National Academies Press: OpenBook

Air Demand in a Dynamic Competitive Context with the Automobile (2019)

Chapter: Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger

« Previous: Chapter 3. Factors Which Influence the Choice of Mode for the Long-Distance Trip
Page 64
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 64
Page 65
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 65
Page 66
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 66
Page 67
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 67
Page 68
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 68
Page 69
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 69
Page 70
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 70
Page 71
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 71
Page 72
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 72
Page 73
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 73
Page 74
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 74
Page 75
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 75
Page 76
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 76
Page 77
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 77
Page 78
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 78
Page 79
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 79
Page 80
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 80
Page 81
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 81
Page 82
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 82
Page 83
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 83
Page 84
Suggested Citation:"Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
×
Page 84

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

50 CHAPTER 4. THE ROLE OF THE AUTOMOBILE IN THE FUTURE OF SMALLER AMERICAN AIRPORTS: LEAKAGE FROM SMALLER TO LARGER 4(A) INTRODUCTION AND STRUCTURE This ACRP study of the relationship between the car and the plane in long-distance travel focuses policy attention on two kinds of trip making which are, arguably, separable into two research areas. First, the auto is a competitor to the airplane for the full long-distance trip: chapters 2 and 3 have focused on the reasons why a traveler would take the trip primarily by plane, or entirely by car. Second, the auto is a competitor to the airplane for the segment which provides access to the longer distance airplane trip segment. Chapter 4 now focuses on specific trends in which the auto is chosen to an increasing degree to gain access to the long-distance flight. This has some serious negative implications for the traffic volumes and financial viability of smaller American airports. INTRODUCTION For most American air travelers, the choice of the airport of departure is not a matter of much concern: fully 53% of flyers either have a large airport nearer to them or have no competing airport at all. The majority of air passengers drive less than 40 miles to gain access to the airport chosen for the particular trip. But those located near a smaller American airport face a dilemma, as the airport to which they might like to be loyal may not be served well by the present service patterns of the major American air carriers. As documented in Chapter 3, the airlines are devoting a smaller and smaller portion of their total service to flights under 500 miles, flights which often serve as feeders to larger hub operations. With the lessening of service options at smaller airports, American air travelers are using automobiles, in effect, to provide the first leg of a multi-segment long-distance trip. A major question for this ACRP project concerns the various possible futures for this pattern of airport selection, particularly as newer technology is incorporated into the automobile and ultimately, automated vehicle operations. New aircraft technology is explored both in terms of its market support and in terms of potential obstacles to successful implementation. This chapter summarizes present knowledge about the selection of an airport, how that selection results in “leakage,” and speculates on how this might change with evolving automotive and aircraft technology. STRUCTURE Chapter 4 begins with basic information about where our national airports are located relative to the flying population. This is followed by a review of the current understanding of the factors influencing the choice of airport; here, an analysis of the project’s 2017 survey results about how the separate factors influence airport choice is presented. The major portion of the chapter is devoted to an analysis of how ground access considerations interact with other factors resulting

51 in “leakage” a pattern in which passengers from the seemingly logical prime area are ‘lost’ to larger competing airports. The role of automobile technology relative to airport choice and leakage is then summarized. Finally, the chapter concludes with a review of the possible implications of newly developing aircraft technology which would potentially lower costs from service to smaller markets, thus affecting the choice of the airport of departure. 4(B) Airports Categorized by Competit ion with Other Airports SUMMARY OF NATIONAL AIRPORT LOCATIONS Our analysis of airport access distance for US residents by Census tract distinguishes major and minor airports, with the “major” airports being the top 60 in the United States in terms of annual enplanements. This distinction helps to better understand the proportion of potential long- distance travelers who have a choice of airports, often with larger airports available at longer distances from the residence location. Approximately 53% of residents are well served in relation to a major airport, with 45% of residents having a major airport closest, and another 8% of residents with the major airport being the only option. Approximately 23% of residents have only a choice of a smaller airport. This leaves 24% of residents who have a choice between a minor airport and a more distant major airport; 10% of residents for whom the minor airport is at least 40 miles closer, and 14% where the major airport is almost as close as the smaller airport (within 40 miles). This suggests that airport choice is a major consideration for 24% of potential air travelers, and for people choosing the departing airport based on factors not influenced by geographic proximity.16 4(C) FACTORS AFFECTING AIRPORT CHOICE: RESULTS FROM THE 2017 SURVEY ATTITUDES TOWARD CHOICE OF AIRPORT, FROM THE SURVEY Observations about the choice of local vs. larger airport can be made based on the attitudes revealed in our 2017 survey. There, more survey respondents agreed than disagreed with the statement, “Assuming that the door-to- door travel times were similar, I would prefer to drive to a larger airport to get more flight choices than take a feeder flight from a closer airport.” Sixty- two percent of survey respondents agreed with the statement, with almost no variation by the length of the referenced trip. The level of agreement with the statement was higher for Millennials than for older age groups, higher for the higher income group than the lower group, and somewhat higher for the males than for the females. Of those who did not fly out of the closest airport, 88% were going to a larger airport, and only 12% were going to a smaller one. Those choosing the more distant airport tended to be younger than those departing from closer to home. 16 Source: FHWA Foundational Knowledge project; the subject of airport location is treated in more detail in the Technical Appendix.

