A deep network with analogous self‐attention for short‐term traffic flow prediction

Abstract Short‐term traffic flow prediction plays a crucial role in research and application of intelligent transportation system. Neural network algorithm can use the big data for training and has more advantages over other prediction models in traffic features extraction. However, it is still a problem to extract the spatiotemporal features of traffic flow in a simple and sufficient way to improve the prediction accuracy. In this paper, a double‐branch deep residual gated convolutional neural network (RGCNN) is proposed to extract features from both time and space based on three‐dimensional traffic data, and scaled exponential linear units is used as an activation function to enhance the convergence effect of network training. In order to increase the ability of the network to fit the traffic data, an analogous self‐attention (ASA) is designed, which retains the advantages of attention while hardly increasing training costs. Simulation experiments are carried out in real traffic data sets, the simulation results of traffic flow prediction tasks in different prediction horizons show that the prediction performance of the proposed prediction model (ASA‐RGCNN) is superior to that of other common prediction models and the proposed model can be applied to the predicting task under different traffic conditions. By visualising ASA weights at different traffic flow levels, the impact of space‐time traffic data on the prediction task can also be found out