Acta Polytechnica CTU Proceedings https://doi.org/10.14311/APP.2024.46.0085 Acta Polytechnica CTU Proceedings 46:85–93, 2024 © 2024 The Author(s). Licensed under a CC-BY 4.0 licence Published by the Czech Technical University in Prague AI-ENABLED TRANSITION TO SMART EUROPEAN CITIES Noor Marji∗, Michal Kohout, Lijun Chen, Gülbahar Emir Isik, Akshatha Ravi Kumar Czech Technical University in Prague, Faculty of Architecture, Thákurova 9, 160 00 Prague 6, Czech Republic ∗ corresponding author: noor.marji@fa.cvut.cz Abstract. Smart cities continue to be discussed throughout Europe as a result of the continent’s rising urbanization and the need for sustainable development. Artificial intelligence (AI) has the potential to significantly promote this shift by assisting cities in becoming more effective, sustainable, and receptive to the requirements of their residents. The goal of this study is to examine the potential and difficulties of AI in urban development and present a framework for incorporating AI into city planning and management in European cities. This is done by analyzing case study examples from European cities and examining primary and secondary data sources, with the aim of providing a comprehensive framework for the sustainable integration of AI systems. This study presents a set of ethical and inclusive AI criteria, such as transparency, inclusion, and accountability, to enable responsible AI research and implementation. It continues by emphasizing the need for efficient AI integration in smart cities and pushing for a holistic AI-enabled transition to inclusive and sustainable smart cities. Keywords: Smart cities, artificial intelligence, Europe, urban planning, transition. 1. Introduction Rapid urbanization and rising demands for sustain- able development have given smart cities prominence across Europe [1, 2]. As cities grow larger and more complex, innovative solutions must be found to assist cities in using resources more efficiently while improving inhabitants’ quality of life and encouraging sustainable development [2]. Artificial intelligence (AI) has emerged as a possible game-changer in this area, providing new avenues for data-driven decision-making, urban system optimization, public participation, and citizen engagement [3, 4]. Integrating AI technology in urban planning and development could yield several advantages, such as enhanced efficiency, lower costs, improved public ser- vices, and citizen involvement [2, 4]. AI’s ability to re- spond to residents’ needs while supporting sustainable growth and mitigating environmental impact makes AI particularly effective at guiding smart cities [2, 5]. However, using AI poses certain hurdles such as ethi- cal concerns regarding algorithm bias as well as data privacy considerations [5, 6]. The identified knowledge gaps in the field of AI- enabled transition to smart European cities are twofold. Firstly, there is a lack of a comprehensive framework that addresses the sustainable integration of AI systems into city planning and management, particularly suited for the diverse urbanization chal- lenges faced nowadays by European cities [2]. Such a framework should encompass ethical considerations and inclusivity criteria. Secondly, existing literature often lacks collective case study analyses from various European cities, essential for understanding diverse contexts, challenges, and successful AI adoption mod- els [3, 4]. Considering these gaps, this paper examines the potential and difficulties of implementing AI in ur- ban development and presents a framework for incor- porating AI into city planning and management in European cities, which is the planned output of this research. 2. Methodology This research incorporates a qualitative data collection and analysis methodology and outlines a systematic approach to investigate the potential and challenges of AI in urban development and the subsequent integra- tion into smart European cities. It covers literature review, case studies, framework development, ethi- cal considerations, data analysis, and integration of findings to drive informed recommendations. (a) Literature Review and Data Collection • Primary and Secondary Sources: review of pri- mary literature such as academic journals, con- ference papers and reports pertaining to AI ap- plications in urban development and smart cities within European contexts. (b) Case Studies • Criteria for Selection: The cities were selected based on their use of artificial intelligence in ur- ban development, considering factors like geo- graphic location, population size, economic pro- file and technological infrastructure. (c) Framework Development • Review of Existing Frameworks: Comprehen- sive evaluation of existing AI-driven urban de- velopment frameworks in order to identify gaps, strengths and weaknesses. 