In the era of digital transformation, deploying artificial intelligence (AI) within urban environments significantly reconfigures regional planning and territorial governance. While smart technologies promise enhanced operational efficiency, evaluating their systemic impact on spatial equity and social sustainability indicators remains a crucial academic challenge. This study delivers a comprehensive scientometric review of global scientific production intersecting smart cities, AI, and territorial justice from 2000 to 2026. A corpus of 3,011 peer-reviewed documents extracted from the Scopus database (restricted to Social Sciences, Economics, and Management) was analyzed using computational science-mapping techniques. The findings reveal a critical paradigm shift in contemporary literature, transitioning from technical infrastructure metrics toward human-centric indicators of social equity, urban inclusion, and the mitigation of socio-spatial inequalities. Thematic clustering further highlights how automated decision-making and digital transformation can either foster inclusion or exacerbate territorial fractures. Conclusively, this paper synthesizes global research trajectories to provide policymakers with a strategic framework that effectively reconciles technological innovation with territorial justice and social sustainability.
Urban sustainability is increasingly challenged by rapid urbanization, environmental degradation, and unequal access to public open and green spaces. These spaces are essential for mitigating urban heat, enhancing biodiversity, and improving human well-being. However, planning and managing them equitably in dense cities remains a complex task. This study explores how artificial intelligence (AI) can enhance the planning, design, and management of green spaces to optimize urban sustainability. Drawing upon recent advances in machine learning, deep learning, and geospatial intelligence, the research synthesizes literature and case studies that demonstrate AI’s potential to improve data-driven decision-making, spatial optimization, and predictive environmental modeling. Integrating AI with Geographic Information Systems (GIS) and multi-criteria decision-making frameworks enables planners to identify underserved areas, forecast future demands, and evaluate the ecological and social performance of green infrastructure. Emerging approaches such as GeoAI and neural network models (e.g., CNNs, LSTMs) support dynamic and adaptive green space allocation that enhances accessibility, resilience, and inclusivity. Despite significant progress, challenges persist regarding data quality, interoperability, ethical transparency, and policy integration. By assessing these opportunities and constraints, this research develops a conceptual framework for embedding AI technologies into sustainable urban planning practices. The findings underscore that AI, when responsibly implemented, can transform POGS into intelligent, adaptive systems that align with equity, ecological health, and long-term urban resilience goals.
Ensuring equitable mobility for individuals with disabilities remains a critical yet under-addressed challenge in Intelligent Transportation Systems (ITS) and smart-city development. While prior ITS reviews have explored technological advancements and, in some cases, interdisciplinary perspectives, limited attention has been given to a comprehensive, accessibility-driven synthesis that integrates artificial intelligence (AI), policy frameworks, ethical considerations, datasets, and real-world deployment challenges. This study presents a systematic review of 2253 studies published between 2021 and 2026, providing a structured, multidimensional analysis of disability-inclusive ITS research. Unlike existing surveys, this work combines rigorous PRISMA-based systematic review methodology with advanced bibliometric analysis to uncover not only research trends but also structural gaps, interdisciplinary disconnects, and limitations in current ITS development. The findings reveal a strong concentration on general accessibility frameworks (66.0%), with significantly lower attention to disability-specific solutions—particularly for cognitive (10.2%) and mobility impairments (5.5%). Although AI and deep learning are increasingly adopted, most proposed systems remain simulation-based, with limited real-world validation, user-centered evaluation, and scalability. The analysis further highlights critical shortcomings, including the lack of accessibility-aware datasets, the absence of standardized evaluation metrics, insufficient policy integration, and the underrepresentation of developing regions. Ethical challenges, including algorithmic bias, data privacy, and transparency, are also inadequately addressed in current ITS implementations. To address these limitations, this review advances a unified framework that integrates technological, human-centered, and policy-driven perspectives for inclusive ITS design. It synthesizes insights across transportation engineering, healthcare, urban planning, and AI to propose actionable directions for developing adaptive, explainable, and accessibility-aware mobility systems. The study also identifies key performance indicators (KPIs) and emphasizes the importance of interdisciplinary collaboration, inclusive field validation, and ethical AI governance. By bridging the gap between technological innovation and real-world accessibility needs, this work provides a comprehensive foundation for future research. It supports the development of scalable, inclusive, and sustainable transportation systems that ensure equitable mobility for all.
This paper examines how artificial intelligence can be responsibly integrated into urban planning institutions, education, and community decision making. It explores the socio-technical frameworks needed to ensure that Urban AI supports equitable, transparent, and effective planning practice beyond methodological innovation alone. The study synthesizes insights from the fifth Urban AI in Planning roundtable held at the 2025 Association of Collegiate Schools of Planning Annual Conference. Drawing on five years of interdisciplinary dialogue, the paper applies qualitative thematic analysis to discussions among scholars and practitioners in geography, planning, architecture, AI, transportation, and professional design practice. Three interconnected themes emerge. First, Urban AI is most effective when functioning as a decision-support capacity amplifier that complements rather than replaces human judgment. Second, uneven geographies of data availability, privacy expectations, and model performance create disparities across urban, rural, and resource-constrained settings. Third, interdisciplinary collaboration, planning education, and community engagement are essential for developing trustworthy and socially responsive AI systems. Across these themes, persistent concerns include data bias, transparency, governance, and ethical accountability. The paper argues that the future of Urban AI depends not only on technological advancement but also on robust socio-technical frameworks integrating ethical oversight, place-sensitive interpretation, interdisciplinary cooperation, and participatory knowledge production. Responsible Urban AI therefore requires alignment with institutional practices, governance systems, and community values to enhance planning legitimacy, inclusiveness, and long-term societal trust.
Urban artificial intelligence (AI) is emerging as a transformative paradigm redefining how cities process data, make decisions, and address systemic challenges. Yet, in African contexts, AI adoption remains constrained by infrastructural limitations, governance gaps, unequal access to technology, and lack of context‐sensitive approaches. This article explores the intersection of urban AI, responsible AI, and collective urban intelligence (CUI) to understand how African initiatives are leveraging AI to foster sustainable, inclusive, and ethical urban transformation processes. Drawing on 14 semi‐structured interviews with practitioners, researchers, and entrepreneurs from across the continent, the study maps the current landscape of AI‐driven tools and services addressing sectors such as planning, health, agriculture, and education. The findings reveal that African‐led AI initiatives are operationalizing key responsible AI principles, showcasing how CUI is promoting distributed intelligence systems where human, institutional, and algorithmic agents co‐produce locally relevant urban knowledge.
Urban food deserts are a serious systemic vulnerability at the nexus of data-driven mobility planning and public health. In order to maximize supply chain logistics and spatial accessibility for community food infrastructure, this article presents an urban intelligence framework. We examine a spatial development project in Baltimore, Maryland, using a multi-method computational framework that combines descriptive statistics, multi-variable regressions, travel demand modeling, and Monte Carlo simulations. Predictive systems measure multi-modal transit results, estimate network traffic generation, and assess accessibility gaps. According to empirical results, the suggested smart logistics node increases transit-accessible food coverage from 31% to 74%, creates 1,240 daily trips, and shortens home journey times by 14.7 minutes. Using an 8% discount rate, system-level transportation benefits result in a Net Present Value of $3.82 million over a five-year period. In the end, this research offers a highly reproducible, data-driven approach for utilizing smart city engineering and predictive analytics to improve urban food security.
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