Spatial-Temporal Indoor Crowd Interpolation and Prediction with Graph Neural Networks

Le résumé fourni par la source

Crowd analysis is crucial for smart city applications, such as space management, safety, and well-being. While studies address spatial, temporal, and spatial-temporal dimensions, spatial-temporal data granularity is often constrained by the high cost and maintenance of high-resolution sensor deployment. Existing approaches focus heavily on prediction and rely on predefined graph structures, overlooking dynamic spatial-temporal dependencies and underperforming in interpolation tasks. To address this, we propose a hybrid model, the Spatial-Temporal Attention-based Interpolation and Prediction Graph Convolutional Gated Recurrent Unit (STAIP-GCGRU), which integrates interpolation and prediction tasks to deliver robust spatial-temporal predictions, even with incomplete graph structures.

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