Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing

• URL: http://arxiv.org/abs/2603.24014v1
• Date: Wed, 25 Mar 2026 07:19:45 GMT
• Title: Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing
• Abstract summary: We propose MAPUS, an LLM-based multi-agent framework for personalized and fair participatory urban sensing.<n>In our framework, participants are modeled as autonomous agents with individual profiles and schedules, while a coordinator agent performs fairness-aware selection.<n>Experiments on real-world datasets show that MAPUS achieves competitive sensing coverage while substantially improving participant satisfaction and fairness.
• Score: 30.447323644062795
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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