The Rise of Smart Cities: Technology’s Role in Urban Planning

Mary Christine Wheatley
Wheatley Research Consultancy, Bagley, MN, USA
Correspondence to: mchristinewheatley@gmail.com

DOI: https://doi.org/10.70389/PJDS.100002

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Mary Christine Wheatley – Conceptualization, Writing – original draft, review and editing
• Guarantor: Mary Christine Wheatley
• Provenance and peer-review:
  Commissioned and externally peer-reviewed
• Data availability statement: N/a

Keywords: Smart cities, Internet of things (IoT), Artificial intelligence (AI), Big data analytics, Urban sustainability.

Peer Review
Received: 30 July 2024
Revised: 22 December 2024
Accepted: 27 December 2024
Published: 15 January 2025

Introduction

Smart cities represent a paradigm shift in how urban environments are designed, managed, and experienced, integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data to optimize city functions and promote economic growth and quality of life.¹ The IoT refers to the network of interconnected devices and sensors that collect and exchange data across the urban infrastructure, enabling automated and more efficient city operations.² AI involves the use of computer systems to mimic human intelligence processes such as learning, reasoning, and self-correction, facilitating more dynamic and responsive urban management.³ Big data refers to the large volumes of data generated by these devices, which, when analyzed, provide insights that drive smarter decision-making and policy development.⁴ These technologies enable real-time analysis of data collected through various sensors and devices scattered throughout the urban landscape, allowing for more efficient resource management, enhanced service delivery, and greater citizen engagement.⁵

This review article outlines the role of these technologies in urban planning, exploring their applications in smart transportation, energy management, and public ­safety, among other areas. It also addresses the societal and ethical implications of digital surveillance and data-driven governance, which are integral to the discussion on smart cities. By providing an overview of the main focus areas—technology integration, case studies, benefits, challenges, and future directions—this review aims to illuminate the transformative impact of smart city technologies on urban development.

Defining Smart Cities

The concept of a “smart city” encompasses a strategic approach to urban development characterized by the integration of information and communication technologies (ICT) to enhance the efficiency, operational effectiveness, and quality of urban services such as transportation, energy, and utilities while ensuring citizens’ well-being. Smart cities leverage technologies such as IoT, AI, and big data analytics to optimize resource use, sustain economic development, and improve governance.⁶ Unlike traditional urban planning that focuses primarily on spatial and regulatory frameworks, smart cities are dynamic and data-driven, focusing on sustainable growth, connectivity, and real-time data-driven decision-making.⁷ The primary goal of smart cities is to enhance urban life through technology, ensuring that city ­infrastructures and services are not only efficient but also proactive in improving residents’ daily lives. By incorporating advanced technologies, smart cities can provide solutions that anticipate and meet community needs, thus fostering a higher quality of urban living and greater engagement among citizens.⁸ This ­approach not only contributes to economic ­sustainability but also enhances accessibility and convenience, transforming how cities meet the evolving needs of their populations.

Integration of Technology in Urban Planning

IoT in Urban Infrastructure

The IoT has become a cornerstone of contemporary urban infrastructure development. This is because it enhances the connectivity and efficiency of city services. For instance, smart traffic management systems utilize IoT to adjust traffic signals in real time based on vehicle flow. This significantly reduces congestion and minimizes commute times. These systems are linked to citywide networks that gather and analyze data from various sensors and cameras, providing a dynamic response to urban mobility challenges.⁹ Furthermore, IoT integration in public safety operations has led to advanced surveillance systems that can detect and respond to emergencies with greater speed and accuracy, thereby improving overall city security.¹⁰

IoT technologies are also revolutionizing utility ­services through smart grids and smart water systems. These systems enable real-time monitoring and ­management of energy and water usage, promoting sustainability by detecting leaks and inefficiencies and allowing for predictive maintenance. The use of smart meters and connected devices not only leads to significant reductions in operational costs but also empowers residents through real-time data, fostering a culture of energy conservation and helping cities manage resources more effectively.¹ The integration of IoT in urban planning not only enhances operational efficiencies but also significantly bolsters resident engagement and environmental sustainability. Smart city initiatives that leverage IoT technologies foster interactive communication platforms that allow residents to report issues, track responses, and even manage their public service needs through mobile applications.¹ This direct interaction not only increases civic participation but also heightens a sense of community and accountability among city dwellers.¹¹

Moreover, IoT’s role in promoting environmental sustainability is manifested through optimized ­resource management. For example, IoT-enabled systems in cities can intelligently manage water and power consumption, reducing waste through real-time data insights and automated adjustments to supply based on actual usage patterns.¹² Such systems help in lowering greenhouse gas emissions and reducing the ecological footprint of urban areas.¹³ Additionally, the deployment of smart sensors throughout the city can monitor air quality and noise levels, providing data that can guide sustainable urban development and mitigate environmental pollutants.¹⁴ These advancements contribute significantly to the quality of urban living, making cities not only more manageable and efficient but also more adaptable to the needs of their growing populations and the challenges posed by climate change.⁸

AI in Urban Design and Management

AI is revolutionizing urban planning by enabling smarter, more efficient decision-making processes. The application of AI for predictive analytics in zoning and land use is transforming how cities plan their growth and manage their resources. Predictive models powered by AI can analyze vast datasets from various sources, such as traffic patterns, demographic changes, and economic trends, to predict urban growth and guide planning decisions.¹⁵ This allows for more dynamic zoning strategies that can adapt to changing urban needs and minimize disruptions.¹⁶

Case studies from cities like Singapore, Toronto, and Barcelona illustrate the successful integration of AI in urban operations. In Singapore, AI not only optimizes public transport routes and schedules to reduce congestion and improve commuter experiences but also enhances environmental monitoring. AI systems in Singapore monitor air quality and water levels, providing real-time data that helps the city respond effectively to environmental challenges and maintain sustainability standards.^17,18 In Toronto, AI’s application in traffic management systems has successfully reduced traffic congestion by predicting peak times and adjusting signal timings accordingly, thereby ­enhancing urban traffic flows and reducing commuter delays.¹⁹ Barcelona has integrated AI into its waste management systems, where AI technologies analyze and predict waste generation patterns, optimizing collection routes and frequencies, which has significantly improved operational efficiencies and reduced the environmental impact of waste collection.²⁰ These examples highlight the significant benefits of AI in enhancing urban efficiency and sustainability.

