M-STAR: Multi-Scale Spatiotemporal Autoregression for Human Mobility Modeling
This work addresses inefficiencies in long-term trajectory generation for applications like transportation planning and epidemic modeling, representing an incremental improvement over existing methods.
The study tackled the problem of generating long-term human mobility trajectories by proposing M-STAR, a framework that uses multi-scale spatiotemporal modeling, resulting in outperforming existing methods in fidelity and significantly improving generation speed on two real-world datasets.
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.