Modeling US Air Passenger Traffic Demand: Dynamic Data

Conventional demand models (e.g., gravity model) in air transport literature tend to rely heavily on the mainstream econometric variables (e.g., distance, population, and GDP), which cannot be dynamically measured or used for short-term predictions. This study seeks to complement the short-term predictability of such conventional approaches by introducing dynamic predictors while alleviating the endogeneity by implementing panel data modeling analysis. Utilizing 40,072 air passenger data stacked in 3,344 city pairs over twelve months in 2020, we demonstrate that a large variability in demand can be explained by a handful of non-conventional variables such as internet search volume and geometric mobility indicators. The performance of our fixed effect model was dramatically improved by adding the regional intensity of google search for “airport” and “flight” and by adding the measure of people’s time spent at residential areas in the origin and destination state (Adj. R2 to .74)

Forecasting Airport Passenger Flow during Periods of Volatility: Comparative Investigation of Time Series vs. Neural Network Models

Artificial Intelligence (AI) models, particularly neural networks, are infrequently utilized in the existing airport management literature for conventional forecasting of airport activities. The limited adoption of these models in the airport management literature might be influenced by their perceived complexity. This perception is likely derived from their common application in intricate tasks within the academic literature. Nevertheless, this research calls for a reevaluation of such perceptions and advocates for the inclusion of RNN and multivariate RNN in the forecasting toolkits of airport managers as credible alternatives to traditional time series models. This study endeavors to discern the forecasting performance of neural network models, providing insights into their effectiveness and applicability in addressing the complexities of passenger flow dynamics through a comprehensive evaluation of RNN, LSTM, GRU, Deep LSTM, BLSTM, multivariate RNN and multivariate LSTM, in comparison to standard time series models (ARIMA, SARIMA and SARIMAX). It was anticipated that the application of neural network techniques in TSA passenger flow v forecasting will yield heightened accuracy when compared to conventional standard time series models. Moreover, the integration of non-standard external factors was expected to enhance the forecasting performance of neural network-based models like RNN and LSTM, further distinguishing them from standard time series models. This investigation rigorously evaluates the robustness of these models by subjecting them to highly volatile historical data to forecast airport security checkpoint passenger flow at five prominent U.S. airports during the pandemic-induced challenges. At Atlanta\u27s Hartsfield-Jackson Airport (ATL), the forecasting precision of RNN notably exceeds that of SARIMA by 34% (DM= 3.44, p\u3c 0.01). This highlights the superior capacity of RNN to manage intricate interactions among variables, complex dependencies between factors and non-linear dynamics, thereby demonstrating its readiness for the emerging data-rich aviation environment. The incorporation of exogenous variables enhances the forecasting accuracies of the multivariate RNN/LSTM (DM=6.82,

Forecasting Airport Passenger Flow during Periods of Volatility: Comparative Investigation of Time Series vs. Neural Network Models

Artificial Intelligence (AI) models, particularly neural networks, are infrequently utilized in the existing airport management literature for conventional forecasting of airport activities. The limited adoption of these models in the airport management literature might be influenced by their perceived complexity. This perception is likely derived from their common application in intricate tasks within the academic literature. Nevertheless, this research calls for a reevaluation of such perceptions and advocates for the inclusion of RNN and multivariate RNN in the forecasting toolkits of airport managers as credible alternatives to traditional time series models. This study endeavors to discern the forecasting performance of neural network models, providing insights into their effectiveness and applicability in addressing the complexities of passenger flow dynamics through a comprehensive evaluation of RNN, LSTM, GRU, Deep LSTM, BLSTM, multivariate RNN and multivariate LSTM, in comparison to standard time series models (ARIMA, SARIMA and SARIMAX). It was anticipated that the application of neural network techniques in TSA passenger flow v forecasting will yield heightened accuracy when compared to conventional standard time series models. Moreover, the integration of non-standard external factors was expected to enhance the forecasting performance of neural network-based models like RNN and LSTM, further distinguishing them from standard time series models. This investigation rigorously evaluates the robustness of these models by subjecting them to highly volatile historical data to forecast airport security checkpoint passenger flow at five prominent U.S. airports during the pandemic-induced challenges. At Atlanta\u27s Hartsfield-Jackson Airport (ATL), the forecasting precision of RNN notably exceeds that of SARIMA by 34% (DM= 3.44, p\u3c 0.01). This highlights the superior capacity of RNN to manage intricate interactions among variables, complex dependencies between factors and non-linear dynamics, thereby demonstrating its readiness for the emerging data-rich aviation environment. The incorporation of exogenous variables enhances the forecasting accuracies of the multivariate RNN/LSTM (DM=6.82,

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 \u3c 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 \u3c 0.01; DM = 2.65, p \u3c 0.01, 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 non-linearity are accentuated during volatile conditions and less pronounced or not pronounced at all during routine periods

Modeling US Air Passenger Traffic Demand: Dynamic Data

Conventional demand models (e.g., gravity model) in air transport literature tend to rely heavily on the mainstream econometric variables (e.g., distance, population, and GDP), which cannot be dynamically measured or used for short-term predictions. This study seeks to complement the short-term predictability of such conventional approaches by introducing dynamic predictors while alleviating the endogeneity by implementing panel data modeling analysis. Utilizing 40,072 air passenger data stacked in 3,344 city pairs over twelve months in 2020, we demonstrate that a large variability in demand can be explained by a handful of non-conventional variables such as internet search volume and geometric mobility indicators. The performance of our fixed effect model was dramatically improved by adding the regional intensity of google search for “airport” and “flight” and by adding the measure of people’s time spent at residential areas in the origin and destination state (Adj. R2 to .74)

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\u27s Hartsfield-Jackson Airport (ATL), the RNN notably surpasses SARIMA\u27s forecasting accuracy by 34% (DM= 3.44, p\u3c .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\u3c.01; DM=2.65, p\u3c.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

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\u27s Hartsfield-Jackson Airport (ATL), the RNN notably surpasses SARIMA\u27s forecasting accuracy by 34% (DM= 3.44, p\u3c .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\u3c.01; DM=2.65, p\u3c.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

The Sizes and Albedos of Centaurs 2014 YY 49_{49} and 2013 NL 24_{24} from Stellar Occultation Measurements by RECON

In 2019, the Research and Education Collaborative Occultation Network (RECON) obtained multiple-chord occultation measurements of two centaur objects: 2014 YY$_{49}$ on 2019 January 28 and 2013 NL$_{24}$ on 2019 September 4. RECON is a citizen-science telescope network designed to observe high-uncertainty occultations by outer solar system objects. Adopting circular models for the object profiles, we derive a radius $r=16^{+2}_{-1}$km and a geometric albedo $p_V=0.13^{+0.015}_{-0.024}$ for 2014 YY$_{49}$, and a radius $r=66 ^{+5}_{-5}$km and geometric albedo $p_V = 0.045^{+0.006}_{-0.008}$ for 2013 NL$_{24}$. To the precision of these measurements, no atmosphere or rings are detected for either object. The two objects measured here are among the smallest distant objects measured with the stellar occultation technique. In addition to these geometric constraints, the occultation measurements provide astrometric constraints for these two centaurs at a higher precision than has been feasible by direct imaging. To supplement the occultation results, we also present an analysis of color photometry from the Pan-STARRS surveys to constrain the rotational light curve amplitudes and spectral colors of these two centaurs. We recommend that future work focus on photometry to more deliberately constrain the objects' colors and light curve amplitudes, and on follow-on occultation efforts informed by this astrometry