Decoupled dynamic spatial-temporal graph neural network for traffic forecasting

We all depend on mobility, and vehicular transportation affects the daily lives of most of us. Thus, the ability to forecast the state of traffic in a road network is an important functionality and a challenging task. Traffic data is often obtained from sensors deployed in a road network. Recent proposals on spatial-temporal graph neural networks have achieved great progress at modeling complex spatial-temporal correlations in traffic data, by modeling traffic data as a diffusion process. However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals. Unfortunately, nearly all previous works coarsely consider traffic signals…

• NN
  National Natural Science Foundation of China

  Awards: 61602197, 2021M703273, 61902382, 61902376
• CP
  China Postdoctoral Science Foundation

  Awards: 2021M703273, RF20210005, 61602197