Integration of Big Data in Urban Planning

Use of Big Data in Urban Decision-Making. Urban planners increasingly rely on big data to enhance ­decision-making processes, utilizing vast amounts of information collected from various sources to optimize urban development and management. This data-driven approach allows for a more granular understanding of urban dynamics, enabling city planners to address issues ranging from traffic congestion to energy consumption with unprecedented precision. Researchers highlight that integrating big data analytics into urban planning leads to smarter, more responsive city management, which adapts in real-time to the needs of its population.²¹

Data Collection, Analysis, and Application. The collection and analysis of big data involve gathering real-time data from sensors, mobile devices, and other IoT implementations that monitor everything from traffic patterns to energy usage. This information is then analyzed to predict trends and model future scenarios, which can profoundly impact the management of city resources. For instance, big data is used to streamline public transportation networks, reduce energy waste, and improve public safety by predicting crime hotspots. The practical application of big data not only improves operational efficiencies but also enhances the quality of life by making cities more livable and sustainable.^22,23 The following case studies illustrate how different cities have implemented these technologies to address specific urban challenges.

Case Studies of Smart Cities Around the World

Singapore: A Model of Smart Urban Management

Singapore stands as a paragon of smart city implementation, integrating advanced technologies such as IoT, AI, and big data to revolutionize urban management and sustainability. One of the most notable applications is its real-time traffic management system, which employs AI to optimize public transport routes and schedules. This system significantly reduces congestion and shortens travel times, directly enhancing the daily commute for millions of residents.²⁴ Furthermore, the deployment of AI and analytics in traffic management allows for dynamic adjustments based on real-time data, helping to mitigate traffic during peak hours and special events.²⁴

Moreover, Singapore’s commitment to environmental sustainability is evident in its extensive sensor network, which monitors a variety of urban metrics ­including air quality, noise pollution, and water levels.²⁵ These sensors provide critical data that supports the city’s proactive responses to environmental challenges, such as haze and flooding, thereby maintaining high standards of public health and safety. The integration of these technologies contributes to the Smart Nation initiative, aiming to reduce the city’s carbon footprint through efficient resource management and sustainable urban practices.²⁶ Additionally, Singapore’s smart urban ecosystem includes sophisticated waste management systems that utilize RFID technology and IoT solutions to optimize garbage collection and recycling processes. These systems not only improve operational efficiencies but also contribute to cleaner and healthier urban environments.²⁷ By leveraging technology to enhance waste management, Singapore demonstrates a holistic approach to urban sustainability that includes meticulous attention to public cleanliness and waste reduction.

Barcelona: Pioneering Smart City Solutions in Europe

Barcelona has emerged as a leader in smart city solutions in Europe, seamlessly integrating technology to enhance urban functionality and citizen well-being. The city’s deployment of IoT-connected smart lighting systems across urban districts not only conserves energy but also reduces light pollution, significantly lowering the city’s carbon footprint and energy costs.²⁸ These smart lighting systems are equipped with sensors that adjust brightness based on pedestrian traffic, further enhancing energy efficiency.²⁹ Additionally, Barcelona’s innovative waste management system uses IoT technology to optimize garbage collection routes and schedules. Sensors in waste containers transmit real-time data on fill levels, allowing for dynamic scheduling of waste collection that minimizes overflow and reduces operational costs.³⁰ This system not only improves cleanliness but also contributes to a substantial reduction in the city’s environmental impact.

The city also leverages digital platforms to engage its citizens in the urban planning process, enhancing participatory governance. Through these platforms, residents can provide instant feedback on urban projects and policies, which helps the city administration to adjust and respond more effectively to public needs and preferences.³¹ This level of civic engagement has fostered a stronger community bond and has been instrumental in enhancing public trust and satisfaction with city services. In terms of public safety, Barcelona utilizes data-driven approaches, including predictive analytics, to optimize the deployment of police resources. By analyzing patterns from a wide range of data sources, the city can predict potential crime hotspots and allocate police presence more strategically. This significantly enhances urban security and reduces crime rates.³²

Dubai: Setting Standards for Smart Infrastructure

Dubai stands as a beacon of technological advancement in urban planning, embracing a wide range of cutting-edge technologies to forge a smart city of the future. The city’s deployment of AI in public safety is notably advanced, featuring a comprehensive network that includes facial recognition technology and predictive policing tools. These systems are designed to enhance the responsiveness and effectiveness of law enforcement agencies, significantly reducing response times to incidents and improving overall public security.³³ In the healthcare sector, Dubai is pioneering the use of AI for predictive health analytics. These systems analyze vast arrays of health data to predict outbreaks, prevent diseases, and optimize the allocation of healthcare resources. This initiative not only enhances patient care but also streamlines the operational aspects of healthcare delivery, ensuring that services are more accessible and timely.³⁴

Moreover, Dubai’s investment in blockchain technology has revolutionized government transactions. By implementing blockchain, Dubai has enhanced the efficiency and transparency of its public services, from visa applications to license renewals. This digital transformation has not only improved citizen satisfaction but also established Dubai as a global leader in digital governance.³⁵ These integrations of futuristic technologies into the urban fabric are complemented by Dubai’s commitment to sustainability. The city has implemented several green initiatives, such as the Smart Majlis, which allows citizens to participate in sustainability efforts and urban planning. This program reflects the city’s broader goals to not only modernize its infrastructure but also ensure that development is sustainable and inclusive.³⁶

Copenhagen: Advancing Urban Sustainability Through Technology

Copenhagen’s commitment to achieving carbon ­neutrality by 2025 is supported by innovative technological integrations throughout the city’s infrastructure. Its smart traffic management systems, including adaptive traffic lights and data-driven public transport schedules, have been pivotal in reducing urban congestion and lowering greenhouse gas emissions. These systems adjust in real-time to traffic conditions, optimizing the flow of vehicles and public transit to minimize idle times and reduce the overall carbon footprint.³⁷ Moreover, Copenhagen’s advanced water management systems exemplify the city’s approach to sustainable urban planning. Utilizing a network of sensors and automated controls, these systems manage water levels and quality across the city’s waterways and public utilities, significantly reducing waste and preventing pollution. This technology not only conserves water resources but also ensures that urban waterways contribute positively to the city’s ecosystem.³⁸

Another notable aspect of Copenhagen’s smart city initiatives is the integration of green spaces within urban areas. Through the use of Geographic Information Systems and IoT, Copenhagen plans and maintains urban green spaces that enhance biodiversity, improve air quality, and provide recreational spaces for residents. This integration supports the city’s environmental objectives while also promoting public health and well-being.³⁹ Copenhagen’s holistic approach to smart city planning is further demonstrated by its investments in renewable energy solutions within urban areas. Solar panels, wind turbines, and bioenergy facilities are integrated into the cityscape, reducing dependency on fossil fuels and supporting the city’s ambitious sustainability goals. These initiatives showcase Copenhagen’s leadership in combining technology with proactive environmental policy to create a sustainable and livable urban future.⁴⁰ These detailed case studies underscore how smart cities globally are harnessing technology to reimagine urban planning and management. The successful integration of technology in these cities not only enhances operational efficiencies and the quality of life but also sets a standard for sustainable urban development. Each city’s unique approach to technology integration showcases the potential of smart solutions in addressing complex urban challenges while improving the lives of their residents.