85 https://doi.org/10.14311/APP.2024.46.0085 https://creativecommons.org/licenses/by/4.0/ https://www.cvut.cz/en N. Marji, M. Kohout, L. Chen et al. Acta Polytechnica CTU Proceedings (d) Ethical Criteria and Guidelines • Exploration of Existing Ethics for AI Implementa- tion, with Emphasis on Transparency, Inclusivity, and Accountability. • Customization for European Context: Adjusting ethical criteria so they fit with European cultural, legal, and societal contexts. (e) Analysis and Evaluation • Qualitative Analysis: Examining qualitative data in the form of case study insights to extract key themes and perspectives related to AI-enabled smart city development. (f) Integration and Recommendations • Synthesis of Findings: From our research results, we propose an AI integration framework in Euro- pean cities that prioritizes responsible and inclu- sive AI practices. 3. Urban planning and sustainable cities in Europe In the 1980s, the Brundtland Report [7] first pro- posed sustainable development as a concept; defined as development that meets present generation needs without undermining future ones’ abilities to meet them [7], its concept has since become the cornerstone for European projects promoting such efforts. The European Union (EU) is a leader in supporting sustainable development through policies and pro- grams, like its 2013 Environment Action Program which established an environmental action frame- work including sustainable cities as an essential focus area [8]. Urban Agenda 2016 also seeks to enhance sustainable urban development while simultaneously raising the quality of life across European cities [1]. Smart cities have recently gained attention as an innovative approach to urbanization and sustainable development issues. A smart city employs technol- ogy and data to enhance inhabitants’ quality of life, improve urban services and promote sustainable de- velopment [3, 4]. AI technology plays a pivotal role in smart city design by offering new avenues for data- driven decision-making and system optimization [2–5]. 4. Artificial Intelligence (AI) in urban planning AI has the potential to revolutionize urban planning by offering new tools for data analysis, modeling, and decision-making. AI utilizes algorithms and machine learning techniques to assess massive datasets in or- der to make predictions or suggestions based on pat- terns found within them [9–11]. AI can assess various data types relevant to urban planning - demographic data, transportation data, energy consumption infor- mation as well as environmental considerations [10, 11]. Through analysis of such data, AI helps urban plan- ners make more educated decisions related to urban growth, land use planning issues along with various other vital variables [3, 6]. Predictive modeling is one of the core applications for AI in urban planning, helping forecast population shifts, fluctuations in energy demand, and transporta- tion congestion based on historical data [12–15]. These predictions can then inform planning decisions as well as investment decisions [2, 3]. Optimizing land use and transportation systems also uses AI extensively: AI may discover more effective methods to utilize ur- ban spaces while carrying people and products, while simultaneously improving traffic flow, decreasing con- gestion levels, and expanding public transportation services [13–15]. AI can also be leveraged to track and manage ur- ban systems in real-time. By collecting and analyzing data from sensors and other sources, it provides real- time feedback on operations of urban systems like energy usage, air quality management, and waste disposal [6]. With this real-time feedback at their dis- posal, planners, stakeholders, and authorities can iden- tify new development opportunities or alter system performance according to changing requirements [2]. Lastly, AI may also help encourage citizen partici- pation in urban planning through providing access to data and information with AI assessments of pub- lic input/preferences assessments to facilitate citizen involvement [16]. 4.1. Key features of AI-enabled urban planning Four key features characterize AI-enabled urban plan- ning in smart cities, which are data-driven decision- making and predictive modeling, optimization of land use and transportation systems, use of sensor data for real-time monitoring and feedback, and integration of citizen input and feedback into the planning process, see Figure 1. 4.1.1. Data-driven decision-making and predictive modeling Urban planners may use AI for data-driven decision- making and predictive modeling. Such evaluation can involve collecting massive amounts of information from sensors, social media posts, and public records before using an algorithm to find patterns, trends, and connections human planners might miss; helping to gain greater insights into complex urban systems such as transportation management systems [17, 18]. AI can also predict future urban development trends by studying historical data and discovering patterns, providing useful forecasting information such as public transit demand or infrastructure expenditure planning requirements. Predictive modeling also allows urban planners to anticipate possible problems before they occur - for instance, flood or other natural disaster pre- dictions inform the creation of robust infrastructures for future development [19]. 