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1 CHAPTER 1. SUMMARY AND MAJOR FINDINGS: UNDERSTANDING THE MARKET BETWEEN AIR AND THE AUTO 1(A) INTRODUCTION AND STRUCTURE OF THIS REPORT INTRODUCTION The air system operates in competition with the automobile to a great extent. Most air trips in the lower 48 states have at least some level of competition between the auto and the plane. Over the past two decades, the role of the auto in providing trips has risen, and the role of air for lesser distance trips has fallen considerably. Looking into the future, there are a wide variety of scenarios in which the role of the air system in accommodating shorter trips could decrease dramatically. This may well occur both with the increase in the auto share for the full long- distance trip, and in the use of the auto for access to less proximate airports â with a parallel decrease in the importance of the smaller airports providing feeder air services to hub airports. The report documents the finding that over the past two decades there has been a major increase in the use of the private automobile for trips over 100 miles in distance, even during a period of relatively flat variation in the use of the automobile in the metropolitan context. This Final Report for ACRP Project 03-40, Air Demand in a Dynamic Competitive Context with the Automobile explores the recent past, the present and speculates about the future role of the automobile in long-distance trip making. The implications for airport managers, and all those involved in providing aviation services to the public are significant. Using market research methods never applied before to this subject, the project has developed five separate illustrative futures for long-distance travel. They range from a scenario in which the automobile provides effortless services to its users to a scenario in which the public increases its preferences for air services to an extent not presently experienced. While one scenario assumes that the competitive position of the largest hub airports will increase significantly, two scenarios explore a combination of services and technology that would increase the relative importance of smaller airports, with more non-stop and direct flights between and among a wide variety of airport sizes and classifications. When compared with the base year of 1995, (the last year in which national long-distance travel survey data was collected on a systematic basis) the airlines are providing relatively less moderate distance seat miles, and more longer distance seat miles; the average distance of an air flight segment has grown by about 18%. While there have been instances of shorter distance air trips moving to rail and bus, this occurs on a significant basis only between New York and Boston, between New York and Washington DC. Other than that, the decrease in the role of the air trip is paralleled by an increase in the role of the auto trip. This pattern has significance for those charged with planning and managing the highway system just as it has significance for those operating the smaller airports not hosting major transfer movements between flights. Over the past two decades, the role of the auto in providing trips has risen, and the role of air for lesser distance trips has fallen considerably.
2 STRUCTURE This ACRP project is presented in the form of this Final Report, with seven chapters and an extended Technical Appendix in electronic format, which provides more detail and technical back up in support of the major content in the main report. Those seven chapters are: Chapter 1. Executive Summary and Major Findings: Understanding the Market Between Air and Auto Chapter 2. Trends and Changes in Auto and Air Markets over Two Decades Chapter 3. Factors Which Influence the Choice of Mode for the Long-Distance Trip Chapter 4. The Role of the Automobile in the Future of Smaller American Airports: Leakage from Smaller to Larger Chapter 5. Attitudes toward the Long-Distance Trip and their Role in Influencing Mode Choice Chapter 6. Methods We Used in this Project Chapter 7. Conclusions and Need for Further Research HIGHLIGHTS Highlights of the findings of the study include: Increased diversion of trips from air to auto ï· The project has revealed a major shift in the use of the automobile for long-distance trip making, with increases in market share particular strong in trips under 1000 miles. ï· This is paralleled by a strong decrease in supply offered by the airline for trips under 500 miles in length. ï· Our project results suggest that the rate of growth of the auto in the long-distance trip is higher than the rate of growth by air, resulting in an increase in overall auto mode share for the long-distance trip since the base year. ï· Cost analysis suggests that the airplane trip is cheaper per person than the auto trip at about 1,500 miles and above. ï· Although beyond the scope of this study, our results suggest a significant shift in the make-up of present VMT generation, with trips above 100 miles comprising as much as 29% of per capita VMT while metropolitan household travel seems generally flat. Demographic differences ï· Millennials have a higher propensity than other age groups to choose air for shorter trips in our 2017 survey, and a lower propensity to choose air for the longest trips. ï· The role of the auto in the very long-distance (over 1,500 mile) non-leisure trip needs to be examined further, as the auto seems to play an important role in work-oriented multi-destination tours which last several days. ï· Similarly, the need for the auto for leisure trips with multiple children also explains why air is not chosen for some trips, even for very long- distance trips. Millennials have a higher propensity than other age groups to choose air for shorter trips in our 2017 survey, and a lower propensity to choose air for the longest trips.
