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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

Arabic

إن البيانات المتعلقة بالحركة البشرية هي الأساسية لخدمات الموقع الإلكتروني وتطبيقات المدن الأوسع، وتقديم إنتاج الحركة المكونة من طرق متكاملة عندما تكون البيانات الحقيقية تكلفة أو غير متوفرة. على الرغم من أن الأنظمة المتعلقة بالحركة السريعة قد وضعت نموذجاً مشتركاً للحركة، فإنها تركز بشكل رئيسي على المراحل الفردية للمستخدم، وتقديم MOTION، إطار متعدد الأطراف للهيئات المجتمعية التي تشكلت من خلال التفاعلات المستخدمة مع الموقع غير متكافئة. في هذه المقالة، نحن نبحث عن المكونات المجتمعية المكونة من المكونات المستخدمة، والمكونات المستخدمة من موقع المكان، والمدير التنفيذي لسياسة الخصوص

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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