Design of Resilient Smart Highway Systems with Data-Driven Monitoring from Networked Cameras

This project aims to develop a systematic way to design smart highway systems with networked video monitoring and control resiliency against environment disruptions and sensor failures. On the video monitoring side, the authors investigate (1) efficient deep learning methods for extracting fine-grained local categorical traffic information from individual surveillance videos (e.g., traffic mixture, environment information, anomaly/extreme-weather detection in the scene), and (2) machine learning-based methods to correlate and propagate the local information through the highway network for global states estimation (e.g., vehicle tracking and reidentification, traffic prediction in unobserved area). On the system design side, the authors (1) establish dynamic models for capacity using video data, (2) model failure in either cyber or physical components, (3) study the relation between sensor deployment and observability for resilient traffic control (e.g. route guidance and ramp metering). The outcome is an implementable approach to designing resilient smart highway systems with trustworthy monitoring capability. The authors also expect their approach (with appropriate modification) to be applicable to general transportation systems.

• Dataset URL:
  https://doi.org/10.5281/zenodo.3986141
• Dataset URL:
  https://doi.org/10.5281/zenodo.4012684
• Record URL:
  https://c2smart.engineering.nyu.edu/wp-content/uploads/69A3551747124-Design-of-Resilient-Smart-Highway-Systems-with-Data-Driven-Monitoring-from-Networked-Cameras.pdf
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/58514
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program. Supporting datasets available at: https://doi.org/10.5281/zenodo.3986141; https://doi.org/10.5281/zenodo.4012684
• Corporate Authors:

  C2SMART Connected Cities with Smart Transportation

  NYU Tandon School of Engineering
  Department of Civil and Urban Engineering
  Brooklyn, NY  United States 

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Jin, Li
  • 0000-0002-5282-2327
  • Feng, Chen
  • 0000-0003-3211-1576
  • Xie, Qian
  • Xu, Xuchu
• Publication Date: 2020-8

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; References; Tables;
• Pagination: 44p

Subject/Index Terms

• TRT Terms: Automated highways; Data collection; Information processing; Machine learning; Sensors; Traffic control; Traffic surveillance; Video
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01763114
• Record Type: Publication
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Feb 3 2021 2:22PM