Efficient and accurate short-term inbound passenger flow forecast is an important prerequisite for urban rail transit operation and management.In order to improve the forecasting precision of short-term inbound passenger flow,a combined model of CNN-Transformer-BiLSTM(ConvTB)was established,which combined the advantages of three fore-casting models:CNN,Transformer and BILSTM.In this model,the convolutional neural network module can extract fea-tures from a multidimensional mass transit passenger traffic data matrix,and the attention mechanism can give greater weight to the more important parts of the extracted features,bidirectional long short-term memory neural network can ex-tract the forward and backward bidirectional correlation in the time series of passenger flow.The performance of this mod-el is trained and tested by taking the incoming passenger flow of different types of stations in Hefei metro as data set,and compared with Arima,SVR,CNN,LSTM,BILSTM,CNN-LSTM,CNN-BiLSTM and so on.Taking the incoming passen-ger flow of different types of stations in Hefei metro as data set,the performance of the model is trained and tested,and compared with the existing models in the literature,the results show that the forecasting errors of ConvTB combination model are smaller than other models in short-term inbound passenger flow time series,and it has higher accuracy and can reflect the changing trend of short-term inbound passenger flow forecasting more effectively.
NETL
NSTL
万方数据
