用多智能体系统提升城市预测准确率，零样本下表现更优
Urban-MAS: Human-Centered Urban Prediction with LLM-Based Multi-Agent System
- 构建三类智能体协同处理城市数据，聚焦关键预测因子
- 在东京、米兰、西雅图测试中误差显著低于单一大模型
- 适合城市规划与智能交通领域，可扩展性强
城市人工智能（Urban AI）已推动以人为中心的城市感知与动态预测任务发展。大型语言模型（LLMs）虽能融合多模态输入以应对复杂城市系统的异构数据，但在特定领域任务上表现有限。本文提出基于大语言模型的多智能体系统（Urban-MAS），用于零样本条件下的以人为本城市预测。该框架包含三类智能体：预测因子引导智能体，通过优先识别关键预测因子来提升压缩城市知识在LLM中的有效性；可靠城市信息提取智能体，通过多重输出比对、一致性验证与冲突重提取增强鲁棒性；多城市信息推理智能体，跨维度整合多源信息完成预测。在东京、米兰和西雅图的运行量预测与城市感知任务上实验表明，Urban-MAS相比单个LLM基线显著降低误差。消融实验显示，预测因子引导智能体对性能提升最为关键，验证了Urban-MAS作为可扩展的人本城市AI预测范式。代码已开源：https://github.com/THETUREHOOHA/UrbanMAS
原文摘要 · Abstract (English)
Urban Artificial Intelligence (Urban AI) has advanced human-centered urban tasks such as perception prediction and human dynamics. Large Language Models (LLMs) can integrate multimodal inputs to address heterogeneous data in complex urban systems but often underperform on domain-specific tasks. Urban-MAS, an LLM-based Multi-Agent System (MAS) framework, is introduced for human-centered urban prediction under zero-shot settings. It includes three agent types: Predictive Factor Guidance Agents, which prioritize key predictive factors to guide knowledge extraction and enhance the effectiveness of compressed urban knowledge in LLMs; Reliable UrbanInfo Extraction Agents, which improve robustness by comparing multiple outputs, validating consistency, and re-extracting when conflicts occur; and Multi-UrbanInfo Inference Agents, which integrate extracted multi-source information across dimensions for prediction. Experiments on running-amount prediction and urban perception across Tokyo, Milan, and Seattle demonstrate that Urban-MAS substantially reduces errors compared to single-LLM baselines. Ablation studies indicate that Predictive Factor Guidance Agents are most critical for enhancing predictive performance, positioning Urban-MAS as a scalable paradigm for human-centered urban AI prediction. Code is available on the project website:https://github.com/THETUREHOOHA/UrbanMAS
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