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

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45 This research on the relationship between the car and the plane in long-distance travel focuses attention on two kinds of tripmaking that are, arguably, separable into two research areas. First, the automobile is a competitor to the airplane for a full long-distance trip: Chapters 2 and 3 of this report focus on the reasons why a traveler would take a trip primarily by plane or entirely by car. Second, the automobile is a competitor to the airplane for the trip segment that provides access to the longer-distance airplane trip segment. This chapter focuses on a specific trend in which an automobile trip is increasingly chosen over an airplane trip to gain access to a long-distance flight. This trend 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 near 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 a particular trip. Those located near a smaller American airport face a dilemma because the airport to which they might like to be loyal may not be well served by the present service patterns of major American air carriers. As documented in Chapter 3, the airlines are devoting an increasingly smaller portion of their total service to flights of less than 500 miles, flights that often serve as feeders to larger hub operations. With the decrease in flight service options at smaller airports, American air travelers are using automobiles, in effect, to provide the first leg of a multisegment long-distance trip. A major question for ACRP Project 03-40 concerned the various possible futures for this pattern of airport selection, particularly as newer technology is incorporated into automobiles and eventually culminates in automated vehicle operations. New aircraft technology is explored both in terms of its market support and in terms of potential obstacles to successful imple- mentation. 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. This chapter begins with basic information about where U.S. 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 and an analysis of the 2017 ACRP Project 03-40 survey results about how the separate factors influence airport choice. A major portion of this C H A P T E R 4 The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller Airports to Larger Ones

46 Air Demand in a Dynamic Competitive Context with the Automobile chapter is devoted to an analysis of how ground access considerations interact with other fac- tors, resulting in a leakage 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. The chapter concludes with a review of the possible implica- tions of newly developing aircraft technology that would potentially lower costs from service to smaller markets, thus affecting the choice of the airport of departure. Airports Categorized by Competition with Other Airports The research team’s analysis of airport access distance for U.S. 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 having a major airport as the only option. Approximately 23% of residents have a minor airport as their only option. This leaves 24% of residents who have a choice between a closer minor airport and a more distant major airport: for 10% of these residents the minor airport is at least 40 miles closer, and for 14% of these residents 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, including people choosing the departing airport based on factors not influenced by geographic proximity.8 Factors Affecting Airport Choice: Results from the 2017 ACRP Project 03-40 Survey Attitudes Toward Airport Choice Observations about the choice of local versus larger airport can be made based on attitudes revealed in the 2017 ACRP Project 03-40 survey. 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 it was for older age groups, higher for the higher income group than for the lower-income group, and somewhat higher for males than for 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. 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 8 Source: FHWA foundational knowledge project (RSG 2015b); the subject of airport location is treated in more detail in ACRP Web-Only Document 38.

The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller Airports to Larger Ones 47 design/operation of the airports, among other attributes. 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 the other attributes. In addition, there are many cases in which air travelers find that one airport dominates all others with respect to all of these attributes. Nonetheless, in most regions with multiple airports, air travelers likely find that different available airports are preferable to others for some attributes, 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 multiple-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 (Brian Campbell and Associates 1977). 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 the airport, and (3) 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 reflected several of the key airport attributes that affect airport choice, it did not consider differences in airfares, flight service (other than flight frequency), or traveler type 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 disaggregated data from airport surveys to statistically estimate parameters of discrete, choice-based (multinomial logit), airport-choice models for that region (Greig 1987). 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 business and nonbusiness travelers make very different trade-offs among these attributes. This was the first of several such studies using San Francisco airport survey data, each making successive improvements to the model (e.g., “Mixed Logit Modelling of Airport Choice in Multi-Airport Regions” [Hess and Polak 2005]). 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 limitations 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 surveys to estimate trade-offs among air travel attributes (Hess et al. 2007). This approach, used also in this research, can provide more robust estimates of the ways in which air travelers trade off attributes when making airport choices.

