Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution

Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent traffic management and urban planning. Although various methods are proposed for spatio-temporal modeling, they ignore the dynamic characteristics of correlations among locations on road network. Meanwhile, most Recurrent Neural Network based works are not efficient enough due to their recurrent operations. Additionally, there is a severe lack of fair comparison among different methods on the same datasets. To address the above challenges, in this article, we propose a novel traffic prediction framework, named Dynamic Graph Convolutional…

• NN
  National Natural Science Foundation of China

  Awards: U20B2060, U1936217, 61971267, and 61972223, U20B2060, 61971267, 61972223, U1936217, U1936217, 61971267, 61972223
• NK
  National Key Research and Development Program of China

  Award: 2020YFB2104005