86 vol. 46/2024 AI-enabled transition to smart European cities Figure 1. Key features of AI-enabled urban planning for smart city transitions. Predictive modeling and data-driven decision- making can improve urban planning precision and effectiveness, helping planners use AI to make more informed decisions based on data rather than intu- ition or preconceptions. Unfortunately, AI algorithms may reflect any biases present in training data which results in decisions being inequitable or unfair [20, 21]; an over-reliance on predictive modeling may result in rigidity within the planning process as planners become overly dependent on previous data without taking into account new patterns or situations chang- ing from what was originally planned. 4.1.2. Optimization of land use and transportation systems AI algorithms can analyze traffic data to use its results to maximize traffic flow and avoid congestion. Traffic lights, for example, can be automatically modified based on real-time traffic conditions to reduce emis- sions and wait times [22]. Furthermore, AI may also be employed in optimizing public transit systems by analyzing passenger demand information such as route performance statistics or vehicle availability data in order to enhance service efficiencies while reducing costs [23, 24]. Urban planners can make more informed decisions regarding land use and urban development projects by uncovering underutilized sections of cities or locations with significant development potential by studying data such as population density, land use patterns, and zoning rules. Optimized transportation system designs have also proven their ability to ease traffic congestion, enhance air quality and make cities more livable [22]. Studies have shown that AI-aided planning for trans- portation networks could lower costs while expanding accessibility for marginalized groups [24–26]. Optimizing transportation systems, however, could increase reliance on vehicles and individual forms of transport, contributing to air pollution and climate change. Meanwhile, optimizing land use could result in the displacement of communities or the removal of green spaces, both of which would negatively impact the quality of life. 4.1.3. Use of sensor data for real-time monitoring and feedback Utilizing sensor data requires placing sensors through- out a city in order to collect information on air quality, noise levels, temperature levels, and energy use [23]. By collecting and analyzing real-time data, urban planners can gain insight into the current state of cities and make more informed decisions regarding re- source allocation and urban growth [24]. Sensors may be utilized to detect levels of air pollution to guide the deployment of air purifiers or mitigation measures, as well as detect energy waste within buildings to provide opportunities for improvement [25]. Sensor data can provide municipal planners with useful data as well as real-time feedback from citi- zens; providing real-time traffic and public transport updates can aid residents in making decisions about their daily commute [26], while air quality monitoring allows consumers to reconsider engaging in outdoor ac- tivities in areas which could harm their health [27]. By giving individuals real-time updates of how their city is being developed they may become more involved in the decision-making process as they advocate for policies reflecting their beliefs and interests [27]. 4.1.4. Integration of citizen input and feedback into the planning process Historically, urban planning was typically carried out from the top down with city officials making choices without community consultation or involvement. AI technologies provide new avenues of participation for citizens, enabling engagement through public partic- ipation opportunities throughout various planning processes. Digital platforms, online forums, social media plat- forms, and mobile applications provide urban planners with opportunities to gather citizen opinions on var- ious urban planning matters such as transportation, housing, and public services. Their feedback can then 87 N. Marji, M. Kohout, L. Chen et al. Acta Polytechnica CTU Proceedings Figure 2. Framework for AI integration towards smart city transition. be studied and integrated into planning processes al- lowing urban planners to design more responsive plans suited to citizen feedback [28, 29]. Urban planners can gain real-time insight into peo- ple’s issues and ambitions using machine learning algorithms applied to social media data analysis, pro- viding real-time insight that allows for enhanced plan- ning decisions as well as greater citizen participation. Furthermore, immersive planning experiences can be created using visualization technologies such as virtual reality or augmented reality technologies for maximum engagement with the proposed environmental changes. One challenge lies in making sure the feedback re- flects all aspects of community opinions rather than simply responding to vocal minority interests, while si- multaneously using it meaningfully as input into plan- ning decisions versus simply giving the appearance of citizen involvement without altering decision-making processes. 