3 ï· The perceived need for a car at the destination is a powerful explanatory factor in the choice of mode, intertwined with both children and trip purpose. Attitudes and preferences ï· The majority of respondents in all demographic subgroups disagree with the concept that they would rather share a car than own it, with Millennials being somewhat more open to the idea. ï· Attitudinal modeling shows that the decision between the two long-distance modes is largely one between the price of the air trip, and the level of stress and distaste associated with the long auto trip. ï· Younger people are more likely to agree that driving for more than a day is unpleasant, and that they want to use a smart device while traveling. Younger respondents express more concern about personal safety, uncertainty, and costs in the long-distance trip than older respondents. ï· Women are more likely than men to dislike driving for more than one day, and those with more income are more likely to dislike it than those with less income. ï· Basic demographic preferences about air and auto should be incorporated into marketing programs to encourage use of the smaller airport. Airport choice ï· Over 60% of our survey respondents agreed with the premise that they would âprefer to drive to a larger airport than take a feeder flight from a closer airport.â ï· Stated preference exercises revealed that travelers would add an extra hour of driving to save one-half hour of flying: these observations reflect the basic fact that time for the full trip by car is more direct than a car+air trip. ï· Extensive national modeling reveals the scale of possible loss for smaller American airports with evolving technology for the automobile. ï· 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. Leakage to more distant airports ï· The overall leakage is estimated to amount to between 15% and nearly 32%, which suggests that a substantial amount of travel is spent on the ground accessing these major hub airports. Younger people are more likely to agree that driving for more than a day is unpleasant, and that they would like to use a smart device while traveling. Over 60% of our survey respondents agreed with the premise that they would âprefer to drive to a larger airport than take a feeder flight from a closer airport.â
4 ï· The report documents significant increases in Interstate roadway traffic attributable to leakage of markets away from smaller airports of departure to larger airports. ï· Across the studyâs airport pairs, the percentage of traffic attributed to travelers driving to a substitute airport is generally between 0.05% and 12%. In sparsely populated airport environs the percentage is higher; in built up urban areas, the percentage is lower. Possible alterations in airline service patterns and aircraft technologies ï· The study concluded that there is a significant market for direct flights between smaller airports, and that evolving aircraft technology might play a supporting role in lowering the operating costs of such flights. ï· Energy and labor cost could reshape the commuter airline landscape. Lower cost commuter flights could supplement the declining frequencies of regional carriers as they shift from 50 to 70-seat planes. ï· Evolving aircraft technology could allow non-stop services on thin short-haul routes where only connecting service is currently available. ï· One scenario was created to examined additional flights, and a second with additional flights and new short/medium distance aircraft technology. In the analysis of new trips, lowered costs are not as strong an explanatory factor as additional service. 1(B) ESTABLISHING A SENSE OF SCALE ACRP Project 03-40 has created a large-scale analytical framework which can help transportation practitioners to understand the dynamic roles played by highways and aviation facilities in facilitating the long-distance trip by North Americans. More than 8.5 million trips are commenced every day in the United States with trip lengths over 100 miles. This equals more than 3.1 billion long-distance trips made by Americans every year. Of these trips, approximately 15% of trips occur via air and approximately 82% of trips occur via private vehicle, as shown in Table 1-1.This research project explores the process by which air competes with the automobile for the billions of such trips made in the United States each year. TABLE 1-1. OVERALL MODE OF LONG-DISTANCE TRIPS (2011) MODE AVERAGE DAILY TRIPS OVER 100 MILES IN DISTANCE SHARE FOR EACH MODE Automobile 6,985,000 82% Bus 195,000 2% Train 82,000 1% Air 1,267,000 15% Total 8,528,000 100% Source: Federal Highway Administration Foundational Knowledge Implementation Report (2016). New aircraft technologies reviewed in this study could supplement the declining frequencies of regional carriers as they shift from 50 to 70-seat planes and support non- stop services on thin short-haul routes where only connecting service is currently available.
