STR-GODEs: Spatial-Temporal-Ridership Graph ODEs for Metro Ridership Prediction
Chuyu Huang
Abstract:The metro ridership prediction has always received extensive attention from governments and researchers. Recent works focus on designing complicated graph convolutional recurrent network architectures to capture spatial and temporal patterns. These works extract the information of spatial dimension well, but the limitation of temporal dimension still exists. We extended Neural ODE algorithms to the graph network and proposed the STR-GODEs network, which can effectively learn spatial, temporal, and ridership co… Show more
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