STAGformer: A Spatio-temporal Agent Graph Transformer for Micro Mobility Demand Forecasting

arXiv:2607.06614v1 Announce Type: new Abstract: Accurate station-level demand forecasting is essential for the efficient operation of bike-sharing systems, yet it remains challenging due to complex spatio-temporal dependencies and the large scale of urban networks. This paper presents STAGformer, a Spatio-Temporal Agent Graph Transformer that achieves efficient global modeling with linear computational complexity. The model introduces a two-step agent attention mechanism, where a small set of le...