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.

翻译：预训练大语言模型（PLMs）通过内容生成、信息抽取、辅助编程、文本分类等技术手段，为城市科学研究提供潜在支撑。本研究探讨了PLMs在城市科学研究中的机遇、挑战与前景。具体而言，我们以ChatGPT为工具，通过七个示例分析了PLMs在城市制度、城市空间、城市信息与市民行为研究中的潜在应用，并从技术与社会双重视角审视了PLMs在城市科学研究中面临的挑战，进而提出其应用前景。研究发现，PLMs能够有效辅助理解城市科学中的复杂概念、促进城市空间形态识别、支持灾害监测及感知公众情绪。但与此同时，PLMs在城市科学中的应用也面临技术局限、安全性、隐私保护及社会偏见等显著威胁。未来基于领域知识与人类-人工智能协作的基础模型开发，可能有助于改进PLMs以更好地支撑城市科学研究。