EAGER: SAI: Cognitive Models of Human Social Wayfinding for the Redesign of Public Spaces

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.In 2020, many public spaces were hastily redesigned to optimize pedestrian flow in order to minimize the spread of COVID-19. Unfortunately, conventional methods for simulating how people move through public spaces do not take into account social factors that affect how people actually navigate in the presence of other people (social wayfinding). For example, these methods do not incorporate how people adjust to avoid others’ personal space, navigate around slower-moving people, or follow instructions from other people. Even worse, existing simulations usually assume everybody has identical abilities, which is rarely true in real populations. The goal of this project is to develop a system for simulating the flow of people through public spaces, including social aspects of human navigation, and incorporating people with a variety of abilities and disabilities. These more realistic simulations will be used to develop novel metrics and protocols for evaluating public spaces, which more thoroughly reflect the rich social behavior of real people. This project develops a new framework for modeling the flow of people through public spaces, called the Social Wayfinding-Inspired InFrasTructure (SWIIFT) design framework. The framework has three interlocking parts: human subjects experiments on human wayfinding, computational simulations of the flow of people through public spaces, and evaluation metrics for assessing design and re-design of real public spaces. In a series of experiments, human subjects will be immersed via Virtual Reality headsets into simulated spaces. These spaces will contain different numbers of simulated people, including people with variations in mobility (using wheelchairs, canes or walkers; pushing strollers; carrying heavy bags), sensory ability (e.g., visual impairments, hearing impairments), knowledge, and attention. Human subjects will receive different cues about which way to go, including visible pathways, signage, and verbal instructions. Data about the choices they make as they navigate through the virtual spaces will be incorporated into simulations, allowing us to develop realistic models of how people move through spaces under natural conditions. Finally, this framework will use these simulation models to evaluate potential modifications to real spaces, allowing potentially expensive changes to be accurately evaluated before they are carried out. The ultimate goal of this work is to enable public spaces to be made more efficient and more accessible for everyone, regardless of ability.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.