52 FACTORS AFFECTING AIRPORT CHOICE Air travelers’ choices among alternative airports can involve trade-offs among a number of attributes that differ among available airports: ground access/egress times and costs, airport flight service frequency, types of flights and airfares available, likely flight delays and the design/operation of the airports, among others. There are likely cases where any one of those attributes causes an air passenger to choose an airport that is less attractive than other alternative airports along all of those other dimensions. And, there are certainly many cases in which air travelers find that one airport dominates all others with respect to all of these attributes. But in most regions with multiple airports, air travelers likely find that different available airports are preferable to others for some, with a selection of several airports quite common. These observations all suggest that there are compelling factors that can cause air travelers to choose a more distant airport. In addition, although airport flight schedules and available airfares are crucial factors in airport choice, other airport amenities and services can have a significant effect on airport choice. Previous research results A number of past studies have analyzed the relationship between airports’ attributes and the share of air passengers that they attract in multi-airport regions. One of the earliest published studies on this topic resulted in the development of the Multiple Airport Demand Allocation Model. This model was originally developed to analyze potential traffic at the New York region’s Stewart Airport but was subsequently applied to the metropolitan Washington region and a number of other multiple airport regions.17 The model assumed that the choice among alternative airports depended on three primary sets of factors: 1) access/egress times and costs, 2) time spent at airport and 3) the schedule frequency. Access costs were converted into equivalent minutes and added along with access times to the time spent at each airport based on expected terminal walking, waiting and delay times. Finally, expected wait times were calculated based on flight schedules to create a total time for each airport. The effect of this total airport time on air shares was calibrated based on observed airport shares. While this model reflects several of the key airport attributes that affect airport choice, it did not consider airfares and flight service differences (other than flight frequency) nor traveler type differences among airports. In addition, the relative weightings of the included attributes were asserted rather than statistically estimated. A later study of the San Francisco region used disaggregate data from airport surveys to statistically estimate parameters of discrete choice-based (multinomial logit) airport choice models for that region.18 That study confirmed the importance of ground access times and costs and of the frequency of air service (particularly direct service) on airport choices and provided statistical estimates of the relative importance of these factors. The study also showed that that business and non-business travelers make very different trade-offs among these attributes. This 17Brian Campbell and Associates, Description of the Multiple Airport Demand Allocation Model, US DOT/FAA Office of Aviation Policy, September 1977. 18 Harvey, Greig, “Airport Choice in a Multiple Airport Region,” Transportation Research Part A, Volume 21, Issue 6, November 1987, Pages 439-449.

53 was the first of several such studies using San Francisco airport survey data, each making successive improvements to the model.19 While the San Francisco study was able to determine the effects of obvious factors such as ground access travel time and flight frequency, the use of observed airport choices (“revealed preferences” or “RP”) alone imposes inherent limitations on the ability to determine trade-offs among attributes such as airfares and airport access travel time. These include the fact that available airfares and air travel options vary significantly for any given itinerary and cannot be accurately estimated from survey responses. In addition, in many markets such as Atlanta, a single airport dominates and there are few if any viable alternatives. As a result, much of the more recent work on airport choices has employed stated preference (“SP”) surveys to estimate trade-offs among air travel attributes.20 This approach, used also in the research project, can provide more robust estimates of the ways in which air travelers trade-off attributes when making airport choices. “Willingness to pay” and airport choice Chapter 6 in this report will provide details of the airport and mode choice models developed for this project but a few observations from those models can illustrate the ways travelers value each of the attributes associated with available airports. These observations come from the estimates of “willingness to pay” derived from these models. Willingness to pay is simply the amount someone would be willing to pay to have more desirable levels or specific attributes. In these models, the willingness to pay is calculated as the amount of additional airfare travelers would absorb to improve their air travel. 1) Travelers on average are willing to pay about an additional $17 on their airfare to save an hour of access time but are willing to spent about double that amount to save an hour of flight time21. This confirms results from many past studies that flight time is considered to be more onerous than ground access time. It implies, for example, that travelers would drive an extra hour to an airport that saves them 30 minutes of flight time. 2) Travelers place a much higher value on additional flights in airports that currently have fewer flights, or in the reverse, are willing to pay much more for airports that have more flights. The effect is strongest for direct flights but also applies to connecting itineraries. However, this effect of a single additional flight diminishes when comparing airports that both have larger numbers of flights. 3) Travelers on average are willing to pay $50-$60 to fly from an airport that has direct flights to their destination rather than connecting itineraries. But there are also many travelers who are willing to pay much more to avoid connections. 19 See, for example, Hess, S. & Polak J.W. (2005), Mixed Logit modelling of airport choice in multi-airport regions, Journal of Air Transport Management, 11(2), pp.59-68. 20 Hess, S., Adler, T. & Polak, J.W. (2007), Modelling airport and airline choice behaviour with the use of stated preference survey data, Transportation Research Part E, Volume 43, pp. 221-233. 21 More information on how trip purpose interacts with willingness to pay in this context is presented in Chapter 6.

54 4) There are also general preferences for specific airports due to their designs, services and amenities and this research found that travelers are willing to pay on average anywhere from $15 to $120 to fly from a particular airport in each region compared to other alternative airports. In all cases, these were the larger hub airports in the regions studied, reflecting the higher levels of amenities and service offered in those airports. These all suggest that there are compelling factors that can cause air travelers to choose an airport other than the one that is closest to them. In addition, although airport flight schedules and available airfares are important factors in airport choice, other airport amenities and services can have a significant further effect on airport choice. The effects of these factors on airport choice can also be observed in the scenario tests that were conducted using these models imbedded in the full national travel simulation system. Simulations were undertaken for both individual factors, and overarching scenarios, themselves combinations of individual factors, testing the effects of airfare increases, auto operating cost increases, smaller airport service frequency increases and increases in non-hub direct flights. All of these changes affect airport choice in the regions that have multiple available commercial airports. But these changes also affect travel frequencies and destinations as well as the split between auto and air trips, somewhat confounding the effects on airport choice. The net effects of the scenarios on airport choice are summarized below. Airfare increases and airport choice The airfare scenario tested the effects of a 25% increase in the costs of all airfares. This increase leads to some shifts to auto and some reductions in travel, most pronounced (-12%) for shorter (200-400 mi.) trips where auto is the most competitive with air, but also extending to a much lesser degree (-2%) to the longest (1600-3200 mi.) trips. The net effect is a reduction in air travel for most airports, but also some noticeable shifts toward and actual trip increases at smaller airports that had low-cost carrier service, such as ISP in the New York region (+4%), MDW in Chicago (+2%), LGB in Los Angeles (+7%), and OAK in San Francisco (+2%). This is because the fares at those airports were generally lower in the model’s 2011 base year and thus the 25% airfare increase results in lower $ increases at those airports. Double digit air trip reductions occur at JFK (-10%), MRY (-17%) and IAD (-13%), caused by a combination of shifts to other regional airport, reductions in travel in general and shifts of shorter trips to auto. Auto cost increases and airport choice The 25% increase in auto operating costs that were tested resulted in more modest shifts in airport choices, with almost all airports seeing passenger volumes increasing by between 1% and 4%, as a result of shifts from auto to air across all trip distances. However, a couple of airports benefited disproportionately from these auto operating cost increases: SWF in the New York region (+5%) and GSO in North Carolina (+7%) and one airport lost net shares (SFO: -0.1%). These effects are likely due to the locations of these airports relative to other competing airports as well as the types of trips served at the airports.

