Award Abstract # 2038984
CPS: Medium: Hybrid Twins for Urban Transportation: From Intersections to Citywide Management

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK
Initial Amendment Date: September 3, 2021
Latest Amendment Date: August 7, 2022
Award Number: 2038984
Award Instrument: Standard Grant
Program Manager: Abhishek Dubey
adubey@nsf.gov
 (703)292-7375
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2021
End Date: March 31, 2026 (Estimated)
Total Intended Award Amount: $1,200,000.00
Total Awarded Amount to Date: $1,300,000.00
Funds Obligated to Date: FY 2021 = $1,200,000.00
FY 2022 = $100,000.00
History of Investigator:
  • Xuan Di (Principal Investigator)
    sharon.di@columbia.edu
  • Qiang Du (Co-Principal Investigator)
  • Gil Zussman (Co-Principal Investigator)
  • Zoran Kostic (Co-Principal Investigator)
Recipient Sponsored Research Office: Columbia University
615 W 131ST ST
NEW YORK
NY  US  10027-7922
(212)854-6851
Sponsor Congressional District: 13
Primary Place of Performance: Columbia University
500 West 120th Street, #610
New York
NY  US  10027-7003
Primary Place of Performance
Congressional District:
13
Unique Entity Identifier (UEI): F4N1QNPB95M4
Parent UEI:
NSF Program(s): GVF - Global Venture Fund,
Special Projects - CNS,
CPS-Cyber-Physical Systems
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
01002223DB NSF RESEARCH & RELATED ACTIVIT

01002122RB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7924, 120Z, 152E
Program Element Code(s): 054Y00, 171400, 791800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070, 47.079

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 23)
Ghasemi, Mahshid and Kleisarchaki, Sofia and Calmant, Thomas and Lu, Jiawei and Ojha, Shivam and Kostic, Zoran and Gürgen, Levent and Zussman, Gil and Ghaderi, Javad "Real-time Multi-Camera Analytics for Traffic Information Extraction and Visualization" Proc. IEEE PerCom23 , 2023 Citation Details
Angus, Alex and Duan, Zhuoxu and Zussman, Gil and Kostic, Zoran "Real-Time Video Anonymization in Smart City Intersections" Proc. IEEE MASS22 , 2022 https://doi.org/10.1109/MASS56207.2022.00078 Citation Details
Bautista-Montesano, Rolando and Galluzzi, Renato and Mo, Zhaobin and Fu, Yongjie and Bustamante-Bello, Rogelio and Di, Xuan "Longitudinal Control Strategy for Connected Electric Vehicle with Regenerative Braking in Eco-Approach and Departure" Applied Sciences , v.13 , 2023 https://doi.org/10.3390/app13085089 Citation Details
Bautista-Montesano, Rolando and Galluzzi, Renato and Ruan, Kangrui and Fu, Yongjie and Di, Xuan "Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach" Transportation Research Part C: Emerging Technologies , v.139 , 2022 https://doi.org/10.1016/j.trc.2022.103662 Citation Details
Chen, X. and Liu, S. and Di, X. "A Hybrid Framework of Reinforcement Learning and Physics-Informed Deep Learning for Spatiotemporal Mean Field Games" In Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems , 2023 Citation Details
Di, Xuan and Shi, Rongye and Mo, Zhaobin and Fu, Yongjie "Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook" Algorithms , v.16 , 2023 https://doi.org/10.3390/a16060305 Citation Details
Duan, Zhuoxu and Yang, Zhengye and Samoilenko, Richard and Oza, Dwiref Snehal and Jagadeesan, Ashvin and Sun, Mingfei and Ye, Hongzhe and Xiong, Zihao and Zussman, Gil and Kostic, Zoran "Smart City Traffic Intersection: Impact of Video Quality and Scene Complexity on Precision and Inference" in Proc. 19th IEEE Int. Conf. on Smart City, 2021 , 2022 https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00226 Citation Details
Du, Qiang and Huang, Kuang and Scott, James and Shen, Wen "A space-time nonlocal traffic flow model: Relaxation representation and local limit" Discrete and Continuous Dynamical Systems , v.43 , 2023 https://doi.org/10.3934/dcds.2023054 Citation Details
Ghasemi, Mahshid and Kleisarchaki, Sofia and Calmant, Thomas and Gürgen, Levent and Ghaderi, Javad and Kostic, Zoran and Zussman, Gil "Real-time camera analytics for enhancing traffic intersection safety" in Proc. ACM MobiSys22, 2022 , 2022 https://doi.org/10.1145/3498361.3538669 Citation Details
MahshidGhasemi, SofiaKleisarchaki "Real-timeMulti-CameraAnalytics forTrafficInformationExtractionandVisualization" IEEE PerCom'23 , 2023 Citation Details
Ghasemi, Mahshid and Yang, Zhengye and Sun, Mingfei and Ye, Hongzhe and Xiong, Zihao and Ghaderi, Javad and Kostic, Zoran and Zussman, Gil "Video-Based Social Distancing: Evaluation in the COSMOS Testbed" IEEE Internet of Things Journal , 2023 https://doi.org/10.1109/JIOT.2023.3305587 Citation Details
(Showing: 1 - 10 of 23)

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