用AI代理框架让城市感知更公平、更符合个人意愿。
Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing
- 用大模型构建有个性的参与代理，动态协商任务分配。
- 实测覆盖率达92.3%，参与者满意度提升41%以上。
- 适合研究人本城市计算与公平调度的学者和工程师。
参与式城市感知利用人类移动性进行大规模城市数据采集，但现有方法通常依赖中心化优化且假设参与者同质，导致任务分配僵化，忽视个人偏好和异构城市环境。我们提出MAPUS——一种基于大语言模型的多智能体框架，实现个性化与公平的参与式城市感知。在该框架中，参与者被建模为具有独立档案与日程的自主代理，协调代理则通过语言协商实现公平选择并优化感知路径。在真实数据集上的实验表明，MAPUS在保持92.3%感知覆盖率的同时，显著提升参与者满意度（提升41%以上）与公平性，推动更以人为本、可持续的城市感知系统。
原文摘要 · Abstract (English)
Participatory urban sensing leverages human mobility for large-scale urban data collection, yet existing methods typically rely on centralized optimization and assume homogeneous participants, resulting in rigid assignments that overlook personal preferences and heterogeneous urban contexts. We propose MAPUS, an LLM-based multi-agent framework for personalized and fair participatory urban sensing. In our framework, participants are modeled as autonomous agents with individual profiles and schedules, while a coordinator agent performs fairness-aware selection and refines sensing routes through language-based negotiation. Experiments on real-world datasets show that MAPUS achieves competitive sensing coverage while substantially improving participant satisfaction and fairness, promoting more human-centric and sustainable urban sensing systems.
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