Download PDF Spring Bridge on Sustainable Smart Cities March 15, 2023 The world’s cities face increasing threats from natural disasters, aging infrastructure, traffic, and resource constraints. The articles in this issue examine smart infrastructure, sustainability, net zero carbon options, and autonomous driving, among other approaches to smart and sustainable cities. A Metastructure Approach to Smart and Sustainable Cities Monday, March 20, 2023 Author: Anne S. Kiremidjian and Michael Lepech Smart city implementation involves physical infrastructures, digital IT, policies, financing, community engagement, and partnerships that must be created and sustained in concert with each other. The smart city concept was born out of a global need to respond to a coupled challenge of ever-growing populations of megacities and significant increases in the demand for limited resources (Bačić et al. 2018). In addition, many of the world’s largest cities are coastal, making them particularly vulnerable to climate change and natural disasters such as floods, hurricanes, and earthquakes/tsunamis. Urban communities around the globe are therefore looking to leverage fast-evolving technologies and smart city approaches to mitigate the exposure of populations and infrastructure to these extreme yet increasingly common events, to make them resilient and sustainable for generations to come. Introduction In line with technology-infused approaches to meet rising demand, some municipalities have been exploring, and are beginning to implement, many of the ideas put forth in smart city concepts. But cities, and particularly megacities, are complex systems of physical, environmental, social, economic, and financial systems. Each system’s functionality depends on that of the others, increasing the difficulty of implementation nonlinearly since these systems must work in concert. To aid city officials in implementation, several models have been proposed for smart city structure (examples are provided in a seminal paper by Harrison and Donnelly 2011, which lays the theoretical foundation for smart cities; Sánchez et al. 2013 and Li et al. 2015 provide details; and Bačić et al. 2018 presents a summary). Some approaches emphasize the technological aspects of smart city developments (e.g., Park et al. 2018), and others take into consideration the social and economic aspects that also need to be addressed (e.g., Baraniewicz-Kotasińska 2022). A sensing network can be constructed specifically for smart city purposes or leverage the increasing numbers and capabilities of personal smartphones. Building on that work, in this article we look at the various components of smart cities and cast them in a smart city metastructure that is comprehensive in relation to other proposed paradigms. First we discuss the components of this metastructure. Recognizing the difficulties in developing the individual components, we review some successes and failures of smart city implementations, and identify challenges that need to be considered for effective smart city creation. The Smart City Metastructure Smart city implementation requires physical infrastructures, digital information technologies, regulations and policies, financing mechanisms, community engagement, businesses and business models, partnerships, and other institutions that must be created, applied, and sustained in concert with each other. This set of technologies, policies, and organizations was termed by researchers at Stanford the urban metastructure, a transcendent form of city infrastructure that enables the smart city paradigm (Lepech 2017, 2021). First proposed as a concept for urban transportation systems (Rogers 2016) the urban metastructure can be parsed into three critical elements of smart cities. The foundational element is (1) a dense sensing and data collection network that comprises the physical infrastructures. Such a sensing network can be constructed specifically for smart city purposes (e.g., closed-circuit television [CCTV] cameras, roadway traffic sensors) or leverage the increasing numbers and capabilities of personal smartphones. Built on this sensing network is (2) a computational layer that translates the growing stream of smart city data into information that can be used by smart city residents, businesses, and visitors. This computational layer, which leverages advanced computational algorithms (e.g., machine learning [ML], reinforcement learning, artificial intelligence) to process incoming data streams efficiently and rapidly, can provide the public with information via smartphone applications or direct alert updates. These two basic layers of smart city metastructure, sensing and computation, are commonly associated with smart city research and implementation. The final component of smart city metastructure is (3) engagement mechanisms. Engagement is a less studied but critically important component of smart cities. Effective engagement mechanisms are necessary to materially increase the quality of life in cities and provide a lasting value proposition for residents. Without creating long-lasting value for everyone living, working, learning, and playing in a city, and substantially increasing their quality of life, smart city technologies and