Short-time Inflow and Outflow Prediction of Metro Stations Based on Hybrid Deep Learning

This paper proposes a prediction model (ResNet-CNN1D) combining convolutional neural network (CNN) and residual network (ResNet) for multi-station short-term passenger volume prediction of urban rail transit. The original passenger volume data is used as input of the model. The deep network composed of two-dimensional CNN and ResNet is used to mine the spatial features between the stations. The one-dimensional CNN is used to mine the temporal features of the passenger flow. Based on the parametric matrix, the temporal and spatial features are weighted to obtain the multi-station inflow and outflow during the research period. The model is verified by the card-swiping data of the Qingdao No.3 metro line. Compared with existing traditional prediction models (ARIMA, SVR, LSTM, CLTFP, ConvLSTM), the proposed ResNet-CNN1D model in this paper has the best prediction accuracy. Copyright © 2020 by Science Press.