基于混合深度学习的地铁站进出客流量短时预测

针对城市轨道交通多站点短时客流量预测问题，本文提出一种将卷积神经网络 (CNN)与残差网络(ResNet)相组合的预测模型(ResNet-CNN1D).模型将原始客流量数据作为输入，利用二维 CNN 与 ResNet 组成深层神经网络，捕捉站点间的空间特征，同时利用一维 CNN捕捉客流量的时间依赖.最后，基于参数矩阵，将时间和空间特征进行加权融合，完成对目标时段中多个站点进出客流量的同时预测.采集青岛市地铁3号线刷卡数据，对模型进行验证. 结果表明，相比现有传统的预测模型(ARIMA，SVR，LSTM，CLTFP，ConvLSTM)，本文 ResNet-CNN1D模型具有更好的预测精度.

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 twodimensional 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.