Template-Type: ReDIF-Article 1.0 Title: The Potentials and Limitations of Agent-Based Models for Urban Digital Twins: Insights From a Surveillance and Behavioral Nudging Simulation File-URL: https://www.cogitatiopress.com/urbanplanning/article/view/8613 File-Format: text/html DOI: 10.17645/up.8613 Journal: Urban Planning Volume: 10 Year: 2025 Number: 8613 Author-Name: Sarah Shtaierman Author-Workplace-Name: Institute for Ethics in Artificial Intelligence, Technical University of Munich, Germany Author-Name: Catarina Fontes Author-Workplace-Name: Institute for Ethics in Artificial Intelligence, Technical University of Munich, Germany Author-Name: Christoph Lütge Author-Workplace-Name: Institute for Ethics in Artificial Intelligence, Technical University of Munich, Germany Abstract: Although urban digital twins are still at an embryonic stage of development, their use cases are multiple, ranging from big data aggregation to simulations. Additionally, predictions can be rendered and quickly implemented using actuators to transform physical environments and influence urban life. In this article, we investigate the potential of an agent-based model in a smart city setting to predict emergent behavior in relation to the suppression of civil violence by implementing crowd management practices. To this end, we designed a simulation environment that includes cameras in public spaces and wearable sensors, and considers nudging and self-nudging processes supported by a surveillance apparatus. Building on Epstein’s threshold-based model of civil violence, the proposed simulation is informed by surveillance theories and contemplates methods for crowd monitoring and social control. The experiments’ results provide insights into how specific measures and combined actions may influence the suppression of civil violence in public spaces and can be useful to inform crowd management activities and policymaking. Moreover, we use the simulation to reflect upon the potentials and limitations of integrating agent-based models into urban digital twins and emphasize the imminent risks for individuals and democratic societies of employing a ubiquitous surveillance apparatus endowed with the autonomy to trigger actuators. Keywords: agent-based model; crowd modeling; smart city; surveillance systems; urban digital twin; urban planning Handle: RePEc:cog:urbpla:v10:y:2025:a:8613 Template-Type: ReDIF-Article 1.0 Title: What Is My Plaza for? Implementing a Machine Learning Strategy for Public Events Prediction in the Urban Square File-URL: https://www.cogitatiopress.com/urbanplanning/article/view/8551 File-Format: text/html DOI: 10.17645/up.8551 Journal: Urban Planning Volume: 10 Year: 2025 Number: 8551 Author-Name: Jumana Hamdani Author-Workplace-Name: School of Information Systems and Technology Management, University of New South Wales, Australia / Department of Spatial Planning, Blekinge Institute of Technology, Sweden Author-Name: Pablo Antuña Molina Author-Workplace-Name: IAAC—Institute for Advanced Architecture of Catalonia, Spain Author-Name: Lucía Leva Fuentes Author-Workplace-Name: IAAC—Institute for Advanced Architecture of Catalonia, Spain Author-Name: Hesham Shawqy Author-Workplace-Name: IAAC—Institute for Advanced Architecture of Catalonia, Spain Author-Name: Gabriella Rossi Author-Workplace-Name: CITA, Royal Danish Academy, Denmark Author-Name: David Andrés León Author-Workplace-Name: IAAC—Institute for Advanced Architecture of Catalonia, Spain Abstract: Plazas are an essential pillar of public life in our cities. Historically, they have been seen as public fora, hosting public events that fostered trade, interaction, and debate. However, with the rise of modern urbanism, city planners considered them as part of a larger strategic development scheme overlooking their social importance. As a result, plazas have lost their function and value. In recent years, awareness has risen of the need to re-activate these public spaces to strive for social inclusion and urban resilience. Geometric and urban features of plazas and their surroundings often suggest what kinds of usage the public can make of them. In this project, we explore the application of machine learning to predict the suitability of events in public spaces, aiming to enhance urban plaza design. Learning from traditional urbanism indicators, we consider factors associated with the features of the public space, such as the number of people and the high degree of comfort, which are evolved from three subcategories: external factors, geometric shape, and design factors. We acknowledge that the predictive capability of our model is constrained by a relatively small dataset, comprising 15 real plazas in Madrid augmented digitally to 2025 fictional scenarios through self-organising maps. The article details the methods to quantify and enumerate quantitative urban features. With a categorical target variable, a classification model is trained to predict the type of event in the urban space. The model is then evaluated locally in Grasshopper by visualising a parametric verified geometry and deploying the model on other existing plazas worldwide