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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

Korean

인간 이동성 구조 데이터는 웹 기반 위치 서비스 및 광범위한 도시 애플리케이션에 근본적이며, 모바일 생성은 실제 데이터가 비싸거나 접근 할 수없는 경우 합성 경로를 제공합니다. 최근의 이동성 생성기는 집단 이동성 패턴을 통합했지만, 그들은 주로 개별 트랙토리오 및 집계 인구 역학에 초점을 맞추고, 사용자 위치 상호 작용에 의해 형성 된 진화하는 커뮤니티 구조를 불충분하게 모델링하는 MOTION을 제안합니다.이 논문에서, 우리는 지역 사회 일관된 인간 이동성 생성을 연구합니다.이 목적을 위해, 우리는 동적 사용자 위치 흐름 그래프의 생성으로 이동성 생성을 정의합니다. MOTION은 역동적 인 사용자 위치 흐름 그래프의 생성으로

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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