Demand for leisure air travel and limits to growth

This paper discusses methodologies for analysing the existence of limits to growth of leisure air travel and defines the concept of demand maturity. It considers the air market as one of a number of inter-related travel markets and applies these concepts to a UK case study. The paper concludes that the UK air international leisure travel market is only at the early stages of maturity; whilst the overall leisure travel market seems to be much nearer to full maturity. This means that if UK air travel is still to experience healthy growth rates, it must be at the expense of the growth of some other UK travel market.     

Price elasticities of demand for passenger air travel: a meta-analysis

The demand for air transport is largely determined by the spending capacity of customers. This paper aims to offer more insight into the determinants of price elasticities in the aviation sector. It seeks to identify both common and contrasting factors that influence the price elasticities, on the basis of a comparative analysis among a large number of empirical studies in this field. By means of meta-analytical methods, the relative importance of several driving forces such as distance, type of ticket and the nature of study is investigated.     

Determinants of air travel demand in Middle Income Countries

The aim of this paper is to calculate the effects of air travel demand determinants in Middle Income Countries (MICs). Through static and dynamic panel data models from 32 countries during the period from 2002 to 2008, we found that the income elasticity is the most important determinant and that it is slightly higher than one. Income growth multiplied by income elasticity accounts for 75 percent of total passenger growth. Public policies such as an open skies agreements with the European Union have a positive effect on passenger growth, whereas structural changes, such as Low Cost Carrier (LCC) growth, have a marginal effect.     

Dynamic analysis of air travel demand in competitive island markets

This paper describes a dynamic demand model, referred to as dynamic abstract mode model, for estimating both the short- and long-term responses of air passengers to changes in relative air-sea travel cost components in competitive markets. The implementation of the model in the competitive market of Aegean islands in Greece demonstrates the importance of considering the past volumes of air passengers and relative travel cost components to explain current air travel demand.     

The impact of airport fees on fares for the leisure air travel market: The case of Spain

Panel data is used to determine the incidence of airport fees on fares in the Spanish leisure market airlines. We also study its structure through an empirical specification of a pricing and demand equation system. The results show the existence of market power, strategic behavior and density economics. We also demonstrate that airlines pass all the airport fees onto customers by increasing fares. Furthermore, we find that the behavior of airlines in routes with the presence of low cost carriers do not differ from other routes.     

Built environment effects on leisure travel for children: Trip generation and travel mode

This study empirically analyzed the effects of built environment on leisure travel among children. Students of three elementary schools, namely Yangmingshan, Sanyu and Shilin, all located in the Shilin District of Taipei, were chosen to provide sample data. The negative binomial regression model and multinomial logit model were used to analyze trip generation and travel mode, respectively. This study reached the following empirical findings: (1) mixed land use, employment density, walkway quality, leisure facility supply and leisure travel distance encouraged generation of leisure trips for children; (2) intersection density, building density, employment density and walkway quality encouraged a child to use transit systems or non-motorized travel modes for leisure travel; and (3) vehicle density and leisure travel distance discouraged walking and biking but encouraged the use of transit systems for leisure travel involving children. Local government can use the empirical findings of this study to develop urban planning strategies to encourage children to perform leisure activities outside the home using transit systems or non-motorized travel modes.     

What is driving the continued growth in demand for air travel? Customer value of air transport

This article introduces the concept of customer value as a driving factor for continued growth in demand and eliminates a subversion of the customer-value theory for the air-transport industry. This theoretical framework includes risk as subcomponents of perceived relative benefits as well as perceived relative costs. Based on data of a survey the weight of the different influential factors is evaluated. Management implications as well as methodological learning for the measurement of customer value are derived. The key conclusion thereby is that a stronger focus on customer value will be a key challenge of airline management. Important developments take place at the lower and upper end of the price and quality ranges. Thereby the orientation should be directed by the real, often hidden and not only to the stated preferences of customers.     

Direct air transport and demand interaction: A vector error-correction model approach

The goal of this paper is to impose a cause–effect structure into the relation between tourism demand and air transport capacity. Specifically, we apply a vector error-correction model to assess if, and to what extent, capacity or passenger demand are first-movers that return to long-run equilibrium following short-run deviations. Using data on international aviation between Australia and our test cases of China and Japan, we find that demand on the Japan–Australia market corrects for short-run deviations from the long-run equilibrium quicker than the China–Australia market. Reasons for such variation in adjustment speeds are discussed and we show that the results are robust to the phenomenon of airlines pre-empting demand when setting capacity.     

Posterior analysis of random taste coefficients in air travel behaviour modelling

Increasing use is being made of random coefficients structures, such as mixed logit, in the analysis of air travel choice behaviour. These models have the advantage of being able to retrieve random variations in sensitivities across travellers. An important issue, however, arises in the computation of willingness to pay indicators, such as the valuation of travel time savings, on the basis of randomly distributed coefficients. Indeed, with the standard approach of using simulation of the ratios across random draws, major problems can be caused by outliers, leading to biased trade-offs, which in turn lead to major issues in policy analyses. Here, a different approach is explored, making use of individual-specific draws from the random distributions, conditioned on the observed sequence of choices for each respondent. An analysis making use of stated preference data for airport and airline choice confirms the advantages of the approach using conditional draws, producing much more realistic distributional patterns for a range of willingness to pay indicators.     