5. Case studies: AI in European smart cities A number of European cities – Barcelona, Amster- dam, and Copenhagen among them – have launched smart city programs covering topics as varied as energy efficiency, transportation needs management, waste removal management, and citizen involvement [4]. Barcelona, Spain: Barcelona has successfully im- plemented an AI-powered traffic control system that uses real-time data to optimize traffic flow and alle- viate congestion. This system collects information from traffic sensors, GPS devices, and public transit systems using AI algorithms that predict patterns to alter lights accordingly; as a result of which travel times and air quality have both seen significant im- provements [30]. Helsinki, Finland: “OmaStadi” is an AI-powered virtual assistant designed to increase citizen participa- tion in city planning and decision-making processes. Utilizing natural language processing and machine learning algorithms, OmaStadi understands citizen inquiries quickly while offering tailored answers – ul- timately increasing transparency, accountability, as well as citizen involvement [28, 29]. Amsterdam, Netherlands: Amsterdam has imple- mented an artificial intelligence-powered energy man- agement system that utilizes data collected via smart meters and building sensors to optimize energy use while simultaneously decreasing greenhouse gas emis- sions. AI algorithms use analysis results in energy- saving actions like changing building temperatures or lighting levels resulting in reduced costs and greater sustainability [29, 31]. Copenhagen, Denmark: Copenhagen has recently implemented an AI-powered trash collection system that utilizes sensor data and artificial intelligence algorithms to optimize waste collection routes and costs by anticipating waste generation patterns, op- timizing collection schedules, and cutting collection costs [29, 32, 33]. These case studies illustrate how AI can be utilized to promote sustainable urban development and en- hance citizen services in smart cities. However, it’s crucial that AI solutions be created and used responsi- bly and inclusively to address potential concerns while upholding ethical AI principles. 6. Framework for AI integration towards smart cities To support the responsible development and applica- tion of AI in smart cities, a framework for integrating AI into municipal planning and management is nec- essary, see Figure 2. The following is a suggested framework by the authors for ethical and inclusive AI guidelines, based on the previously conducted analysis and review: Identify Opportunities: The initial step should in- volve identifying areas where AI may help enhance urban systems and citizen services. For this to work effectively, collaboration among municipal officials, 88 vol. 46/2024 AI-enabled transition to smart European cities citizens, technology specialists, and experts must oc- cur to find opportunities that fit with city values and interests. Assess Risks: Once opportunities have been iden- tified, the next step should be evaluating any risks posed by AI deployment. Threats to data privacy, ethics, and social justice need to be assessed before AI implementation begins; residents and stakeholders should be included as much as possible during this process to address any potential concerns. Develop Guidelines for Ethical and Inclusive AI : Once potential risks have been assessed, guidelines must be created that address ethical and inclusive AI rules. Recommendations such as those set forth by international bodies (for example, the European Commission Ethics Recommendations for Trustworthy AI) should serve as inspiration when crafting such guidelines; principles like transparency, accountability, and inclusivity should all be included within such recommendations. Implement AI Solutions: Once ethical and inclusive AI principles have been established, the next step should be implementing solutions that comply with them. This involves selecting appropriate algorithms, data sources, and governance frameworks to guarantee reliable, transparent, and accountable AI services. Evaluate and Adapt: Finally, AI solutions must be constantly evaluated and modified so they remain productive while adhering to ethical and inclusive AI norms. This involves monitoring their effects on citizens and civic processes as well as, when needed, revising algorithms or governance frameworks accord- ingly. Cities can ensure AI development and application take an inclusive, responsible approach which improves citizen services and quality of life while contributing to sustainable development by following such frame- works. 