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

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, the authors 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. At Atlanta’s Hartsfield-Jackson Airport (ATL), the RNN notably surpasses SARIMA’s forecasting accuracy by 34% (DM = 3.44, p < 0.01). This underscores RNN’s superiority in handling complex interactions among variables and non-linear dynamics, demonstrating its readiness for the emerging data-rich environment. Including exogenous variables enhances the forecasting accuracies of the multivariate RNN/LSTM (DM = 6.82, p < 0.01; DM = 2.65, p < 0.01, respectively), while the SARIMAX struggles with the added complexity. The authors observed the same patterns at the other four airports studied (DEN/ORD/LAX/DFW) during the pandemic period. However, during the normal airport traffic period, the clear superiority of RNN became much less pronounced, obscuring the performance gap between RNN and SARIMA. This suggests that the inherent advantages of RNN in capturing non-linearity are accentuated during volatile conditions and less pronounced or not pronounced at all during routine periods.

• Record URL:
  • https://doi.org/10.1016/j.jairtraman.2023.102525
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0969699723001680
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/31005945
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Hopfe, David H
  • Lee, Kiljae
  • Yu, Chunyan
• Publication Date: 2024-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 102525
• Serial:
  • Journal of Air Transport Management
  • Volume: 115
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0969-6997
  • Serial URL: http://www.sciencedirect.com/science/journal/09696997

Subject/Index Terms

• TRT Terms: Airports; Forecasting; Passenger volume; Volatility
• Subject Areas: Aviation; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01904292
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 9 2024 9:09AM