Bringing Social Distancing to Light: Crowd Management Using AI and Interactive Floor Projection

With the spread of COVID-19, social distancing has become an integral part of our everyday lives. Worldwide, efforts are focused on identifying ways to reopen public spaces, restart businesses, and reintroduce physical togetherness. We believe that architecture plays a key role in the return to a healthy public life by providing a means for controlling distances between people. Making use of computational processing power and data accessibility, we propose a multipronged approach that will promote healthy and efficient movement through public space. The goal of our research is to develop: 1) a computational tool that utilizes machine learning to predict people's movement and provides suggestions for adapting existing spaces through local physical interventions; and 2) a physical intervention system based on light projections that provides direct realtime information about safe trajectories and movement behavior for pedestrians. The computational tool will use existing visual data from target case study spaces, identifying movement patterns and translating those into behavior rules. This data will be combined with swarm behavior knowledge from natural systems to provide an initial movement prediction. At the same time, the installation of the camera-projection system will allow us to evaluate the efficiency of the proposed measures, monitor flow, and inform the predictive model. Ultimately, we expect to identify strategies for efficient trajectory planning and repurposing of public space, while continually learning from their direct implementation. As such, we hope to identify novel spatial typologies pertaining to improved public health, resulting in planning rules that will reshape the built environment.