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Predicting Citi Bike Demand Evolution Using Dynamic Graphs

arXiv 2212.09175 ↗First posted 18 Dec 2022cs.LGcited by 0

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Abstract (arXiv)

Bike sharing systems often suffer from poor capacity management as a result of variable demand. These bike sharing systems would benefit from models to predict demand in order to moderate the number of bikes stored at each station. In this paper, we attempt to apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 3 references that are in the map. First posted on arXiv 18 Dec 2022.

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2 of 3 references.

References · 2

  1. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  2. 2018Comparison between DeepESNs and gated RNNs on multivariate time-series predictioncited by 4

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