Benefits of Smart Cities

Environmental Sustainability

Smart cities play a pivotal role in promoting environmental sustainability through efficient resource management. Technologies like IoT-enabled infrastructure can drastically reduce energy consumption and greenhouse gas emissions. For instance, smart grids and renewable energy systems optimize energy use to reduce the carbon footprint of urban centers. These systems not only conserve energy but also ensure that it is generated and used sustainably, contributing significantly to the cities’ goals of becoming carbon-neutral.⁴¹ Increasing the efficiency of urban energy infrastructures is crucial in smart cities’ quest for environmental sustainability. Recent research by Almihat et al. predicted significant reductions in CO2 emissions due to smart technologies, with smart energy systems in Europe alone accounting for 31.8% of total sales in 2020.⁴² Furthermore, the global market for smart energy is anticipated to expand annually by 27.1%, reaching $652 billion by 2027.⁴² Complementing these findings, Şerban and Lytras discussed how AI enhances the renewable energy sector in Europe, with AI-driven systems optimizing energy production and consumption.⁴² Their research highlights the efficiency gains in the transformation processes of renewable energy, driven by AI technologies, which allow for a more precise management of energy flows, thereby reducing urban areas’ ecological footprints significantly.

Economic Efficiency

The integration of technology in urban areas drives economic efficiency by streamlining city operations and services. Smart technologies improve the logistics of transportation and utilities, reducing costs and enhancing service delivery. For example, smart traffic management systems reduce commute times and fuel consumption by optimizing traffic flow, thus saving millions in public spending and boosting economic productivity. Furthermore, smart infrastructure projects attract investments, fostering economic growth by creating tech-driven markets and jobs.⁴³ Efficiencies in Energy and Economic Gains. Smart cities are poised to transform economic paradigms by integrating smart technologies that significantly enhance energy efficiency and reduce operational costs. Through the strategic deployment of ICT, urban energy management becomes more optimized, leading to considerable economic savings.⁴⁴ Innovations in energy consumption and storage, facilitated by smart grids and renewable resources, not only decrease energy costs but also support sustainable economic development.⁴⁵ These improvements in energy efficiency can translate into a 95.4% increase in operational efficiency, underpinning substantial reductions in public spending and fostering an environment conducive to economic growth and enhanced public service delivery.⁴⁶

While the potential for energy savings in smart cities is substantial, Wang and Moriarty posited that the ­realization of these savings is contingent upon the implementation of robust supporting policies.⁴⁵ These policies are essential not only for facilitating technological integration but also for addressing broader sustainability challenges such as data privacy and security concerns. By ensuring that smart cities are supported by appropriate policy frameworks, municipalities can enhance their energy efficiency, thereby translating into economic gains through reduced operational costs and a lesser environmental impact.⁴⁵ Job Creation in New Tech Sectors. Smart city ­initiatives, characterized by the integration of ­digital infrastructure and smart technologies, have catalyzed transformations across urban landscapes and ­economic sectors, presenting both opportunities and challenges for job creation. The creation of new jobs, particularly in tech-centric sectors, directly contributes to economic efficiency by fostering a more dynamic labor market and enhancing productivity through the adoption of innovative technologies.

Estebsari and Werna explored the implications of smart grids and smart homes, emphasizing the restructuring of vocational training to adapt to new technologies.⁴⁶ These innovations necessitate a more skilled workforce, potentially enhancing working conditions while also threatening to reduce the overall number of jobs, necessitating a balance between technological advancement and social protection. The transformation of the labor market in the smart cities era, as explored by Silva-Morales, underscores the shift toward new smart service systems and the requisite development of new job skills across various service industries.⁴⁷ This transition is not only about adopting new technologies but also about developing policies that adapt to these changes, ensuring that the workforce is equipped to handle the demands of new, technologically advanced urban environments.

In China, the deployment of Smart City Construction (SCC) has been found to have a dual impact on employment. According to Cao et al., while SCC promoted digital capitalization, leading to efficiencies and job creation in new tech sectors, it also facilitated a significant reduction in low-skilled and non-production roles due to automation and technological substitution.⁴⁸ This restructuring of the labor market reflects a broader global trend where technological advancements necessitate a shift toward higher skill requirements and greater adaptability among the workforce. Together, these studies paint a complex picture of the job market in the smart cities era: one where technological advancements drive the creation of new job opportunities and the demand for new skills, while also posing significant challenges to traditional employment structures. The transformation thus calls for thoughtful policy interventions that not only foster innovation but also ensure that the benefits of smart cities are equitably distributed, preventing social disparities and supporting those displaced by technological change.

Savings from Optimized Traffic Management ­Systems. Investments in intelligent transportation systems (ITS) are increasingly justified by their potential to save energy and reduce emissions, crucially impacting economic efficiency in urban settings. Smart mobility solutions in cities like Amsterdam, Barcelona, and Singapore have shown that ITS can drastically reduce travel, increase modal shifts, and decrease per-km energy consumption.⁴⁹ For instance, signal systems in Richmond, Virginia, reduced fuel consumption by 10–12%; in Los Angeles by 13%, and in Syracuse, New York, by 9–13%.⁴⁹ Additionally, a transit signal priority system in Southampton, England, reduced bus fuel consumption by 13% and in Helsinki, Finland, by 3.6%.⁴⁹ These changes directly translate into lower fuel costs and reduced congestion, delivering measurable economic benefits by optimizing traffic flow and reducing idle times. Further enhancing these capabilities, Lilhore et al. introduced an adaptive traffic management system utilizing machine learning and IoT technologies.⁵⁰ This system dynamically adjusts traffic signals based on real-time traffic conditions, significantly decreasing vehicle waiting times and smoothing traffic flow. Such advancements not only improve travel times but also contribute to economic savings by lowering the overall energy expenditure of urban transport systems.

