AI for Cities: Generative AI, Digital Twin Simulators for Advanced Urban Efficiency, Quality of Life, and Resiliency
Across the globe, cities are grappling with the dual challenge of sustaining growth while ensuring an improved quality of life for their citizens. Technological innovations—particularly in the realm of Artificial Intelligence (AI)—are revolutionizing how urban environments are planned, managed, and experienced. The integration of advanced technologies such as Generative AI, Digital Twin Simulators, and Agentic AI is transforming urban environments, enhancing urban efficiency, quality of life, and resilience. These technologies offer innovative solutions to address the challenges posed by rapid urbanization, resource depletion, and environmental sustainability. From Generative AI models that design optimal information management solutions and citizen engagement to Digital Twin Simulators that replicate real-world conditions in virtual space to help managers make the best-informed decision, and on to Agentic AI systems capable of semi-autonomous decision-making helping our civil servants with complex tasks and always using all available knowledge, these advancements open up new frontiers in urban innovation.
Generative AI is not only being utilized to enhance information management and citizen services but also urban planning and governance by enabling anticipatory forms of governance. This involves using AI to predict future urban scenarios and inform policy-making, as seen in the development of city brains that manage urban domains like transport and safety through large-scale AI platforms (Cugurullo & Xu, 2024). Additionally, Generative AI is integrated into Urban Digital Twin (UDT) frameworks to improve predictive analytics and adaptive learning, as demonstrated by the Large Flow Model (LFM) which models urban flows to support sustainable urban environments (Huang et al., 2025).
Digital Twins, virtual replicas of urban environments, are crucial for real-time monitoring and simulation of urban systems. They enable cities to adapt to challenges by providing a platform for testing scenarios and identifying bottlenecks, thus enhancing urban efficiency and resilience (Gkontzis et al., 2024). AI-powered Digital Twins, when combined with IoT, optimize energy use and enhance resilience in urban contexts, contributing significantly to sustainability (Alnaser et al., 2024). In smart city infrastructure, AI-enhanced Digital Twins have been shown to improve energy efficiency and reduce carbon footprints, demonstrating their potential in reshaping city planning and resource consumption (Li, 2025).
Agentic AI, through platforms like Urban Generative Intelligence (UGI), leverages Large Language Models to create embodied agents for urban tasks. These agents simulate complex urban systems and provide a multidisciplinary approach to managing urban complexity, addressing issues such as traffic congestion and pollution (Xu et al., 2023). This approach signifies a transformative step in urban intelligence, harnessing AI to unravel and address the intricate dynamics of urban systems.
Despite the advancements, challenges remain in integrating these technologies into practical urban solutions. The complexity of network architectures in 6G and the need for accurate and flexible Digital Twin constructions are ongoing challenges. Generative AI is being explored as a solution to these challenges, offering efficient data generation for network simulation and optimization (Tao et al., 2023; Li et al., 2025; Chai et al., 2024). Future research is needed to address these challenges and fully realize the potential of AI-driven technologies in urban environments.
In conclusion, the integration of Generative AI, Digital Twin Simulators, and Agentic AI is pivotal in advancing city efficiency, quality of life, and resilience. These technologies offer innovative solutions for sustainable urban development, though challenges remain in their practical implementation and integration. Continued research and development are essential to overcome these challenges and harness the full potential of these technologies in creating advanced, sustainable cities.
Such AI-driven frameworks can yield significant improvements in efficiency, resiliency, and overall quality of life. They enable city managers and policymakers to make proactive, data-informed decisions, predict and respond to changing conditions, and foster inclusive, citizen-centric development.
In the current climate of emerging and adopting new technologies, with a dizzying acceleration of new announcements and possibilities, especially in the field of applied AI, cities need to incorporate these innovations to improve the quality of service to their citizens and their attractiveness to talent and investors. Due to the long and complex public contracting processes, they need to seek out the most advanced, most recently developed systems. Nor can they wait for these new developments to become commercial products (this would add extra months to the solution's age, rendering it almost obsolete). The only solution is to explore the latest technology, the latest proposals, “what's cooking in the oven, then prepare the table”. That is the reason we are holding this call: to connect cities with the leading-edge technologies in AI for Cities.
To further accelerate this knowledge exchange and practical impact, this monographic issue seeks to bring together groundbreaking research from universities, research institutes, start-ups and companies experimental department, especially those projects that have reached minimum a Technology Readiness Level of 5 (TRL5)—indicating validated prototypes or pilot implementations in real-world contexts but not reaching the category of commercially available products.
