Demand for Air Travel in the United States: Bottom-Up Econometric Estimation and Implications for Forecasts by Origin-Destination Pairs

Bhadra, Dipasis

doi:10.2514/6.2002-5861

Cited by 34 publications

(38 citation statements)

References 3 publications

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“…In the bottom panel of the table we decompose the own-price elasticity estimates according to market nonstop flight distance categories. Consumers seem to be less pricesensitive in short-haul distance markets relative to long-haul distance markets, which is consistent with findings in Bhadra (2003). It is possible that many of the passengers who choose to use air travel on relatively short distances are business travelers.…”

Section: Own-price Elasticity Of Demandsupporting

confidence: 79%

On the Extent to which the Presence of Intermediate-stop(s) Air Travel Products Influences the Pricing of Nonstop Air Travel Products

Gayle¹,

Wu²

2014

Review of Network Economics

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Analysts of air travel markets, which include antitrust authorities, are interested in understanding the extent to which the presence of intermediate stop(s) products influences the pricing of nonstop products. This paper uses a structural econometric model to investigate the potential pricing interdependence between these two product types in domestic air travel markets. Counterfactual experiments using the estimated model suggest that in many (but far from a majority) markets the current prices of nonstop products are at least 5% lower than they would otherwise be owing to the presence of intermediate-stop(s) products. Keywords: Substitutability and Pricing Interdependence between Differentiated Air TravelProducts; Discrete Choice Demand Model. JEL Classification codes: L13, L40, L93Acknowledgement: We thank the editor, Julian Wright, two anonymous referees, Philippe Belley, Leilei Shen, Dong Li, Tian Xia, Kate Ho, and Yang-Ming Chang for very helpful comments and suggestions. Any remaining errors are our own.

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Section: Own-price Elasticity Of Demandsupporting

confidence: 79%

On the Extent to which the Presence of Intermediate-stop(s) Air Travel Products Influences the Pricing of Nonstop Air Travel Products

Gayle¹,

Wu²

2014

Review of Network Economics

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“…The present available literature on air traffic forecasting can be summarizedas in Figure 1. To be specific, Ashford [31] is among the earliest researchers who have pointed out the basic impact factors on air passenger flow, which are economical, technical, and operational ones; later on, Bhadra [32] has analyzed the local original-destination (OD) features and found their impact on air passenger flow; Hsiao and Hansen [33] have added the competition between airlines into consideration; while Bhadra et al [34] have utilized the impact from those various factors to estimate the flight schedules; and, when it comes to the area which has many tourist places of interest, Fang [35] have made a deep analysis in those impact factors which may just affect the tourist passenger flow. To be specific, Ashford [31] is among the earliest researchers who have pointed out the basic impact factors on air passenger flow, which are economical, technical, and operational ones; later on, Bhadra [32] has analyzed the local original-destination (OD) features and found their impact on air passenger flow; Hsiao and Hansen [33] have added the competition between airlines into consideration; while Bhadra et al [34] have utilized the impact from those various factors to estimate the flight schedules; and, when it comes to the area which has many tourist places of interest, Fang [35] have made a deep analysis in those impact factors which may just affect the tourist passenger flow.…”

Section: On Specific Models For Air Traffic Forecastingmentioning

confidence: 99%

An EMD–SARIMA-Based Modeling Approach for Air Traffic Forecasting

Nai¹,

Liu

Wang³

et al. 2017

Algorithms

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Abstract:The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities.In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode decomposition (EMD) and seasonal auto regressive integrated moving average (SARIMA) has been proposed in this paper. The model proposed decomposes the original time series into components at first, and models each component with the SARIMA forecasting model, then integrates all the models together to form the final combined forecast result. By using the monthly air cargo and passenger flow data from the years 2006 to 2014 available at the official website of the Civil Aviation Administration of China (CAAC), the effectiveness in forecasting of the model proposed has been demonstrated, and by a horizontal performance comparison between several other widely used forecasting models, the advantage of the proposed model has also been proved.

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“…This manual was originally developed in 1985 using traditional modelling techniques (Alekseev and Seixas 2009). Historically, multiple linear regression (MLR) models have generally been used to forecast airline passenger traffic demand (see, for example, Aderamo 2010;Ba-Fail et al 2000;Bhadra 2003;Kopsch 2012;Sivrikaya and Tunç 2013).…”

Section: Traditional Air Travel Demand Forecasting Approachesmentioning

confidence: 99%

A Forecasting Tool for Predicting Australia’s Domestic Airline Passenger Demand Using a Genetic Algorithm

Srisaeng

Baxter

Richardson³

et al. 2015

J.Aerosp. Technol. Manag.

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This study has proposed and empirically tested for the first time genetic algorithm optimization models for modelling Australia's domestic airline passenger demand, as measured by enplaned passengers (GAPAXDE model) and revenue passenger kilometres performed (GARPKSDE model). Data was divided into training and testing datasets; 74 training datasets were used to estimate the weighting factors of the genetic algorithm models and 13 out-of-sample datasets were used for testing the robustness of the genetic algorithm models. The genetic algorithm parameters used in this study comprised population size (n): 200; the generation number: 1,000; and mutation rate: 0.01. The modelling results have shown that both the quadratic GAPAXDE and GARPKSDE models are more accurate, reliable, and have greater predictive capability as compared to the linear models. The mean absolute percentage error in the out of sample testing dataset for the GAPAXDE and GARPKSDE quadratic models are 2.55 and 2.23%, respectively.

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Demand for Air Travel in the United States: Bottom-Up Econometric Estimation and Implications for Forecasts by Origin-Destination Pairs

Cited by 34 publications

References 3 publications

On the Extent to which the Presence of Intermediate-stop(s) Air Travel Products Influences the Pricing of Nonstop Air Travel Products

On the Extent to which the Presence of Intermediate-stop(s) Air Travel Products Influences the Pricing of Nonstop Air Travel Products

An EMD–SARIMA-Based Modeling Approach for Air Traffic Forecasting

A Forecasting Tool for Predicting Australia’s Domestic Airline Passenger Demand Using a Genetic Algorithm

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