55 Changes in smaller airport service frequency and directness Changes in the frequency of service to non-hub generally result in direct effects on all airports: decreases in air trips when service is reduced and increases when service is increased. This is because smaller airports serve as important feeders to hub airports, increasing OD trips where one end is a smaller airport and the other end is a hub airport as well as trips where both ends are non-hub airports. There are, however, notable exceptions, where increases in non-hub service result in decreases in hub airport trips: LAX and SFO (each -2%), BOS (-1% compared to MHT and PVD, each +7%) and DCA (-0.5% compared to IAD +8%). Additional smaller airport direct service results in overall increases in air trip volumes as a result of shifts from auto and increased travel frequencies but also results in significant shifts from hub to non-hub airports. Almost all hub airports lose passengers to smaller airports. The most extreme shifts occur in the New York region where LGA and EWR traffic changes by -3% and - 2% respectively while HPN, ISP and SWF passengers increase by 41%, 40% and 59% respectively. 4(D) HOW AUTO ACCESS RELATES TO AIRPORT MARKET LEAKAGE UNDERSTANDING “LEAKAGE” Airport passenger “leakage” is the phenomenon of air travelers choosing to drive relatively long- distances to access larger hub airports, bypassing their local airport. Air passengers “leak” across regions to take advantage of convenient flight schedules, lower airfares, and other amenities at the larger (substitute) airport – features that can override the added cost of driving longer distances. Interregional airport passenger leakage has been documented for decades from airports in small or rural cities.22 However, in more recent years, there is evidence that airline mergers, alliances, and decisions to cut operational costs and increase efficiency have reduced and degraded air services from small and medium-size airports. This has further resulted in these airports losing passengers to neighboring large airports23. But the subject of passengers “leaking” from a U.S. airport designated a small or medium hub (FAA, 2016) to large hub airports,24 often travelling well over 100 miles, has received less scrutiny from the research community. Despite airport competition and leakage being an issue worldwide, this chapter will focus specifically on the U.S. context in light of changes in the U.S. aviation industry. Airport passenger leakage is a concern for airport operators, airlines, and ultimately, the traveler. Airport sponsors – typically cities or sub-state governmental authorities – have long sought to attract airlines to their airports, believing that air services stimulate regional economic development. In fact, air service is viewed as so critical to a local economy that many airport sponsors throughout the world provide incentive packages funded by airport revenue to retain and build new service. Moreover, as passengers “leak” to an out-of-region airport, airlines will 22 Kanafani & Abbas, 1987; Innes & Doucet, 1990; Grubesic & Wei, 2012; Wittman, 2014 23 Sharkey, 2015; Ryerson, 2016b 24 These airports make up roughly the top 30 U.S. airports, each handling over 1% of the country's annual passenger boardings.

56 experience depressed demand at the local airport, leading to reduced flight schedules and higher fares. This vicious cycle is a detriment to passenger service, business development, and tourism at small and mid-sized cities. This section reviews the extent of this practice occurring in the present day with present day auto vehicle technology. Later, the chapter will extrapolate that to a future scenario where automobiles are more automated and “easier” to drive. Airport market leakage: definitions and scope An airport market, or catchment, is the land area from which passengers are expected to originate and use the services of a particular airport. To orient the reader to the concept of an air traveler accessing a local airport or distant airports, Figure 4-1 is presented. A traveler could decide to use their local airport for their air trip, and from that airport travel to their destination on a non- stop flight or a connecting flight through a hub airport. An air traveler could also decide to “leak” to an out-of-region airport and access a large hub airport (typically by driving). From the hub airport the traveler will likely travel by a non-stop flight to their destination. The motivations of an air traveler to leak to a distant market are linked to the spatial characteristics of airport markets, as well as differences in airfares and schedule frequency across airports. It is clear that the concept of a local vs. neighboring out of region airport is linked to the definition of catchment. However, there is no general industry-wide consensus on how to define and measure catchment. The definition of airport catchment and the definition of a traveler leaking from one airport catchment to another are intrinsically linked; if an air passenger within an airport’s catchment chooses to travel to a substitute airport for their air travel, this passenger is “leaking” from their airport market to another. Air passengers may leak to a large or medium airport when that airport has more air service than their local airport. Consider that large airports as defined by the FAA (including the hub airports of Atlanta, Chicago, New York, and Los Angeles) have substantial runway and airport infrastructure and many serve as transfer points for passengers and freight. Medium airports enplane between 1.9 and 6.1 million passengers per year and do not serve as hubs for a major airline; thus, they tend to have a mix of flights that travel directly to a large hub to facilitate connecting traffic and some non-stop service to other, non-hub markets. Small airports enplane between 400,000 and 1.8 million passengers per year and serve significantly fewer flights compared with large and medium airports. Small airports tend to have mostly service to large hub airports, enabling connections to a wide range of destinations but not offering non-stop service themselves. An environment ripe for airport market leakage Between 2008 and 2013, six major U.S. carriers merged into three – United Airlines with Continental Airlines (2010), Delta Air Lines with Northwest Airlines (2008), and American Airlines with US Airways (2013) – during a period of large variations in fuel price and economic recession. These newly merged airlines consolidated their networks and hub operations and established fewer, more concentrated airline hubs. Hub airports situated in the largest cities saw their air service strengthen while airports in smaller metropolitan areas lost significant service.

57 The result is a widening discrepancy in flight frequency, number of destinations served, and airfares at airports with significant service versus those without. AIRPORT MARKET LEAKAGE TODAY This section seeks to establish a baseline quantity of airport market leakage, focusing on a specific subset of airports: small and medium airports within 70-300 miles of a large airport in regions of the country. The analysis uses U.S. airports ranked in the top 30-75 of enplaned passengers, meaning they are designated by FAA as small or medium airports and are within 70- 300 miles of a large (top 30 airport by enplaned passengers) designated as a hub by a U.S. airline. The airport pairs (small/medium airport and proximate large airport) that are 1) made up of one small or medium airport and one large airport; 2) located in separate Metropolitan Statistical Areas; 3) at least 70 miles apart and no more than 300 miles apart. Airport pairs for which the small or medium airport is within 300 miles of two large airports were eliminated from consideration, since it would be difficult to isolate the leakage volumes to each of the large. The range of up to 300 miles was used based on the existing studies which have estimated and found airport market leakage in the range of 200-300 miles. FIGURE 4-1. DIAGRAM OF TRAVELER PATTERNS FOR LOCAL AND MEGAREGIONAL AIRPORT ACCESS.  Twelve airport pairs and four substitute airports meet our requirements, as presented in Figure 4-2. The sample includes four large airline hubs: the major airports in the cities of Atlanta, Charlotte, Phoenix, and Dallas/Fort Worth and one to five local airports surrounding each hub. Data on these airports from 2007 to 2015 was collected; this date range covers the change in air