Potential Research Goals
- Urban Efficiency and Optimization
- Harnessing AI (Generative, Agentic, or otherwise) to improve public service efficiency, streamline resource distribution, traffic control, and infrastructure maintenance.
- Leveraging digital twins for real-time operational insights, predictive analytics and solutions to problems simulation and virtual testing.
- Integrating AI-driven solutions to improve urban mobility, public health, and environmental monitoring.
- Demonstrating how AI-based services can enhance everyday experiences and citizen engagement for diverse city populations, always with citizen front and ahead of all service proposals.
- Using digital twin simulations and AI to anticipate, model, and mitigate climate-related risks or infrastructural failures. Improve physical and social resiliency.
- Exploring adaptive frameworks that help cities maintain stability and continuity under various stressors.
- Addressing regulatory requirements, equitable data governance, and privacy considerations in AI-empowered urban ecosystems.
- Studying the social and economic implications of AI urban deployment, from public trust to job market effects.
- Showcasing case studies / pilots where TRL5 research has been deployed in real-world city settings.
- Examining pathways for larger-scale adoption, including stakeholder engagement, funding models, and policy support.
Suggested or Proposed Lines of Analysis (examples as guidance, not all-inclusive)
- 1. AI-Augmented Urban Planning and Policy
- Data-driven frameworks to optimize zoning, resource allocation, and city services.
- Tools for multi-stakeholder engagement, co-creation, and transparent decision-making processes.
- 2. Real-Time Monitoring and Predictive Analytics
- Application of AI algorithms for anomaly detection and rapid response in transit, utilities, and public safety.
- Integration of IoT sensors with digital twins to manage and forecast urban dynamics.
- 3. Human-Centric Design and User Experience
- Methods for engaging citizens and community organizations in AI-enabled projects (e.g., participatory design, processes and tools co-creation, virtual assistants).
- Techniques to enhance trust, transparency, accessibility and inclusivity in digital twin environments.
- 4. Data Privacy, Security, and Ethical Compliance
- Approaches to safeguarding personal and infrastructural data, including federated learning or anonymization. Blockchain based identity and digital rights management.
- Frameworks to ensure responsible AI adoption, including bias mitigation and fairness assessments.
- 5. Pilot Deployments (TRL5) and Best Practices
- Empirical results and evaluations from pilot projects, highlighting successes, challenges, and lessons learned.
- Approaches for long-term maintenance, updates, and scalability of AI-based urban solutions.
We invite contributors to submit high-quality, original research papers detailing minimum validated pilot-level results (TRL5) and offering novel insights into AI applications for city-level transformation. Submissions should describe methodologies, present data-driven findings, and explore practical implications for urban settings.
Deadline: 1 SEPTEMBER 2025
For more information on submission guidelines, deadlines, and the review process, please consult the editorial board’s detailed instructions: LINK HERE
We look forward to your contributions shaping the next generation of AI-driven, resilient, and citizen-focused cities.
Keywords
Generative artificial intelligence; Generative spatial artificial intelligence; Urban digital twin; Sustainable smart cities; Agentic AI for Urban management; Citizen-centric Urban AI; Social resiliency
Coordination:
Prof. José Antonio Ondiviela is Director Citizen-Centric Intelligent Cities Research Institute at UFV (Universidad Francisco Vitoria, Madrid). In addition, he serves as Strategy Senior Advisor at City of Zaragoza (Spain) and he participates as Associate Member of Urban Innovators Global. https://www.urbaninnovators.global/ . Former Microsoft Western Europe Public Sector Government Industry Advisor for Cities&Regions. (29 years) (1 March 2024), he is a frequent speaker at International events like SmartCityExpo, SmartCityExpo-MIAMI, Mobile World Forum, TEDTalk, EU Events… and he is also UNESCO SmartCities SME Consultant. TEDx Organizer/Licensee TEDxUFV (Mar23, May24, June25). Telecommunications Engineer MS at UPM Madrid, Human Sciences MS at UFV Madrid, Enterprise and Institutional Communication Management MS at UAB Barcelona, and Financial Business MS online INSEAD. PhD at UFV, Madrid with Thesis “Beyond SmartCities: How to create an Attractive City for Talented Citizens”. 175 World Cities Analysed since 2020. All research information at: http://attractivecities.com ORCID (Academic Profile): https://orcid.org/0000-0001-6732-8754
