  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 correlations without the
limitation of dividing data into equal-sized intervals on the timeline. While
learning the spatial relations and the temporal correlations, we modify the
GODE-RNN cell to obtain the ridership feature and hidden states. Ridership
information and its hidden states are added to the GODESolve to reduce the
error accumulation caused by long time series in prediction. Extensive
experiments on two large-scale datasets demonstrate the efficacy and robustness
of our model.

Machine Learning