A Passenger Flow Prediction Model of Urban Rail Transit Based on ICEEMDAN Decomposition and TCN-LSTM-CAM Module
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With the rapid development of intelligent operation and management of urban rail transit, accurate passenger flow prediction is crucial for management and operation. However, the complex, nonlinear and non-smooth characteristics make detecting passenger flow evolution features challenging. In this regard, a novel decomposition integration model, IC-TLCA, is proposed. The model first decomposes the raw passenger flow data into multiple sublayers with different frequencies using an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm to better capture the intrinsic structure of the data. Then, the temporal convolutional network (TCN), long-short-term memory network (LSTM) and channel attention module (CAM) are combined to predict each sublayer separately. Finally, by combining the prediction results of each sublayer, the final passenger flow prediction is obtained. After comparing with all baseline models and conducting ablation experiments, the IC-TLCA model has been validated to be innovative in theory and superior in practical applications, thereby providing an effective solution for passenger flow prediction.
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