58 service immediately prior to the wave of airline mergers and the economic recession through the present day. Data on air service, airfares, travel distances to air destinations, and surface ground access distances for our local and substitute airports were collected. For each airport (substitute or local), the list of destinations for which a connecting and a non-stop itinerary was purchased from the Bureau of Transportation Statistics (BTS) Airline Origin and Destination Survey (DB1B, a 10% sample of all air itineraries purchased), retaining the connecting and non-stop itinerary information in separate lists for each airport. Finally, the analysis uses the number of non-stop flights per quarter and the total in-flight time from the BTS Air Carrier Statistics (T- 100), and the average airfare per ticket in each quarter from DB1B for each non-stop and connecting flight itinerary. The ground distance a traveler must cover to access their local airport is the driving distance (network distance) to the local airport from the center of the metropolitan area. The ground distance a traveler must cover to access the substitute airport is the network distance between the local and the substitute airport. Door-to-door airport access times were based on the network distances, assuming speeds of 30 mph (local) or 55 mph (highway). Status Quo differences in air service The analysis compared the local and substitute airport on the following key metrics over the study period: domestic passengers (and split across airlines); international and domestic departures; and airfare. This is done to understand 1) the quantity of air service, and the quality of that air service, across airports and regions, and 2) the change in the distribution and quality of air service over time. These comparisons begin with Table 4-1 which captures the departures per year at the substitute and local airports, with the departures per year at the local airport shown as a percentage of the departures at the substitute airport. Relationship between dominant and local airports Overall, it is clear that the substitute hub airports overwhelm the local airports in terms of the number of departures. Consider that Atlanta airport experienced fluctuations of 40,000 departures per year during the study period, roughly 9% of the total departures 2015; these minor fluctuations are roughly double the number of total departures at the local airports (the local airports have between 8,000 and 27,000 departures per year). The differences in the number of departures are less dramatic for the other substitute/local airport pairs yet the gap is still stark, with the large hubs typically having 10 times the departures compared to the local airport. There is variability in the number of departures in both the hub and local airports. This variability could be attributed to an airline introducing a new route and then cancelling that route, flights added or reduced at the substitute airport, or airlines using larger aircraft and thus consolidating flights into fewer departures. The variability over time trend mirrors the national trend in the supply of air service. Airlines significantly reduced their supply of flights at the local airports and increased airfares at the local airport after the recession and the peak in fuel prices in 2008. Post 2008, flight levels have grown tentatively at small and medium airports.

59 LOCAL AIRPORT SUBSTITUTE AIRPORT AND MEGAREGION DISTANCE, LOCAL TO SUBSTITUTE AIRPORT (MILES) INTERSTATE CONNECTION MAP Chattanooga Metropolitan (CHA) Atlanta (ATL), Piedmont Atlantic Megaregion 123 I-75 Huntsville (HSV) 201 I-20 Birmingham (BHM) 152 I-20 Savannah (SAV) 240 I-16 Knoxville (TYS) 224 I-75 Columbia Metropolitan (CAE) Charlotte Douglas (CLT), Piedmont Atlantic Megaregion 105 I-77 Charleston (CHS) 204 I-77 Greensboro (GSO) 102 I-85 Oklahoma City (OKC) Dallas/Fort Worth (DFW), Texas Triangle and Gulf Coast 195 I-35 Shreveport Regional (SHV) 202 I-20 Tucson (TUS) Phoenix (PHX), Arizona Sun Corridor 117 I-10 FIGURE 4-2. LOCAL AND SUBSTITUTE AIRPORT PAIRS, INCLUDING THEIR INTERSTATE CONNECTIONS. In addition, the research team estimated percent difference in fares for itineraries from the local and substitute airport with the same destination (discarding destinations served by the substitute airport that are not directly served by the local destination). This is based on a comparison between 1) non-stop itineraries from the local and the substitute airports and 2) connecting

60 itineraries from local airports and non-stop itineraries from substitute airports, using a weighted average of each percent difference across destinations for each local-substitute airport pair. 25 In general, the results show airfares at the local airports are between 20% lower and 60% higher compared with those at their substitute airports across our study airport pairs. Airfares at local airports are mostly – and overwhelmingly in some cases – higher than those offered at their substitute airports. Airfares for connecting itineraries at the local airport generally appear to be higher than for non-stop flights at their substitute airports, in some cases 20-60% higher. Airfares for direct itineraries at the local airport are between 20% lower and 10% higher than those at the substitute airport. While direct flights from local airports might be relatively commensurately priced with those from the substitute airports. There is a great deal of variability (and volatility) across the study years of 2007-2015 in terms of fares, with fares at the local airports, compared to the substitute airports, tending to be their highest from 2007-2012. ESTIMATING LOCAL AIRPORT MARKET SHARE AND AIRPORT MARKET LEAKAGE For each local-substitute airport pair, the passenger market share has been predicted for travel to a destination from the local versus substitute airport for travelers beginning their trip proximate to the local airport. This is accomplished by applying a simple binary logit model, under the assumption that air travelers in the local airport market catchment will choose to depart from either the local or substitute airport. This estimate is used to calculate the estimated number of passengers “leaked” from the local airport market catchment in each year of the study period across all identified travel destinations. This predicts market share for air service provided at the local airport for all destinations served by the local airport, both via direct service and connecting service. The model estimates the number of travelers who are likely to use the local airport, as well as the market share of the local airport for travel to different destinations. As the most conservative values result from varying the coefficients that capture ground access distance and time, we here present the results when the coefficients on ground access distance and time represent “high” values of time as our lower bound. However, a traveler having a “high” value of time or access distance is not reflective of a future which includes new vehicle technologies. In a future with new vehicle technologies it is possible that travelers will place little value on ground access distance or time. Scholars surmise that connected vehicles that assist drivers in finding the routes with the lowest traffic, and maintain a safe distance from other vehicles, and autonomous vehicles which perform the driving function will reduce a traveler’s effective value of time. A long drive to access an airport with higher levels of service may be of little consequence to a traveler with an autonomous vehicle. Under these scenarios, the coefficient on ground access time and distance might trend toward zero, thus increasing the likelihood that a passenger leaks to the substitute airport. In this case, then our base estimates are actually a lower bound rather than an upper bound on the potential for airport market leakage.   25 The charts showing the difference in fares are presented in full in Ryerson and Kim, 2018.

