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Beyond Trajectory Statistics: Community-Consistent Human Mobility Generation via Evolving User-Location Graphs

Bo Liu, Tong Li, Jiapeng Zhang, Zhu Xiao, Zhuo Tang, Kenli Li · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28467 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

Os dados da mobilidade humana são fundamentais para os serviços de localização baseados na Web e aplicações urbanas mais amplas, e a geração de mobilidade fornece trajetórias sintéticas quando os dados reais são caros ou inacessíveis. Embora os geradores de mobilidade recentes tenham incorporado padrões de mobilidade coletiva, eles se concentram principalmente em trajetórias individuais e dinâmicas populacionais agregadas, deixando as estruturas comunitárias em evolução formadas por interações de usuário-localização modeladas de forma insuficiente. Neste artigo, estudamos a geração de agentes de localização humanos coerentes com a comunidade. Para este fim, formulamos a geração de mobilidade 4.0 como a geração de gráficos de fluxo de usuário dinâmico para modelar estruturas comunitárias e propomos MOTION

Machine translation (Meta M2M100); the English original follows.

Abstract

Human mobility data are fundamental to Web-based location services and broader urban applications, and mobility generation provides synthetic trajectories when real data are costly or inaccessible. Although recent mobility generators have incorporated collective mobility patterns, they primarily focus on individual trajectories and aggregate population dynamics, leaving evolving community structures formed by user-location interactions insufficiently modeled. In this paper, we study community-consistent human mobility generation. To this end, we formulate mobility generation as the generation of dynamic user-location graphs to model community structures and propose MOTION, a Multi-agent framework for cOmmuniTy-consIstent human mObility geNeration. MOTION consists of user agents, location agents, and a global manager agent. MOTION first detects coherent communities from historical mobility using the Leiden algorithm and extracts community-conditioned interaction priors to guide the generation process. Guided by these priors, the user agents plan mobility, search destination candidates, and infer user movements from the individual view. In parallel, selectively activated location agents infer likely user arrivals from the community view. The manager coordinates both views, diagnoses interactions causing deviations, and attributes targeted reflection signals to revise user decisions, location attraction, and source-user selection. Experiments on two real-world datasets show that MOTION outperforms baselines by at least 4.05% across reported metrics of individual trajectories, population and flow distributions, and community and graph structures. Further analyses confirm its robustness, privacy preservation, and downstream utility. The code is available at https://anonymous.4open.science/r/MOTION-04C4.

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Topics: Computer science, Mobility model, Graph, Population, Trajectory, Theoretical computer science

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