48 Air Demand in a Dynamic Competitive Context with the Automobile Willingness to Pay and Airport Choice Chapter 6 of this report will provide details on the airport and mode choice models developed for ACRP Project 03-40, but a few observations from those models can illustrate the ways that 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 of specific attributes. In the ACRP Project 03-40 airport and mode choice models, 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 of airfare to save an hour of access time but are willing to spend about double that amount to save an hour of flight time.9 This confirms results from many past studies that flight time is considered 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 to $60 to fly from an airport that has direct flights to their destination rather than connecting itineraries. Nevertheless, there are also many travelers who are willing to pay much more to avoid connections. 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 services offered in those airports. The four items listed above 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 embedded in the full national travel simulation system. Simulations were undertaken for both individual factors and overarching scenarios, themselves combinations of individual factors, to test the effects of airfare increases, auto- mobile operating cost increases, smaller airport service frequency increases, and increases in non-hub direct flights. All of these factors and scenarios affect airport choice in regions that have multiple available commercial airports. These factors and scenarios also affect travel frequencies and destinations as well as the mode split between automobile and air trips, somewhat confounding their 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 led to some shifts to automobile and some reductions in travel. These effects were most pronounced (−12%) for shorter (200- to 400-mile) trips, where automobile travel is most 9 More information on how trip purpose interacts with willingness to pay in this context is presented in Chapter 6.

The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller Airports to Larger Ones 49 competitive with air travel, but the effects also extended, to a much lesser degree (−2%), to the longest (1,600- to 3,200-mile) trips. The net effect was 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. These airports included Long Island MacArthur Airport (ISP) in the New York region (+4%), Midway International Airport (MDW) in Chicago (+2%), Daugherty Field (LGB) in Los Angeles (+7%), and Oakland International Airport (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 in airfare dollar amounts at those airports. Double-digit air trip reductions occur at JFK (−10%), Monterey Regional Airport (MRY) (−17%) and Dulles International Airport (IAD) (−13%), caused by a combination of shifts to other regional airports, reductions in travel in general, and shifts of shorter trips to automobile. Automobile Cost Increases and Airport Choice The 25% increase in automobile operating costs that were tested resulted in more modest shifts in airport choices, with almost all airports seeing passenger volumes increase between 1% and 4% because of shifts from automobile travel to air travel across all trip distances. How- ever, a couple of airports benefited disproportionately from these automobile operating cost increases: New York Stewart International Airport (SWF) (+5%) and GSO in North Carolina (+7%). One airport, SFO, lost net shares (−0.1%). These effects are likely due to the locations of these airports relative to competing airports and the types of trips served at the airports. Changes in Smaller Airport Service Frequency and Directness Changes in the frequency of service to non-hub airports 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 O-D 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: Los Angeles International Airport (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 automobile 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 La Guardia Airport (LGA) and Newark Liberty International Airport (EWR) traffic changes by −3% and −2%, respectively, while Westchester County Airport (HPN), ISP, and SWF passengers increase by 41%, 40%, and 59%, respectively. How Automobile Access Relates to Airport Market Leakage Understanding Leakage Airport passenger leakage is the phenomenon of air travelers choosing to drive relatively long distances, bypassing their local airport, to access larger hub airports. 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

50 Air Demand in a Dynamic Competitive Context with the Automobile of driving longer distances. Interregional airport passenger leakage has been documented for decades from airports in small or rural cities (Kanafani and Abbas 1987, Innes and Doucet 1990, Grubesic and Wei 2012, Wittman, 2014). However, more recently, 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 airports (Sharkey 2015, Ryerson 2016). An issue that has received less scrutiny from the research community is passengers “leaking” from a U.S. airport designated as a small or medium hub (FAA 2016) to a large hub airport,10 often traveling well over 100 miles. Although airport competition and leakage is 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 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 in small and mid-sized cities. This section reviews the extent of this practice occurring in the present day with present-day automobile vehicle technology. Later in the chapter, current practice will be extrapolated 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, traveling from that airport 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 large hub airport the traveler will likely travel via a non-stop flight to their destination. The air traveler’s motivations for leaking 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 versus 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 to 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 one 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 10 These airports make up roughly the top 30 U.S. airports, each handling over 1% of the country’s annual passenger boardings.

