mlsanityData to 14 Oct 2026

M-STAR: Multi-Scale Spatiotemporal Autoregression for Human Mobility Modeling

arXiv 2512.07314 ↗First posted 8 Dec 2025cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 59 references that are in the map. First posted on arXiv 8 Dec 2025.

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References · 31

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19 more
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