C2SMART Featured in IEEE Spectrum

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.