The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller Airports to Larger Ones 51 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 than do 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 merged with Continental Airlines (2010), Delta Air Lines merged with Northwest Airlines (2008), and American Airlines merged 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. 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 to 300 miles of a large airport in regions of the country. The analysis uses U.S. airports ranked in the top 30 to 75 for enplaned passengers, meaning these airports are designated by FAA as small or medium airports and are within 70 to 300 miles of a large airport (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) are (1) made up of one small or medium airport and one large airport; (2) located in separate Metropolitan Statistical Areas; and (3) located at least 70 miles apart and no more than 300 miles Traveler Beginning a Trip from the Local Airport Figure 4-1. Diagram of traveler patterns for local and megaregional airport access.

52 Air Demand in a Dynamic Competitive Context with the Automobile 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 airports. The range of up to 300 miles was used based on exist- ing studies, which have estimated and found airport market leakage in the range of 200 to 300 miles. Eleven possible pairs of small/medium airport with large hub airport met these requirements, (shown in Table 4-1). The sample includes four large airline hubs: the major airports in the cities of Atlanta, Charlotte, Dallas/Fort Worth, and Phoenix and one to five local airports surrounding each hub. Data on these airports were collected from 2007 to 2015; this date range covers the change in air 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, BTS Airline Origin and Destination Survey (DB1B, a 10% sample of all itineraries purchased) data was applied, creating the both 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 the 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 research period: domestic passengers (and split across airlines), international and domestic departures, and airfare. This was 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. Relationship Between Dominant and Local Airports Table 4-2 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. Overall, it is clear that the substitute hub airports overwhelm the local airports in terms of the number of departures. Consider that Hartsfield Jackson Atlanta International Airport (ATL) experienced fluctuations of 40,000 departures per year during the study period, roughly 9% of the total departures in 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 substitute hubs typically having 10 times the number of departures of 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 trend of variability over time mirrors the national trend in the supply of air service. Airlines significantly reduced their supply of flights at local airports and increased airfares at local airports after the recession and the peak in fuel prices in 2008. Since 2008, flight service levels have grown tentatively at small and medium airports.

The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller Airports to Larger Ones 53 LOCAL AIRPORT SUBSTITUTE AIRPORT AND MEGAREGION MAP Lovell Field Airport (CHA) (Chattanooga, TN) Hartsfield Jackson Atlanta International Airport (ATL) Piedmont Atlantic Megaregion 123 I-75 Huntsville International Carl T. Jones Field Airport (HSV) Huntsville, AL 201 I-20 Birmingham- Shuttlesworth International Airport (BHM) Birmingham, AL 152 I-20 Savannah Hilton Head International Airport (SAV) Savannah, GA 240 I-16 McGhee Tyson Airport (TYS) Knoxville, TN 224 I-75 Columbia Metropolitan Airport (CAE) Columbia, SC Charlotte Douglas International Airport (CLT) Piedmont Atlantic Megaregion 105 I-77 Charleston Air Force Base- International (CHS) Charleston, SC 204 I-77 Piedmont Triad International Airport (GSO) Greensboro, NC 102 I-85 Will Rogers World Airport (OKC) Oklahoma City, OK Dallas/Fort Worth International Airport (DFW) Texas Triangle and Gulf Coast 195 I-35 Shreveport Regional Airport (SHV) Shreveport, LA 202 I-20 Tucson International Airport (TUS) Tucson, AZ Phoenix Sky Harbor International Airport (PHX) Arizona Sun Corridor 117 I-10 DISTANCE BETWEEN LOCAL AND SUBSTITUTE AIRPORT (MILES) INTERSTATE CONNECTION Low Traffic Point Low Traffic Point (SHV) High Traffic Point (SHV) High Traffic Point (CHS, CAE) High Traffic Point (GSO) High Traffic Point High Traffic Point (OKC) Low Traffic Point (OKC) Low Traffic Point (CHS, CAE) Low Traffic Point (GSO) High Traffic Point (SAV) High Traffic Point (CHA, TYS) High Traffic Point (BHM, HSV) Low Traffic Point (BHM, HSV) Low Traffic Point (CHA, TYS) Low Traffic Point (SAV) Table 4-1. Local and substitute airport pairs including their interstate connections.

