Abstract
In the context of mitigating viral transmission, particularly the case of COVID-19, engineering controls have proven to be highly effective. A critical facet of bolstering the efficacy of engineering controls lies in exploring the correlation between the movements of occupants and the building morphology. While extant investigations have predominantly centered on scenarios involving emergency evacuations or day-to-day wayfinding, a knowledge gap exists in our understanding of how occupants’ movements relate to building morphology.To bridge this gap and furnish a more robust foundation for engineering controls during pandemics, this paper introduces a data-driven methodology tailored to studying this relationship both pre-pandemic and during pandemics. The methodology hinges on two primary datasets: building features and the occupants' movements. Initially, building morphology is characterized by discerning pertinent architectural features distilled from prior research. The tracking of occupants’ movements is achieved through a thorough analysis of occupancy and trajectory data. Subsequently, Association Rule Mining (ARM) is used to investigate meaningful relationships. These generated association rules are subsequently elucidated using the proposed interpretation approach.The study's outcomes underscore the pivotal role played by certain building features, most notably floor size, floor level, and room functions, in shaping patterns of occupancy and transit. Moreover, a comparative analysis of occupants' movements before and during the COVID-19 pandemic demonstrates the tangible benefits of engineering control measures. The research contribution unveils the relationship between occupants and building morphology. It proposes a baseline for conducting investigations on relationships between buildings and occupants. Beyond pandemic preparedness, the implications of this research extend to building design, operation, and management.
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