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Predicting metro passenger flow precisely is of great importance for dynamictraffic planning. Deep learning algorithms have been widely applied due totheir robust performance in modelling non-linear systems. However, traditionaldeep learning algorithms completely discard the inherent graph structure withinthe metro system. Graph-based deep learning algorithms could utilise the graphstructure but raise a few challenges, such as how to determine the weights ofthe edges and the shallow receptive field caused by the over-smoothing issue.To further improve these challenges, this study proposes a model based onGraphSAGE with an edge weights learner applied. The edge weights learnerutilises socially meaningful features to generate edge weights. Hypergraph andtemporal exploitation modules are also constructed as add-ons for betterperformance. A comparison study is conducted on the proposed algorithm andother state-of-art graph neural networks, where the proposed algorithm couldimprove the performance.

Hyper-GST: Predict Metro Passenger Flow Incorporating GraphSAGE, Hypergraph, Social-meaningful Edge Weights and Temporal Exploitation