Smart City Gnosys
Smart city article details
| Title | Building The Smart City Of Tomorrow: A Bibliometric Analysis Of Artificial Intelligence In Urbanization |
|---|---|
| ID_Doc | 13108 |
| Authors | Karger E.; Rothweiler A.; Brée T.; Ahlemann F. |
| Year | 2025 |
| Published | Urban Science, 9, 4 |
| DOI | http://dx.doi.org/10.3390/urbansci9040132 |
| Abstract | Urbanization is a global trend that continues to grow, leading to an increasing number of people to live in cities. This rapid expansion creates challenges such as traffic congestion, environmental pollution, and the need to ensure high living standards for all residents. To address these challenges, many cities adopt digital technologies to become smarter, more efficient, and more sustainable. Among these technologies, artificial intelligence (AI) has gained significant attention in recent years due to its transformative potential. In the context of smart cities, AI offers innovative solutions across various domains, including mobility, waste management, and energy optimization. Due to its multidisciplinary nature and rapid advancements, research on AI in smart cities has grown significantly. A comprehensive approach is needed to understand its role in urban transformation and identify key research gaps. This paper aims to synthesize existing knowledge on AI in smart cities, providing valuable insights for both researchers and practitioners. We define the scope of AI-related research by analyzing scientific literature and offer three main contributions. First, we provide a holistic overview of the field by conducting a bibliometric analysis to map the status and structure of existing knowledge. Second, we identify major research themes through co-citation clustering. Third, we outline a future research agenda by analyzing the most recent and influential journal articles. Our findings have both theoretical and practical implications for a wide range of disciplines, including computer science, energy, transportation, and security. Furthermore, our results can facilitate collaboration by identifying leading researchers and institutions, highlight critical research gaps, and foster discussions on the benefits and challenges of AI-driven smart city solutions. © 2025 by the authors. |
| Author Keywords | artificial intelligence; deep learning; machine learning; smart city; urban AI; urbanization |
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