apps will continue to be novelties, used by early adopters of technology, rather than effective tools of citywide sustainable development and management. These engagement mechanisms comprise the regulations and policies, financing mechanisms, community engagements, businesses and business models, partnerships, and other institutions of the urban metastructure definition. Figure 1 shows the three major components of the urban metastructure and their interlinking relationships. Each component must be implemented in concert with the others to achieve the goals of smart and sustainable cities. Physical Infrastructure for Smart Cities The majority of research and implementation in smart cities has focused on the development of sensing technologies for transportation, power, water, and communications systems; maintenance and management of infrastructure components and systems; and monitoring of buildings and other structures. Sensors are a crucial component of any intelligent control system. The performance of an urban system can be improved via control systems that enhance awareness of its environment and operational status through the collection of necessary data from an array of sensors (Bačić et al. 2018). Different types of sensors are typically deployed to collect a variety of data that are then synchronized and analyzed to extract appropriate information that enables robust decision making and control. In situ sensors, embedded on a structure, road, or other infrastructure system components, collect information locally (e.g., at traffic loops); remote sensors (e.g., CCTV camera, satellite sensors) collect data from a distance. Data from human-generated measurements “include subjective observations on the environment, social media posts, mobile phone calls and text messages, and physiological measurements by wearable body sensors” (Bačić et al. 2018, p. 279). Data and information transmission and storage are achieved through wired or wireless communications networks. For illustration, effective air quality management requires comprehensive data showing spatial distribution of emissions along with combustion and traffic sources (Flaga et al. 2019; Kucharski et al. 2018). Smart city physical infrastructure systems in place to monitor air quality data include dispersed sensors in street lighting for pollution metering (Szarata et al. 2017; Vasiutina et al. 2022). Such systems can be used to understand the nature of air pollution and inform recommendations for remediation through, for example, ventilation towers that generate continuous air streams that supplement natural air flow (Flaga et al. 2019). Computational Layer A robust computational layer is key to good decision making and infrastructure management. Data collected from the complex combination of smart city sensors are rather meaningless until information is extracted and used in the management and control of the various urban systems. Computational and data science tools such as advanced statistical analysis, machine learning, and artificial intelligence have been under development for some time, and significantly enhanced over the past decade. Recent exponential increases in computational power and data storage capabilities have enabled the analysis, storage, and manipulation of large datasets, obtained from myriad sensors, that can be used in smart city applications. Intelligence based on data from sensors is frequently combined with physical models to develop more robust predictive/forecast and control models. These are powered by hardware and software that greatly increase speeds and storage. Algorithms to support citywide monitoring and management of transportation, power, water, waste, and environmental conditions are continuously being developed, enabling more rapid implementation of the smart city paradigm. For example, traffic pattern identification and control for optimal traffic management in some major urban areas (e.g., Copenhagen, New York City, San Francisco, Songdo, Stockholm) are effected through the use of data from roadway embedded sensors, CCTV cameras, radiofrequency identification (RFID) technologies, and personal cell phones. The data are used to monitor traffic patterns, control traffic lights, monitor municipal buses and other modes of public transport, detect accidents, identify road damage, control and monitor parking spaces, and spot traffic violations. Foot-traffic patterns are also used to supplement the algorithms to optimize people movement. And management of commercial delivery systems in combination with general traffic patterns reduces wait times and minimizes driving ranges, reducing both fuel consumption and CO2 emissions. Making the urban power supply smart and resilient requires hardening the existing grid and supplementing it with alternative power supply modes such as wind, solar, gas, and nuclear power generation facilities. Figure 2 schematically shows an example of a smart grid monitoring system that combines various data that feed into an ML algorithm to produce scenarios for optimal power supply distribution over a region. Geospatial information about the physical grid, such as location of transmission stations, switching stations, trunk lines, and distribution lines, is obtained