Impact analysis of September 11 on air travel demand in the USA

This study uses an intervention model to look at the impact of the September 11, 2001 terrorist attack on air transport passenger demand in the US. The result showed that both domestic and international air traffic was significantly impacted for 1 and 2 months, respectively. The impact pattern was thus abrupt and temporary, instead of gradual and permanent. The approach also provides better forecasts than the seasonal ARIMA benchmark.     

A literature review and citation analyses of air travel demand studies published between 2010 and 2020

Accurate forecasting of air travel demand is vital for the resource planning of the air transportation industry. Therefore, identifying contributing factors and understanding the effect of these factors in causing the variation of air travel demand have been one of the key focus areas in air transportation research. This article reviews 87 air travel demand studies published from 2010 to 2020 and summarizes these studies using their input data and primary analytical methods. We also devise and conduct three citation analyses to further explore the relationships among the reviewed studies. Our review finds that a typical empirical study of air travel demand analysis would focus on the demand at the national level, employ time-series data concerning socio-economic and airline operational factors and use time-series based methods to estimate the relationship among the selected time-series. These studies are mostly applying existing analytical frameworks to specific problems rather than developing original methods, therefore their relationship to each other is parallel rather than sequential. A small number of references are frequently cited by the reviewed studies primarily because of their methodological contribution to time-series analysis. A common limitation of existing literature is that very few reviewed studies provide validation of their analyses. In addition, methods that are not regression or time-series based have very limited application in this area so far, so are the non-convention data such as mobility data or social media data. Besides providing a systematic summary of recent publications in a specific field, this review uses a relatively objective and replicable framework to compare and link studies by their references, which can be visualized by the figures included in this review. This review is expected to benefit future researchers that are interested in either air transportation or the application of time-series forecasting in an applied domain.     

Predicting the impact of Trans Java Toll Roads on demand for intercity air travel in Indonesia

This paper aims to explore the factors influencing a potential air passenger shift to autos due to the completion of the Trans Java Toll Road and an increase in airfares. The study focuses on intercity trips for nonbusiness purposes. A face-to-face interview survey of 751 air passengers was conducted in three main airports on Java Island, Indonesia. Both the theory of planned behavior and the discrete choice model were applied to understand the factors for influencing toll road use among air passengers. The empirical results reveal that psychological factors, consisting of attitudes, subjective norms, and perceived moral obligations significantly influenced intentions to use the toll road. Meanwhile, perceived control of external resources was essential for airline passengers in their intention to use toll roads. Sociodemographic factors such as age, gender, income, frequency of airplane use, travel time, and cost also significantly influenced the decision to drive. Female, older passengers, high-income passengers, and frequent flyers were more likely to continue flying. The finding confirms that the Trans Java Toll Road affected a decline in air demand on Java Island. However, traveling with family members, rather than airfare hikes, became a more substantial reason for air passengers to switch to driving the toll roads. This research found that air passengers were inelastic related to changes in travel time, while 6% were willing to switch due to airfare increases of 10%.     

Bi-level optimization approach for configuring population and employment distributions with minimized vehicle travel demand

Compact development aims to lead to dense and mixed distributions of population and employment in land use planning which may result in reduced vehicle miles traveled (VMT). However, determining an optimum or desirable configuration of population and employment distribution to achieve the compact development goal remains challenging due to numerous competitive development alternatives or possible exacerbated traffic congestion as a result of over-intensification of urban land use. To address this challenge, a systematic approach is proposed with a bi-level optimization model aiming to find out an efficient population and employment map for vehicle travel reduction. The upper level of the model is formulated to minimize VMT and vehicle hours traveled (VHT) varying with possible changes in population and employment densities. The lower level is based on a tour-based travel demand model to mechanistically represent the response of travel choices to those changes. The programming is solved by a Genetic Algorithm. The proposed method is demonstrated through a case study of Hamilton County, Ohio, U.S. The results indicate that a more compact urban form reduces VMT; however, it may cause longer VHT depending upon the density. To avoid urban overconcentration and reduce both VMT and VHT simultaneously, a compact urban development configuration with a population density < 5289 persons/mi² and the employment density < 3282 jobs/mi² is recommended for the area. As a negative relationship demonstrated between transit headway reduction and vehicle travel demand, strategies for improving transit provision are helpful to reducing vehicle travels in compact development scenarios.     

Evaluating travel website motivational communication using a structural equation modelling approach

This study examines online information value structure and its motivational communication competence using the motivational language theory (MLT). MLT explains how communication acts directed by managers to employees generate a greater motivation for work. Five information value dimensions — functional, hedonic, innovation, aesthetic and sign — were identified through literature on the subject of MLT and applied to online information. This study confirmed the multidimensionality of online information and proposed four online information dimensions: experiential (hedonic–innovation), sign, functional and aesthetic. The findings also suggested that experiential (hedonic–innovation) and functional information values have significant effects for travel motivation, including exploration, family togetherness, novelty, escape and socialisation. Copyright © 2010 John Wiley & Sons, Ltd.     

