Engineering Smarter Cities with Smart City Digital Twins

Abstract

The special collection on Engineering Smarter Cities with Smart City Digital Twins is available in the ASCE Library (https:// ascelibrary.org/jmenea/smart_city_digital_twins). Urbanization challenges and the rapid adoption of technological advancements by cities are generating complex interdependencies between humans, infrastructure systems, and technologies, which-as cities grow-is resulting in increasing uncertainties, unreliable predictions, and poor management decisions. Understanding and managing these increasingly complex interdependent processes requires a paradigm shift in how urban infrastructure is understood, influenced, and ultimately managed. Decision makers in cities require an enhanced ability to collaboratively model, understand, and anticipate the dynamics across human, infrastructure, and technology systems as an integrated entity. Smart city digital twins (SCDTs), from their inception (Mohammadi and Taylor 2017), have been regarded as intelligent adaptive systems that pair the physical and digital worlds using iterative data-driven feedback loops. By generating a parallel virtual version of the city infrastructure along with the connectivity, analytical, and visualization capabilities enabled by the Internet of Things (IoT) and emerging virtualization technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), SCDTs holistically capture the dynamic interactions in cities. Synergistic feedback loops between the physical infrastructure and human systems enable hyperlocal data-driven decision making and help decision makers evaluate community-and stakeholder-led policies and initiatives through what-if scenario analysis and prediction. Enabling simulation of what-if scenarios and anticipating emergent behavior can further provide insights into cities' operational readiness and help analysts understand how cities equipped with smart technologies will likely perform under various economic, environmental, and social conditions, and identify the drivers of possible disruptions. Such understanding is critical in assessing whether or not smart growth strategies are effective, minimizing the gap between smart utopia and smart reality. Developing such integrated cyber-physical city infrastructures can enable dynamic simulation of infrastructure processes, environmental qualities, and human activities, as well as adaptation to changing conditions. This is a highly complex task that requires new technological and methodological advancements and collaborative participation from various disciplines. In order to solidify the theoretical and scholarly foundation of a smart city digital twin paradigm, largely established in a National Science Foundation Smart City Digital Twin Convergence Conference (Taylor et al. 2019), and highlight the latest discoveries across the spectrum of SCDT research, in this special collection we feature papers that explore topics ranging in foci from theory, scale, and system architecture for smart city digital twins (Lu et al. 2020; Austin et al. 2020); data, sensing, IoT, and analytics for smart city digital twins (Ruhlandt et al. 2020; Zhao et al. 2020; Lu et al. 2020; Francisco et al. 2020); human-infrastructure interdependencies, connectivity, and citizen engagement (Lee et al. 2020; Ham and Kim 2020; Fan et al. 2020); information management and decision support for smart city digital twins (Du et al. 2020; Lin and Cheung 2020; Francisco et al. 2020); digital twin virtualization (virtual reality/augmented reality/mixed reality) of smart cities (Chen et al. 2020; Ham and Kim 2020); to implications for operational readiness, context-aware simulation, and crisis management (Ford and Wolf 2020; Fan et al. 2020; Du et al. 2020; Lee et al. 2020). The following list contains a short summary of each collected article's contribution to engineering smarter cities with smart city digital twins: • Lu et al. (2020) present an SCDT system architecture, scaled at both buildings and cities focused on the operation and maintenance (O&M) phase in the asset life cycle. Using the University of Cambridge as a case study, they explore the methodo-logical and implementation challenges of developing the SCDT of the West Cambridge Campus, including integration of heterogeneous data, analysis, and decision-making processes in O&M management. • Austin et al. (2020) further explore various approaches and challenges of architecting the operation of SCDT from a combined multidomain semantic modeling and rule-based reasoning perspective. Taking the Chicago metropolitan area as a case study, they propose a semantic modeling and machine learning integrated SCDT system architecture that supports data collection and processing, identification of events, and automated decision making. • Ruhlandt et al. (2020) investigate the implications of SCDT from the perspective of smart cities' utilization of data and ana-lytics by identifying the condition (e.g., structures, leadership, strategy, culture, data infrastructure, data governance, budgets) and outcome variables (e.g., intention, frequency, purpose) that could influence or have an impact on cities' decision-making process from both theoretical and practical standpoints. • Zhao et al. (2020) further explore integration of data and ana-lytics in decision making for national infrastructure planning and construction by developing an SCDT simulation model for smart mobility that determines the optimal design of the energy storage system (ESS) for a given network of charging stations. This novel model leverages the holistic integration of the charging station network and energy storage system and buffers multiple charging stations through microgrids to achieve minimum cost and zero energy gap. • Francisco et al. (2020) establish an SCDT-enabled energy management platform built around temporally segmented building energy benchmarks leveraging smart meter electricity data to © ASCE 02021001-1 J. Manage. Eng.