Predicts each venue zone's crowd density 5 ticks ahead from the current densities and the venue's walkway graph. Trained on synthetic runs from the Crowd Flow Optimiser simulator.

Two-layer GraphSAGE. Message passing is the whole point: a gate backing up should raise the predicted risk of the walkway feeding it, which a per-zone threshold cannot see.

features rows must be in this exact column order:

density, trend, capacity_norm, degree_norm, neighbour_max_density, density_delta

edge_index is [all_sources, all_targets] — PyTorch Geometric's [2, num_edges] layout, not a list of pairs.

Note the second zone: 44% full, but scored 0.80 because its neighbour is at 93% and pushing into it. That is the behaviour this model exists to provide.

ai-service/app/clients/hf_gnn_client.py in the Crowd Flow Optimiser, which falls back to a local linear model whenever this endpoint is unset or unreachable.