54 Air Demand in a Dynamic Competitive Context with the Automobile In addition, the research team estimated the percentage of difference in fares for itineraries from the local and substitute airport with the same destination (discarding destinations served by the substitute airport that were not directly served by the local destination). This is based on a comparison between (1) non-stop itineraries from the local and substitute airports and (2) connecting 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.11 In general, the results show airfares at the local airports are between 20% lower and 60% higher than airfares at the substitute airports across the airport pairs. Airfares at local airports were mostly and overwhelmingly, in some cases, higher than airfares offered at the associated Atlanta and Local Airports Year Departures per Year at Substitute Airport 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 at Substitute Airport Departures at the Local Airport as a Percentage of Departures at the Substitute Airport Charlotte (CLT) Greensboro (GSO) Columbia Metropolitan (CAE) Charleston (CHS) 2007 227,189 11.09% 7.11% 9.22% 2011 245,243 7.55% 5.10% 8.43% 2015 250,222 6.29% 4.26% 9.01% Dallas and Local Airports Year Departures per Year at Substitute Airport Departures at the Local Airport as a Percentage of Departures at the Substitute Airport Dallas (DFW) Shreveport (SHV) Oklahoma City (OKC) 2007 321,106 3.14% 9.40% 2011 305,210 2.56% 8.37% 2015 322,070 2.18% 7.20% Phoenix and Local Airport Year Departures per Year at Substitute Airport Departures at the Local Airport as a Percentage of Departures at the Substitute Airport Phoenix (PHX) Tucson (TUS) 2007 223,623 13.50% 2011 200,513 12.74% 2015 189,177 12.26% Table 4-2. Departures per year at the substitute airport and departures at the local airport as a percentage of departures at the substitute airport. 11 The charts showing the difference in fares are presented in full in “A Drive for Better Air Service: How Air Service Imbalances Across Neighboring Regions Integrate Air and Highway Demands” (Ryerson and Kim 2018).

The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller Airports to Larger Ones 55 substitute airports. Airfares for connecting itineraries at the local airport generally appeared to be higher than for non-stop flights at the associated substitute airport, in some cases 20%–60% higher. Airfares for direct itineraries at the local airport were between 20% lower and 10% higher than airfares for direct itineraries 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) from 2007 to 2015 in terms of fares, with fares at local airports, as compared to fares at substitute airports, tending to be their highest from 2007 to 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 either from the local airport or from the 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 process predicts market share for air service provided at the local airport for all destinations served by the local airport, via both 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. The most conservative values result from varying the coefficients that capture ground access distance and time; therefore, the research team here presents the results when the coefficients on ground access distance and time represent “high” values of time as the lower bound. However, a traveler having a “high” value of time or access distance is not reflective of a future that 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 a traveler’s effective value of time will be reduced in connected vehicles that assist drivers in finding routes with the lowest traffic and maintaining a safe distance from other vehicles and in autonomous vehicles that perform the driving function. 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 would leak to the substitute airport. In this case, the base estimates are actually a lower bound rather than an upper bound on the potential for airport market leakage. 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.12 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 to 2015). Market shares for local airports were at their lowest between 2008 and 2012, but they appeared to rise between 2012 and 2015. This reflects the contraction of the aviation market from 2008 12 The full results of this analysis are available on an airport-by-airport basis in “A Drive for Better Air Service: How Air Service Imbalances Across Neighboring Regions Integrate Air and Highway Demands” (Ryerson and Kim 2018).

