Published Paper Details:

This paper aims to conduct a bibliometric analysis on the future of urban models in the Big Data and AI era. A city is perceived to be a dynamic structure. It is made up of many interconnected subsystems that are influenced by a variety of factors such as government land policies, demographic development, transportation networks, and market behavior. In the long run, land use and transportation networks are known as the two most critical subsystems defining urban structure and shape. Meanwhile, one of the most relevant subjects in urban modeling is urban growth, and the key driving factors are demographic growth and infrastructure progress. Modeling and simulation are thought to be useful instruments for investigating the processes of urban evolution and providing planning assistance in growth management [1]. The rise of urban big data is reshaping current science concepts in urban studies. In China, the use of big data in urban studies and development activities exploded in 2014. Big data has the benefit of disclosing features instead of a description, which is compatible with the principle of urbanization and urban-rural planning. To address the methodological problems, new hypotheses and analytical methods are needed. This essay explores the impact of the Big Data and AI transition in urban management on the complexities of urban science [1]. It concentrates on two fields of urban science: one on academic research on big data and the other on artificial intelligence. First, we monitor the development of AI and Big Data parameters in these fields. The second is to study the development of the proportion of publications concerning urban big data and AI in various urban studies applications. The paper will depend on the information from literature reviews to conduct this bibliometric research. The thesis's ultimate aim is to explore how urban modeling could be utilized in the future in the age of Big Data and AI.