Transactions on Data Analysis in Social Science

Transactions on Data Analysis in Social Science

Artificial Intelligence in Urban Development: A Systematic Review of Applications, Challenges and the Role of Urban Science

Document Type : Original Article

Author
R. Fotouh
Departement of Management, Faculty of Islamic Governance, Islamic Azad University, Khorasgan Branch
10.22034/tdas.2026.256209
Abstract
Background: Rapid global urbanization and escalating environmental and social pressures call for more sophisticated approaches to urban management. Artificial intelligence (AI) has become a cornerstone of the smart-city paradigm. Its integration into urban development, however, is often hindered by algorithmic opacity, data bias and fragmentation, and the lack of robust socio-ethical frameworks. Methods: Following the PRISMA guidelines, a systematic literature review was conducted of peer-reviewed studies published between 2015 and 2025 and indexed in Scopus, Web of Science, IEEE Xplore and Google Scholar. Of 387 records identified, 58 duplicates were removed, 329 were screened, 156 were assessed in full text and 112 met the inclusion criteria. The included studies were synthesized thematically by AI technology, urban application domain and reported challenges. Results: The literature covers machine learning, deep learning, computer vision and, increasingly, generative models (generative adversarial networks, diffusion models and large language models). Five application domains dominate: intelligent transportation, environmental monitoring and sustainability, urban planning and design, public health and social services, and governance and citizen engagement. Six cross-cutting challenges recur: data quality and availability, algorithmic bias, explainability and trust, ethical and legal accountability, skills gaps, and the digital divide. Without deliberate countermeasures, these challenges can reproduce or deepen existing urban inequalities. Conclusions: AI should converge with the “New Urban Science”, which grounds computational algorithms in human dynamics and in the theory of cities as complex adaptive systems. Realizing sustainable, equitable and resilient cities requires transparent and interoperable open-data infrastructures, explainable AI, algorithmic auditing, interdisciplinary capacity building, and inclusive participatory tools such as AI-enabled urban digital twins. These measures can ensure that technological innovation serves the public good.
Keywords
Artificial intelligence
Smart cities
Urban planning
Generative AI
Urban science
Algorithmic governance
Digital twins
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Transactions on Data Analysis in Social Science
Volume 8, Issue 2
Spring 2026
Pages 138-148

  • Receive Date 02 February 2026
  • Revise Date 17 April 2026
  • Accept Date 04 May 2026