7. Challenges, limitations, and further research 7.1. Challenges and considerations for AI-enabled urban planning AI is an exciting prospect when applied to urban planning; however, its use also presents significant hurdles and potential pitfalls which must be considered carefully before embarking upon its application. Below we explore several of these considerations in more depth for AI-enabled urban planning: 7.1.1. Ethical considerations and algorithmic bias With AI’s growing use in urban planning comes an in- creased need to consider ethical concerns and algorith- mic bias. AI systems can only ever be as objective as the data on which they are trained; if such data contains prejudices and inequities, then the AI re- produces or amplifies them [20, 34]. This could have serious ramifications for underrepresented groups as it will exacerbate existing imbalances [21, 35]. Studies have demonstrated how using biased datasets with AI algorithms can lead to discriminatory outcomes across several areas, including criminal justice, em- ployment practices, and lending practices [21, 35]. It is imperative that training data is carefully curated and assessed to reduce biases and promote fairness within deployed AI systems [36, 37]. AI’s use in life- altering decisions such as housing, employment, and social services raises ethical concerns. AI algorithms may make judgments on age, color, gender, or socioe- conomic status that lead to discrimination or unfair treatment [38]. Therefore it is crucial that these al- gorithms’ decision-making processes remain visible, responsible, and equitable [39]. Data privacy is also an ethical concern of AI sys- tems. AI relies heavily on collecting, using, and storing personal data; any changes could have profound ram- ifications on individuals’ rights to privacy [40]. In order to uphold individuals’ privacy rights effectively, specific criteria for data collection, utilization, and storage must be developed [41, 42]. AI system development presents many risks related to algorithmic bias, which may arise at various points during AI’s development process – for instance during data collection, algorithm design, or model training processes. Training data used for an AI system’s construction should ideally not contain bias if possi- ble [35]. Keeping in mind that implicit bias can only be avoided with sufficient variety and representation among datasets for the training of these models. To address these concerns, ethical and legal frame- works regulating AI systems used for urban planning development and deployment must be set in place. This involves setting ethical rules relating to AI cre- ation and use, conducting audits for prejudice or dis- crimination issues within AI systems, as well as hon- oring individuals’ privacy rights [41]. Furthermore, greater collaboration among developers, urban plan- ners, affected communities, and affected individuals must occur to guarantee the ethical implementation of AI technologies [43, 44]. 7.1.2. Data privacy and security Since AI-enabled urban planning heavily relies on data, data privacy, and security are of critical importance. Cities collect massive amounts of data from sources including sensors, social media accounts, and mobile phones which contain personal data including location details, health details, and biometric indicators [42] that could compromise people’s privacy rights if used for AI planning applications without consent. General Data Protection Regulation (GDPR), in- troduced by the European Union in 2018, requires organizations to gain consent before collecting or us- ing personal data, identify its goals, and implement security measures aimed at protecting individual data rights while simultaneously imposing sanctions for 89 N. Marji, M. Kohout, L. Chen et al. Acta Polytechnica CTU Proceedings noncompliance [43]. When processing personal data in AI-enabled urban planning applications, compli- ance with GDPR standards is of utmost importance in order to preserve individuals’ rights [44–48]. Gaining individuals’ consent before collecting or using their data, installing necessary safeguards against illegal access or usage, and giving individuals the option of access or removal is essential for ethical AI integra- tion [44–50]. As part of GDPR compliance, cybersecurity risks associated with AI-enabled urban planning must also be assessed. Intrusions and ransomware attacks can have severe repercussions for individuals as well as cities alike; cities should employ safeguards like en- cryption, multi-factor authentication, vulnerability assessments, penetration testing, as well as incident response processes in order to keep data protected against cyber threats [41]. 