STR-GODEs: Spatial-Temporal-Ridership Graph ODEs for Metro Ridership Prediction

TL;DR

This paper introduces STR-GODEs, a novel graph neural ODE model for metro ridership prediction that captures spatial, temporal, and ridership correlations without relying on fixed interval segmentation, improving accuracy and robustness.

Contribution

The paper extends Neural ODEs to graph networks for ridership prediction, addressing temporal limitations of previous methods and enhancing long-term prediction accuracy.

Findings

Outperforms existing models on large-scale datasets

Effectively captures complex spatial-temporal-ridership relationships

Reduces error accumulation in long-term predictions

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 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.