In 2023, C2SMARTER is focused on reducing congestion and ensuring...Read More
C2SMART Director Kaan Ozbay, along with Senior Research Associate Jingqin Gao, were interviewed by IEEE Spectrum’s Dexter Johnson for an article titled AI Tool for COVID Monitoring Offers Solution for Urban Congestion, in the June 2022 issue.
.@c2smartnyu Director @kaanozbay & researcher @JingqinGao developed a cost-effective & innovative method for using camera-based object detection & #AI image analysis w/ @NYC_DOT video feeds that offers #cities solutions to #urban challenges #NYUTandonMade https://t.co/UP18YKVhj5— NYU Tandon (@nyutandon) June 10, 2022
The article explores the motive for and development of C2SMART’s Mobility Dashboard, as well as projections for future use cases using computer vision and upscaling potential of the methodology:
In the midst of the COVID-19 pandemic, in 2020, many research groups sought an effective method to determine mobility patterns and crowd densities on the streets of major cities like New York City to give insight into the effectiveness of stay-at-home and social distancing strategies. But sending teams of researchers out into the streets to observe and tabulate these numbers would have involved putting those researchers at risk of exposure to the very infection the strategies were meant to curb.
Researchers at New York University’s (NYU) Connected Cities for Smart Mobility towards Accessible and Resilient Transportation (C2SMART) Center, a Tier 1 USDOT-funded University Transportation Center, developed a solution that not only eliminated the risk of infection to researchers, and which could easily be plugged into already existing public traffic camera feeds infrastructure, but also provided the most comprehensive data on crowd and traffic densities that had ever been compiled previously and cannot be easily detected by conventional traffic sensors.
This work was made possible through the joint efforts of many researchers, including Dr. Hong Yang (ODU), Dr. Joseph Chow, Yubin Shen, Zilin Bian, Fan Zuo, Suzana Duran Bernardes, Abhinav Bhattacharyya, Ding Wang, Yueshuai He, Siva Sooryaa Muruga Thambiran, and Nicholas Hudanich.
This work has allowed led to interest from several New York agencies, including NYCDOT and NYC DDC, and we continue to work with them. To read papers on this project and beyond, see below.
- Impact of COVID-19 behavioral inertia on reopening strategies for New York City transit
- Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic
- Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit