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Short-term forecasting airport passenger flow during periods of volatility: Comparative investigation of time series vs. neural network models

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  • Hopfe, David H.
  • Lee, Kiljae
  • Yu, Chunyan

Abstract

Recurrent Neural Networks (RNNs), known for handling complex data tasks like language translation and speech recognition, are seldom employed in airport management practice for daily and weekly passenger flow forecasting tasks. In this paper, we evaluate the effectiveness and adaptability of various neural network models (RNN, LSTM, GRU, Deep LSTM, Bidirectional LSTM, multivariate RNN, and multivariate LSTM) against standard time series models (ARIMA, SARIMA, and SARIMAX) for a short-term forecasting airport security checkpoint passenger flows at five major U.S. airports during the pandemic.

Suggested Citation

  • Hopfe, David H. & Lee, Kiljae & Yu, Chunyan, 2024. "Short-term forecasting airport passenger flow during periods of volatility: Comparative investigation of time series vs. neural network models," Journal of Air Transport Management, Elsevier, vol. 115(C).
    • Handle: RePEc:eee:jaitra:v:115:y:2024:i:c:s0969699723001680
    DOI: 10.1016/j.jairtraman.2023.102525
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