Accurately obtaining information of road traffic conditions is of great significance to people’s travel planning and arrangement of social shared resources, and has become a major research focus in the field of smart cities. Accurately predicting road conditions poses a huge challenge due to the complex spatial correlations and nonlinear temporal dependencies of real-time traffic networks. In this paper we propose a Multi-Range Attention-Based Dynamic Graph Convolutional Network (MRA-DGCN) to model complex traffic networks. The MRA-DGCN model uses a bicomponent modules to separate different periodicity to extract refined traffic signal. In the MRA-DGCN model, we use the adaptive spatial-temporal network block (ASTnet block), which includes dynamic graph convolution and temporal attention, to mine complex spatial correlations and nonlinear temporal dependencies, respectively. In the adaptive spatial-temporal network block, we use dynamically generated adjacency matrices instead of existing distance-based adjacency matrices to perform graph convolution operations to aggregate information between nodes during model training. Instead of hierarchically extracting spatial-temporal signal, we adopt temporal attention to capture the spatial-temporal information synchronously to improve the prediction performance. Furthermore, we propose a residual gated network to control the flow of information passed to the next hidden layer to enhance the predictive accuracy. Extensive experiments on two real-world traffic datasets, METR-LA and PeMS-BAY, show that the MRA-DGCN achieves the state-of-the-art results.

Accurate long-term traffic forecasting is of great significance to intelligent transportation systems, but long-term traffic forecasting is very challenging due to the complexity of the spatial and temporal relationship of traffic data. This paper proposes a model for prediction of traffic data. The model adopts an encoder-decoder architecture, in which the encoder and decoder are composed of a Graph Generator module, a Multi-Head Convolutional Self-Attention module, and a Dynamic Convolution Recurrent module to simulate the impact of spatial and temporal factors on traffic conditions. The encoder encodes the input features and the decoder predicts the output sequence. Between the encoder and the decoder, we use a Transformation mechanism to re-encode the traffic features to generate new sequence representations as the input of the decoder, which helps alleviate the problem of error propagation during prediction. We conduct the traffic prediction task on two real-world traffic datasets and the experimental results show that our model performs better than other baseline models.