Impact of aircraft size and seat availability on airlines’ demand and market share in duopoly markets

Abstract

We build a nested logit model to study the roles of aircraft size, together with service frequency, seat availability and fare, in airlines’ market share and total demand in non-stop duopoly markets. We find that airlines can obtain higher returns in market share from increasing service frequency than from increasing aircraft size, and our study confirms an S-curve effect of service frequency on airlines’ market share. We find that the available capacity per flight––net of capacity absorbed by connecting passengers––affects market share in the same manner whether it is derived from a larger proportion of a smaller aircraft or smaller proportion of a larger one.

Introduction

In the late 20th century, when most major airports in the United States were congested and the flight delays were a major concern to both passengers and carriers, the airlines often demanded airport capacity enhancement through building new runways or installing more sophisticated traffic control systems, both of which were very expensive. In the meantime, the airport managers, government policy makers and aircraft manufacturers have been asking the questions of whether the airlines would increase the size of aircraft in their fleet, rather than the number of flights, to accommodate increasing travel demand, and how airlines’ choice of aircraft size would influence demand, market share and profit.

The tragedy of September 11 and the slowdown of the economy in both domestic and international markets in the new millennium have significantly changed the airline business. Travel demand has diminished due to security concerns and economy downturn; low cost carriers are competing more aggressively and penetrating in more markets; passengers are unwilling to pay for a premium price due to more transparent prices available on the Internet. To account for these factors, most network carriers, such as American Airlines and United Airlines, are in the process of reconstructing their business models. Simplifying and reconstructing aircraft fleet is a critical component in these reorganization plans, which would result in only the most profitable aircraft type(s) being retained in the fleet. Thus the same question posed by the old environment is equally salient in the new one: what are the market share and profit implications of varying flight frequency and aircraft size to provide a given level of air transport capacity?

The recognition and study of the impact of aircraft size and frequency on airline demand started with the introduction of the concept of “schedule delay”, first introduced by Douglas and Miller (1974), and subsequently applied by Viton (1986). “Schedule delay” has two components. The first is frequency delay, which represents the elapsed time between an individual traveler’s preferred time and the time of a scheduled flight. The second component is stochastic delay, which represents the additional elapsed time when preferred flights are fully booked. Douglas and Miller estimated empirical frequency and stochastic delay functions by using regression and simulation methods. Frequency delay decreases with frequency, while stochastic delay decreases with frequency and aircraft size, and increases with demand in the market. For the same service frequency provided by the airlines, the larger the aircraft, the higher the probability that a passenger can get a seat on a preferred flight and therefore enjoy a more convenient service. The concept of “schedule delay” was used in a linear regression model by Abrahams (1983) to estimate total air travel demand in a single market. In order to specify “schedule delay,” Abrahams used the frequency delay function introduced by Eriksen (1977), and the stochastic delay function introduced by Swan (1979). These two functions have the same form as those proposed by Douglas and Miller (1974), but the parameter values are different. Thus these models capture effects of both frequency and aircraft size.

Instead of using the negative term “schedule delay”, Eriksen (1977) and Russon and Hollingshead (1989) used the terms “level of service” or “quality of service”––which are functions of service frequency and aircraft size in a format similar to “schedule delay”––in their models of air passenger travel demand.

Other researchers focused on “service frequency” or “frequency delay” to study the influence of airlines’ service on travel demand. Hansen (1990) used service frequency, fare and flight distance to specify a passenger’s utility function, and built a logit model for demand analysis. Norman and Strandens (1990) directly related service frequency to the waiting time and cost of passengers, and built a probabilistic air travel demand model under the assumption of uniform distribution for desired departure times over a time interval. Nikulainen (1992) built a similar model based on the assumption that passenger demand at any time is a function of the distribution of all flight departure times. But aircraft size was not explicitly taken into consideration in these models, which emphasized the role of service frequency in air travel demand.

More recently, Coldren et al. (2003) built an itinerary level market share model using aggregate multinomial logit methodology. Aircraft size and type, together with such variables as fares, time of day, carrier market presence, itinerary level-of-service (non-stop, direct, single-connect, or double-connect) and connecting quality, are taken as independent variables in the model to measure various itinerary characteristics. Proussaloglou and Koppelman (1995) and Nako (1992) both applied the logit model to study airlines’ demand using the survey data from individual passenger. They both investigated the effectiveness of the frequent flyer programs, but did not take aircraft size or type as a factor influencing passengers’ choice of airlines.

In practice, some commercial carriers in the US apply the Quality of Service Index (QSI) method to forecast each carrier’s weekly market share based on the schedules of all airlines serving in the market. Each service is given a QSI score, which is a weighted metric determined by the schedule “quality” or attributes, including aircraft type used for the flight, whether it is non-stop, one-stop or two-stop connecting service, the carrier’s historical dominance in the market, day of the week of this service, its proximity to the “best” non-stop service, and so on. Each carrier’s market share is determined by the total QSI scores of the services provided by this carrier in relative to the total QSI scores of all the services available in the market. Surprisingly, the weights assigned for each schedule attribute to calculate QSI for each service are always judgmentally, if not arbitrarily, determined rather than statistically calibrated. There is a commercial software package that takes the QSI score as the “utility” in the discrete choice framework, and thus calibrates the weights for QSI in a logit model for all services in all markets. But this software does not deal properly with some basic statistical issues in the calibration process, such as endogenity and collinearity.

In this paper, we focus on the analysis of the role of aircraft size on airlines’ demand and market share in a duopoly competitive environment at the market level, with one major airport in origin and one major airport in destination. Our studies will not only update previous estimates for the role of aircraft size in airlines’ demand, but also obtain new estimation results for the specific case of duopoly market based on the data from the homogenous duopoly markets, where exactly two airlines compete with each other. We will study the roles of aircraft size both in an individual airline’s market share and in total air travel demand in the market. While all previous studies assume that all the seats in an aircraft are available to local passengers, which is rare in reality, this research takes into account the situation in which some of the seats are taken by connecting passengers. Therefore, not only aircraft size but also the proportion of aircraft capacity available to local passengers will be taken into consideration in our research.

Our research subject is jet aircraft, and does not include small regional aircraft normally with fewer than 60 seats. The seat capacity for current generation jet aircraft ranges from 100 seats to a little more than 400 seats (Airbus 380 is not included in this study). Since different airlines may have different seat configurations for the same type of aircraft, we use the actual number of seats available in the aircraft as aircraft seat capacity to characterize the size of the aircraft operated by each airline in the market.

The rest of this paper is arranged as follows: Section 2 describes in detail an air travelers’ choice model; Section 3 applies statistical methods to estimate the coefficients in the model, and discusses the implication and application of the developed model; and Section 4 is a summary of this paper.