大语言模型赋能的参与式城市规划
Large language model empowered participatory urban planning
AI总结：
本研究提出一种基于大语言模型智能体的参与式城市规划框架，通过角色扮演、协作生成和反馈迭代解决社区土地利用任务，在满意度和包容性上超越人类专家，并可与强化学习方法媲美，提供可调整且包容的解决方案。
摘要：
参与式城市规划是现代城市规划的主流，涉及不同利益相关者的积极参与。然而，传统的参与模式在时间和人力方面面临挑战，而生成式规划工具无法提供可调整且包容的解决方案。本研究提出了一种创新的城市规划方法，将大语言模型（LLMs）整合到参与过程中。该框架基于精心设计的大语言模型智能体，包含角色扮演、协作生成和反馈迭代，解决了一个面向1000种不同利益的社区级土地利用任务。在不同城市社区进行的实证实验展示了LLM在多种规划场景中的适应性和有效性。结果通过四个指标进行评估，在满意度和包容性方面超越人类专家，在服务和生态方面可与最先进的强化学习方法相媲美。进一步分析显示，LLM智能体在通过自然语言推理和强可扩展性提供可调整且包容的解决方案方面具有优势。在实施模拟人类行为的最新进展用于规划的同时，这项工作展望了规划者和公民都能从低成本、高效的大语言模型智能体中受益，这对于增强参与和实现参与式城市规划至关重要。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