The impact of carbon emission fees on passenger demand and air fares: A game theoretic approach

The implementation of an environmental market-based measure on U.S. aviation industry is studied. Under this policy, each airline pays a carbon fee for the carbon dioxide emissions it generates. The impact on ticket prices and corresponding market shares is investigated via the joint estimation of an air travel demand model and an airlines' behavior model. In the demand model, aggregate air traffic data is used to determine the marginal effects of flight attributes that are specific to itinerary, airline and airport on market share. The airline's behavior model incorporates the carbon fee in the airline marginal cost. After the implementation of the carbon policy, the increased cost forces airlines to adjust ticket prices in order to maximize profits. The results obtained by the proposed model indicate a moderate price increase which strongly depends on the per tonne carbon price. Air travel demand falls from 2.4% to 21% depending on the carbon price level.     

Forecasting air passenger demand with a new hybrid ensemble approach

Analyzing and modeling passenger demand dynamic, which has important implications on the management and the operation in the entire aviation industry, are deemed to be a tough challenge. Air passenger demand, however, exhibits consistently complex non-linearity and non-stationarity. To capture more precisely the aforementioned complex behavior, this paper proposes a hybrid approach VMD-ARMA/KELM-KELM for the short-term forecasting, which consists of variational mode decomposition (VMD), autoregressive moving average model (ARMA) and kernel extreme learning machine (KELM). First, VMD is adopted to decompose the original data into several mode functions so as to reduce their complexity. Then, the unit root test (ADF test) is employed to classify all the modes into the stable and unstable series. Meanwhile, the ARMA and the KELM models are used to forecast both the stationary and non-stationary components, respectively. Lastly, the final result is integrated by another KELM model incorporating the forecasting results of all components. In order to prove and verify the feasibility and robustness of the proposed approach, the passenger demands of Beijing, Guangzhou and Pudong airports are introduced to test the performance. Also, the experimental results show that the novel approach does have a more obviously advantage than other benchmark models regarding both accuracy and robustness analysis. Therefore, this approach can be utilized as a convincing tool for the air passenger demand forecasting.     

Testing the differentiated impact of the COVID-19 pandemic on air travel demand considering social inclusion

The economic downturn and the air travel crisis triggered by the recent coronavirus pandemic pose a substantial threat to the new consumer class of many emerging economies. In Brazil, considerable improvements in social inclusion have fostered the emergence of hundreds of thousands of first-time fliers over the past decades. We apply a two-step regression methodology in which the first step consists of identifying air transport markets characterized by greater social inclusion, using indicators of the local economies’ income distribution, credit availability, and access to the Internet. In the second step, we inspect the drivers of the plunge in air travel demand since the pandemic began, differentiating markets by their predicted social inclusion intensity. After controlling for potential endogeneity stemming from the spread of COVID-19 through air travel, our results suggest that short and low-density routes are among the most impacted airline markets and that business-oriented routes are more impacted than leisure ones. Finally, we estimate that a market with 1% higher social inclusion is associated with a 0.153%–0.166% more pronounced decline in demand during the pandemic. Therefore, markets that have benefited from greater social inclusion in the country may be the most vulnerable to the current crisis.     

A nodal approach for estimating potential cycling demand

Establishing cycling as a prominent utility mode is recognised as central to creating sustainable transport systems in many cities around the world. Strategies of starter cycling cities are often biased to the supply of infrastructure along prominent corridors without acknowledging the nature, quantum or location of potential demand for cycling. Decisions are frequently left to local knowledge and experience of local needs, resulting in a bias with little opportunity for repeatability or reproducibility. This study proposes a data-driven approach to estimate the potential market for cycling geographically. Estimates are based on the number of potential cyclists in close proximity to the destinations they want to access. The paper demonstrates the method using Cape Town, South Africa as a case study. Given the virtual absence of utility cycling in the city, characteristics of cyclists in cities where cycling is popular are used to identify potential cyclists. Destination nodes are stratified in terms of the characteristics of their users, while home locations of persons with these characteristics are identified from a publicly available synthetic population for Cape Town. Analysis provides an order of magnitude indication of the cycling potential of selected nodes. It also shows areas with many potential cyclists that are not in proximity of desired destination. The results enable city authorities to focus detailed investigations and interventions where a critical mass of cycling may be achieved with the least effort and in the shortest time.     

The travel cost method and the economic value of leisure time

Recent estimates of high values for tourist related recreation USA amenity values indicate that allocation of basic water and terrestrial resources to recreation activities should be given precedence over conventional market oriented activities that often degrade or even extirpate the resource. We discuss at length the travel cost method (TCM), a survey based technique that quantifies the non‐market benefits of trips to recreation sites. The TCM has been cast in the role of an ‘umpire’ in recent resource allocation debates. Understanding the key role of the TCM in the debate will aid tourist agency officials throughout the world. This article is a U.S. Government work and is in the public domain in the U.S.A.