On the factors that affect airline flight frequency and aircraft size

https://doi.org/10.1016/j.jairtraman.2009.08.001Get rights and content

Abstract

This paper assesses the determinants of aircraft size and frequency of flights on US airline routes by considering market demographics, airport characteristics, airline characteristics and route characteristics. It shows that frequency and aircraft size increase with population, income, and runway length. An increase in the proportion of managerial workers in the labor force or the proportion of population below the age of 25 results in greater frequency with the use of small planes. Slot constrained airports and an increase in the number of nearby airports lead to lower flight frequency with the use of smaller planes. Hubs and low cost carriers are associated with larger plane sizes and higher frequency, while regional airline ownership leads to higher frequency and the use of smaller planes. An increase in distance between the endpoints leads to lower frequency with the use of larger planes. As airport delay rises, airlines reduce frequency and use smaller planes, though when airport cancellations rise, flight frequency increases with the use of larger planes.

Introduction

Flight delays have become rampant in the airline industry. US on-time performance for the summer of 2007 was the worst on record, with over 30% of all commercial flights delayed. Airlines blame bad weather and an outdated air traffic control system. Government officials blame airlines for scheduling more flights than the system is designed to handle. At the same time, airlines are increasingly using small regional aircraft, which are capable of carrying between 30 and 100 passengers. These smaller planes utilize the same resources as larger planes in terms of landing slots and air traffic control, while carrying fewer passengers than mainline aircraft. This paper examines the determinants of aircraft sizes and the frequency of flights between airports.

Airlines may choose to serve a market1 with a particular aircraft size and frequency. A market that has a high concentration of passengers with high time costs, business travelers, might be served by smaller aircraft with greater frequency, while a market with a high concentration of low time cost passengers (leisure travelers) might be serviced by larger aircraft with lower frequency. Conversely, markets with a high concentration of business or affluent passengers could benefit from the use of larger jets, as they have more first class seats, than markets with fewer business or affluent passengers. Distance is also a significant factor in the use of a particular aircraft type on a route. As the distance between the two endpoints increases, longer-range (and thus larger) aircraft are needed.

The determinants of aircraft size and frequency can have major policy implications. The Federal Aviation Administration (FAA) has proposed aircraft size targets at New York's LaGuardia airport, as airlines' use of regional jets over mainline jets in the airport's limited number of landing slots has led to congestion and under-utilization of passenger terminals. A minimum size requirement, however, may lead to under-provision of desired flight frequencies or over-provision of seat capacity. Thus, while a it may lead to more efficient use of terminal infrastructure, it may ultimately reduce welfare.

Attention has been paid to the determinants of aircraft size and frequency in city-pair markets. Bhadra (2005) uses the number of passengers, distance, and the type of airport hub at the route endpoints to explain aircraft choice in the US. A shortcoming of the model is that neither airline behavior nor economic factors affecting passenger demand are considered in explaining aircraft choice. Givoni and Reitveld (2006) take a different approach by considering the implications of route factors and airport characteristics on the aircraft size decision and investigate the impact of distance, market size, market concentration, slot constraints, hub status, and the number of runways on aircraft choice in the US, Europe and Asia. They find that the choice of aircraft size is mainly influenced by route characteristics, including distance, level of demand, and competition. Further, airport characteristics, such as the number of runways and whether the airport is a hub or slot constrained, do not influence aircraft choice.

Other factors may also influence aircraft sizes. An airline may opt to use larger aircraft on a route due to economies of scale in aircraft operation.2 This effect, however, may be offset by higher labor costs. Pilots receive higher salaries for flying larger aircraft, that can result in airlines using smaller aircraft in short-haul, high density markets (Wei and Hansen, 2003). Forbes and Lederman (2005) consider the relationship between service quality and the integration of regional carriers with major airlines. On routes where schedule disruptions are costly to the major carrier and likely to occur (i.e., on hub routes and routes-prone to bad weather), they show that majors are likely to rely on their regional carrier subsidiaries, rather than on independent regional carriers. Thus, factors such as unit costs and relationship to regional carriers may play a factor in an airline's aircraft size and frequency decisions.

We focus on the US market using market demographics, airport characteristics, airline characteristics and route characteristics to investigate the determinants of aircraft size and flight frequency. Such variables provide insight into the demand characteristics and operational constraints that airlines face. This approach differs from past studies that consider operational constraints without any attention to demand factors and uses several continuous variables as hub controls instead of a dummy variable. This allows for deeper analysis of “focus cities” that have service to many destinations, but are not considered a hub by the carrier. In addition, the impact of delays and cancellations on frequency and aircraft size are considered.

Section snippets

Theoretical framework and implications

To understand the implications of demographics on aircraft size and frequency, a review of the monopoly scheduling model of Brueckner (2004) is useful. For simplicity, the airline serves three equidistant cities, A, B, and H, as shown in Fig. 1. Demand for travel exists between each pair of cities, yielding three city-pair markets: AH, BH, and AB. In a point-to-point network, the airline operates flights between each pair of cities, so that nonstop travel occurs in each city-pair market. In a

Empirical model and data

To conduct this study, the following regression model is estimatedGijkt=αijkt+β1Wi+β2Wj+β3Wikt+β4Xjkt+β5Yk+β6Zij+vijktwhere Gijkt is the dependent variable (frequency or aircraft size) on the route from airport i to airport j on airline k in month t. Many studies that focus on the airline industry remove directionality in the data (i.e., treat flights from j to i the same as flights from i to j). Due to the nature of the hub control variables used in this study, however, the directional nature

Empirical results

The results of the regressions with frequency and aircraft size as the dependent variables are presented in Table 4 and Table 5, respectively. The first column in each table is the base specification. Base-specification results for flight frequency and subsequently aircraft size are now presented, before turning attention to the effect of delay and cancellations.

Conclusion

The US airline industry has experienced rampant delays, and expectations are that the delay problem will become more severe. Airlines are utilizing smaller planes with greater frequency, while the government is relying on an air traffic control system that was not meant for handling the number of flights being flown. This paper explores the factors that might lead airlines to exacerbate the problems of over-utilized infrastructure by examining the determinants of aircraft sizes and the

Acknowledgements

I thank Jan Brueckner, Volodymyr Bilotkach, Ken Button, Linda Cohen, Iris Franz, Nick Rupp, Ken Small, Kurt Van Dender, Kat Wong, and two anonymous referees for their feedback. I am grateful to the University of California Transportation Center for generous financial support. Credit for any errors remains with the author.

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