Graph ODE Recurrent Neural Networks for Traffic Flow Forecasting

Traffic flow prediction is the core problem of traffic planning management and the basic basis of traffic control. However, the existing methods often do not fully consider the differences of different road nodes when modeling spatial dependence. In this paper, we propose a traffic flow prediction framework called graph neural ordinary differential equation recurrent neural network (GODE-RNN). Our method fuses graph convolution into neural ordinary differential equation to capture the spatial correlation, while the temporal correlation is extracted by sequence-to-sequence model. Specifically, we use neural ordinary differential equation (ODE) to expand the hidden state of graph convolution network, and different weights are used to weight and sum the hidden states of ODE for different road nodes, so that the network can learn the difference of spatial correlation of different road nodes independently. On the real-world data set, our method is significantly better than the baseline.