In the broader scope of total transportation management, Nguyen et al. described a system where extensive sensor networks and IoT integration allow for sophisticated management strategies that span all forms of urban transport.⁵¹ This system supports a decrease in travel times and energy use, leading to reduced traffic jams and significant energy savings by providing users with real-time data for rerouting traffic and adjusting speed limits based on this information. By reducing congestion and smoothing traffic flows, these intelligent systems contribute significantly to the economic and environmental sustainability of smart cities. Together, these technologies represent a shift toward more efficient, economically beneficial urban transportation networks, showcasing how smart city initiatives can harness technology to enhance both the quality of life and economic efficiency.

Improved Civic Engagement

Smart cities also enhance civic engagement by leveraging digital platforms that facilitate communication between citizens and government. These platforms not only improve transparency but also allow for more direct involvement of citizens in governance processes. For instance, mobile apps and online portals enable residents to report issues, participate in public consultations, and access services more efficiently, which strengthens community bonds and enhances democratic participation.⁵²

Resilience and Adaptation

Additionally, smart cities are more resilient to environmental, economic, and social shocks. Technologies such as predictive analytics and real-time data monitoring enable cities to anticipate and respond to crises swiftly, minimizing impacts. These systems are crucial in managing everything from natural disasters to economic downturns, ensuring that cities can quickly adapt and recover.⁵³ By integrating advanced technologies, smart cities not only enhance current urban living standards but also pave the way for future developments. These cities stand as beacons of progress, demonstrating the potential of technology to create sustainable, efficient, and inclusive urban environments.

Challenges of Smart Cities

Privacy Concerns

The vast amount of data collected through IoT devices, AI applications, and other digital platforms raises substantial privacy concerns.⁵⁴ The potential for ­surveillance and data misuse is a significant risk, as personal information can be exploited if not adequately protected. Studies have shown that without stringent security measures and transparent data management policies, the information collected can be vulnerable to breaches, causing privacy invasions and loss of trust among citizens.⁵⁵

Regulatory Challenges

Regulatory frameworks often struggle to keep pace with the rapid development of technologies used in smart cities. There is a pressing need for laws that specifically address the nuances of smart technologies, including data protection rules and standards for cross-border data flows. Without these regulatory measures, it is challenging to safeguard citizen rights and ensure equitable benefits from smart city initiatives. Moreover, inconsistencies in regulations across different jurisdictions can complicate the deployment of smart technologies on a global scale.⁵⁶

Ethical Considerations

The deployment of smart city technologies also raises ethical concerns, particularly regarding equity and ­access. There is a risk that these technologies might primarily benefit those in higher socioeconomic brackets, thereby widening the digital divide and exacerbating existing inequalities. Ethical frameworks need to guide the development and implementation of smart technologies to ensure they serve all segments of ­society fairly and without bias.⁵⁷

Logistical Issues in Construction and Assembly

In the development of smart cities, addressing logistical challenges in the construction and assembly of infrastructure is crucial. These challenges, ranging from financial constraints to technical difficulties, can significantly influence the overall success and timely implementation of smart city projects. Understanding and managing these obstacles is essential for ensuring that smart city initiatives deliver their intended benefits efficiently and sustainably, without unexpected delays or escalated costs.

High Initial Investment Costs. The development of smart city infrastructure necessitates substantial initial investments, which can pose significant financial barriers.⁵⁸ The deployment of advanced technologies such as IoT systems, smart grids, and integrated transportation solutions often requires upfront capital that can strain municipal budgets.⁵⁸ A study highlighting the financial impact on cities undertaking smart transformations reported that the initial costs for implementing IoT frameworks alone could run into millions of dollars, depending on the scale and scope of the project.⁵⁹

For instance, Barcelona’s ambitious smart city initiative included deploying IoT technologies across multiple urban systems, which was initially budgeted with substantial public funds amounting to $230 million. This investment aimed to rejuvenate an industrial area, fostering the growth of local technology startups and IoT applications, and was part of a broader strategy to enhance city operations and services. The financial commitment significantly influenced the city’s budget, channeling funds into technological upgrades that ­included the installation of 19,500 smart meters to improve energy efficiency, integration of a citywide fiber network to support extensive WiFi connectivity, and the development of advanced transportation and parking systems. These improvements not only optimized resource management but also anticipated generating considerable savings—estimated at $58 million annually on water and $37 million through smart lighting, alongside a boost in parking revenues of $50 million per year.⁶⁰ Such examples underscore the need for careful ­financial planning and exploration of various funding mechanisms, including public–private partnerships, to manage the high initial costs associated with smart city projects.

Coordination Among Various Stakeholders. The complexity of smart city projects often requires coordination among a diverse group of stakeholders, including government bodies, private companies, and local communities. This coordination is critical as it involves managing the expectations and responsibilities of different groups, each with their unique interests and objectives.⁶¹ The smart city initiative in Amsterdam provides a clear example of such coordination challenges. The project required alignment between local government, technology providers, and citizen groups to implement sustainable energy solutions and smart transportation systems.⁶² Conflicts arose due to differing priorities, such as the local government’s focus on cost-effectiveness versus citizens’ concerns for privacy and security. Effective management of these conflicts involved structured dialogue sessions and transparent decision-making processes, which were crucial for maintaining the project’s momentum and ensuring stakeholder buy-in.⁶²

Technical Challenges. Integrating new smart technologies with existing urban infrastructures presents a range of technical challenges that directly impact the feasibility and operational effectiveness of smart city initiatives. Compatibility issues often arise when newer, advanced technologies must work in concert with older systems that were not originally designed to accommodate them.⁵² For example, the proposal to electrify and enhance Boston’s commuter rail system, including adopting regional rail, presents significant technical and logistical challenges. These challenges involve integrating new electric train technologies into an existing network that currently relies on diesel-powered locomotives.⁶³ The transition not only requires substantial infrastructure upgrades but also faces hurdles in aligning with ongoing safety directives and managing projected budget deficits.