56 Air Demand in a Dynamic Competitive Context with the Automobile to 2012, when the airlines reduced their services, particularly in short-haul markets (as docu- mented 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 region did not hold after 2013. In this region, Shreveport has fewer than 10,000 flights per year and Oklahoma City has 30,000, as compared with Dallas–Fort Worth’s 320,000; in addition, the gap between air service frequencies at these airports has grown over time. Dallas–Fort Worth strengthened its position as a hub not just relative to local airports but also with actual growth in air service; this development was to the detriment of the local airports. To further contextualize the number of total travelers who may leak, Table 4-3 includes the percent of leaked passengers from a local airport in 2015 divided by the total passengers carried by the local airport that year. The research team found this percentage to be generally in the range of 16% to 32% for the different airport pairs. The interpretation of this percentage is that each local airport is not capturing a possible 16% to 32% in additional passengers that it could have carried in any particular year. The numbers in Table 4-3 are broken down by direct and connecting flights. 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 much higher than the market shares for connecting travel. The local airport passenger market shares for direct flights are between 67% and 89%, with most values around 80%; for connecting flights, the range is between 15% and 67%, with most values in the 20% to 30% range. In short, a local airport typically commands the largest share of its passenger catchment market when it offers 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 16% and nearly 32%, which suggests that local airport passenger leakage may be occurring at significant rates in various regions of the United States, which in turn suggests a substantial amount of travel on the ground to access major hub airports. 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. Share of passengers within the local airport catchment that were estimated to prefer a flight option from the local airport in 2015.

The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller Airports to Larger Ones 57 Highway Traffic Due to Airport Market Leakage The research team’s analysis of the effects of leakage included an estimation of the proportion of traffic on interstate highways connecting local to substitute airports that could be attributed to travelers driving long distances between the catchment of the local airport to/from a substitute airport. The estimate is based on 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. AADT values were collected for two points along each interstate highway route connecting the airport pairs from state DOT websites. The first point (see Table 4-1) is the lowest volume point on the corridor; the second point is the highest volume point. Collecting two AADT values for each corridor, each year, makes it possible to identify an upper and lower bound of traffic between the local and substitute airport. Table 4-1 shows the locations where the AADT values were collected. The number of passengers leaked from the local airport to the substitute airport was divided by the AADT. For local airports that share a route to the substitute airport with another local airport, the numbers 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 for purposes of calculation the research team assumed that each passenger travels in their own vehicle. This assumption is supported by data from DB1B, the 10% sample of all air itineraries collected by the FAA and BTS. Table 4-4 shows the percentage of AADT that might be attributable 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 categorized into 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 the airport pairs considered in this research, the percentage of traffic attributable 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, SUBSTITUTE AIRPORT LOCAL AIRPORT DATA SOURCE HIGHWAY LOW AADT HIGH AADT HIGH BASE HIGH BASE Atlanta Chattanooga & Knoxville GA 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 GA 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 AL DOT I-20 8.12% 8.94% 1.62% 1.78% Table 4-4. Data sources for highway AADT and estimates of highway traffic attributable to leaked passengers accessing a substitute airport in 2015.