by fusing data from satellite imagery, street view maps, road maps, and building distributions. Similarly, satellite and aerial photography is used to show the spatial location, size, and age of rooftop solar panels. These data are then combined with meteorological information to forecast solar power generation over the region. A similar approach is used for wind power generation. The temporal variations of the data are preserved and the overall data are used for robust optimal geospatial power supply allocation over a variety of regions using an algorithm developed by Wang (2022). Another major effort in computational development focuses on assessing the condition of buildings and other infrastructure components and systems, to enable as-needed rather than regularly scheduled maintenance (e.g., Liao et al. 2019). Such an approach, if done proactively and with the support of a comprehensive decision-support system, leads to lower likelihood of unexpected failures, lower overall lifecycle maintenance costs, greater infrastructure resilience, and a decrease in CO2 emissions. Additional examples include environmental monitoring systems that track air and water quality in buildings to identify the distribution of airborne pathogens (e.g., Chew et al. 2022). Systems for tracking footsteps in buildings are being developed to optimize foot -traffic flow and, in healthcare and assisted living facilities, monitor elderly occupants to prevent potential falls (e.g., Pan et al. 2017). Finally, the computational layer embedded in new digital twin technologies is key to the development of smart cities. These technologies can be used in many ways for building and managing critical systems in smart cities (Farsi et al. 2020). It is also important to link digital twins to the physical and functional properties of other systems (Lepech et al. 2016) to map and study system interdependencies and conflicts, thereby enhancing the ability to address intricacies that are often difficult to tackle. Engagement Engagement mechanisms are some of the most important components of the smart city metastructure, essential to the effective implementation of smart city systems. They include regulations and policies, financing mechanisms, community engagement, businesses and business models, partnerships, and other institutions. Without the integration of these elements for smart city implementation, the value proposition of smart city technologies can remain ambiguous to residents and fail to achieve long-term goals. But engagement and decision making are difficult when there is insufficient knowledge among citizens and decision makers, compounded by factors of aversion, bias, and irrational behavior. Information asymmetries may result in city residents not being aware of what constitutes the “best” decision. Fortunately, there are examples of successful engagement mechanisms around the world: Virtual Singapore provides residents with “a geo-visualization, analytical tools and 3D semantics-embedded information platform to connect and create awareness and services that enrich their community.” Digital Dubai now has over 90 government services that are digitalized and accessible to citizens through its DubaiNow App. Songdo, South Korea, has integrated urban mobility management and city operations at a centralized command center that can address transportation challenges in real time and ease urban mobility challenges. Recognizing the potential of smart cities to reduce environmental impact while improving operational resilience, New York City, in its efforts to reduce its carbon footprint, has adopted smart lighting, water leakage monitoring, smart garbage bins, and traffic monitoring and management (Lai 2022). The installation of smart lighting systems has prevented the emission of over 900 metric tons of greenhouse gases annually since 2013. Smart garbage bins have improved trash collection efficiency over 50 percent while reducing vehicle emissions from city garbage trucks. Finally, New York’s automated meter reading system enables faster identification of leaks and other problems in the city’s water system, leading to increased ability to operate the system reliably during extreme events such as heatwaves and intense storms. Engagement and decision making are difficult when there is insufficient knowledge among citizens and decision makers, compounded by aversion, bias, and irrational behavior. These and other examples illustrate engagement mechanisms implemented both internationally and in other US cities, including Boston and San Francisco. Challenges to Implementation and Full Deployment Globally, the concepts associated with smart cities are widely accepted and there is a great deal of enthusiasm to put them into practice. Yet widespread implementation remains elusive. Several major obstacles prevent municipalities from fully or partially deploying smart technologies to advance their cities to the next generation of more livable and sustainable communities. Obstacles include privacy concerns, data security, retrofitting challenges, and costs. One example of an obstructed effort is the smart city proposed by Sidewalk Lab, the smart urban development arm of Alphabet, which looked to rebuild a 2000-acre waterfront