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

TL;DR

This paper introduces MAPUS, a multi-agent framework using language models to enable personalized, fair, and efficient participatory urban sensing by modeling participants as autonomous agents with individual preferences.

Contribution

The paper presents a novel LLM-based multi-agent system that incorporates personal preferences and fairness in urban sensing, improving upon centralized and homogeneous approaches.

Findings

Achieves comparable sensing coverage to existing methods.

Significantly enhances participant satisfaction and fairness.

Promotes human-centric and sustainable urban sensing systems.

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.