A traffic prediction method for missing data scenarios: graph convolutional recurrent ordinary differential equation network by Ming Jiang, Zhiwei Liu, Yan Xu

“…To address this challenge, this paper proposes a neural network architecture designed specifically for missing data scenarios—graph convolutional recurrent ordinary differential equation network (GCRNODE). GCRNODE combines recurrent networks based on ordinary differential equation (ODE) with spatiotemporal memory graph convolutional networks, enabling accurate traffic prediction and effective modeling of dynamic graph structures even in the presence of missing data. …”
Get full text