Traffic flow prediction based on graph convolutional networks with a parallel attention network and stacked gate recurrent units

Accurate traffic flow prediction is essential to address traffic issues and assist traffic managers make informed decisions in intelligent transportation systems. Extracting potential features from traffic data is challenging due to the complex topology of urban road networks and the time-varying traffic flow. To capture the global spatiotemporal characteristics of traffic flow, we propose a novel model based on graph convolutional networks with a parallel attention network and stacked gated recurrent units (PAGCN-SGRU). First, the parallel attention (PA) network enhances the feature representation of global traffic road nodes and road segments. Then, the graph convolutional networks (GCN) are designed to extract spatial characteristics. Next, the stacked gate recurrent units (SGRU) are employed to capture temporal features. Finally, PAGCN-SGRU discovers global spatiotemporal features for traffic flow prediction. The experimental results demonstrate that the accuracy of PAGCN-SGRU under the SZ-dataset is improved by 9.76%, 72.54%, 5.76%, 16.07%, 2.07%, 1.82%, 3.35%, and 6.59%, respectively, compared to that of HA, ARIMA, SVR, GCN, T-GCN, A3T-GCN, ST-GCN, and DCRNN. In the Los-dataset, the accuracy values increase by 7.11%, 8.94%, 6.66%, 7.77%, 3.43%, 2.45%, 2.28%, and 4.09%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.