Spatial Graph Coarsening: Weather and Weekday Prediction with London's Bike-Sharing Service using GNN

Yuta Sato, Pak Hei Lam, Shruti Gupta, Fareesah Hussain
8/30/2023
cs.LG

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Abstract

This study introduced the use of Graph Neural Network (GNN) for predicting the weather and weekday of a day in London, from the dataset of Santander Cycles bike-sharing system as a graph classification task. The proposed GNN models newly introduced (i) a concatenation operator of graph features with trained node embeddings and (ii) a graph coarsening operator based on geographical contiguity, namely "Spatial Graph Coarsening". With the node features of land-use characteristics and number of households around the bike stations and graph features of temperatures in the city, our proposed models outperformed the baseline model in cross-entropy loss and accuracy of the validation dataset.

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Cite this paper

@article{sato2023spatial,
  title  = {Spatial Graph Coarsening: Weather and Weekday Prediction with London's Bike-Sharing Service using GNN},
  author = {Yuta Sato and Pak Hei Lam and Shruti Gupta and Fareesah Hussain},
  year   = {2023},
  eprint = {2308.16122},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.16122v1}
}

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