LAUSR

Metro passenger flow prediction is an essential part of crowd flow forecasting and intelligent transportation management systems. However, two challenges still need to be addressed to achieve a more accurate prediction: (1) accounting for featural dependence instead of considering only the temporal connection and spatial relations; (2) utilising graph structures to address non-European relationships of spatial and featural dependence. To address these challenges, we developed a novel model called the multi-featured spatial-temporal (MFST) and dynamic multi-graph convolutional network (DMGCN) model. Temporal connections are learned from both the local and global information in a time-series sequence using the combination of a time-trend feature mapping block and a gated recurrent unit block. Spatial relation and featural dependence are separately captured by two DMGCN blocks. Each DMGCN block encodes various relationships by constructing multiple graphs consisting of predefined and non-defined topologies. The results of evaluations conducted of the MFST tensor and the DMGCN on the real-world Beijing subway dataset indicate that the prediction performance of the proposed model is superior to that of the existing baselines. The proposed model thus contributes significantly to the improvement of public safety by providing early warnings of large passenger flow and enabling the smart scheduling of resources.