61 TABLE 4-1. DEPARTURES PER YEAR AT THE SUBSTITUTE AIRPORT AND DEPARTURES AT THE LOCAL AIRPORT AS A PERCENTAGE OF DEPARTURES AT THE SUBSTITUTE AIRPORT. Atlanta and Local Airports Year Departures per Year Departures at the Local Airport as a Percentage of Departures at the Substitute Airport Atlanta (ATL) Birmingham (BHM) Knoxville (TYS) Savannah (SAV) Huntsville (HSV) Chattanooga (CHA) 2007 472,369 5.68% 4.72% 3.40% 2.99% 1.75% 2011 445,553 5.10% 4.47% 3.17% 3.04% 1.66% 2015 426,365 4.43% 3.89% 3.33% 2.24% 1.71% Charlotte and Local Airports Year Departures per Year Departures at the Local Airport as a Percentage of Departures at the Substitute Airport Charlotte (CLT) Greensboro (GSO) Columbia Metropolitan (CAE) Charleston (CHS) 2007 227189 11.09% 7.11% 9.22% 2011 245243 7.55% 5.10% 8.43% 2015 250222 6.29% 4.26% 9.01% Dallas and Local Airports Year Departures per Year Departures at the Local Airport as a Percentage of Departures at the Substitute Airport Dallas (DFW) Shreveport (SHV) Oklahoma City (OKC) 2007 321106 3.14% 9.40% 2011 305210 2.56% 8.37% 2015 322070 2.18% 7.20% Phoenix and Local Airport Year Departures per Year Departures at the Local Airport as a Percentage of Departures at the Substitute Airport Phoenix (PHX) Tucson (TUS) 2007 223623 13.50% 2011 200513 12.74% 2015 189177 12.26% At each individual local airport, the results showed that approximately 58,000 to 700,000 travelers annually leak from a local airport to a large substitute airport, with a median value of 201,514 travelers in 2015. The implication is that each local airport is unable to capture and serve these passengers because, for these passengers, the service at the substitute airport is more attractive.26 26 The full results of this analysis are available on an airport-by-airport basis in Ryerson and Kim, 2018

62 The results of this analysis show that the trends in the number of leaked passengers change over time, with the most marked changes occurring in the later years (about 2012-2015). The market shares for the local airports were at their lowest between 2008 and 2012, but they appear to rise between 2012 to 2015. This reflects the contraction of the aviation market from 2008 to 2012, when the airlines reduced their services, particularly in short haul markets, as documented in Chapter 2. As flights were added after 2012 when the aviation market experienced some expansion, the market shares for local airports began to grow and the number of leaked passengers decreased. This trend of decreased market leakage for local airports in the Texas Triangle/Gulf Coast regions does not hold post-2013. In this region, Shreveport has fewer than 10,000 flights per year and Oklahoma City has 30,000 compared with Dallas Fort Worth’s 320,000; in addition, the gap between air service frequencies at these airports has grown over time. Dallas strengthened its position as a hub not just relatively to the local airports but with actual growth in air service; this was to the detriment of the local airports. To put the number of total travelers who may “leak” into further context, Table 4-2 includes the percent of leaked passengers from a local airport in a year divided by the total passengers carried by the local airport that year. We find this percentage to generally be in the 16-32% range for the different airport pairs. The interpretation of this percentage is that each local airport is not capturing a possible 16- 32% more passengers than it carried in any particular year. The numbers in Table 4-2 are broken down by non-stop and connecting travel. The median shares of passengers captured at local airports are 83.4% in 2015 for direct itineraries and 42.3% in 2015 for connecting itineraries. The local airport market shares for direct flights are well above the market shares for connecting travel. The local airport passenger market shares for direct flights are between 67-89%, with most values around 80%; for connecting flights this ranges between 15-67%, with most values in the 20-30% range. In short, a local airport typically commands the largest share of their passenger catchment market when they offer the option of flying directly to destinations; when passengers must connect to their final destination, they are much less likely to use their local airport. The overall leakage is estimated to amount to between 15% and nearly 32%, which suggests that airport passenger leakage may be occurring at significant rates in the various regions of the U.S., which in turn suggests a substantial amount of travel (if not distance, then at least the time) spent on the ground accessing these major hub airports. HIGHWAY TRAFFIC DUE TO AIRPORT MARKET LEAKAGE Our analysis of the effects of leakage estimated the proportion of traffic on interstate highways connecting local to substitute airports that may be attributed to travelers driving long-distances between the catchment of the local airport to/from a substitute airport. This uses the ratio of the travelers leaked to the substitute airport per day and the Average Annual Daily Traffic (AADT) on the major interstate highway that links the two airports. Average Annual Daily Traffic (AADT) are shown for two points along each interstate highway route connecting the substitute airport pairs from State DOT websites (see Figure 4-2). The first is the lowest volume point on the corridor; the second is the highest volume point. Collecting two AADT values for each corridor, each year, allows us to identify an upper and lower bound of traffic between the local and substitute airport. Figure 4-2 shows the locations for the AADTs. Passengers leaked from

63 the local to the substitute airport is divided by the AADT. For local airports that share a route with another local airport to the substitute airport, the number of passengers leaked to the substitute from both local airports are added together to estimate a share of highway traffic attributed to both local airports. It should be noted that we assume that each passenger travels in their own vehicle for purposes of calculation. This assumption is supported by data from DB1B, the 10% sample of all air itineraries collected by the FAA and BTS. TABLE 4-2. SHARE OF PASSENGERS WITHIN THE LOCAL AIRPORT CATCHMENT THAT ARE ESTIMATED TO PREFER A FLIGHT OPTION FROM THE LOCAL AIRPORT. LOCAL AIRPORT LOCAL MARKET SHARE, DIRECT FLIGHTS LOCAL MARKET SHARE, CONNECTING FLIGHTS % TOTAL LOCAL PASSENGERS LEAKED Atlanta Knoxville 80.4% 32.1% 25.4% Huntsville 83.3% 26.4% 25.2% Birmingham 87.1% 42.3% 21.5% Savannah 89.2% 59.9% 15.7% Chattanooga 71.5% 66.7% 16.2% Charlotte Charleston 67.0% 58.6% 26.0% Greensboro 81.7% 59.9% 19.3% Columbia 84.2% 65.3% 19.9% Dallas Oklahoma City 85.0% 31.1% 26.9% Shreveport 85.4% 15.0% 31.8% Phoenix Tucson 83.4% 26.1% 31.1% Table 4-3 shows the percent of daily traffic that might be able to be attributed to passengers leaking to a substitute airport in 2015. The results are organized by local and substitute airport pairs, either a single local-substitute pair or multiple local airports that might be connected to the substitute airport by the same highway, and two AADT levels (high and low points along the highway). The high AADT points represent segments of the highway that are very close to the large cities served by the substitute airport, while the low AADT points represent rural, less trafficked areas. Across our study airport pairs, the percentage of traffic attributed to travelers driving to a substitute airport is generally between 0.05% and 12%, depending on the year and airport pair. The range between the high and the low AADT sections can be quite large across highways connecting the different airport pairs studied. The low AADT sections of highway clearly see the highest percentage of traffic attributable to airport market leakage, as these sections see relatively low levels of traffic. Low trafficked areas, such as those seen in the more rural areas of Arizona, South Carolina, Alabama, and Oklahoma, might see up to 10-12% of their daily traffic coming

