Gated Recurrent Graph Convolutional Attention Network for Traffic Flow Prediction

Traffic flow prediction is an important function of intelligent transportation systems. Accurate prediction results facilitate traffic management to issue early congestion warnings so that drivers can avoid congested roads, thus directly reducing the average driving time of vehicles, which means less greenhouse gas emissions. However, traffic flow data has complex spatial and temporal correlations, which makes it challenging to predict traffic flow accurately. A Gated Recurrent Graph Convolutional Attention Network (GRGCAN) for traffic flow prediction is proposed to solve this problem. The model consists of three components with the same structure, each of which contains one temporal feature extractor and one spatial feature extractor. The temporal feature extractor first introduces a gated recurrent unit (GRU) and uses the hidden states of the GRU combined with an attention mechanism to adaptively assign weights to each time step. In the spatial feature extractor, a node attention mechanism is constructed to dynamically assigns weights to each sensor node, and it is fused with the graph convolution operation. In addition, a residual connection is introduced into the network to reduce the loss of features in the deep network. Experimental results of 1-h traffic flow prediction on two real-world datasets (PeMSD4 and PeMSD8) show that the mean absolute percentage error (MAPE) of the GRGCAN model is as low as 15.97% and 12.13%, and the prediction accuracy and computational efficiency are better than the baselines.

Automated summary

Traffic flow prediction is a vital component of intelligent transportation systems, and accurate predictions can help issue early congestion warnings and reduce greenhouse gas emissions. A novel model called GRGCAN is proposed, which incorporates temporal and spatial feature extractors, an attention mechanism, and a residual connection. Experimental results on real-world datasets demonstrate that GRGCAN achieves better prediction accuracy and computational efficiency compared to baseline models, with a low MAPE of 15.97% and 12.13%.