Hyper-GST: Predict Metro Passenger Flow Incorporating GraphSAGE, Hypergraph, Social-meaningful Edge Weights and Temporal Exploitation. (arXiv:2211.04988v1 [cs.LG])

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 …

arxiv edge exploitation flow hypergraph social temporal