64 from people driving to Dallas for air service. However, those same volumes from leaking passengers are less than 2% of the traffic in high traffic areas. TABLE 4-3. DATA SOURCES FOR HIGHWAY AADT AND ESTIMATES OF HIGHWAY TRAFFIC ATTRIBUTED TO LEAKED PASSENGERS ACCESSING A SUBSTITUTE AIRPORT IN 2015. SUBSTITUTE AIRPORT LOCAL AIRPORT DATA SOURCE HIGHWAY LOW AADT HIGH AADT HIGH BASE HIGH BASE Atlanta Chattanooga & Knoxville Georgia DOT I-75 2.53% 2.74% 1.34% 1.45% Charlotte Greensboro NC DOT I-85 1.31% 1.49% 0.91% 1.04% Atlanta Savannah Georgia DOT I-16 2.25% 2.60% 0.24% 0.27% Dallas Shreveport TX DOT I-20 2.56% 2.64% 1.00% 1.03% Phoenix Tucson AZ DOT I-10 8.57% 9.32% 1.49% 1.62% Dallas Oklahoma City OK & TX DOT I-35 11.04% 12.07% 2.18% 2.38% Charlotte Columbia & Charleston SC DOT I-77 7.23% 10.09% 1.97% 2.75% Atlanta Huntsville & Birmingham Alabama DOT I-20 8.12% 8.94% 1.62% 1.78% 4(E) FUTURE AUTOMOBILES AND THE FUTURE OF AIRPORT MARKET LEAKAGE This ACRP study finds the existence of airport market leakage from local airports to hub airports 100-300 miles apart occurring in present day, presently operating without connected autonomous vehicles. Our estimates suggest that 16-32% of the total passengers living proximate to a small or mid-sized airport have the incentive to leak; the range 11-33% for travelers facing a non-stop itinerary from their local airport and 33-85% for travelers facing connecting travel. We find that passengers leaking from a local to a hub airport could contribute 1-3% of average daily highway traffic at heavily congested portions of the interstate highways connecting airports and up to 10- 12% of traffic on low density portions of the highway. Self-driving or Highly Automated Vehicle technologies, now undergoing public trials in major cities, are positioned to bring about transformative change to the entire transportation system. Far from being a distant innovation, retail autonomous vehicles have already been announced by various manufacturers for release as early as 2018, complementing estimates that on-road HAVs will reach market ubiquity as part of a $7 trillion passenger economy by 2055.27 Transportation planners and policymakers are welcoming autonomous vehicles for their potential to positively impact traffic safety by fundamentally changing the interaction and relationship between drivers and vehicles, and how drivers and vehicles collect and process information from their 27 Intel. Accelerating the Future: The Economic Impact of the Emerging Passenger Economy [Internet]. June 1, 2017. https://newsroom.intel.com/newsroom/wp-content/uploads/sites/11/2017/05/passenger-economy.pdf

65 environment. However, given the analysis above, it is clear that autonomous vehicles will greatly change the relationship between air and aircraft in the intercity transportation system. Our analysis shows that, in a scenario where auto dominates, short distance aircraft flights and passengers will decline significantly. As presented in Chapter 1, Figure 1-3 showed the change in air passengers by airport size in a scenario where automobiles are advanced beyond those we have today. Chapter 1 reported that small airports might see a 34% reduction in passengers due to advancements in vehicle technology –consistent with our findings in the airport market leakage study as well. The decline in passengers is significantly smaller for the largest 30 airports; these airports will still continue to attract passengers for both long-distance and short distance trips based on their ability to be competitive in terms of fare and travel time with vehicles. When we dive deeper into the types and distances of routes that will likely be replaced by passengers driving more advanced automobiles, we find that airport market leakage will be accelerated. Passengers who might replace flying with a more advanced automobile are those traveling on routes that are within 600 to 1400 miles, with the largest share being between 600 and 1000 miles. One reason that routes of a distance 600 to 1000 miles lose the highest percentage of passengers is that short distance routes have already experienced large quantities of passengers “leaking” and/or substituting drives for their air trips. Routes above 600 miles may have been safer from being replaced given the sheer distance. However, as vehicles become more advanced and automated, the effective cost of driving becomes significantly depressed. As a result, travelers may find that a 600-1000 mile drive, where they can relax or otherwise not operate a vehicle, is more attractive to them than accessing the airport and flying. CONCLUSION: AIRPORT LEAKAGE AND DIVERSIONS TO THE AUTOMOBILE Our research indicates the strength of the connection between the air and intercity surface transportation system and provides justification for integrated air-highway transportation planning. Policies and actions by airports and airlines at large airports have significant implications not just on neighboring local airports but on the interstate highway system: one possible contribution to the deal with congestion on the highway is to increase air service at a small local airport. Our findings on the significant link between the air and intercity transportation system open up a new area of inquiry in the field of intercity transportation. While there are institutions and scholars focused on the link between the local transportation system and airports, few focus on the concept of long-distance airport access and airport market leakage. This study indicates that leaking from a local to a larger airport market is a widespread practice in which travelers engage and one that has a significant impact on the surface transportation system and the economic health of small metropolitan areas. Implications of results The results of this study will help to shape the evolving role of airport managers in controlling demand and delay at major hub airports and in building and managing air service at smaller airports across the U.S. Managers of small airports could stem market leakage by focusing not on building air service to connecting hub airports but to new, unique destinations. This is a

