Center for Sustainable Mobility, Virginia Tech Transportation Institute, Virginia Tech, Blacksburg, VA 24061, USA.
College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria 21500, Egypt.
Sensors (Basel). 2021 Mar 18;21(6):2127. doi: 10.3390/s21062127.
The transportation system has evolved into a complex cyber-physical system with the introduction of wireless communication and the emergence of connected travelers and connected automated vehicles. Such applications create an urgent need to develop high-fidelity transportation modeling tools that capture the mutual interaction of the communication and transportation systems. This paper addresses this need by developing a high-fidelity, large-scale dynamic and integrated traffic and direct cellullar vehicle-to-vehicle and vehicle-to-infrastructure (collectively known as V2X) modeling tool. The unique contributions of this work are (1) we developed a scalable implementation of the analytical communication model that captures packet movement at the millisecond level; (2) we coupled the communication and traffic simulation models in real-time to develop a fully integrated dynamic connected vehicle modeling tool; and (3) we developed scalable approaches that adjust the frequency of model coupling depending on the number of concurrent vehicles in the network. The proposed scalable modeling framework is demonstrated by running on the Los Angeles downtown network considering the morning peak hour traffic demand (145,000 vehicles), running faster than real-time on a regular personal computer (1.5 h to run 1.86 h of simulation time). Spatiotemporal estimates of packet delivery ratios for downtown Los Angeles are presented. This novel modeling framework provides a breakthrough in the development of urgently needed tools for large-scale testing of direct (C-V2X) enabled applications.
随着无线通信的引入以及联网出行者和联网自动驾驶车辆的出现，交通系统已经发展成为一个复杂的网络物理系统。此类应用产生了开发能够准确捕捉通信和交通系统相互作用的高保真度交通建模工具的迫切需求。本文通过开发一种高保真度、大规模动态和集成的交通和直接蜂窝车对车（简称 V2X）建模工具来满足这一需求。这项工作的独特贡献在于：(1) 我们开发了一种可扩展的分析通信模型实现，能够在毫秒级水平上捕捉数据包的移动；(2) 我们实时地将通信和交通仿真模型进行了耦合，开发出了一种完全集成的动态连通车辆建模工具；以及 (3) 我们开发了可扩展的方法，根据网络中并发车辆的数量来调整模型耦合的频率。通过在考虑早高峰交通需求（145000 辆车）的洛杉矶市中心网络上运行，该可扩展建模框架得到了验证，在常规个人计算机上的运行速度快于实时速度（1.5 小时即可运行 1.86 小时的仿真时间）。展示了洛杉矶市中心的数据包传递率的时空估计。这种新颖的建模框架在开发急需的大规模直接（C-V2X）应用测试工具方面取得了突破。
