Predict bike flow at station-level with multi-graph CNNs.
arXiv research
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This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; G…
Study improves cross-modal bike-share and transit demand prediction.
Paper benchmarks and customizes energy forecasting methods.
Proposes AtCoR for predicting bike station usage, improving station network reconfiguration.
Sparse point observations can provide useful local constraints, but their benefit for radar-like fields depends on the training loss, uncertainty representation, and how observation support is encoded in the model.