Scalability is another critical concern, as solutions that work effectively on a small scale may encounter difficulties when expanded city-wide.64 Additionally, the technical expertise required for the installation, operation, and maintenance of sophisticated smart city technologies often exceeds the capabilities available within local government staff. To address this, cities like Seoul have invested heavily in training programs to enhance the skills of their workforce to meet the demands of new smart city technologies.⁶⁵ The challenges of building smart cities, including technical difficulties and logistical hurdles, highlight the complexity of integrating advanced technologies with existing urban infrastructures. Technical challenges demand substantial investments and continuous commitment to staff training and system upgrades, while logistical issues require careful coordination among diverse stakeholders and significant upfront costs. Addressing these alongside privacy, regulatory, and ethical concerns through robust policies that prioritize data security, transparency, and inclusiveness is crucial. Engaging with various stakeholders ensures that solutions are comprehensive and sensitive to the diverse needs of urban populations, paving the way for the successful implementation of smart cities.

Future Directions and Innovations

As smart city initiatives continue to evolve, the potential for technological innovation remains vast. Future developments are likely to focus on further integrating advanced technologies like quantum computing, edge computing, and next-generation AI to enhance urban management.⁶⁶ These technologies promise to dramatically increase the speed and efficiency of data processing, enabling real-time analytics at an unprecedented scale.⁶⁷

Use of AR/VR in Planning and Management

The integration of augmented reality (AR) and virtual reality (VR) into urban planning and management is poised to transform how cities engage with citizens and plan future developments. These tools could revolutionize public participation in urban development, allowing residents to visualize and provide feedback on urban projects before they are implemented.⁶⁸ AR and VR in City Modeling. AR and VR are transforming urban planning by enabling the creation of interactive, detailed 3D models of cities. VR technology allows urban planners to visualize new projects and infrastructure changes in a comprehensive 3D ­environment before actual construction begins. This pre-visualization helps in assessing the potential impacts of developments on the urban landscape and aids in making more informed decisions.⁶⁹ For example, the use of VR in the redevelopment plans of the London King’s Cross area provided planners and stakeholders with a realistic view of the project’s outcomes, facilitating better spatial understanding and project approval processes.⁷⁰

Conversely, AR brings additional layers of data to the real world, allowing planners and engineers to overlay digital information onto the physical environment. This capability is crucial for situational analysis and on-site decision making. AR applications can project data such as property boundaries, underground infrastructure, and historical data directly onto development sites, enhancing the accuracy and efficiency of fieldwork. In Helsinki, AR technologies have been strategically implemented to enhance urban planning by enabling the visualization of future developments directly within the current cityscape. This application allows both city planners and the public to ­interactively engage with and visualize proposed architectural projects, thereby significantly enhancing the transparency of planning processes and improving public consultation and feedback.⁷¹ Together, AR and VR provide a powerful toolkit for modern urban planning, combining detailed pre-visualization with real-time data overlay, which streamlines the planning process and enhances collaborative decision-making.

Public Engagement and Participation

AR and VR technologies are significantly enhancing public engagement and participation in urban planning. These technologies offer immersive experiences that allow citizens to visualize potential changes in their ­environment before they occur, making the abstract elements of planning tangible and understandable.⁷² For instance, VR platforms can host virtual town hall meetings where community members can experience upcoming development projects in a controlled, virtual environment, enhancing transparency and fostering a stronger sense of community involvement.⁷³

AR adds a layer of interaction to urban planning processes by enabling people to view proposed ­changes superimposed onto their current environments through their mobile devices. In Valencia, Spain, AR was successfully employed to enhance public participation by allowing citizens to interact with potential urban designs overlaid on the existing landscape. This approach facilitated a more engaging consultation process and gathered substantial public support. These participatory approaches significantly enhance the quality of feedback from residents and increase their satisfaction with the planning process, as they can visually comprehend and influence the proposed design outcomes directly.⁷⁴ Such dynamic interaction mechanisms facilitated by AR and VR are proving crucial in breaking down the barriers between city planners and the communities they serve, paving the way for more democratic and inclusive urban development.

Challenges and Considerations

Implementing AR and VR in urban planning is fraught with several challenges that can affect the feasibility and effectiveness of these technologies. Technical limitations, such as the processing power required to run detailed simulations and the sophistication needed for accurate real-world overlays, pose significant hurdles.⁷⁵ Additionally, the high costs associated with ­deploying AR and VR technology, from initial setup to ongoing maintenance, can be prohibitive for many municipalities.⁵⁸ The requirement for specialized skills is another significant barrier. Urban planners and technical staff need specialized training to design, manage, and maintain AR and VR systems effectively, which can be a substantial investment in time and resources.⁷⁶

Furthermore, addressing privacy and cybersecurity concerns is crucial when deploying AR and VR technologies in urban settings. The use of AR in smart ­cities involves the collection and processing of extensive data sets, including some that are highly sensitive and personal. The integrity and security of this data are paramount, not only to protect against privacy violations but also to maintain public trust. Effective cybersecurity measures are essential to prevent data breaches that could severely undermine public confidence in municipal initiatives and expose citizens to significant risks.69 These challenges necessitate comprehensive planning, substantial investment in technology and human resources, and robust data governance policies to ensure the successful integration of AR and VR into urban planning processes.⁷⁷

Conclusion

In this review, we have explored the significant ­impact of technology on urban planning and the development of smart cities. Integrating IoT, AI, and big data ­analytics into urban infrastructure has not only ­enhanced the operational efficiency of cities but has also significantly improved the quality of life for their residents. From optimizing traffic management to ­enhancing public safety and promoting environmental sustainability, smart cities utilize cutting-edge ­technology to address complex urban challenges comprehensively. The expansion of smart city capabilities into areas like job creation, energy efficiency, and optimized traffic management systems illustrates the dynamic ways these technologies are being leveraged to foster economic growth and sustainability. Moreover, the integration of AR and VR in urban planning presents exciting new possibilities for enhancing public engagement and making urban planning more inclusive and interactive.

As we move forward, the role of technology in urban environments is only expected to grow. The continuous advancement of digital tools will play a crucial role in shaping the future of urban living, making cities more responsive, efficient, and sustainable. Technologies such as quantum computing, edge computing, AR, and VR are poised to transform urban management and planning further. These innovations can potentially engage citizens more actively in shaping their environments, making urban planning more inclusive and interactive. However, the challenges of integrating these ­sophisticated technologies with existing infrastructure, managing substantial initial investments, and ensuring data privacy and security must also be addressed. As cities navigate these challenges, the importance of strategic planning, stakeholder engagement, and robust policy frameworks cannot be overstated.

