Determinants of air travel demand: The role of low-cost carriers, ethnic links and aviation-dependent employment

Boonekamp, T.; Zuidberg, J.; Burghouwt, Guillaume

doi:10.1016/j.tra.2018.01.004

Cited by 38 publications

(26 citation statements)

References 22 publications

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“…Wei and Hansen [36,37] analyse the effects of aircraft size, flight frequency, and seat availability on air passenger flows and find that rising flight frequency can attract more passengers, and Pitfield et al [20] also find a positive relationship between passenger flows and flight frequency. Airfare is also a key determinant of demand, with airfare and demand found to be negatively related [22,38]. With the advent of low-cost carriers (LCCs) entering the air service market, air travel has become more accessible to passengers, influencing demand [31,39].…”

Section: Demand Side: Air Passenger Flowsmentioning

confidence: 99%

“…As the presence of LCCs on the air transport market becomes more established, lower airfare on a route results, which subsequently promotes the demand for air travel. Boonekamp et al [38] also document that the presence of LCCs results in a significant increase in demand, particularly on routes that are active with Europe's leading LCCs such as Ryanair and easyJet.…”

Section: Demand Side: Air Passenger Flowsmentioning

confidence: 99%

See 1 more Smart Citation

Linking the Determinants of Air Passenger Flows and Aviation Related Carbon Emissions: A European Study

Yue

Byrne

2021

Sustainability

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Increasing global concern regarding the negative consequences of climate change will see the introduction of comprehensive policy governing aviation-related carbon emissions. With demand for air travel set to accelerate over the next three decades airlines are faced with the task of catering to increased demand while simultaneously achieving emission reductions. In this study we identify the determinants of air passenger flows and flight frequency and assess their impact on carbon emissions and carbon efficiency. Confining our analysis to the European market, we employ Fixed-Effects (FEIV) and Random-Effects (REIV) instrumental variables modelling techniques to 150 intra-Europe routes and find that the factors that are significant in influencing the operational planning decisions of an airline often lead to carbon inefficiencies. Our findings have important implications for both airlines and policymakers. For airlines, we show how the decisions they make to optimise operations can have negative environmental consequences, while we make suggestions as to how policymakers can incentivise airlines to achieve emission reductions.

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Section: Demand Side: Air Passenger Flowsmentioning

confidence: 99%

Section: Demand Side: Air Passenger Flowsmentioning

confidence: 99%

Linking the Determinants of Air Passenger Flows and Aviation Related Carbon Emissions: A European Study

Yue

Byrne

2021

Sustainability

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“…[Yang et al 2017] studied on the demand elasticities of air travel and used the Log-Linear Regression models for the estimations of the China mainland. [Boonekamp et al 2018] applied the Gravity Model (GM) to analyze the air travel demand among the European airports. They used a data set for 2010 that consists of the route-based air travel data.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Air Travel Demand for Selected Destinations Using Machine Learning Methods

Fırat¹,

Yiltas-Kaplan²,

Şamlı³

2021

jucs

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Over the past decades, air transportation has expanded and big data for transportation era has emerged. Accurate travel demand information is an important issue for the transportation systems, especially for airline industry. So, “optimal seat capacity problem between origin and destination pairs” which is related to the load factor must be solved. In this study, a method for determining optimal seat capacity that can supply the highest load factor for the flight operation between any two countries has been introduced. The machine learning methods of Artificial Neural Network (ANN), Linear Regression (LR), Gradient Boosting (GB), and Random Forest (RF) have been applied and a software has been developed to solve the problem. The data set generated from The World Bank Database, which consists of thousands of features for all countries, has been used and a case study has been done for the period of 2014-2019 with Turkish Airlines. To the best of our knowledge, this is the first time that 1983 features have been used to forecast air travel demand in the literature within a model that covers all countries while previous studies cover only a few countries using far fewer features. Another valuable point of this study is the usage of the last regular data about the air transportation before COVID-19 pandemic. In other words, since many airline companies have experienced a decline in the air travel operation in 2020 due to COVID-19 pandemic, this study covers the most recent period (2014-2019) when flight operation performed on a regular basis. As a result, it has been observed that the developed model has forecasted the passenger load factor by an average error rate of 6.741% with GB, 6.763% with RF, 8.161% with ANN, and 9.619 % with LR.

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“…Obviously, the market structure of each destination differs and determines such results (Moreno-Izquierdo, Ramón-Rodríguez and Perles-Ribes 2016). However, most articles have shown that LCC entry has a positive impact on international arrivals (Graham and Dennis 2010; Rey, Myro, and Galera 2011; Chung and Whang 2011; Alsumairi and Tsui 2017; Boonekamp, Zuidberg, and Burghouwt 2018; Bilotkach et al 2019) and domestic arrivals (Pulina and Cortés-Jiménez 2010; Tsui 2017). LCCs have been observed to mimic the seasonal pattern of incumbent air companies; thus, seasonality remains similar (Graham and Dennis 2010; Chung and Whang 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

Have Low-Cost Carriers Crowded Out Full Services and Charter Carriers in Tourism Destinations? A Trivariate Structural Time Series Analysis

Eugenio-Martin

Pérez-Granja

2020

Journal of Travel Research

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Long-haul tourist arrivals depend on the airline market, its size, and the degree of competition. This article studies the entry and exit of full service carriers (FSCs), charter carriers (CCs) and low-cost carriers (LCCs) from two origins: the United Kingdom and Germany, and five sun-and-beach destinations in Spain. The relationship among all types of airlines is captured with a trivariate structural time series model to disentangle the airlines’ responses under common shocks of airlines’ entry and/or exit and provides estimates of immediate responses and indicators of responses over time. The results show that in the British market, the entry of LCCs has crowded out FSCs and CCs. However, in the German market, the results are heterogeneous and overall do not support the existence of such crowding out effect.

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Determinants of air travel demand: The role of low-cost carriers, ethnic links and aviation-dependent employment

Cited by 38 publications

References 22 publications

Linking the Determinants of Air Passenger Flows and Aviation Related Carbon Emissions: A European Study

Linking the Determinants of Air Passenger Flows and Aviation Related Carbon Emissions: A European Study

Forecasting Air Travel Demand for Selected Destinations Using Machine Learning Methods

Have Low-Cost Carriers Crowded Out Full Services and Charter Carriers in Tourism Destinations? A Trivariate Structural Time Series Analysis

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