  Participatory urban planning is the mainstream of modern urban planning that
involves the active engagement of residents. However, the traditional
participatory paradigm requires experienced planning experts and is often
time-consuming and costly. Fortunately, the emerging Large Language Models
(LLMs) have shown considerable ability to simulate human-like agents, which can
be used to emulate the participatory process easily. In this work, we introduce
an LLM-based multi-agent collaboration framework for participatory urban
planning, which can generate land-use plans for urban regions considering the
diverse needs of residents. Specifically, we construct LLM agents to simulate a
planner and thousands of residents with diverse profiles and backgrounds. We
first ask the planner to carry out an initial land-use plan. To deal with the
different facilities needs of residents, we initiate a discussion among the
residents in each community about the plan, where residents provide feedback
based on their profiles. Furthermore, to improve the efficiency of discussion,
we adopt a fishbowl discussion mechanism, where part of the residents discuss
and the rest of them act as listeners in each round. Finally, we let the
planner modify the plan based on residents' feedback. We deploy our method on
two real-world regions in Beijing. Experiments show that our method achieves
state-of-the-art performance in residents satisfaction and inclusion metrics,
and also outperforms human experts in terms of service accessibility and
ecology metrics.