58 Air Demand in a Dynamic Competitive Context with the Automobile 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 10% to 12% of their daily traffic coming 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. Future Automobiles and the Future of Airport Market Leakage This research has found present-day airport market leakage from local airports to hub airports 100 to 300 miles away. Estimates based on this research suggest that 16% to 32% of the total passengers living proximate to a small or mid-sized airport have the incentive to leak; the range is 11% to 33% for travelers with a non-stop itinerary from their local airport and 33% to 85% for travelers with connecting travel. The research team found that passengers leaking from a local to a hub airport could contribute 1% to 3% of average daily highway traffic at heavily congested portions of the interstate highways connecting airports and 10% to 12% of traffic on low-density portions of the highway. Self-driving, or highly automated, vehicles now undergoing public trials in major cities are positioned to transform the entire transportation system. Far from being a distant inno- vation, retail autonomous vehicles have already been announced by various manufacturers for testing operations in the immediate future, although timing of administrative approvals is less clear. It is estimated that on-road, highly automated vehicles will reach market ubiquity as part of a $7 trillion passenger economy by 2055 (Intel 2017). Transportation planners and policymakers are welcoming autonomous vehicles for their potential to positively affect traffic safety by fundamentally changing the interaction and relationship between drivers and vehicles and by changing how drivers and vehicles collect and process information from their environment. However, given the analysis above, it is clear that autonomous vehicles will greatly change the relationship between automobiles and aircraft in the intercity trans- portation system. The ACRP Project 03-40 analysis shows that in a scenario where automobile dominates, short-distance aircraft flights and passengers will decline significantly. Figure 1-3 of this report shows the change in air passengers by airport size in a hypothesized scenario with autonomous vehicles. Chapter 1 reports that small airports could see a 34% reduction in passengers due to advancements in vehicle technology, which is consistent with the research team’s findings in the airport market leakage study. The decline in passengers due to the introduction of autono- mous vehicles is significantly smaller for the largest 30 airports; these airports will 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. Further inquiry into the types and distances of routes that will likely be replaced by passen- gers driving more advanced automobiles shows 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 1,400 miles, with the largest share being routes between 600 and 1,000 miles. One reason that routes of 600 to 1,000 miles lose the highest percentage of passen- gers is that short-distance routes have already experienced large quantities of passengers leaking and/or substituting drives for their air trips. Routes of more than 600 miles may be safer from being replaced because of the longer distance. However, as vehicles become more advanced and automated, the effective cost of driving will be significantly depressed. As a result, travelers may find that a 600- to 1,000-mile drive, during which they can relax or otherwise not operate a vehicle, is more attractive to them than accessing the airport and flying.

The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller Airports to Larger Ones 59 Airport Leakage and Diversions to the Automobile The research conducted under ACRP Project 03-40 indicates the strength of the connection between the air and surface intercity transportation systems and provides justification for integrated air-highway transportation planning. Policies and actions taken by large airports and airlines at large airports have significant implications for both neighboring local airports and the interstate highway system. For example, one possible contribution to addressing congestion on a highway may be to increase air service at a small local airport. The findings of this research on the significant link between the air and surface intercity transportation systems open up a new area of inquiry in the field of intercity transportation. While there are institutions and scholars focused on the link between local transportation systems and airports, few focus on the concept of long-distance airport access and airport market leakage. This research indicates that leaking from a local to a larger airport market is a widespread practice among travelers 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 research will help to shape the evolving role of airport managers in con- trolling demand and delay at major hub airports and in building and managing air service at smaller airports across the United States. Managers of small airports could stem market leakage by focusing on building air service to new, unique destinations rather than focusing on service to connecting hub airports. However, this is a challenging prospect. Small and medium airport managers have been markedly less successful than large hub airports in building new service in recent years. The findings of this research also indicate the complexity of the challenges that large airport managers face: they must balance providing air service for their region against starving neigh- boring regions of air service and causing increasing surface highway traffic. While dealing with many complex issues in aviation, managers of large airports 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—its hub airline. The findings of this research may suggest to managers of large airports some alternate solutions for tackling congestion at busy airports—namely, encouraging air traffic at local airports to stem leakage. Nonetheless, 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. 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 by the research team to support such an analysis; its supply input assumptions are the same as those of Scenario 2, with the addition of new aircraft technology. Scenario 2 assumed that the number of flights to non-hub airports increases, the number of direct flights from smaller airports increases,

