From tools to partners: How large language models are transforming urban planning
Fang Pan, Xiaolin Huang, Y. J. Bi, Yunfan Gao, Ye Yu, Haofen Wang
Abstract
Recent advances in large language models have transformed urban planning from passive tool-assisted workflows to active human–AI collaborative partnerships, enabling natural language-driven design generation, multi-agent stakeholder simulation, and intelligent decision support. This survey systematically examines the integration of LLMs in urban planning, establishing a comprehensive taxonomy covering task categories, technical paradigms, and collaboration patterns. Furthermore, the survey identifies critical evaluation frameworks and benchmark datasets while examining implementation challenges, including domain knowledge integration, scalability constraints, and ethical implications. The work bridges theoretical advances with practical deployment considerations, providing guidance for selecting appropriate LLM approaches across different urban planning contexts and scales. • We survey LLMs in urban planning, covering capabilities, alignment, and workflow use. • LLMs speed summarizing and ideation but planners must steer input and vet outputs. • We clarify uses and risks, bridge AI and planning, and chart a path for practice.