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

• URL: http://arxiv.org/abs/2308.16122v1
• Date: Wed, 30 Aug 2023 16:21:02 GMT
• Title: Spatial Graph Coarsening: Weather and Weekday Prediction with London's Bike-Sharing Service using GNN
• Authors: Yuta Sato, Pak Hei Lam, Shruti Gupta, Fareesah Hussain
• Abstract summary: This study introduced the use of Graph Neural Network (GNN) for predicting the weather and weekday of a day in London. With the node features of land-use characteristics and number of households around the bike stations, our proposed models outperformed the baseline model in cross-entropy loss and accuracy of the validation dataset.
• Score: 0.40964539027092917
• License: http://creativecommons.org/licenses/by/4.0/
• 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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