a transportation physical object or process, such as a traffic signal controller, and thereby a two-way real-time data exchange between the physical twin and digital twin. This paper introduces a digital twin approach for adaptive traffic signal control (ATSC) to improve a traveler's driving experience by reducing and redistributing waiting time at an intersection. While an ATSC combined with a connected vehicle concept can reduce waiting time at an intersection and improve travel time in a signalized corridor, it is nearly impossible to reduce traffic delay for congested traffic conditions. To remedy this defect of the traditional ATCS with connected vehicle data, we have developed a digital twin-based ATSC (DT-based ATSC) that considers the waiting time of approaching vehicles towards a subject intersection along with the waiting time of those vehicles at the immediate upstream intersection. We conducted a case study using a microscopic traffic simulation, Simulation of Urban Mobility (SUMO), by developing a digital replica of a roadway network with signalized intersections in an urban setting where vehicle and traffic signal data were collected in real-time. Our analyses reveal that the DT-based ATSC outperforms the connected vehicle-based baseline ATSC in terms of average cumulative waiting time, distribution of drivers' waiting time, and level of services for each approach for different traffic demands and therefore demonstrates our method's superior efficacy.

中文翻译：

---

自适应交通控制系统的交通数字孪生方法

交通数字孪生代表交通物理对象或过程的数字版本，例如交通信号控制器，从而实现物理孪生和数字孪生之间的双向实时数据交换。本文介绍了自适应交通信号控制 (ATSC) 的数字孪生方法，通过减少和重新分配交叉路口的等待时间来改善旅行者的驾驶体验。虽然 ATSC 与联网车辆概念相结合，可以减少在交叉路口的等待时间并改善在信号走廊中的旅行时间，但几乎不可能减少拥堵交通状况下的交通延误。为了弥补传统 ATCS 的这一缺陷，连接车辆数据，我们开发了一种基于数字孪生的 ATSC（基于 DT 的 ATSC），它考虑了接近目标交叉路口的车辆的等待时间以及这些车辆在直接上游交叉路口的等待时间。我们使用微观交通模拟进行了一个案例研究，即城市交通模拟 (SUMO)，通过在城市环境中开发具有信号交叉口的道路网络的数字副本，在该环境中实时收集车辆和交通信号数据。我们的分析表明，基于 DT 的 ATSC 在平均累积等待时间、驾驶员等待时间分布以及针对不同交通需求的每种方法的服务水平方面优于基于联网车辆的基线 ATSC，因此证明了我们方法的优越功效.