In conclusion, the evolution of smart cities is intrinsically linked to the progress in technological innovations. The ability of cities to adapt and integrate new technologies will be key to their continued success and sustainability. As we look to the future, the ongoing challenge for urban developers and policymakers will be to harness these technological advancements responsibly and inclusively, ensuring that smart city initiatives not only lead to smarter but also more equitable urban futures.

References

1. Bibri SE, Krogstie J. Smart sustainable cities of the future: an extensive interdisciplinary literature review. Sustain Cities Soc. 2017;31:183-212.
https://doi.org/10.1016/j.scs.2017.02.016
 
2. Talari S, Shafie-Khah M, Siano P, Loia V, Tommasetti A, Catalão JP. A review of smart cities based on the internet of things concept. Energies. 2017;10(4):421.
https://doi.org/10.3390/en10040421
 
3. Saba D, Sahli Y, Maouedj R, Hadidi A, Medjahed MB. Towards artificial intelligence: concepts, applications, and innovations. Enab AI Appl Data Sci. 2021:103-46.
https://doi.org/10.1007/978-3-030-52067-0_6
 
4. Vassakis K, Petrakis E, Kopanakis I. Big data analytics: Applications, prospects and challenges. In Mobile big data: a roadmap from models to technologies. Springer International Publishing; 2018. p. 3-20.
https://doi.org/10.1007/978-3-319-67925-9_1
 
5. Angelidou M, Psaltoglou A, Komninos N, Kakderi C, Tsarchopoulos P, Panori A. Enhancing sustainable urban development through smart city applications. J Sci Technol Policy Manag. 2018;9(2):146-69.
https://doi.org/10.1108/JSTPM-05-2017-0016
 
6. Chourabi H, Nam T, Walker S, Gil-Garcia JR, Mellouli S, Nahon K,
 
et al. Understanding smart cities: an integrative framework. 2012 IEEE 53rd Hawaii International Conference on System Sciences (HICSS). IEEE; 2012.
 
7. Angelidou M. Smart cities: A conjuncture of four forces. Cities. 2015;47:95-106.
https://doi.org/10.1016/j.cities.2015.05.004
 
8. Kitchin R. The real-time city? Big data and smart urbanism. GeoJournal. 2014;79(1):1-14.
https://doi.org/10.1007/s10708-013-9516-8
 
9. Townsend AM. Smart cities: Big data, civic hackers, and the quest for a New Utopia. W.W. Norton & Company; 2013.
 
10. Allam Z, Newman P. Redefining the smart city: culture, metabolism and governance. Smart Cities. 2018;1(1):4-25.
https://doi.org/10.3390/smartcities1010002
 
11. Hancke GP, Hancke Jr GP. The role of advanced sensing in smart cities. Sensors. 2013;13(1):393-425.
https://doi.org/10.3390/s130100393
 
12. Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M. Internet of things for smart cities. IEEE Int Things J. 2014;1(1):22-32.
https://doi.org/10.1109/JIOT.2014.2306328
 
13. Colding J, Nilsson C, Sjöberg S. Smart cities for all? Bridging digital divides for socially sustainable and inclusive cities. Smart Cities. 2024;7(3):1044-59.
https://doi.org/10.3390/smartcities7030044
 
14. Batty M. Big data, smart cities and city planning. Dial Human Geogr. 2013;3(3):274-9.
https://doi.org/10.1177/2043820613513390
 
15. Kitchin R, Lauriault TP, McArdle G. Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards. Reg Stud Reg Sci. 2015;2(1):6-28.
https://doi.org/10.1080/21681376.2014.983149
 
16. Abduljabbar R, Dia H, Liyanage S, Bagloee SA. Applications of artificial intelligence in transport: an overview. Sustainability. 2019;11(1):189.
https://doi.org/10.3390/su11010189
 
17. Shaikh RA, Naidu H, Kokate PA. Next-generation WSN for environmental monitoring employing big data analytics, machine learning and artificial intelligence. In Evolutionary computing and mobile sustainable networks: proceedings of ICECMSN 2020 2021;(pp. 181-96). Springer Singapore.
https://doi.org/10.1007/978-981-15-5258-8_20
 
18. El-Tantawy S, Rezaee K, Abdulhai B. Closed loop optimal adaptive traffic signal and ramp control: a case study on downtown Toronto. In 2015 IEEE 18th international conference on intelligent transportation systems 2015;(pp. 2398-404). IEEE.
https://doi.org/10.1109/ITSC.2015.387
 
19. Wilts H, Garcia BR, Garlito RG, Gómez LS, Prieto EG. Artificial intelligence in the sorting of municipal waste as an enabler of the circular economy. Resources. 2021;10(4):28.
https://doi.org/10.3390/resources10040028
 
20. Kovacs-Györi A, Ristea A, Havas C, Mehaffy M, Hochmair HH, Resch B, et al. Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning. ISPRS Int J Geo-Inf. 2020;9(12):752.
https://doi.org/10.3390/ijgi9120752
 
21. Silva BN, Khan M, Jung C, Seo J, Muhammad D, Han J, Yoon Y, Han K. Urban planning and smart city decision management empowered by real-time data processing using big data analytics. Sensors. 2018;18(9):2994.
https://doi.org/10.3390/s18092994
 
22. Babar M, Arif F, Jan MA, Tan Z, Khan F. Urban data management system: towards Big Data analytics for Internet of Things based smart urban environment using customized Hadoop. Fut Gen Comp Syst. 2019;96:398-409.
https://doi.org/10.1016/j.future.2019.02.035
 
23. Meduri K, Nadella GS, Gonaygunta H, Meduri SS. Developing a fog computing-based AI framework for real-time traffic management and optimization. Int J Sustain Dev Comput Sci. 2023;5(4):1-24.
 