5 In cases of trips between 100 miles and 500 miles, the automobile dominates, capturing over 90% of the trips. However, air travel dominates in trips between 1,000 and 2,000 milesâ reducing the automobileâs share to 20% of those trips. Research undertaken for the FHWA in 2016 concluded that the automobile is down to 2â3% of the market for the smaller number of city pairs over 2,000 miles. Table 1-2 documents nearly 300,000 city pairs in which the split TABLE 1-2. AIR AND AUTO MODE SHARE OF TRIPS OVER 100 MILES, BY DISTANCE DISTANCE (MILES) AIR AUTOMOBILE NUMBER OF CITY PAIRS, BY DISTANCE 100â499 5% 91% 81,000 500â999 37% 61% 146,000 1,000â1,999 79% 20% 150,000 2,000â2,999 97% 2 % 56,000 3,000+ 97% 3% 7,000 Source: Federal Highway Administration Bus Ridership Study (2016). between automobile and air travel will vary sharply, influenced by several factors, for trips between 500 and 1,999 miles. Note that in this report, an âair tripâ is defined as a trip from the origin airport to the destination airport and may include multiple flight segments in cases where transfers are made. This is different from âenplanementsâ which are individual flight segments. 1(C) THE BASE CASE FOR ANALYSIS OF ALTERNATIVE SCENARIOS While most definitions of the long-distance trip include trips over 100 miles, our project found that most realistic competition with between air trips and auto trips occurs above 200 miles. Thus, our research has created a new national air/auto modeling process which simulates some 1,834,000,000 annual domestic trips in the lower 48 states with trip lengths of 200 miles and over. Overwhelmingly, most of these trips are by auto, with some 422,000,000 annual domestic air trips. The simulation process is presented in Chapter 6 of this report and is documented in more detail in the Technical Appendix. The modeling results in a simulation of long-distance trip making that utilizes some 70,000 analysis zones (Census tracts) for the calculation of airport access and egress trip segments. The model application leveraged the output of an existing national modeling framework for long-distance passenger travel, developed by RSG for the Federal Highway Administration (RSG 2015; Bradley et al. 2016). The mode/airport choice models developed for this project incorporate aspects of consumer preferences, attitudes, and values that are not in the current FHWA model. The new models have been applied using an extension of the FHWA model framework to develop scenarios to understand how different market developments are likely to affect demand for air and auto. This process is designed to enhance practitionersâ understanding of the issues associated with auto as an alternative to air.
6 TABLE 1-3. NUMBER OF AIR TRIPS AND MODE SHARE FOR BASE CASE Source: Project Scenario Testing Model DISTANCE in MILES ANNUAL NUMBER OF AIR TRIPS AIR AS PERCENT OF AIR +AUTO 200-400 45,934,525 5% 400-600 55,789,218 17% 600-800 54,936,454 28% 800-1000 49,634,860 41% 1000-1200 50,415,726 56% 1200-1400 39,257,989 65% 1400-1600 20,678,987 72% 1600-3200 105,219,047 84% Total 421,866,805 23% Table 1-3 shows the number of air trips modeled in the base case scenario, which resulted from calibration to recreate trip making conditions in 2011, a year for which FHWA has generated long-distance travel estimates for each major mode. 1(D) DEVELOPING THE RESEARCH TOOLS For this study, a major national survey was conducted in 2017 in four metropolitan areas to collect long- distance travel behavior and attitudes as well as administer a series of Stated Preference (SP) experiments that deal with the choice between car and air. The project survey Survey Design. The objective of this study was to examine the choice to use car or air for long- distance travel in the United States. With this objective in mind, the sample for the study was comprised of travelers who have taken an automobile or air trip of greater than 300 miles within the past year. Quota sampling, a technique that sets a minimum number of respondents for each respective category, was used to establish a diverse sample of respondents. The resulting sample size is 4,223. Questionnaire. A questionnaire was designed to understand present mode choice behavior and the sociodemographic characteristics of each survey respondent. The survey instrument also collected basic information concerning attitudes affecting the propensity to choose traveling by car or plane for trips of 300 miles or more. Three kinds of kinds of models The project developed three separate kinds of mathematical models to aid in the understanding of the choice of mode between the auto and the airplane in the long-distance trip. Attitudes were explored in the first model: the Structural Equations Model (SEM). The SEM model was designed to emphasize the importance of âsoftâ variables, including values, preferences and attitudes in the selection of modes for the long-distance trip; by design, this model does not emphasize the trip-based times and costs.