regarding geographical proximity to Madrid, where to share or not the same cultural and environmental conditions. Despite these limitations, our findings offer valuable insights into the potential of machine learning in urban planning, suggesting pathways for future research to expand upon this foundational study. Keywords: data classification; event prediction; machine learning; Madrid; plaza; public squares; self-organising maps; urban planning Handle: RePEc:cog:urbpla:v10:y:2025:a:8551 Template-Type: ReDIF-Article 1.0 Title: A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change File-URL: https://www.cogitatiopress.com/urbanplanning/article/view/8562 File-Format: text/html DOI: 10.17645/up.8562 Journal: Urban Planning Volume: 10 Year: 2025 Number: 8562 Author-Name: Julia Forster Author-Workplace-Name: Institute of Spatial Planning, TU Wien, Austria Author-Name: Stefan Bindreiter Author-Workplace-Name: Institute of Spatial Planning, TU Wien, Austria Author-Name: Birthe Uhlhorn Author-Workplace-Name: Institute of Landscape Development, Recreation and Conservation Planning, University of Natural Resources and Life Sciences, Austria Author-Name: Verena Radinger-Peer Author-Workplace-Name: Institute of Landscape Development, Recreation and Conservation Planning, University of Natural Resources and Life Sciences, Austria Author-Name: Alexandra Jiricka-Pürrer Author-Workplace-Name: Institute of Landscape Development, Recreation and Conservation Planning, University of Natural Resources and Life Sciences, Austria Abstract: The impacts on living conditions and natural habitats deriving from planning decisions require complex analysis of cross-acting factors, which in turn require interdisciplinary data. At the municipal level, both data collection and the knowledge needed to interpret it are often lacking. Additionally, climate change and species extinction demand rapid and effective policies in order to preserve soil resources for future generations. Ex-ante evaluation of planning measures is insufficient owing to a lack of data and linear models capable of simulating the impacts of complex systemic relationships. Integrating machine learning (ML) into systemic planning increases awareness of impacts by providing decision-makers with predictive analysis and risk mitigation tools. ML can predict future scenarios beyond rigid linear models, identifying patterns, trends, and correlations within complex systems and depicting hidden relationships. This article focuses on a case study of single-family houses in Upper Austria, chosen for its transferability to other regions. It critically reflects on an ML approach, linking data on past and current planning regulations and decisions to the physical environment. We create an inventory of categories of areas with different features to inform nature-based solutions and backcasting planning decisions and build a training dataset for ML models. Our model predicts the effects of planning decisions on soil sealing. We discuss how ML can support local planning by providing area assessments in soil sealing within the case study. The article presents a working approach to planning and demonstrates that more data is needed to achieve well-founded planning statements. Keywords: GIS analysis; machine learning; nature-based solutions; spatial analysis; spatial planning Handle: RePEc:cog:urbpla:v10:y:2025:a:8562 Template-Type: ReDIF-Article 1.0 Title: The Potential of AI in Information Provision in Energy-Efficient Renovations: A Narrative Review of Literature File-URL: https://www.cogitatiopress.com/urbanplanning/article/view/8660 File-Format: text/html DOI: 10.17645/up.8660 Journal: Urban Planning Volume: 10 Year: 2025 Number: 8660 Author-Name: C. Koray Bingöl Author-Workplace-Name: Management in the Built Environment, Delft University of Technology, The Netherlands Author-Name: Tong Wang Author-Workplace-Name: Management in the Built Environment, Delft University of Technology, The Netherlands Author-Name: Aksel Ersoy Author-Workplace-Name: Management in the Built Environment, Delft University of Technology, The Netherlands Author-Name: Ellen van Bueren Author-Workplace-Name: Management in the Built Environment, Delft University of Technology, The Netherlands Abstract: Energy-efficient renovation (EER) is a complex process essential for reducing emissions in the built environment. This research identifies homeowners as the main decision-makers, whereas intermediaries and social interactions between peers are highly influential in home renovations. It investigates information and communication barriers encountered during the initial phases of EERs. The study reviews AI tools developed within the EERs domain to assess their capabilities in overcoming these barriers and identifies areas needing improvement. This research examines stakeholders, barriers, and the AI tools in the literature for EERs. The discussion compares the functionalities of these tools against stakeholder needs and the challenges they face. Findings show that tools often overlook methodologies in human–computer interaction and the potential of textual and visual AI