Bo Liu, Tong Li, Jiapeng Zhang, Zhu Xiao, Zhuo Tang, Kenli Li · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28467 · open-access PDF
Los datos de movilidad humana son fundamentales para los servicios de ubicación basados en la Web y las aplicaciones urbanas más amplias, y la generación de movilidad proporciona trayectorias sintéticas cuando los datos reales son costosos o inaccesibles.Aunque los generadores de movilidad recientes han incorporado patrones de movilidad colectiva, se centran principalmente en las trayectorias individuales y la dinámica de la población agregada, dejando estructuras comunitarias en evolución formadas por interacciones de usuario-lugar insuficientemente modeladas.
Machine translation (Meta M2M100); the English original follows.
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
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