Research on subway passenger flow prediction method based on attention mechanism and residual network
Research on subway passenger flow prediction methods based on attention mechanism and residual network,aiming to improve the accuracy of passenger flow prediction and provide data guarantee for urban traffic intelligent management.On the basis of describing the problem of subway passenger flow prediction,a passenger flow prediction model based on attention mechanism and re-sidual network is constructed.Historical passenger flow data is divided into time series data of nearest neighbor,daily cycle,and weekly cycle modes,which are used as model inputs along with external influencing factors of passenger flow,The ST-SANet network and LSTM network,which combine the ResNet34 residual module and the segmentation attention mechanism module,are used to cap-ture their deeper multi-scale information features.The fully connected layer is used to fuse and concatenate the output features of each part.After processing with the activation function,the subway passenger flow prediction results are output.The experimental re-sults show that this method can achieve subway passenger flow prediction with a learning rate parameter of 1 x At 10 to 4 hours,the predicted loss of subway passenger flow is the lowest;When the prediction period is set to 15 minutes,the predicted curve has the highest fit with the actual passenger flow curve.
万方数据
维普