7 The second models were designed to emphasize the immediately relevant factors of travel time and costs, in addition to other traditional variables such as travel party size. These models are called the Multinomial Logit and Mixed Multinomial Logit Choice Models. The third model is designed to integrate all relevant factors into the prediction of (in our case) long-distance travel mode. The model takes the form of an Advanced Hybrid Choice model, which is also described as an Integrated Choice Latent Variable model. It was developed to support the development of a nationwide travel demand modeling process, referred to here as the Scenario Testing Model. The models allowed the research team to explore five separate market futures which would influence the choice between air and auto. 1(E) THE FIVE OVERARCHING SCENARIOS The survey and model development process supported the development of the projectâs Scenario Testing Model. In its application, the project created five overarching scenarios for the future of long-distance travel in addition to the base case simulation. They are: ï· Scenario 1. Auto dominates the future. If, somehow, the auto trip becomes less stressful, and somewhat less costly, long multi-day trips were less onerous, and riders could stay connected on the auto trip, (like automated vehicles) then air demand would decrease by about 16%. ï· Scenario 2. An optimistic scenario for the smaller airports. If 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, then air demand would increase by about 10%. ï· Scenario 3. Smaller airports benefit from new cheaper short distance planes. Same as Scenario 2, but short distance flights would have lower ticket prices for short distances only; more short distance, direct flights; and, less stress at smaller airports. In this scenario, air demand would increase by about 14% ï· Scenario 4. An aggressive scenario for the hub airports. Hub airports lower their parking charges, decrease the amount of stress, and increase the frequency of direct flights. In this scenario, air demand would increase by 14% ï· Scenario 5. Air dominates the future. Congestion on the highways makes longer travel times for the auto trip. The price of gas goes up. As youth grow older, their concerns about the long-distance highway trip remain, and preference for private vehicle ownership goes down. The price of air trip goes down and flight frequencies increase. In this scenario, air demand would increase by about 16%. Although the magnitude of the overall effects of the five scenarios are similar, they have very different effects when analyzed along dimensions such as trip distance, airport size, travel purpose, and region of the country. Such differences are highlighted in the following section. The project created five overarching scenarios for the future of long-distance travel, and specifically the market contest between aviation and the automobile
8 UNDERSTANDING THE FIVE POSSIBLE FUTURES FOR LONG-DISTANCE TRAVEL The five scenarios reflect a wide range of possible roles for both air and auto in the long-distance trips of the future. When examined in terms of futures designed purposefully to illustrate possible trends, (rather than to predict any specific future), the role of possible changes in the technology of the automobile jumps from the data. One of the five future scenarios was designed to explore the possible impacts of autonomous vehicles on the selection of mode for the long- distance trip. The sheer range of futures examined The forecast range of possible outcomes for air demand is dramatic. The difference in futures is graphed in Figure 1-1, comparing a most auto-oriented scenario (âAuto Dominatesâ) with the four more optimistic scenarios for the airport community. For the national system as a whole, the impacts range from a decrease in air travel of 16% to an increase of 16%. As will be discussed further, the smallest airports would suffer most from a dramatic change in automobile technology in Scenario 1, (-34%) and would benefit most from the hypothesized new services to smaller airports in Scenario 3, (+55%). As noted, the scenarios are purposefully designed to examine extreme changes in input assumptions and should not be considered to be forecasts of travel. FIGURE 1-1. COMPARISON NATIONAL CHANGE IN AIR TRIPS, 5 SCENARIOS â16% 10% 14% 14% 16% SCENARIOÂ 1.Â AUTOÂ DOMINATESÂ THEÂ FUTURE SCENARIOÂ 2.Â OPTIMISTICÂ FORÂ SMALLERÂ AIRPORTS SCENARIOÂ 3.Â NEWÂ SHORTÂ DISTANCEÂ PLANES SCENARIOÂ 4.Â AGGRESSIVEÂ FORÂ THEÂ HUBÂ AIRPORTS SCENARIOÂ 5.Â AIRÂ DOMINATESÂ THEÂ FUTUREÂ Â Ch an ge Â in Â A irÂ Tr ip s ChangeÂ inÂ NationalÂ AirÂ Trips The smallest airports would suffer most from a dramatic change in automobile technology (-34%) and benefit most from the hypothesized new services to smaller airports (+55).