7.1.3. Need for community engagement and participation In AI-enabled urban planning, community engagement and participation are key for successful implementa- tion. AI can assist urban planners in processing and analyzing large amounts of data but cannot replace the human element in urban planning. Community participation enables residents to provide input and feedback into planning decisions, ensuring that plan- ning decisions meet residents’ needs and values [44]. Public meetings, internet forums, surveys, and semi- nars are among many forms of community engagement strategies that allow citizens to express concerns, pose questions to planners, and receive replies [45]. Community participation presents many unique challenges to all members of a given community, partic- ularly people with impairments and limited language proficiency. Reaching out to underrepresented popula- tions such as low-income households or minorities that might otherwise not participate is equally vital [46]. Integrating community input effectively into plan- ning decisions presents another challenge, necessitat- ing a two-way dialogue between citizens and plan- ners requiring active listening from both parties and a clear explanation of how input from this source has been considered [47]. When properly conducted, community engagement and participation can lead to more inclusive and informed planning decisions while building trust between citizens and planners through greater transparency and accountability in planning processes. Moreover, it reveals community concerns and priorities which might not be evident through data analysis alone [48]. 7.1.4. Interdisciplinary collaboration and knowledge sharing To effectively incorporate AI in urban planning for transition to smart European cities, urban planners, policymakers, data scientists, social scientists, and community members must come together across dis- ciplines to share knowledge. Each brings different experiences, skillsets, perspectives, and interests that contribute towards crafting more holistic and equi- table AI-powered urban planning approaches [3, 49]. Technical language barriers between various fields make multidisciplinary collaboration challenging. Ur- ban planners might lack experience with AI while data scientists could struggle with the social and cultural context of urban planning. To overcome this barrier, organizations must promote communication and col- laboration as much as possible, in addition to trans- lating technical terms so nontechnical stakeholders can understand [24, 30]. Moreover, interdisciplinary teams for AI-enabled urban planning projects must be formed where representatives from several depart- ments or agencies come together as one team [46, 47]. This would aid in overcoming the fragmented decision- making trees and unnecessary bureaucratic elements typically involved in large-scale masterplanning. Knowledge exchange is key for successfully applying AI in urban planning, with best practices, case studies, and research findings shared among municipalities and regions to reduce duplication. Cities that share their knowledge can avoid reinventing the wheel while learning from the successes and failures of other cities – an approach encouraged by platforms such as the European Union’s Smart Cities Marketplace, where cities share and collaborate across boundaries [50]. 7.2. Research limitations This study presents a conceptual framework for the incorporation of artificial intelligence (AI) in urban planning and deployment of smart cities using the European context as an example. However, it must be acknowledged that AI use for urban planning remains in its infancy, with challenges yet to be overcome, and ethical and social considerations to be carefully taken into consideration during the deployment of solutions incorporating this technology. While this paper explores the potential advantages and challenges of artificial intelligence in sustainable urban planning in Europe, its findings may not ap- ply equally elsewhere or under different conditions. Furthermore, while the examples provide indications of potential AI applications within urban planning and transition to smart city solutions; they do not encompass all possibilities and specific AI technolo- gies should be studied in further detail, preferably in defined urban contexts. This research proposes ethical and inclusive AI de- velopment and implementation criteria, although these should not be seen as exhaustive; their implementa- tion should be regularly assessed and evaluated as AI technology rapidly advances and ethical concerns arise along with it. Finally, this research does not assess the financial and technical feasibility of using AI for urban planning. Policymakers, urban planners, and stakeholders must carefully examine both costs and resources necessary 90 vol. 46/2024 AI-enabled transition to smart European cities for adopting this form of planning, as well as the technical talent required for success. 