Traffic forecasting is an important issue in intelligent traffic systems
(ITS). Graph neural networks (GNNs) are effective deep learning models to
capture the complex spatio-temporal dependency of traffic data, achieving ideal
prediction performance. In this paper, we propose attention-based graph neural
ODE (ASTGODE) that explicitly learns the dynamics of the traffic system, which
makes the prediction of our machine learning model more explainable. Our model
aggregates traffic patterns of different periods and has satisfactory
performance on two real-world traffic data sets. The results show that our
model achieves the highest accuracy of the root mean square error metric among
all the existing GNN models in our experiments