66 challenging prospect, however, as small and medium airport managers have been markedly less successful in building new service in the recent years compared with large hub airports. The findings of our study also indicate the complexity of the challenges large airport managers face: they must balance providing air service for their region against starving neighboring regions of air service and causing increasing surface highway traffic. While we assert their role in complex, large airport managers are not necessarily concerned with surface traffic or the health of small airports. Large airport managers are well known for protecting their hub airline and trying to grow their airport to better serve – and retain – that hub airlinei. While our findings help broaden the solution space over which an airport manager of a large airport may look to tackle congestion at a busy airport – namely, encourage air traffic at a local airport to stem leakage – it may take the intervention of federal regulators or a powerful megaregional planning body to actually encourage the managers of the national system to consider the implications of their plans on the health of the broader aviation and roadway systems. 4(F) HOW EVOLVING AIRCRAFT TECHNOLOGY MIGHT INFLUENCE THE CHOICE OF AIRPORTS EXAMINING THE SCENARIO RESULTS In examining various strategies to encourage more utilization of existing non-major airports, it is worthwhile to examine the possible role of new aircraft technology in such a coordinated, multi- faceted effort. As discussed in Chapter 1, Scenario 3 was created to support such an analysis; its supply input assumptions are the same as Scenario 2, except with the addition of new aircraft technology. Scenario 2 assumed that the number of flights to non-hub airports increased, the number of direct flights from smaller airports increased, stress at larger airports increased, tickets became cheaper, the stress of driving increased, the relative cost of driving increased and future generations are somewhat less auto-oriented. Isolating the impacts of new aircraft technology This ACRP study shows significant levels of market support for the combination of assumptions included in Scenario 3. In the aggregate, Scenario 3 increases nationwide air passenger traffic by 14% over the base case. More relevantly, increases in trips of between 15% and 25% were predicted in the scenario testing process for distance bands under 1200 miles. In order to isolate the predicted impact of the aircraft technology per se, Table 4-4 shows the incremental improvement attributable to the addition of new aircraft technology into the new scenario. Nationwide, some 31% of the additional passengers in Scenario 3 can be attributed to the aircraft assumptions, with such portions ranging from 28% to 40%, in the relevant bands under 1200 miles. TABLE 4-4. ADDITIONAL AIR TRIPS ATTRIBUTABLE TO NEW AIRCRAFT TECHNOLOGY ASSUMPTIONS

67 In sum, the addition of aircraft technology to an already optimistic set of additional market assumptions does indeed make a significant improvement to the number of trips attracted to air. However, it must be noted that for no distance band does the contribution of the aircraft technology explain the majority of gain in air trips: lowered costs, per se, would not be as strong an explanatory factor as additional service --itself a difficult condition to predict. And, consistent with patterns revealed throughout this study, its role in attracting auto users to air is strongest for the shortest trips. UNDERSTANDING THE NEW AIRCRAFT TECHNOLOGY While it is beyond the scope of this study to predict the future of new aircraft technology, it is worthwhile to review the potential for major improvements in air vehicle technology. It is possible that these advances could lead to more attractive service options for thin, short-haul, markets and thereby improve the fortunes of small airports. In essence, the new aircraft technologies favor smaller air vehicles and thereby weaken the “tyranny of economies of scale” that encourages concentration of traffic on high density routes and the airports they connect. Distributed Elective Propulsion One such improvement is distributed elective propulsion (DEP). DEP exploits the fact that electric propulsion, unlike jets or turboprops, is efficient at small scale. Small, high-performance electric motors are therefore possible. Accordingly, electric aircraft can be powered by many small electric motors, each turning its own propeller, instead of one or two large ones. The small propellers can be placed at different locations on the airframe so as to optimize aerodynamic performance. They can also be activated or deactivated as appropriate for different phases of flight. For example, some of the motors can be placed on the aircraft wing so as to generate air flow over the wing surface when high lift is required, and deactivated (as well as folded to reduce drag) for cruise. A recent analysis (Harish et al, 2016) compared the cost of a hypothetical commuter service using DEP to an existing piston aircraft, the Cessna 402, using information obtained from Cape Air, a Part 135 commuter operator, as a case study. Both aircraft seat nine passengers. Detailed cost models for energy, maintenance, labor, and battery replacement were developed and applied to both the existing Cessna’s and the hypothetical DEP. Base estimates suggest a 20% lower direct operating cost for the DEP aircraft, almost entirely as a result of reduced energy costs. TRIP DISTANCE BAND GROWTH IN AIR TRIPS IN SCENARIO 3 OVER THE BASE CASE ADDITIONAL PASSENGERS FOR FULL SCENARIO 3 NEW PASSENGERS ATTRIBUTABLE TO NEW AIRCRAFT TECHNOLOGY PORTION OF SCENARIO 3 BENEFIT ATTRIBUTABLE TO NEW AIRCRAFT TECHNOLOGY 200-400 +25% 11,682,877 4,688,436 40% 400-600 +20% 11,014,150 3,992,823 36% 600-800 +18% 9,711,670 3,079,029 32% 800-1000 +19% 9,217,575 2,703,917 29% 1000-1200 +15% 7,425,019 2,069,237 28%

