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

David H. Hopfe, Kiljae Lee and Chunyan Yu

Journal of Air Transport Management, 2024, vol. 115, issue C

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.

Keywords: Airport traffic flow; Forecasting; RNN; LSTM; ARIMA; SARIMA (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jaitra:v:115:y:2024:i:c:s0969699723001680

DOI: 10.1016/j.jairtraman.2023.102525

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