Short-Term Metro Origin–Destination Forecasting for Large Events Using a Dual-Stream Multi-Graph Spatiotemporal Network

Large events concentrate arrival and departure demand at core metro stations. We propose DMG-STNet, a dual-stream multi-graph spatiotemporal network that separately encodes origin–destination (OD) histories and station margins derived from the same data. The model combines cross-station graph information, event conditions at each prediction step, and arrival/departure sample weighting, with joint supervision of a single OD output and its derived margins. It predicts the next 15 min interval from 120 min histories within a 42-station subnetwork containing 984 valid OD pairs. Evaluation covers 122 core-station-linked OD pairs on two ordinary days and two event days, including two concerts at the National Stadium (Bird’s Nest). Across both event days, OD weighted absolute percentage error (WAPE) over the event-window union is 52.94%, 3.54 percentage points lower than Pairwise Ridge; paired time-block resampling gives an approximate 95% interval of [1.20, 5.73] percentage points for this difference. Station-aggregate WAPE is 24.02% over the event-window union and 23.04% over the arrival/departure-window union. With common log(1 + x) historical-flow inputs, DMG-STNet achieves lower mean OD and station-aggregate WAPE than the three deep baselines in both window sets. By combining fine-grained OD allocation with station-aggregate demand, the method supports short-term demand assessment for the planned large events examined here.