Smart cities of the future

By Michael Batty, Kay W. Axhausen, Fosca Giannotti, A. Pozdnoukhov, Armando Bazzani, M. Wachowicz, Georgios K. Ouzounis, Y. Portugali. The European Physical Journal Special Topics, 2012

1,059 citations recorded by the source corpus.

Here we sketch the rudiments of what constitutes a smart city which we define as a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies. We first sketch our vision defining seven goals which concern: developing a new understanding of urban problems; effective and feasible ways to coordinate urban technologies; models and methods for using urban data across spatial and temporal scales; developing new technologies for communication and dissemination; developing new forms of urban governance and organisation; defining critical problems relating to cities, transport, and energy; and identifying risk, uncertainty, and hazards in the smart city. To this, we add six research challenges: to relate the infrastructure of smart cities to their operational functioning and planning through management, control and optimisation; to explore the notion of the city as a laboratory for innovation; to provide portfolios of urban simulation which inform future designs; to develop technologies that ensure equity, fairness and realise a better quality of city life; to develop technologies that ensure informed participation and create shared knowledge for democratic city governance; and to ensure greater and more effective mobility and access to opportunities for urban populations. We begin by defining the state of the art, explaining the science of smart cities. We define six scenarios based on new cities badging themselves as smart, older cities regenerating themselves as smart, the development of science parks, tech cities, and technopoles focused on high technologies, the development of urban services using contemporary ICT, the use of ICT to develop new urban intelligence functions, and the development of online and mobile forms of participation. Seven project areas are then proposed: Integrated Databases for the Smart City, Sensing, Networking and the Impact of New Social Media, Modelling Network Performance, Mobility and Travel Behaviour, Modelling Urban Land Use, Transport and Economic Interactions, Modelling Urban Transactional Activities in Labour and Housing Markets, Decision Support as Urban Intelligence, Participatory Governance and Planning Structures for the Smart City. Finally we anticipate the paradigm shifts that will occur in this research and define a series of key demonstrators which we believe are important to progressing a science of smart cities.

Subfields: Computer Science Applications, Media Technology, Transportation

View Paper: https://www.semanticscholar.org/paper/8682e66b99e08ccffe80af8a8f4a8c98141d77d7

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Citations in the corpus

Big Data, new epistemologies and paradigm shifts (2014, result): “The potential exists for a new era of computational social science that produces studies with much greater breadth, depth, scale, and timeliness, and that are inherently longitudinal, in contrast to existing social sciences research (Lazer et al., 2009; Batty et al., 2012).”
The real-time city? Big data and smart urbanism (2013, background): “Here, the city is envisaged as ‘‘constellations of instruments across many scales that are connected through multiple networks which provide continuous data regarding the movements of people and materials’’ and the status of various structures and systems (Batty et al. 2012: 482).”
Complex systems: physics beyond physics (2016, background): “City growth has been shown to exhibit self-similar (or fractal) patterns, an observation that might imply a universality of processes that drive city agglomeration and clustering [76–78].”
Big data and human geography (2013, background): “At a fundamental level is the need for new methods of handling and analysing data sets that consist of millions or billions of observations that are being generated on a dynamic basis in a variety of forms (Batty et al., 2012).”
New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings (2016, background): “Big data are starting to have a paradigm-shifting impact on social science research, and data from Internet-based interactions have the potential to reshape our understanding of collective human dynamics (Watts 2007; Batty et al. 2012).”
Smart Cities: A Survey on Data Management, Security, and Enabling Technologies (2017, background): “c) Machine learning and smart cities: Machine learning can be used in smart city projects to extract useful data for assessing the way the city is working [165].”
Returners and explorers dichotomy in human mobility (2015, background): “…microscope has attracted scientists from diverse disciplines, from physics and network science 1–4 to data mining 5–8 , and has fuelled advances from public health 9–14 to transportation engineering 15–17 , urban planning 18–21 , ofﬁcial statistics 22,23 and the design of smart cities 24–27 .”
The Role of Smart City Characteristics in the Plans of Fifteen Cities (2017, background): “Works Expert Working Group,2011; Townsend et al., 2011; Batty et al., 2012).”
Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2023, background): “[128,129]), paving the way for merging digital technologies with urban infrastructures and coordinating and integrating these through digital instrumentation and hyper-connectivity.”
Big data, smart cities and city planning (2013, background): “…these kinds of data which so far deal with movement and transport, some energy and utility flows, and in time may well extend into spatial financial market data – housing markets and point of sales data pertaining to other kinds of consumption – are the stuff of smart cities (Batty et al., 2012).”
Smart cities with big data: Reference models, challenges, and considerations (2018, background): “…examples (Chen et al., 2017; Cicirelli et al., 2017), bene ﬁ ts and opportunities (Al Nuaimi et al., 2015; Solanas et al., 2014), challenges and requirements (Batty et al., 2012; Hashem et al., 2016), and models and frameworks (Bibri, 2018b; Díaz-Díaz et al., 2017; Pramanik et al., 2017).”