methods. Digital tool development also lacks insights from social science and user feedback, potentially limiting the practical impact of these innovations. This article contributes to the EERs literature by proposing an AI-supported framework and outlining potential research areas for future exploration, particularly improving tool effectiveness and stakeholder engagement to scale up the EER practice. Keywords: AI; energy-efficient renovations; information and communication barriers; stakeholders Handle: RePEc:cog:urbpla:v10:y:2025:a:8660 Template-Type: ReDIF-Article 1.0 Title: Past, Present, and Future Perspectives on the Integration of AI Into Walkability Assessment Tools: A Systematic Review File-URL: https://www.cogitatiopress.com/urbanplanning/article/view/8518 File-Format: text/html DOI: 10.17645/up.8518 Journal: Urban Planning Volume: 10 Year: 2025 Number: 8518 Author-Name: Yasin Delavar Author-Workplace-Name: School of Architecture, University of Florida, USA Author-Name: Sarah Gamble Author-Workplace-Name: School of Architecture, University of Florida, USA Author-Name: Karla Saldana-Ochoa Author-Workplace-Name: School of Architecture, University of Florida, USA Abstract: This study employs a systematic literature review (PRISMA methodology) to investigate the integration of Artificial Intelligence (AI) in walkability assessments conducted between 2012 and 2022. Analyzing 34 articles exploring data types, factors, and AI tools, the review emphasizes the value of utilizing diverse datasets, particularly street view images, to train supersized AI models. This approach fosters efficient, unbiased assessments and offers deep insights into pedestrian environment interactions. Furthermore, AI tools empower walkability assessment by facilitating mapping, scoring, designing pedestrian routes, and uncovering previously unconsidered factors. The current shift from large-scale spatial data analysis (allocentric perspective) to a ground-level view (egocentric perspective) and physical and perceptual features of walking introduces a subjective lens into current walkability assessment tools. However, the efficacy of current methods in addressing non-visual aspects of human perception and their applicability across diverse demographics remains debatable. Finally, the lack of integration of emerging technologies like virtual/augmented reality and digital twin leaves a significant gap in research, inviting further study to determine their efficacy in enhancing the current methods and, in general, understanding the interaction of humans and cities. Keywords: artificial intelligence; digital twin; human perception; urban built environment; walkability; walkability assessment; walkable environment Handle: RePEc:cog:urbpla:v10:y:2025:a:8518 Template-Type: ReDIF-Article 1.0 Title: From Vision to Reality: The Use of Artificial Intelligence in Different Urban Planning Phases File-URL: https://www.cogitatiopress.com/urbanplanning/article/view/8576 File-Format: text/html DOI: 10.17645/up.8576 Journal: Urban Planning Volume: 10 Year: 2025 Number: 8576 Author-Name: Frank Othengrafen Author-Workplace-Name: Department of Spatial Planning, TU Dortmund, Germany Author-Name: Lars Sievers Author-Workplace-Name: Department of Spatial Planning, TU Dortmund, Germany Author-Name: Eva Reinecke Author-Workplace-Name: Department of Spatial Planning, TU Dortmund, Germany Abstract: In an urban context, the use of artificial intelligence (AI) can help to categorise and analyse large amounts of data quickly and efficiently. The AI approach can make municipal administration and planning processes more efficient, improve environmental and living conditions (e.g., air quality, inventory of road damages, etc.), or strengthen the participation of residents in decision-making processes. The key to this is “machine learning” that has the ability to recognise patterns, capture models, and learn on the basis of big data via the application of automated statistical methods. However, what does this mean for urban planning and the future development of cities? Will AI take over the planning and design of our cities and actively intervene in and influence planning activities? This article applies a systematic literature review supplemented by case study analyses and expert interviews to categorise various types of AI and relate their potential applications to the different phases of the planning process. The findings emphasize that AI systems are highly specialised applications for solving and processing specific challenges and tasks within a planning process. This can improve planning processes and results, but ultimately AI only suggests alternatives and possible solutions. Thus, AI has to be regarded as a planning tool rather than the planning solution. Ultimately, it is the planners who have to make decisions about the future development of cities, taking into account the possibilities and limitations of the AI applications that have been used in the planning process. Keywords: artificial intelligence; decision-making; digital participation; planning phases; smart city; urban planning Handle: RePEc:cog:urbpla:v10:y:2025:a:8576