24. Shamsuzzoha A, Nieminen J, Piya S, Rutledge K. Smart city for sustainable environment: a comparison of participatory strategies from Helsinki, Singapore and London. Cities. 2021;114:103194.
https://doi.org/10.1016/j.cities.2021.103194
 
25. Ferro-Escobar R, Vacca-González H, Gómez-Castillo H. Smart and sustainable cities in collaboration with IoT: the Singapore success case. In Machine learning for smart environments/cities: An IoT approach 2022;(pp. 213-243). Springer International Publishing.
https://doi.org/10.1007/978-3-030-97516-6_12
 
26. Zhou J, Li L, Wang Q, Van Fan Y, Liu X, Klemeš JJ, et al. Household waste management in Singapore and Shanghai: experiences, challenges and opportunities from the perspective of emerging megacities. Waste Manag. 2022;144:221-32.
https://doi.org/10.1016/j.wasman.2022.03.029
 
27. Mora H, Peral J, Ferrandez A, Gil D, Szymanski J. Distributed architectures for intensive urban computing: a case study on smart lighting for sustainable cities. IEEE Access. 2019;7:58449-65.
https://doi.org/10.1109/ACCESS.2019.2914613
 
28. Xia X, Wu X, BalaMurugan S, Karuppiah M. Effect of environmental and social responsibility in energy-efficient management models for smart cities infrastructure. Sustain Energy Technol Assess. 2021;47:101525.
https://doi.org/10.1016/j.seta.2021.101525
 
29. Capdevila I, Zarlenga MI. Smart city or smart citizens? The Barcelona case. J Strateg Manag. 2015;8(3):266-82.
https://doi.org/10.2139/ssrn.2585682
 
30. Smith A, Martín PP. Going beyond the smart city? Implementing technopolitical platforms for urban democracy in Madrid and Barcelona. In Sustainable smart city transitions 2022
https://doi.org/10.4324/9781003205722-14
 
(pp. 280-299). Routledge.
 
31. Bibri SE, Krogstie J. The emerging data-driven smart city and its innovative applied solutions for sustainability: the cases of London and Barcelona. Energy Inform. 2020;3(1):5.
https://doi.org/10.1186/s42162-020-00108-6
 
32. Al Shamsi HR, Safei SA. Artificial intelligence adoption in predictive policing to predict crime mitigation performance. Int J Sustain Constr Eng Technol. 2023;14(3):289-98.
https://doi.org/10.30880/ijscet.2023.14.03.025
 
33. Khadragy S, Elshaeer M, Mouzaek T, Shammass D, Shwedeh F, Aburayya A, Jasri A, Aljasmi S. Predicting diabetes in United Arab Emirates healthcare: artificial intelligence and data mining case study. South East Eur J Public Health. 2022. https://doi.org/10.70135/seejph.vi.406
 
34. Khan S, Shael M, Majdalawieh M, Nizamuddin N, Nicho M. Blockchain for governments: the case of the Dubai government. Sustainability. 2022;14(11):6576.
https://doi.org/10.3390/su14116576
 
35. Hernàndez CA. The role of citizens in smart cities and urban infrastructures. In Solving urban infrastructure problems using smart city technologies 2021 (pp. 213-234). Elsevier.
https://doi.org/10.1016/B978-0-12-816816-5.00010-3
 
36. Adamec V, Herman D, Schullerova B, Urbanek M. Modelling of traffic load by the data from sky system in the smart city concept. In Smart governance for cities: Perspectives and experiences. Springer; 2020. p. 135-52.
https://doi.org/10.1007/978-3-030-22070-9_7
 
37. Feilberg M, Mark O. Integrated urban water management: improve efficient water management and climate change resilience in cities. In Sustainable water management in urban environments. Springer; 2016. p. 1-32.
https://doi.org/10.1007/978-3-319-29337-0_1
 
38. Krähmer K. Are green cities sustainable? A degrowth critique of sustainable urban development in Copenhagen. Eur Plan Stud. 2021;29(7):1272-89.
https://doi.org/10.1080/09654313.2020.1841119
 
39. Damsø T, Kjær T, Christensen TB. Implementation of local climate action plans: copenhagen-towards a carbon-neutral capital. J Clean Product. 2017;167:406-15.
https://doi.org/10.1016/j.jclepro.2017.08.156
 
40. Eltigani D, Masri S. Challenges of integrating renewable energy sources to smart grids: a review. Renew Sustain Energy Rev. 2015;52:770-80.
https://doi.org/10.1016/j.rser.2015.07.140
 
41. Almihat MG, Kahn MT, Aboalez K, Almaktoof AM. Energy and sustainable development in smart cities: an overview. Smart Cities. 2022;5(4):1389-408.
https://doi.org/10.3390/smartcities5040071
 
42. Şerban AC, Lytras MD. Artificial intelligence for smart renewable energy sector in europe-Smart energy infrastructures for next generation smart cities. IEEE Access. 2020;8:77364-77.
https://doi.org/10.1109/ACCESS.2020.2990123
 
43. Barba-Sánchez V, Arias-Antúnez E, Orozco-Barbosa L. Smart cities as a source for entrepreneurial opportunities: evidence for Spain. Technol Forecast Soc Change. 2019;148:119713.
https://doi.org/10.1016/j.techfore.2019.119713
 
44. Li B. Effective energy utilization through economic development for sustainable management in smart cities. Energy Rep. 2022;8:4975-87.
https://doi.org/10.1016/j.egyr.2022.02.303
 
45. Wang SJ, Moriarty P. Energy savings from Smart Cities: a critical analysis. Energy Proc. 2019;158:3271-6.
https://doi.org/10.1016/j.egypro.2019.01.985
 
46. Estebsari A, Werna E. Smart cities, grids, homes and the workforce: challenges and prospects. In Current state of art in artificial intelligence and ubiquitous cities. Springer; 2022. p. 17-39.
https://doi.org/10.1007/978-981-19-0737-1_2
 
47. Silva-Morales MJ. French labour market transformation in the smart cities era expansion: new smart service systems-New job skills policy development?. 2023. https://hal.science/hal- 04100946/document
 
48. Cao Y, Hu Y, Liu Q, Lu M, Shan Y. Job creation or disruption? Unraveling the effects of smart city construction on corporate employment in China. Technol Forecast Soc Change. 2023;195:122783.
https://doi.org/10.1016/j.techfore.2023.122783
 
49. Chen Y, Ardila-Gomez A, Frame G. Achieving energy savings by intelligent transportation systems investments in the context of smart cities. Transp Res Part D Transp Environ. 2017;54:381-96.
https://doi.org/10.1016/j.trd.2017.06.008
 
50. Lilhore UK, Imoize AL, Li CT, Simaiya S, Pani SK, Goyal N, Kumar A, Lee CC. Design and implementation of an ML and IoT based adaptive traffic-management system for smart cities. Sensors. 2022;22(8):2908.
https://doi.org/10.3390/s22082908
 
