In order to further improve the accuracy of short-term passenger flow prediction of urban rail stations,an improved LSTM method combining ensemble empirical pattern decomposition algorithm and Bayesian optimization algorithm was proposed.Firstly,the EEMD is used to decompose the passenger flow data of subway stations to reduce the interference of data noise.Then,the BOA is used to optimize the hyper parameters of the LSTM,so as to promote the parameter accuracy of the model.Compared with the single LSTM and single-layer combination model,the testing results of prediction show that the Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)of the two-layer combined model named EEMD-BOA-LSTM,are respectively reduced by 21.8%～44.8%,16.9%～47.4%,the error of the prediction results for short-term passenger flow has been significantly improved.
维普
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