Bridge augments a graph neural network backbone with time-aware retrieval from a memory of region-time windows to improve cold-start and cross-city urban delivery demand forecasting.
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SynHAT uses a novel two-stage spatio-temporal diffusion framework with Latent Spatio-Temporal U-Net to synthesize realistic human activity traces, outperforming baselines by 52% on spatial and 33% on temporal metrics across four cities.
A GNN-based foundation model on aggregated US geospatial data produces embeddings achieving SOTA on all 27 interpolation tasks and 25/27 extrapolation/super-resolution tasks across health, socioeconomic and environmental domains, plus improved forecasting when combined with TimesFM.
citing papers explorer
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Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand
Bridge augments a graph neural network backbone with time-aware retrieval from a memory of region-time windows to improve cold-start and cross-city urban delivery demand forecasting.
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SynHAT: A Two-stage Coarse-to-Fine Diffusion Framework for Synthesizing Human Activity Traces
SynHAT uses a novel two-stage spatio-temporal diffusion framework with Latent Spatio-Temporal U-Net to synthesize realistic human activity traces, outperforming baselines by 52% on spatial and 33% on temporal metrics across four cities.
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General Geospatial Inference with a Population Dynamics Foundation Model
A GNN-based foundation model on aggregated US geospatial data produces embeddings achieving SOTA on all 27 interpolation tasks and 25/27 extrapolation/super-resolution tasks across health, socioeconomic and environmental domains, plus improved forecasting when combined with TimesFM.