7.3. Recommendations for further research While AI may offer many benefits in urban planning, it’s essential to study alternative techniques as well. Participatory planning approaches, which integrate citizen participation and feedback directly, may pro- duce different outcomes and perspectives than AI- enabled planning approaches. Long-term Impacts Assessment: Many potential advantages of AI for urban planning may not become immediately evident and require further examination; future studies may focus on exploring long-term eco- nomic, environmental, and social impact associated with AI-enabled urban planning solutions. AI Implementation Evaluation: As AI technology becomes an integral part of urban planning processes, its efficacy needs to be assessed carefully. Further research efforts might focus on measuring whether AI-enabled planning strategies meet their intended goals effectively. Moreover, future studies should take steps to develop more detailed recommendations to address the ethical and societal repercussions of using AI in various stages of urban planning. Future research should also assess how AI could be applied in urban planning across different regions and countries in order to gain a fuller picture of its deploy- ment and efficacy in each setting, with a specific focus on tools such as computer vision and machine learning capabilities of AI. Continued research of AI-enabled urban planning is necessary in order to guarantee its appropriate and efficient application, in the hopes of creating more liveable, equitable, and sustainable cities. 8. Conclusion This article examined the potential and difficulties of AI in urban development and presented a framework for incorporating AI into city planning and manage- ment in European cities. Cities could achieve in- creased efficiency, sustainability, and livability with AI being integrated into urban planning processes – such as data-driven decision-making, predictive mod- eling, optimizing land use and transportation as well as real-time monitoring using sensor data and involv- ing citizens in the planning process are some aspects that have been presented herein – showing it has great promise as an instrument of building resilient European cities. This paper also explored several AI applications for sustainable urban planning in Europe, such as green infrastructure and urban biodiversity, energy-efficient building design and retrofitting, smart transportation systems and mobility planning, and resilient urban design and disaster mitigation. However, AI adoption presents its own set of unique challenges, such as ethical concerns and algorithmic bias, data privacy and security, the need for community engagement and participation, and interdisciplinary collaboration and information exchange. Responding to the identified gaps in research and in order to promote responsible and inclusive AI de- velopment and implementation, this article presents a framework for implementing AI technologies into transition to smart cities, which is the output of this research, that aims to facilitate responsible AI research and deployment. Policymakers, urban planners, and stakeholders are encouraged to explore AI as a way of creating more livable cities that foster equality and sustainability while remaining cognizant of limitations associated with this study and further investigation on its efficacy as an urban planning strategy tool. Future research should examine in more detail the capabilities of specific AI tools in defined urban con- texts, which could provide a basis for examining the implementation of emerging technological tools in Europe and elsewhere. Furthermore, exploring its ca- pacity for solving global concerns like climate change, pandemics, and natural disasters. When used appro- priately with ethical considerations in mind, AI can serve as an indispensable resource for creating more resilient, inclusive cities in Europe and elsewhere. Acknowledgements This research was supported by grant: SGS23/081/OHK1/ 1T/15 by the Faculty of Architecture, Czech Technical University in Prague. References [1] European Commission. Urban agenda for the EU, 2016. [2023-05-01]. https://ec.europa.eu/futurium/en/urban-agenda [2] S. E. Bibri. The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. 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[2023-05-01]. https://smart-cities-marketplace.ec.europa.eu/ 93 https://doi.org/10.1145/3290605.3300830 https://doi.org/10.1177/2053951715622512 https://doi.org/10.1038/s42256-019-0088-2 https://doi.org/10.1080/1464935032000057209 https://doi.org/10.1080/01944366908977225 https://doi.org/10.1016/j.apgeog.2013.11.004 https://doi.org/10.1080/01944368208976167 https://smart-cities-marketplace.ec.europa.eu/ Acta Polytechnica CTU Proceedings 46:85–93, 2024 1 Introduction 2 Methodology 3 Urban planning and sustainable cities in Europe 4 Artificial Intelligence (AI) in urban planning 4.1 Key features of AI-enabled urban planning 4.1.1 Data-driven decision-making and predictive modeling 4.1.2 Optimization of land use and transportation systems 4.1.3 Use of sensor data for real-time monitoring and feedback 4.1.4 Integration of citizen input and feedback into the planning process 5 Case studies: AI in European smart cities 6 Framework for AI integration towards smart cities 7 Challenges, limitations, and further research 7.1 Challenges and considerations for AI-enabled urban planning 7.1.1 Ethical considerations and algorithmic bias 7.1.2 Data privacy and security 7.1.3 Need for community engagement and participation 7.1.4 Interdisciplinary collaboration and knowledge sharing 7.2 Research limitations 7.3 Recommendations for further research 8 Conclusion Acknowledgements References