Preparing for Future Pandemics: Subway Crowd Management to Minimize Airborne Transmission of Respiratory Viruses (Way-CARE)
Lead PI:
Xuan Sharon Di

This Smart and Connected Communities (S&CC) project focuses on strengthening the preparedness and resilience of transit communities facing public health disasters through the development of a sociotechnical system for crowd management. Following the substantial drop in public transportation ridership across the globe during the pandemic, how can subway systems respond to and recover from a future pandemic? Mass transit, especially subways, are essential to the economic viability and environmental sustainability of cities. This research will elevate U.S. leadership and economic competitiveness in recovery from pandemics, and will improve the social, economic, and environmental well-being of those who live, work, and travel within cities. The goal of this study is to equip public transit communities (i.e., agencies, workers, and riders) with a sociotechnical system, “Way-CARE" (Subway Crowd Management to Minimize Airborne Transmission of REspiratory Viruses) that: 1) enables transit riders to make informed decisions and adapt travel behavior accordingly; and 2) provides transit agencies engaged in planning and policymaking with recommendations for mitigating virus transmission risks to riders and workers. People in low-income communities are among the most impacted and are in a disadvantaged position due to reduced accessibility to perceived safer travel modes. As such, the broader impacts of this study include helping identify needs, target resources, and develop more effective approaches to better ensure health and wellness, accessibility and inclusivity, and economic vitality for residents of low-income communities. The accompanying educational plan aims to broaden participation in engineering of underrepresented groups via outreach programs, including programs for Harlem public school teachers and K-12 students, as well as annual student data science challenges.

True health risks inside subway systems and future commuting patterns are unknown after the pandemic. The technological propeller of the project is the integration of sensing, crowd and airflow modeling, and public health knowledge on a microscale applied to subway crowd management. Coupled airborne dispersion and epidemiological models will be developed that account for microscale processes (transport of droplets and aerosols) affecting respiratory virus transmission opportunities. The social catalyst of the award is the integration of behavioral science evidence to inform travel choices and policy making. The Metropolitan Transportation Authority (MTA) and two local rider communities (Harlem and Columbia) will be engaged in the development and assessment of the sociotechnical dimensions of the project. To assure project success, a 2-phase evaluation plan is presented to pilot the system and the technologies. Transferability and scalability will be investigated with input from the engaged communities.

Xuan Sharon Di
Balancing theory and application, Xuan (Sharon) Di studies travel behavior and transportation systems, both of which are being transformed by emerging communications and sensing technologies. Her research helps transportation planners and managers maximize efficiency and sustainability. In particular, her work on travel behavior during disrupted networks, such as after a hurricane or structural failure, contributes to the design of resilient infrastructure. Di applies optimization, game theory, and data analytics to large data collected from various types of traffic sensors, including individual tracing devices such as GPDs. Her studies of travel behavior focus on such factors as travel demand, high-occupancy travel lanes, and the effects of ride-hailing services like Uber, as well as on the future role of connected and automated vehicles. Di is also a committee member of the Center for Smart Cities, at Columbia’s Data Science Institute. Di received a BS in traffic engineering, summa cum laude, in 2005 and an MA in transportation information and control engineering in 2008 from Tongji University, China. She received a PhD in civil, environmental, and geo-engineering from the University of Minnesota, Twin Cities, in 2014. Di received a Chan Wui & Yunyin Rising Star Workshop Fellowship for Early Career Professionals from the Transportation Research Board in 2016. As a graduate student, she developed an interactive multi-player game, Multi-Agent Route Choice, for undergraduate transportation engineering students.
Performance Period: 01/01/2023 - 12/31/2026
Institution: Columbia University
NSF Award ID: 2218809