60 Air Demand in a Dynamic Competitive Context with the Automobile stress at larger airports increases, tickets become cheaper, the stress of driving increases, the relative cost of driving increases, and future generations are somewhat less automobile oriented. Isolating the Impacts of New Aircraft Technology This research showed 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 significantly, increases in trips of 15% to 25% were predicted in the scenario testing process for distance bands under 1,200 miles. Table 4-5 shows the incremental increases attributable to the addition of new aircraft technology into the scenario structure. 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 1,200 miles. In sum, the addition of aircraft technology to an already optimistic set of additional market assumptions does indeed result in a significant increase in the number of air trips. However, it must be noted that for no distance band does new aircraft technology explain the majority of gain in air trips: lowered costs, per se, would not be as strong an explanatory factor as addi- tional service—itself a difficult condition to predict. Moreover, consistent with patterns revealed throughout this research, the role of new technology in attracting automobile users to air is strongest for the shortest trips. Understanding the New Aircraft Technology While it is beyond the scope of this research to predict the future of new aircraft technology, it is worthwhile to review the potential for major improvements in aircraft 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 air traffic on high-density routes and the airports they connect. Distributed Electric Propulsion One new technology is distributed electric propulsion (DEP). DEP exploits the fact that electric propulsion, unlike jets or turboprops, is efficient on a 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 motors. The small propellers can be placed at different locations on the airframe to optimize aerodynamic 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% 800–1,000 +19% 9,217,575 2,703,917 29% 1,000–1,200 +15% 7,425,019 2,069,237 28% Table 4-5. Additional air trips attributable to new aircraft technology assumptions.

The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller Airports to Larger Ones 61 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 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 (Harish et al. 2016). Both aircraft seat nine passengers. Detailed cost models for energy, maintenance, labor, and battery replacement were developed and applied to both the existing Cessna 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. 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 previously, the cost advantage of the DEP increases to 25% when the higher speed is taken into account (assuming a passenger load of seven). Deployment of electric propulsion aircraft in commercial operations is being led by nations such as Norway, with a 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 serving all routes with flight times of 1.5 hours or less with 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 (“Norway Aims,” 2018). Crew Assumptions The previous analysis assumes a two-person flight crew, the same as that 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 near future, the possibility of single-pilot opera- tion (SPO) 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 ensuing years, combined more recently with a 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. Vu et al. (2018) describe two such concepts: (1) ground based and (2) replacing the co-pilot. Ground based. 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 transi- tion between the high-workload, dedicated mode and the low-workload, multitasking mode is a crucial requirement for this concept of SPOs. Replacing the co-pilot. An alternative to ground-based SPOs 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,

62 Air Demand in a Dynamic Competitive Context with the Automobile which would limit the ability to take appropriate action when intervention is required. 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 the 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 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. Regulatory, institutional, and political barriers exist in addition to these technical issues. Aviation authorities, insurance companies, pilots, and 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 concluded, “The nationwide pilot shortage is the dominant theme in many of today’s challenges to small community air service” and focused on reducing flight hour requirements for first officer qualification to address this problem— making no mention of SPOs. Arguably, however, the obstacles mentioned in the previous paragraph increase the prospects for SPOs providing a benefit to thin routes and small airports. While SPOs on large jet trans- ports 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 (operated 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 fewer for commuter airlines than for other segments of the airline industry. SPOs could substantially reduce the cost disadvantage of commuter airlines compared to regional and mainline carriers. A rough estimate, based on a comparison between the nine-seat DEP and Cessna 402 presented earlier, suggests that shifting to SPOs 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% and would approach 30% if traveler value of time were included. Summary: New Aircraft Technology and the Future of Smaller Airports Energy and labor cost savings resulting from DEP and/or SPOs, along with the travel time savings in the case of DEP, could reshape the commuter airline landscape. Lower cost commuter flights could serve several purposes. First, lower cost commuter flights could supplement the declining service 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

The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller Airports to Larger Ones 63 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 feedback generated 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 regula- tors, insurance companies, airport operators, and pilots, to test them in the marketplace. 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 technology.