Spatio-temporal multi-graph convolution network for multi-station metro passenger flow prediction

Accurate inbound and outbound passenger flow prediction is an essential task of urban rail transit intelligence, which guarantees the operation scheduling, and planning of stations and stations. Considering multiple complex relationships and time-varying characteristics among stations, the urban rail transit passenger flow prediction model based on multigraph convolution and Transformer model is proposed. Three graph relations, namely the connectivity graph, association graph, and interaction graph, are proposed from the passenger flow characteristics and topology between rail transit stations. The multi-graph convolution constructed based on the three graph relations and Gated Recurrent Units (GRU) are combined to form a multi-graph convolution unit, effectively simultaneously capturing local spatio-temporal dependencies. At the same time, the output state of each multigraph convolutional unit is stitched into the Transformer model to mine the global features of the long-range time dimension. The model's validity is verified after finishing with the AFC data of Hangzhou Metro 2019 total stations, and the results are obtained after repeated experiments. The proposed passenger flow prediction model has better prediction ability as the values of three error evaluation indexes are smaller than other traditional benchmark models.