68 Moreover, the DEP aircraft has a cruise speed that is 70% greater (363 vs 213 mph). Using the willingness to pay for flight time savings of $34/hr cited earlier in this chapter, the cost advantage of the DEP increases to 25% when the higher speed is taken into account (assuming a passenger load of 7). Deployment of electric propulsion aircraft is commercial operations is being led by nations such as Norway, with strong commitment to electrification as a means of greenhouse gas reduction. Avinor, the public operator of Norway’s airports, hopes to test a commercial route served by a 19-seat electric plane in 2025, with the ultimate goal of having all routes with flight times of 1.5 hours or less being served by electric propulsion by 2040. While greenhouse gas reduction is a primary motivation for Avinor’s initiative, they also anticipate that the shift will yield cost savings.28 Crew assumptions The above analysis assumes a two-person flight crew, the same as is required for all modern commercial jets, including those with more than 50 times as many seats. This creates a significant unit cost advantage for larger planes. While it is widely believed that large jet transports will have two-person crews for the foreseeable future, the possibility of single pilot operations (SPO’s) is under active exploration for smaller aircraft. Over time, the required flight crew for commercial flights has steadily fallen, from five in the 1950s to two by the 1980s. Improvements in automation and information technology over the ensuring years, combined more recently with the pilot shortage that resulted from more stringent air transport pilot certification requirements, have kindled considerable research into developing SPO concepts that are as safe as the two-pilot standard. Ground based. Vu et al. describe two such concepts. In the ground-based approach, the first officer works remotely, in a manner somewhat similar to pilots of unmanned aerial systems. To realize economic savings, the ground-based first officer cannot be dedicated to a single flight over its whole mission but does concentrate on a single flight during periods of high workload such as during approach or when diverting to a new flight plan. During low workload periods, this individual is performing other duties, such a co-piloting multiple flights or dispatching. The ability to seamlessly transition between the high workload, dedicated, mode and the low workload multi-tasking mode is a crucial requirement for this concept of SPOs. Replacing the co-pilot. An alternative to ground-based single pilot operations is to replace the human co-pilot altogether, using cockpit-based automation. The challenge is to devise an autonomous co-pilot agent whose interaction with the human pilot-in-command avoids the pitfalls that arise when human functions are merely shifted to an autonomous agent which is then monitored by a human. These include limited human capacity for vigilance tasks (remaining alert so that malfunctions can be detected even when they are very rare) and reduced situational awareness, which would limit the ability to take appropriate action when intervention is required. 28 https://www.theguardian.com/world/2018/jan/18/norway-aims-for-all-short-haul-flights-to-be-100-electric-by- 2040

69 To avoid these problems, many believe that successful replacement of a first officer with an automated agent will require more than simply automating specific tasks. Rather, there must be mutual “understanding”—on the part of the human operator of actions and recommendations of the autonomous agent, and on the part of the agent about the mental state of the human. The term Human-Autonomy Teaming (HAT) has been proposed to describe how a human pilot-in- command and autonomous agent First Officer should cooperatively interact under cockpit-based automation. Development of autonomous agents capable of such interaction is in fairly early stages. Obstacles Despite the technological possibilities and potential economic benefit, there are many obstacles to widespread adoption of SPOs for commercial air travel. Pilot incapacitation, while exceedingly rare, is a scenario that must be addressed. A remote pilot could take command in such a circumstance, but this would make flight survival completely dependent on the integrity of the air-ground communications link. Another threat, tragically illustrated by the German Wings crash, is the possibility of a rogue pilot. Indeed, in the wake of this tragedy, many airlines now require two persons in the cockpit at all times, even when a pilot must use the lavatory. Another concern is how a shift to SPOs would affect pilot training, in which experience as a First Officer now plays a critical role. In addition to these technical issues, there is a host of regulatory, institutional, and political barriers. Aviation authorities, insurance companies, pilots, and not least the traveling public would have to accept and adapt to commercial SPOs in order for them to become a reality. It is telling that a recent report by the Congressionally mandated Working Group on Improving Air Service to Small Communities, while concluding that “the nationwide pilot shortage is the dominant theme in many of today’s challenges to small community air service,” focuses on reducing flight hour requirements for First Officer qualification to address this problem, while making no mention of SPO’s. Arguably, however, the obstacles detailed above increase the prospects that SPOs can benefit thin routes and small airports. While SPO’s on large jet transports remain a distant prospect, they may come to commuter operations much sooner. Many commuter models, including the Cessna 402 and the more modern Pilatus PC-12 (operating by Boutique air) are already certified for a single pilot flight crew. Most commuter airlines have yet to use this option for many of the reasons cited above—concerns of government regulators, insurers, the traveling public, and pilots—but the barriers to doing so are far less for commuters than other segments of the airline industry. Doing so could substantially reduce the cost disadvantage of commuters compared to regional and mainline carriers. As a rough estimate based on the comparison between the 9-seat DEP and Cessna 402 presented earlier, shifting to SPO’s might yield a 40% reduction in flight crew costs. When combined with the DEP energy savings, the reduction in direct operating cost would be about 27% percent, and approach 30% if traveler value of time is included. SUMMARY: NEW AIRCRAFT TECHNOLOGY AND THE FUTURE OF SMALLER AIRPORTS Energy and labor cost savings resulting from DEP and/or SPO’s, along with the travel time savings in the case of DEP, could reshape the commuter airline landscape. Lower cost commuter

70 flights could serve several purposes, first they could supplement the declining frequencies of regional carriers as they shift from 50 to 70-seat planes. Secondly, they could allow non-stop services on thin short-haul routes where only connecting service is currently available. A non- stop flight to a desired destination from the local airport may deter leakage, and in some instances induce a mode shift from driving for the entire trip. Lastly, these new technology aircraft may create additional feeder services, either connecting smaller airports to more large hubs, or in some cases connecting airports currently without commercial service to the airline network. These effects are enhanced by the positive feedbacks that result as more air travelers use the improved, and less expensive, services, creating a virtuous cycle. Thus, the higher frequencies and additional connections envisioned in Scenario 3, while speculative in their details, are entirely plausible. Realizing a future along the lines of Scenario 3 requires a commitment to research and development on the part of the public and private sectors to create mature technologies, and an innovative spirit on the part of government regulators, insurance companies, airport operators, pilots, to test them in the market place. With these ingredients, there is every reason to hope that the recent trends in service and traffic that have harmed small airports can be reversed, and that the challenges posed by improvements in automotive technology can be met with equally dramatic advances in aircraft. .

Next: Chapter 5. Attitudes Toward the Long-Distance Trip and Their Role in Influencing Mode Choice »
Air Demand in a Dynamic Competitive Context with the Automobile Get This Book
×
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

TRB’s Airport Cooperative Research Program has released a pre-publication version of ACRP Research Report 204: Air Demand in a Dynamic Competitive Context with the Automobile. The report establishes a new approach to the analysis of future consumer demand for shorter distance air travel in comparison with travel by automobile.

According to the report, future demand for shorter-range airline trips is both volatile and unstable, affected by changes in technology as well as consumer preferences. Through application of new research tools that support scenario analysis, the report suggests that evolving automobile technology could diminish demand for shorter-range air trips, both in terms of distance to ultimate destination as well as access to larger airports.

Alternatively, changes in aircraft technology could increase demand for short-distance air travel by creating improvements that decrease operating cost of short flights. Most probably, the future will bring changes affected by both emerging trends.

The report may help managers of smaller airports develop a better understanding of how consumers choose between flying out of a smaller hometown airport to connect to a larger airport versus a longer automobile drive bypassing the smaller airport, traveling directly to a larger airport.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!