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In order to improve the accuracy of subway traffic prediction, an empirical mode decomposition (EMD) and long short-term memory (LSTM) model is proposed, and the model is verified by taking Xinjiekou Station of the Nanjing Subway as an example. The study shows that the EMD-LSTM model has the lowest root-mean-square error (RMSE) and mean absolute percentage error (MAPE) compared with the autoregressive integrated moving average (ARIMA) model and the LSTM model, which indicates that the model has high prediction accuracy and can be well applied to the prediction of subway passenger flow.

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EMD-LSTM-Based Prediction of Passenger Flow in and Out of Subway Stations

  • 章节

  • 发表日期：

    2026年

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  • Jiahui Yang,
  • Zhe Ji,
  • Fengwu Wang

摘要

In order to improve the accuracy of subway traffic prediction, an empirical mode decomposition (EMD) and long short-term memory (LSTM) model is proposed, and the model is verified by taking Xinjiekou Station of the Nanjing Subway as an example. The study shows that the EMD-LSTM model has the lowest root-mean-square error (RMSE) and mean absolute percentage error (MAPE) compared with the autoregressive integrated moving average (ARIMA) model and the LSTM model, which indicates that the model has high prediction accuracy and can be well applied to the prediction of subway passenger flow.