9 Understanding the implications of a very automobile oriented future (Scenario 1) As shown in Figure 1-2 . A Hypothetical Scenario with autonomous vehicles shows impacts on air travel which vary by the length of the long-distance trip. While the impact of number of air passengers in the system as a whole is a decrease of about 16%, there is considerable variation of impact by trip length. For example, a decrease of 24% is shown for trips between 600 and 800 miles in length. FIGURE 1-2 . A HYPOTHETICAL SCENARIO WITH AUTONOMOUS VEHICLES SHOWS IMPACTS ON AIR TRAVEL WHICH VARY BY THE LENGTH OF THE LONG-DISTANCE TRIP In the model inputs and simulation results, airports were segmented along four size classes according to their ranking in terms of the number of O-D air passenger trips in 2011: ï· Large hubs: The 30 largest airports ï· Smaller hubs: Airports ranked 31-60 ï· Smaller airports: Airports ranked 61-120 ï· Regional airports: Airports ranked lower than 120th. In the most pessimistic future for air, Scenario 1 shows decrease in air passengers at the 30 largest airports might be as small as 12%, while decreases at the smallest airports might range as high as 34%, as shown in Figure 1-3. The implications from both charts show how the increases in the relative desirability of the auto vary widely by trip distance. Over and over in this study, it becomes clear that the vulnerability and instability of the air market occurs for the lower distance trips, and not for the longest distance trip. The choice between the car and the airplane is overwhelmingly a function of 200â 400 400â 600 600â 800 800â 1000 1000â 1200 1200â 1400 1400â 1600 1600â 3200 Change â17.7% â15.0% â24.1% â22.3% â18.4% â15.8% â12.8% â8.4% â25.0% â20.0% â15.0% â10.0% â5.0% 0.0% De cr ea se Â in Â A irÂ Tr ip sÂ DistanceÂ ofÂ TripÂ inÂ Miles DecreaseÂ inÂ AirÂ TripsÂ fromÂ AutomousÂ Vehicles In the Autonomous Vehicles scenario, decrease in air passengers at the 30 largest airports might be as small as 12%, while decreases at the smallest airports might be as severe as 34%.
