Large language model empowered participatory urban planning
Zhilun Zhou Yuming Lin Yong Li
Participatory urban planning is the mainstream of modern urban planning and
involves the active engagement of different stakeholders. However, the
traditional participatory paradigm encounters challenges in time and manpower,
while the generative planning tools fail to provide adjustable and inclusive
solutions. This research introduces an innovative urban planning approach
integrating Large Language Models (LLMs) within the participatory process. The
framework, based on the crafted LLM agent, consists of role-play, collaborative
generation, and feedback iteration, solving a community-level land-use task
catering to 1000 distinct interests. Empirical experiments in diverse urban
communities exhibit LLM's adaptability and effectiveness across varied planning
scenarios. The results were evaluated on four metrics, surpassing human experts
in satisfaction and inclusion, and rivaling state-of-the-art reinforcement
learning methods in service and ecology. Further analysis shows the advantage
of LLM agents in providing adjustable and inclusive solutions with natural
language reasoning and strong scalability. While implementing the recent
advancements in emulating human behavior for planning, this work envisions both
planners and citizens benefiting from low-cost, efficient LLM agents, which is
crucial for enhancing participation and realizing participatory urban planning.