51. Nguyen DD, Rohács J, Rohács D, Boros A. Intelligent total transportation management system for future smart cities. Appl Sci. 2020;10(24):8933.
https://doi.org/10.3390/app10248933
 
52. Javed AR, Shahzad F, ur Rehman S, Zikria YB, Razzak I, Jalil Z, Xu G. Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects. Cities. 2022;129:103794.
https://doi.org/10.1016/j.cities.2022.103794
 
53. Büyüközkan G, Ilıcak Ö, Feyzioğlu O. A review of urban resilience literature. Sustain Cities Soc. 2022;77:103579.
https://doi.org/10.1016/j.scs.2021.103579
 
54. Bari L, O’Neill DP. Rethinking patient data privacy in the era of digital health. Health Affairs Forefront. 2019.
 
55. Ma C. Smart city and cyber-security; technologies used, leading challenges and future recommendations. Energy Rep. 2021;7:7999-8012.
https://doi.org/10.1016/j.egyr.2021.08.124
 
56. Weber M, Podnar Žarko I. A regulatory view on smart city services. Sensors. 2019;19(2):415.
https://doi.org/10.3390/s19020415
 
57. Martínez-Ballesté A, Pérez-Martínez PA, Solanas A. The pursuit of citizens’ privacy: a privacy- aware smart city is possible. IEEE Commun Mag. 2013;51(6):136-41.
https://doi.org/10.1109/MCOM.2013.6525606
 
58. Wolniak R, Stecuła K. Artificial intelligence in smart cities-Applications, barriers, and future directions: a review. Smart Cities. 2024;7(3):1346-89.
https://doi.org/10.3390/smartcities7030057
 
59. Halegoua G. Smart cities. MIT Press; 2020.
https://doi.org/10.7551/mitpress/11426.001.0001
 
60. Laura Adler. How smart city Barcelona brought the internet of things to life. Harv Bus Rev. 2016. https://datasmart.hks.harvard.edu/news/article/how-smart-city- barcelona-brought-the-internet-of-things-to-life-789
 
61. Viale Pereira G, Cunha MA, Lampoltshammer TJ, Parycek P, Testa MG. Increasing collaboration and participation in smart city governance: a cross-case analysis of smart city initiatives. Inform Technol Dev. 2017;23(3):526-53.
https://doi.org/10.1080/02681102.2017.1353946
 
62. Voorwinden A. Regulating the smart city in European municipalities: a case study of Amsterdam. Eur Pub Law. 2022;28(1):155-180.
https://doi.org/10.54648/EURO2022008
 
63. Zukowski D. Boston commuter rail lines need overhaul, advocates say, as transit agency tackles safety, financial issues. Environ Health Perspect. 2023. https://www.smartcitiesdive.com/news/boston-transit-agency-mbta-electrify-commuter-regional-rail/652117/
 
64. Sánchez-Corcuera R, Nuñez-Marcos A, Sesma-Solance J, Bilbao-Jayo A, Mulero R, Zulaika U, et al. Smart cities survey: Technologies, application domains and challenges for the cities of the future. Int J Distrib Sen Net. 2019;15(6):1550147719853984.
https://doi.org/10.1177/1550147719853984
 
65. Lee J, Babcock J, Pham TS, Bui TH, Kang M. Smart city as a social transition towards inclusive development through technology: a tale of four smart cities. Int J Urban Sci. 2023;27(Supp 1):75-100.
https://doi.org/10.1080/12265934.2022.2074076
 
66. Bonab AB, Fedele M, Formisano V, Rudko I. Urban quantum leap: A comprehensive review and analysis of quantum technologies for smart cities. Cities. 2023;140:104459.
https://doi.org/10.1016/j.cities.2023.104459
 
67. Khan LU, Yaqoob I, Tran NH, Kazmi SA, Dang TN, Hong CS. Edge-computing-enabled smart cities: a comprehensive survey. IEEE Internet Things J. 2020;7(10):10200-32.
https://doi.org/10.1109/JIOT.2020.2987070
 
68. Hunter M, Soro A, Brown R. Enhancing urban conversations for smarter cities: augmented reality as an enabler of digital civic participation. Interact Des Architect. 2021;48:75-99.
https://doi.org/10.55612/s-5002-048-004
 
69. Alzahrani NM, Alfouzan FA. Augmented reality (AR) and cyber-security for smart cities-A systematic literature review. Sensors. 2022;22(7):2792.
https://doi.org/10.3390/s22072792
 
70. Abdelsalam AE. Resilient design for London’s elevated social spaces: exploring challenges, opportunities, and harnessing interactive virtual reality co-design approaches for community engagement (Doctoral dissertation, University of Nottingham). 2023.
 
71. Hämäläinen M. A framework for a smart city design: Digital transformation in the Helsinki smart city. Entrepreneurship and the community: a multidisciplinary perspective on creativity, social challenges, and business. Springer; 2020. p. 63-86.
https://doi.org/10.1007/978-3-030-23604-5_5
 
72. Wang Y, Lin YS. Public participation in urban design with augmented reality technology based on indicator evaluation. Front Virt Real. 2023;4:1071355.
https://doi.org/10.3389/frvir.2023.1071355
 
73. Anim P. Exploring the citizen engagement metaverse: Testing virtual reality for citizen feedback on public space design options (Master’s Thesis, University of Regina). 2022.
 
74. Yagol P, Ramos F, Trilles S, Torres-Sospedra J, Perales FJ. New trends in using augmented reality apps for smart city contexts. ISPRS Int J Geo-Inf. 2018;7(12):478.
https://doi.org/10.3390/ijgi7120478
 
75. Kaji S, Kolivand H, Madani R, Salehinia M, Shafaie M. Augmented reality in smart cities: applications and limitations. J Eng Technol. 2018;6(1):28-45.
 
76. Allam Z, Sharifi A, Bibri SE, Jones DS, Krogstie J. The metaverse as a virtual form of smart cities: opportunities and challenges for environmental, economic, and social sustainability in urban futures. Smart Cities. 2022;5(3):771-801.
https://doi.org/10.3390/smartcities5030040
 
77. Cesario E. Big data analytics and smart cities: applications, challenges, and opportunities. Front Big Data. 2023;6:1149402.
https://doi.org/10.3389/fdata.2023.1149402

Cite this article as:
Wheatley MC. The Rise of Smart Cities: Technology’s Role in Urban Planning. Premier Journal of Data Science 2024;1:100002

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