10 trip distance, which emerges as more important than such factors as age, income, education level or details of services provided. . FIGURE 1-3. THE HYPOTHESIZED ACCEPTANCE OF AUTONOMOUS VEHICLES WOULD IMPACT THE SMALLER AIRPORTS MORE SEVERELY THAN THE LARGEST Based on the US Census National Regions shown in Figure 1-4, Figure 1-5 shows that different parts of the nation would react differently in response to a massive change in the technology of the private automobile. While the Pacific, Mountain and New England regions would show least impact in loss of air trips, the Southeast1 Central and Northeast Central Regions would be most vulnerable to change in mode away from aviation. One reason for such differences is the range of trip distances seen in different regionsâpeople in the Mountain and Pacific regions tend to make more trips in the longer distance ranges where the car is a less competitive option. 1 Concerning National Regions, this report uses the more colloquial terminology of âSouthwestâ (etc.) rather than âWest Southâ (etc.) used by the Census in Figure 1-4 Smallest Airports Smaller Larger LargestÂ 30 Airports %Â Change â34% â25% â17% â12% â40% â35% â30% â25% â20% â15% â10% â5% 0% Ch an ge Â in Â A irÂ Pa ss en ge rs Â ImpactÂ ofÂ AutonomousÂ VehiclesÂ onÂ AirÂ Demand,Â byÂ AirportÂ SizeÂ
11 FIGURE 1-4 DEFINITIONS OF US CENSUS NATIONAL REGIONS USED (census.gov) FIGURE 1-5. IMPACT OF AUTONOMOUS VEHICLES, BY NATIONAL REGION The scale of the range of futures for aviation from the viewpoint of airports of different size can be observed in the scenario results in this chapter. The results support the conclusion that the shortest flights and the smallest airports experience the most volatility and uncertainty in their futures. By way of example, Table 1-4 allows the direct comparison of the two extreme conditions for the short distance air trip (between 200 and 400 miles), resulting a range between 18% decline and 25% growth associated with the two extreme futures. The same comparison of futures is presented by size of airport in Table 1-6 where, again, the smallest airports are associated with the widest variation of possible futures; in this case varying from a pessimistic future of over 30% loss, to an optimistic vision of over 50% gain. SEÂ Cent NEÂ Cent NWÂ Cent SWÂ Cent MidÂ Atl SouÂ Atl NewÂ Eng Mountn Pacific Decrease â25% â20% â19% â18% â17% â17% â15% â12% â9% â25% â20% â19% â18% â17% â17% â15% â12% â9% DecreaseÂ inÂ AirÂ DemandÂ inÂ AutonomousÂ VehicleÂ Scenario,Â byÂ NationalÂ RegionÂ
12 More optimistic scenarios for airports Just as the project concluded that the smallest airports are most vulnerable to loss of riders from changes in auto technology, the project also examined what factors might come together to improve the fate of the smaller airports, reported here as Scenario 2 and Scenario 3, as defined above. Figure 1-6 shows the changes in air trips by trip length for Scenario 3, which includes both optimistic assumptions about how new shorter-distance aircraft technology might lower operating costs (and thus ticket prices) and equally optimistic assumptions about how many such direct flights would be operated by the airlines. Air trips under 600 miles in distance are shown to increase by over 20% in the scenario which includes new smaller aircraft technology. FIGURE 1-6. INCREASE IN AIR TRIPS UNDER OPTIMISTIC ASSUMPTIONS FOR SMALLER AIRPORTS Comparing the five scenarios together Table 1-4 shows the change in air trip-making for all five scenarios against the base case by the distance of the air trip. Table 1-5 shows the impact of the five scenarios by national region, (i.e., census district). It is perhaps counter-intuitive that the âaggressive hubsâ scenario gains the most air trips in the shorter distance classes. The explanation is that the hub airports already dominate the market for the longer air trips, but in this scenario, lower prices, lower stress, and more direct flights makes using hub airports more competitive with auto for the shorter trips as well. Conversely, a reason why improving service from small airports increases air demand in the longer distance ranges is that more attractive flights from these airports can also be used to reach hub transfer airports for long air trips. 25% 20% 18% 19% 15% 13% 10% 5% 200â400 400â600 600â800 800â1000 1000â12001200â14001400â16001600â3200 Pe rc en tÂ I nc re as eÂ in Â A irÂ Tr ip sÂ OptimisticÂ FutureÂ forÂ SmallerÂ AirportÂ withÂ NewÂ AircraftÂ Technolgy,Â byÂ Distance Air trips under 600 miles in distance are shown to increase by over 20% in the scenario which includes new smaller aircraft technology.
