A Bi-Level Model to Estimate the US Air Travel Demand

A single-level optimization model (i.e., a Route Flow Estimator (RFE)) has been proposed to estimate the historical air travel demand. However, the RFE may require a significant amount of additional data collection effort when applied to estimate travel demand in small or medium-sized networks. We propose a novel bi-level model as an alternative to the RFE to handle demand estimation for small or medium-sized networks. The upper-level model is designed as a constrained least square (LS) model. The lower-level model is designed based on the RFE. The bi-level model estimates travel demand by considering travelers' choice behaviors and some observed data. It requires less data collection effort yet it produces estimation results consistent with those from the RFE. A Gauss–Seidel type (GST) algorithm is proposed to solve the bi-level model. To solve the upper-level model, we propose a heuristic algorithm, which is designed to solve the dual of the upper-level model. The estimation results from the two models are compared using two numerical examples: a small-sized example with one OD pair and a medium-sized example with 400 OD pairs.

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Long Term Forecasting of International Air Travel Demand in Nigeria (2018-2028)

American International Journal of Multidisciplinary Scientific Research ◽

10.46281/aijmsr.v1i2.184 ◽

2018 ◽

Vol 1 (2) ◽

pp. 16-24

Author(s):

Adeniran, Adetayo Olaniyi ◽

Kanyio, Olufunto Adedotun

Keyword(s):

Travel Demand ◽

Air Travel ◽

Least Square ◽

Coefficient Of Determination ◽

High Tendency ◽

Passenger Travel ◽

Percent Increase ◽

Air Travel Demand ◽

Long Term Forecasting

This study examines long term forecasting of international air travel demand in Nigeria. Yearly data from 2001 to 2017 were collected from secondary sources. Ordinary Least Square (OLS) regression was used to forecast the ten years (2018 to 2028) demand for international air passenger travel in Nigeria. The demand for international air passenger in Nigeria from year 2001 to 2017 was compared with the forecast. Calculation reveals that the coefficient of determination R2 is 0.815, while the computed reveals that the coefficient of determination R2 is 0.769, this difference can be attributed to approximations to two decimal places for calculated test. The calculated test and computed test reveals that the error term is minimal and the explanation level is high; hence the prediction or forecast is reliable. The forecast for years 2020, 2025 and 2028 are 5,282,453, 6,342,519, and 6,978,559 respectively which are about 48 percent increase, 78 percent increase, and 95 percent increase respectively from demand in year 2017. The forecast of ten years from year 2018 to year 2028 reveals that there will be more increase in the demand for international air passenger travel in Nigeria. The implication of this increment is that existing air transport infrastructures should be upgraded, and new infrastructures should be procured and installed; airport and airline operations should be reviewed and strategized such that they will meet the expectations of airline and airport users. Other concerned business stakeholders should use this data to plan and invest as there is high tendency for profit making.

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Tracing the Woes: An Empirical Analysis of the Airline Industry

American Economic Journal Microeconomics ◽

10.1257/mic.2.3.1 ◽

2010 ◽

Vol 2 (3) ◽

pp. 1-43 ◽

Cited By ~ 88

Author(s):

Steven Berry ◽

Panle Jia

Keyword(s):

Airline Industry ◽

Structural Model ◽

Travel Demand ◽

Low Cost ◽

Air Travel ◽

Demand And Supply ◽

The Us ◽

Low Cost Carriers ◽

Air Travel Demand ◽

The Impact

The US airline industry went through tremendous turmoil in the early 2000s, with four major bankruptcies, two major mergers, and various changes in network structure. This paper presents a structural model of the industry, and estimates the impact of demand and supply changes on profitability. Compared with 1999, we find that, in 2006, air-travel demand was 8 percent more price sensitive, passengers displayed a stronger preference for nonstop flights, and changes in marginal cost significantly favored nonstop flights. Together with the expansion of low-cost carriers, they explain more than 80 percent of legacy carriers' variable profit reduction. (JEL L13, L25, L93)

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A Demand Forecasting Method Based on Stochastic Frontier Analysis and Model Average: An Application in Air Travel Demand Forecasting

Journal of Systems Science and Complexity ◽

10.1007/s11424-018-7093-0 ◽

2018 ◽

Vol 32 (2) ◽

pp. 615-633 ◽

Cited By ~ 2

Author(s):

Xinyu Zhang ◽

Yafei Zheng ◽

Shouyang Wang

Keyword(s):

Stochastic Frontier Analysis ◽

Travel Demand ◽

Demand Forecasting ◽

Stochastic Frontier ◽

Air Travel ◽

Frontier Analysis ◽

Travel Demand Forecasting ◽

Model Average ◽

Forecasting Method ◽

Air Travel Demand

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Model Calibration and Forecasts of Air Travel Demand with Categorized Household Socioeconomic Attributes

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120919111 ◽

2020 ◽

Vol 2674 (6) ◽

pp. 363-371

Author(s):

Jungin Kim ◽

Ikki Kim ◽

Jaeyeob Shim ◽

Hansol Yoo ◽

Sangjun Park

Keyword(s):

Travel Behavior ◽

Household Income ◽

Travel Demand ◽

Behavioral Model ◽

Income Group ◽

Air Travel ◽

Jeju Island ◽

Demand Model ◽

Air Travel Demand ◽

Travel Demand Model

The objectives of this study were to (1) construct an air demand model based on household data and (2) forecast future air demand to explain the relationship between air demand and individual travel behavior. To this end, domestic passenger air travel demand at Jeju Island in South Korea was examined. A multiple regression model with numerous explanatory variables was established by examining categorized household socioeconomic data that affected air demand. The air travel demand model was calibrated for 2009–2015 based on the annual average number of visits to Jeju Island by households in certain income groups. The explanatory variable was set using a dummy variable for each household income group and the proportion of airfare to GDP per capita. Higher household income meant more frequent visits to Jeju Island, which was well-represented in the model. However, the value of the coefficient for the highest income was lower than the value for the second-highest income group. This suggested that the highest income group preferred overseas travel destinations to domestic ones. The future air demand for Jeju airport was predicted as 26,587,407 passengers in 2026, with a subsequent gradual increase to approximately 33,000,000 passengers by 2045 in this study. This study proposed an air travel demand model incorporating household socioeconomic attributes to reflect individual travel behavior, which contrasts with previous studies that used aggregate data. By constructing an air travel model that incorporated socioeconomic factors as a behavioral model, more accurate and consistent projections could be obtained.

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