CPS: Medium: Hybrid Twins for Urban Transportation: From Intersections to Citywide Management

这笔网络物理系统(CPS)赠款将专注于城市交通管理系统的开发，这是由公众对提高大都市区内的安全性、机动性和可靠性的需求推动的。未来的城市将被物联网(IoT)彻底改变，物联网将在物理基础设施、移动资产、人员和控制系统之间提供无处不在的连接。特别是，物联网和智能交通管理有可能显著改善日益摇摇欲坠的交通系统，这些系统占温室气体排放的25%以上，每年造成超过1万亿美元的经济和社会损失。该项目开发了一种混合双胞胎，利用机器学习和边缘计算，以实时分辨率与真实世界并行运行，以监控周围交通，向联网的脆弱用户发送安全警告，并为红绿灯和自动车辆提供基于学习的控制。因此，更广泛的影响包括促进对城市交通建模、计算和模拟的理解，以及用数据科学丰富交通科学。随之而来的教育计划旨在通过外联项目扩大少数族裔和女性对计算和工程的参与，包括针对哈莱姆公立学校教师和K-12学生的项目，以及新的研究生课程开发。该项目的目标是开发一个分层和分布式混合双胞胎，以支持城市交通管理系统，同时利用人工智能(AI)、边缘云计算和下一代通信网络。混合双胞胎由虚拟(即现有的交通模拟)和数字双胞胎组成，它们集成了基于物理的模型，并吸收了从基础设施和车载传感器获得的数据，用于交通建模、预测和管理。基础研究的贡献是数据分析和机器学习，包括用于控制的实时学习。交通管理系统将通过计算机模拟和实验在NSF PAWR COSMOS城市规模的无线试验床上进行验证和评估，该试验床正部署在西哈莱姆，靠近哥伦比亚校园。这一独特的城市试验床将为系统设计和评估过程提供一个现实的环境，也将作为当地社区外展的平台。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Cyber-Physical Systems (CPS) grant will focus on the development of an urban traffic management system, which is driven by public needs for improved safety, mobility, and reliability within metropolitan areas. Future cities will be radically transformed by the Internet of Things (IoT), which will provide ubiquitous connectivity between physical infrastructure, mobile assets, humans, and control systems. In particular, IoT and smart traffic management have the potential to significantly improve increasingly faltering transportation systems that account for over 25% of greenhouse gas emissions and over one trillion dollars of annual economic and social loss. The project develops a hybrid twin that operates in parallel with the real world at real-time resolution, leveraging machine learning and edge computing, to monitor surrounding traffic, send safety warnings to connected vulnerable users, and provide learning-based controls to traffic lights and automated vehicles. As such, the broader impacts include advancing the understanding of urban traffic modeling, computation, and simulation, and enriching transportation science with data science. The accompanying educational plan aims to broaden participation in computing and engineering by underrepresented minorities and women via outreach programs, including programs for Harlem public school teachers and K-12 students, as well as new graduate course development.The project’s goal is to develop a hierarchical and distributed hybrid twin to support urban traffic management systems while leveraging Artificial Intelligence (AI), edge cloud computing, and next generation communication networks. A hybrid twin consists of a virtual (i.e., existing traffic simulation) and a digital twin, which integrate physics-based models and assimilate data acquired from infrastructure and in-vehicle sensors for traffic modeling, prediction, and management. The foundational research contributions are data analytics and machine learning including real-time learning for control. The traffic management system will be validated and evaluated via computer simulation and experimentation in the NSF PAWR COSMOS city-scale wireless testbed that is being deployed in West Harlem next to the Columbia campus. This unique urban testbed will provide a realistic environment for the system design and evaluation process, and will also serve as a platform for local community outreach.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.