13 TABLE 1-4. AIR DEMAND CHANGE VS. BASE SCENARIO BY DISTANCE BAND DISTANCE (MILES) AUTO DOMINATES OPTIMISTIC FOR SMALL AIRPORTS NEW SHORT DISTANCE PLANES AGGRESSIVE HUBS AIR DOMINATES 200-400 -17.7% 15.2% 25.4% 42.4% 34.0% 400-600 -15.0% 12.6% 19.7% 25.7% 24.6% 600-800 -24.1% 12.1% 17.7% 16.8% 19.3% 800-1000 -22.3% 13.1% 18.6% 11.9% 15.9% 1000-1200 -18.4% 10.6% 14.7% 7.9% 12.4% 1200-1400 -15.8% 9.4% 12.7% 5.8% 10.4% 1400-1600 -12.8% 7.6% 9.9% 4.1% 8.3% 1600-3200 -8.4% 4.0% 4.5% 2.2% 5.6% Total -16.1% 9.9% 14.4% 13.8% 15.6% TABLE 1-5. AIR DEMAND CHANGE, BY CENSUS NATIONAL REGION AUTO DOMINATES OPTIMISTIC FOR SMALL AIRPORTS NEW SHORT DISTANCE PLANES AGGRESSIVE HUBS AIR DOMINATES New England -12.8% 6.9% 10.7% 17.9% 14.7% Mid Atlantic -17.0% 9.1% 13.7% 22.0% 20.2% NE Central -18.6% 10.8% 16.2% 17.2% 18.3% NW Central -21.2% 13.3% 18.8% 11.8% 18.4% South Atlantic -19.0% 10.7% 15.3% 15.3% 17.4% SE Central -26.9% 19.8% 27.6% 9.8% 20.4% SW Central -17.6% 11.3% 16.1% 11.3% 16.3% Mountain -13.1% 8.0% 10.5% 7.2% 9.6% Pacific -6.7% 6.1% 9.6% 10.3% 9.2% 1(F) IMPLICATIONS FOR AIRPORTS, BY SIZE The extensive modeling of possible futures for the relationship between the auto system and the air system has revealed a very clear and consistent pattern of travel behavior: it is the smaller airports, and more generally the shorter trip segments, that are most intensively intertwined with the future of auto travel. This report will examine the impact of various scenarios and future on the smaller airports in two ways. First, the overall change in boardings will be presented. Second, the impacts of changes in the selection of the first airport of departure (the airport used from the home end of a round-trip journey) will be analyzed separately: Chapter 4 will further explore concept of âleakageâ from closer, smaller airports to larger, less proximate airports âusually by substituting an auto trip for the first leg of a total trip. The smaller airports and the shorter trip segments are most intensively intertwined with the future of auto travel.
14 TABLE 1-6. AIR DEMAND CHANGE AGAINST BASE SCENARIO, BY AIRPORT SIZE AUTO DOMINATES OPTIMISTIC FOR SMALL AIRPORTS NEW SHORT DISTANCE PLANES AGGRESSIVE HUBS AIR DOMINATES Largest 30 Hubs -12.2% 5.2% 8.6% 18.4% 14.2% Larger -16.8% 6.8% 10.1% 13.2% 15.4% Smaller -25.0% 25.9% 31.0% -2.7% 13.5% Smallest Airports -34.3% 35.8% 54.7% 4.4% 35.6% To explore the relationship between the choice of the closer airport with the larger airport, the research team modeled several geographic areas covered in our survey which had both a dominant airport and smaller ones at various distance from it. By way of example, Table 1-7 shows how each of the five scenarios might influence both the dominant airport in Boston, (Logan International) and the two smaller national airports in Providence and Manchester. TABLE 1-7. AN EXAMPLE OF A REGION WITH BOTH A DOMINANT AND SUBDOMINANT AIRPORTS AUTO DOMINATES OPTIMISTIC FOR SMALL AIRPORTS NEW SHORT DISTANCE PLANES AGGRESSIVE HUBS AIR DOMINATES BOS -12% 3% 7% 23% 14% PVD -14% 5% 10% 4% 16% MHT -16% 24% 27% -26% -7% ALL -12% 5% 8% 18% 13% Manchester NH airport (MHT) is revealed to be very susceptible to supply side changes in Boston (BOS) reflected in the Aggressive Hubs scenario, while Providence (PVD) is less so; MHT suffers a 26% loss under the Aggressive Hub assumptions, with PVD declining by only 4%. This same pattern of high volatility to supply side assumption finds MHT far more likely to benefit from a national pattern of new short distance flights than PVD. TABLE 1-8. A SMALLER AIRPORT IN A REGION WITH TWO LARGER HUBS AUTO DOMINATES OPTIMISTIC FOR SMALL AIRPORTS NEW SHORT DISTANCE PLANES AGGRESSIVE HUBS AIR DOMINATES CLT -19% 4% 7% 23% 17% RDU -20% 4% 6% 16% 16% GSO -28% 58% 64% -11% 18% ALL -20% 8% 11% 18% 16% Table 1-8 examines the North Carolina air market with two major airports, serving Charlotte (CLT) and Raleigh/Durham (RDU), and a smaller airport serving Greensboro, commonly known as the Piedmont Triad International Airport, (GSO). Here the market would evidently benefit from a national pattern of increased amounts of short distance, and possibly cheaper, flight segments, with around a 60% increase in trips commencing here. The volatility factor is also
