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

CPS：中：城市交通的混合双胞胎：从十字路口到全市管理

• 批准号: 2038984
• 负责人: Xuan Di
• 金额: $ 120万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038984&HistoricalAwards=false
• 关键词: CPS; Medium; Hybrid; Twins; Urban

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.

该网络物理系统（CPS）赠款将侧重于城市交通管理系统的开发，这是由大都市地区内提高安全性，流动性和可靠性的公共需求驱动的。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、移动的资产、人类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量的25%以上，每年造成的经济和社会损失超过1万亿美元。该项目开发了一个混合双胞胎，以实时分辨率与真实的世界并行运行，利用机器学习和边缘计算来监控周围的交通，向连接的脆弱用户发送安全警告，并为交通信号灯和自动驾驶汽车提供基于学习的控制。因此，更广泛的影响包括推进对城市交通建模、计算和模拟的理解，以及用数据科学丰富交通科学。配套的教育计划旨在通过外展计划，包括针对哈莱姆公立学校教师和K-12学生的计划，以及新的研究生课程开发，扩大代表性不足的少数民族和妇女对计算和工程的参与。该项目的目标是开发一个分层和分布式的混合孪生模型，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，下一代通信网络。混合孪生子由虚拟的（即，现有的交通模拟）和数字孪生，它集成了基于物理的模型，并吸收从基础设施和车载传感器获取的数据，用于交通建模，预测和管理。基础研究贡献是数据分析和机器学习，包括实时学习控制。交通管理系统将通过计算机模拟和实验在NSF PAWR COSMOS城市规模的无线测试平台上进行验证和评估，该测试平台将部署在哥伦比亚校园旁边的西哈莱姆。这个独特的城市试验台将为系统设计和评估过程提供一个现实的环境，也将作为一个平台，为当地社区outreach.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。

项目成果

Smart City Intersections: Intelligence Nodes for Future Metropolises

• DOI: 10.1109/mc.2022.3206273
• 发表时间: 2022-12-01
• 期刊: COMPUTER
• 影响因子: 2.2
• 作者: Kostic, Zoran;Angus, Alex;Raychaudhuri, Dipankar
• 通讯作者: Raychaudhuri, Dipankar

Uncertainty Quantification of Car-following Behaviors: Physics-Informed Generative Adversarial Networks

跟车行为的不确定性量化：基于物理的生成对抗网络

• 发表时间: 2022
• 期刊: the 28th ACM SIGKDD in conjunction with the 11th International Workshop on Urban Computing (UrbComp2022
• 作者: Zhaobin Mo;Xuan Di
• 通讯作者: Xuan Di

Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach

• DOI: 10.1016/j.trc.2022.103662
• 发表时间: 2022-04-21
• 期刊: TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
• 影响因子: 8.3
• 作者: Bautista-Montesano, Rolando;Galluzzi, Renato;Di, Xuan
• 通讯作者: Di, Xuan

A space-time nonlocal traffic flow model: Relaxation representation and local limit

时空非局部交通流模型：松弛表示和局部极限

• DOI: 10.3934/dcds.2023054
• 发表时间: 2023
• 期刊: Discrete and Continuous Dynamical Systems
• 影响因子: 1.1
• 作者: Du, Qiang;Huang, Kuang;Scott, James;Shen, Wen
• 通讯作者: Shen, Wen

Real-Time Video Anonymization in Smart City Intersections

• DOI: 10.1109/mass56207.2022.00078
• 发表时间: 2022-10
• 期刊: 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
• 作者: Alex Angus;Zhuoxu Duan;G. Zussman;Z. Kostić