ERI: Optimizing Healthcare Accessibility by Integrating Human Mobility Behaviors into Facility Planning

This Engineering Research Initiation (ERI) award supports research that aims to improve healthcare facility planning by incorporating how people actually travel to and use healthcare services, enabling more effective and data-driven decision-making. Access to quality healthcare is essential for improving health outcomes and strengthening communities, yet many populations still face barriers such as distance, limited service availability, affordability and other social and structural constraints. These barriers can delay treatment and reduce care effectiveness, highlighting the importance of how healthcare services are planned and distributed. However, traditional planning approaches often rely on where services are located relative to the population and may not fully reflect how people choose and access care in practice. This project develops new methods that combine emerging human mobility data with optimization models to capture how mobility patterns influence healthcare facility choices. The research will analyze differences between expected and actual patterns of healthcare use, develop models to represent how individuals select healthcare facilities, and design improved planning approaches that account for these behaviors. The project will also support student training through hands-on experiences in mobility data analytics and optimization, and will disseminate tools and educational materials to broaden impact. This project supports healthcare access decision-making by integrating mobility behaviors modeling with facility location optimization. First, exploratory data analysis will examine differences between potential and realized access using network theory. Next, a probabilistic behavioral model, grounded in discrete choice theory, will be developed to capture both access constraints and preferences, while revealing latent facility choices and decision-making processes. Finally, mobility-aware facility location models will be formulated by relaxing the nearest-facility assumption. Efficient and scalable solution algorithms will be developed to ensure computational tractability by exploiting structural properties of the behavioral models. The proposed models will be validated across multiple geographic regions and benchmarked against traditional approaches. Ultimately, this project will provide insights to support the optimization of healthcare accessibility and service provision through the lens of human mobility behavior. 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. NSF Award ID: 2553462 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Zhiyuan Wei | Institution: California Polytechnic State University Foundation, SAN LUIS OBISPO, CA | Award Amount: $199,999 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2553462 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2553462.html