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, 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.

At Atlanta's Hartsfield-Jackson Airport (ATL), the RNN notably surpasses SARIMA's forecasting accuracy by 34% (DM= 3.44, p< .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<.01; DM=2.65, p<.01, respectively), while the SARIMAX struggles with the added complexity.

We 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 nonlinearity are accentuated during volatile conditions and less pronounced or not pronounced at all during routine periods.