  Pre-trained large language models (PLMs) have the potential to support urban
science research through content creation, information extraction, assisted
programming, text classification, and other technical advances. In this
research, we explored the opportunities, challenges, and prospects of PLMs in
urban science research. Specifically, we discussed potential applications of
PLMs to urban institution, urban space, urban information, and citizen
behaviors research through seven examples using ChatGPT. We also examined the
challenges of PLMs in urban science research from both technical and social
perspectives. The prospects of the application of PLMs in urban science
research were then proposed. We found that PLMs can effectively aid in
understanding complex concepts in urban science, facilitate urban spatial form
identification, assist in disaster monitoring, and sense public sentiment. At
the same time, however, the applications of PLMs in urban science research face
evident threats, such as technical limitations, security, privacy, and social
bias. The development of fundamental models based on domain knowledge and
human-AI collaboration may help improve PLMs to support urban science research
in future.