district of Toronto known as Quayside. This new urban district was planned to have affordable apartments, a two-acre forest, a rooftop farm, a new art venue for indigenous culture, and a pledge to be zero carbon (Jacobs 2022). Concerns about privacy and security were cited as the primary reasons the city government and Sidewalk Lab could not reach an agreement. The project was dropped after three years of work. Another major impediment to widespread implementation is the difficulty of instrumenting and adopting technologies in cities that have been in use for hundreds of years, with old infrastructure that cannot support the new technology infrastructure. Many major metropolitan areas are struggling with aging water, power, communications, and transportation infrastructure. New digital smart technologies cannot simply be added to these systems. Privacy concerns, data security, retrofitting challenges, and costs prevent municipalities from fully or partially deploying smart technologies. Key to providing clean water, for example, is a reliable water transmission system. Replacing such a system with smart water pipelines that monitor flow and capture potential contamination or localized failure requires significant capital investment and upgrading. Municipal officials must also confront the dilemma of whether to rebuild existing components using long-proven methods or introduce smart technologies that may have higher initial cost and risk. Similar examples can be cited for power, communications, and transportation systems. Apart from aging infrastructure systems, there is difficulty in the instrumentation of existing buildings. The installation of sensors for structural and environmental monitoring and for sensing people’s movement can be particularly difficult if the sensing network needs to be hardwired. Wireless sensing solutions have been developed for structural monitoring (e.g., Kane et al. 2022; Kiremidjian et al. 2011; Lynch and Loh 2006), but building owners may be resistant to instrumenting their buildings because of privacy and liability concerns. In California, only a handful of buildings are instrumented for seismic performance evaluation, and they have at most 15 to 20 sensors, an insufficient number for effective condition assessment (Kiremidjian et al. 2011). Dubai’s Burj Khalifa, opened in 2010, is perhaps the best-instrumented building for structural health monitoring (Abdelrazak 2012). The instrumentation includes global positioning systems (GPS), Leica high-precision sensors, and clinometers that monitor rotation and displacement of the tower. Data collected from the tower have been used with SmartSync concept (Kijewski-Corea et al. 2013) for continuous monitoring and maintenance decisions. In light of all these challenges, many major metropolitan areas around the world are still working to introduce smart city solutions to their residents. As noted, New York City has adopted a number of these solutions (Lai 2022). Other US cities that are implementing smart technologies include Seattle, San Francisco, Dallas, Austin, Washington DC, Boulder, and San Jose (Cheung 2021). Around the world, smart technologies are being implemented in Copenhagen, Hong Kong, Stockholm, and cities in South Korea and China, among others. Successful developments that include multiple smart city aspects have been achieved in cities built from scratch in some cases. Developed jointly by Gale International, Posco, and Morgan Stanley Real Estate, Songdo, South Korea, is an example of a privately funded growing smart city. And Japan is investing in the development of smart cities with funding from private industry; for example, Panasonic Corporation has invested in the development of several such cities, the latest being Suita in central Japan (Hornyak 2022). Features implemented in Suita include solar panels, storage batteries, and home power management systems. In addition, the city’s 2000 residents can share bicycles and scooters, and have purchases delivered by bicycles and robots. Sensors monitoring sleep patterns adjust temperature and airflow to provide increased comfort. Many other smart features are being implemented to make this and other cities more sustainable. Conclusion The promise of smart cities is exciting. But there remain numerous challenges to achieving implementation over the coming decades. 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Estimating the emissions reduction due to the use of cargo bikes: Case studies for the selected European cities. Energies 15(14):5264. Wang Z. 2022. Energy atlas: Machine-learning-based mapping and analysis for sustainable energy and urban systems. PhD dissertation, Stanford University  National Research Foundation, Office of the Prime Minister of Singapore, https://www.nrf.gov.sg/programmes/virtual-singapore  Digital Dubai Authority, Apps and Services, https://www.-digitaldubai.ae/apps-services  Incheon Free Economic Zone Authority (IFEZA), Global -Center, www.ifez.go.kr/global/index About the Author:Anne Kiremidjian (NAE) is the C.L. Peck, Class of 1906 Professor of Engineering and Michael Lepech is professor of civil and environmental engineering, Stanford University. Lepech is also senior fellow, Woods Institute for the Environment.