用概率图生成真实人类移动轨迹，兼顾个体与群体行为模式。
Markovian Reeb Graphs for Simulating Spatiotemporal Patterns of Life
- 将拓扑结构的李伯图改造为带概率转移的生成模型，模拟时空移动路径。
- 在两个数据集上验证，混合型模型在五项指标中均表现优异。
- 只需少量轨迹数据，无需额外信息，适合城市规划等实际应用。
精准建模人类移动对城市规划、流行病学和交通管理至关重要。本文提出马尔可夫李伯图（Markovian Reeb Graphs），将李伯图从描述性工具转变为生成式轨迹模型。该方法捕捉个体与群体层面的生活模式（PoLs），通过在李伯图结构中嵌入概率转移，生成保留基础行为且具随机变异的真实轨迹。提出两种变体：用于个体代理的序列李伯图（SRGs）和融合个体与群体生活模式的混合李伯图（HRGs）。在Urban Anomalies和Geolife数据集上，使用五项移动性统计指标评估，结果表明HRGs在多数指标上表现良好，仅需少量轨迹数据且无需特殊附加信息。本工作确立了马尔可夫李伯图作为城市环境中轨迹模拟的有力框架。
原文摘要 · Abstract (English)
Accurately modeling human mobility is critical for urban planning, epidemiology, and traffic management. In this work, we introduce Markovian Reeb Graphs, a novel framework that transforms Reeb graphs from a descriptive analysis tool into a generative model for spatiotemporal trajectories. Our approach captures individual and population-level Patterns of Life (PoLs) and generates realistic trajectories that preserve baseline behaviors while incorporating stochastic variability by embedding probabilistic transitions within the Reeb graph structure. We present two variants: Sequential Reeb Graphs (SRGs) for individual agents and Hybrid Reeb Graphs (HRGs) that combine individual with population PoLs, evaluated on the Urban Anomalies and Geolife datasets using five mobility statistics. Results demonstrate that HRGs achieve strong fidelity across metrics while requiring modest trajectory datasets without specialized side information. This work establishes Markovian Reeb Graphs as a promising framework for trajectory simulation with broad applicability across urban environments.
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