M-STAR高效生成长期人流轨迹，支持多尺度时空建模。
M-STAR: Multi-Scale Spatiotemporal Autoregression for Human Mobility Modeling
- 分阶段粗到细预测，结合多尺度编码与Transformer解码
- 在两个真实数据集上轨迹保真度更高，生成速度提升显著
- 适合交通规划、疫情模拟等需要长期轨迹的应用
人类移动建模对交通规划、疫情模拟等应用至关重要。随着AIGC范式兴起，近期研究尝试使用自回归和扩散模型生成合成轨迹。尽管这些方法在单日轨迹生成上表现良好，但长期生成（如周级轨迹）效率低，且缺乏显式的时空多尺度建模。本文提出多尺度时空自回归框架M-STAR，通过粗到细的时空预测流程生成长期轨迹。M-STAR结合多尺度时空分词器编码层次化移动模式，并采用基于Transformer的解码器进行下一尺度自回归预测。在两个真实世界数据集上的实验表明，M-STAR在轨迹保真度上优于现有方法，且生成速度显著提升。代码与数据已公开于https://github.com/YuxiaoLuo0013/M-STAR。
原文摘要 · Abstract (English)
Modeling human mobility is vital for extensive applications such as transportation planning and epidemic modeling. With the rise of the Artificial Intelligence Generated Content (AIGC) paradigm, recent works explore synthetic trajectory generation using autoregressive and diffusion models. While these methods show promise for generating single-day trajectories, they remain limited by inefficiencies in long-term generation (e.g., weekly trajectories) and a lack of explicit spatiotemporal multi-scale modeling. This study proposes Multi-Scale Spatio-Temporal AutoRegression (M-STAR), a new framework that generates long-term trajectories through a coarse-to-fine spatiotemporal prediction process. M-STAR combines a Multi-scale Spatiotemporal Tokenizer that encodes hierarchical mobility patterns with a Transformer-based decoder for next-scale autoregressive prediction. Experiments on two real-world datasets show that M-STAR outperforms existing methods in fidelity and significantly improves generation speed. The data and codes are available at https://github.com/YuxiaoLuo0013/M-STAR.
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