A deep neural network model with GCN and 3D convolutional network for short-term metro passenger flow forecasting

Rail transit has many advantages, such as large passenger capacity, convenience, safety, and environmental protection, making it the preferred travel mode for most passengers. Deep learning has become an effective method for short-term rail passenger flow prediction. A deep learning model which combines a graph convolutional network and a three-dimensional Convolutional Neural Network improved by a residual module and an attention mechanism (ARConv-CGN) is proposed. First, historical passenger inflow and outflow data are aggregated into three patterns: recent pattern, daily pattern, and weekly pattern separately. The GCN is applied to capture the spatiotemporal and topological information of passenger flows in each pattern. Second, a three-dimensional convolutional neural network is used to deeply integrate three patterns of passenger flow information. Additionally, a residual module is used to increase the number of neural network layers and prevent the gradient of the deep neural network from disappearing. Finally, an attention mechanism is also introduced to adjust the importance of passenger flow in different pattern, so that improving the performance of the model. Training with passenger flow data from automatic fare collection on weekdays in Beijing and Xiamen provides a good demonstration of the superiority of ARConv-CGN in metro passenger flow forecasting.