The Research Landscape of AI in Urban Planning: A Topic Analysis of the Literature with ChatGPT - Thomas W. Sanchez; Xinyu Fu; Tan Yigitcanlar; Xinyue Ye (Urban Science)
This study investigated the current state of artificial intelligence (AI) in urban planning by analyzing 744 research publications. Utilizing topic modeling analysis with latent Dirichlet allocation (LDA) and ChatGPT, we interpreted and categorized weighted keywords from this analysis, and then generated topic names based on these insights. The analysis identified 16 key themes within the corpus, encompassing a range of topics including urban and transport planning, urban and architectural design methods, as well as algorithms and predictive modeling techniques. The most prevalent topic identified was “Urban Design and Architectural Methods”, emphasizing the integration of AI in urban design strategies. Other significant themes included “Smart Urban Development and Social Governance” and “Algorithms and Predictive Modeling in Transportation”. The findings demonstrate the diverse applications of AI in urban planning, such as enhancing public services, optimizing transportation systems, and managing urban development. This study underscores the increasing application of AI in addressing urban challenges and provides a comprehensive overview of the current state of research, offering useful insights for future studies and potential implementations in urban planning. The study findings offer researchers and practitioners invaluable insights, uncovering both opportunities and gaps in the literature that can guide and shape future research and practical initiatives.