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