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 learnable spatial and temporal agent tokens first aggregate global information and then broadcast it b
The increasing complexity and scale of urban mobility data necessitate more efficient and accurate forecasting models to optimize operations and resource allocation.
Improved demand forecasting for micro-mobility directly impacts urban planning, resource efficiency, and the development of intelligent transportation systems critical for smart cities.
This model introduces a more efficient way to process complex spatio-temporal data, potentially leading to more reliable predictions for dynamic systems like bike-sharing.
- · Micro-mobility operators
- · Urban planners
- · Smart city technology providers
- · Inefficient forecasting model developers
- · Operators without advanced data analytics
Micro-mobility services can optimize bike and scooter distribution, reducing operational costs and improving user satisfaction.
Better managed micro-mobility networks could reduce traffic congestion and carbon emissions in urban areas.
The underlying methodology could be applied to other complex spatio-temporal forecasting challenges, accelerating the development of advanced AI agents for logistics and resource management.
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Read at arXiv cs.LG