Smart Building, Smart Community, and Smart City Digital Twins
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ItemModeling aggregate human mobility patterns in cities based on the spatial distribution of local infrastructure( 2021-01-05)Understanding human mobility patterns in urban areas is key to solving a wide range of socio-technical problems at the human-infrastructure interface. Extending the intervening opportunities concept, we showcase a data-driven, network-based model that reproduces aggregate mobility patterns in cities. Using this model, we create a digital replication of daily travel across different trip purposes in 5 U.S. metropolitan areas and compare results against publicly available reference data. We find that our proposed model explains a large fraction of the variation in mean and median travel distance across the 5 cities. In particular, it accurately captures the effect of density on aggregate travel patterns. These findings add to evidence that human mobility patterns are strongly governed by the structure of the built environment. We discuss implications for the ongoing transformation of cities and for developing more sophisticated models that replicate human behavior based on crowd-sourced, spatio-temporal data streams.
ItemCommunity Dynamics in Smart City Digital Twins: A Computer Vision-based Approach for Monitoring and Forecasting Collective Urban Hazard Exposure( 2021-01-05)Urbanization and the growth of human population are leading to increased complexities in the interactions of citizens with public spaces, creating cities that must be more responsive to community dynamics. Despite the critical need for community-based city management, current decision-making approaches are often uninformed by the collective behavior of communities across time and in different locations. Here, we introduce a computer vision-based monitoring and forecasting approach for Smart City Digital Twins (SCDT) that enables integrating collective behavior into the spatiotemporal assessments of exposure to urban heat stress. Our results from a pilot case study in the city of Columbus, GA, demonstrate the significance of integrating community dynamics into monitoring and forecasting urban hazard exposure. This ongoing study highlights how SCDT can make community dynamics more accessible to and responsive by city managers.