15 reflected in the finding that loss in the Autonomous Vehicles scenario would be much more severe for them than in a dominant airport like Charlotte. The changes in enplanements presented in Table 1-8 are influenced by two kinds of travel behavior change: first, there is the replacement of the air trip to a trip fully by car; second, by the increased use of the car for the first segment of the trip. Changes in the selection of the airport of origin are addressed extensively in Chapter 4. 1(G) CHANGE FROM MAJOR COMPONENT FACTORS USED IN SCENARIOS As noted earlier, each of the five overarching scenarios was assembled by inclusion of key input variables to describe a future assumption. At the same time, the input factors were examined separately. A range of initial component factors was run to gauge the sensitivity of the model to various types of changes in the input assumptions. The base scenario was run along with several input variables, some of which are summarized here. ï· Air fares up 25%: Fares for all routes are increased by 25% over their 2011 levels. This includes both Economy and Business/First Class fares. For the whole population, this results in a decrease in air trips of about 6%, with the Business travel purpose less sensitive than the other travel purposes. ï· Car operating cost up 25%: For all O-Ds the perceived auto operating cost for the car mode is increased by 25% over their 2011 levels, from 20 cents/mile to 25 cents per mile. This resulted in an average increase in air trips by 3%. ï· All incomes up 25%: The household income for each tour is increased by 25%. This resulted in an increase in air trips of 1%. ï· Smaller airport frequencies down 50%: For any airports smaller than the largest hubs, the frequencies for both direct and connecting flights are reduced by 50% on all routes. This resulted in a decrease in air trips of 1%. ï· Smaller airport frequencies up 100%: For any airports smaller than the largest hubs, the frequencies for both direct and connecting flights are doubled on all routes. This resulted in systemwide increase in air trips of 3%. ï· More direct flights to/from smaller airports: For flights between the second rank of airports (here labeled as âlarger airportsâ) ranked within 1000 miles, and flights between the smallest airports and all other airports within 1000 miles, the minimum frequency for direct flights is 2 flights per day. This resulted in a systemwide increase in air trips of 3%. ï· Large airports more stressful: For airports on each end that are large hubs the latent variable for âAirport Stressâ is shifted 0.5 units towards higher stress. For the group of airports here labeled âlarger,â the latent variable for âAirport Stressâ is shifted 0.25 units towards higher stress. Thus, the maximum shift is 1.0, which is the standard deviation of the random component of the latent variables. This resulted in a decrease in air trips of A range of separate component factors was run to gauge the sensitivity of the model to various types of changes in the input assumptions.
16 7%. ï· Stressful long-distance driving and auto orientation. An increase of 1.0 in the latent variable for stress in driving would result in an increase in air trips of 4.5%; a 1-unit shift in the latent variable for reliance on the private car is associated with a 6% increase in air trips. ï· All people adopt "under 35" attitudes: For computing the latent variables, all people are treated as if they are in the under 35 age group, reflecting a future scenario where everyone has the attitudes of todayâs younger peopleâat least as far as the five latent variables are concerned. This resulted in an increase in air trips of 5%. EXAMPLES OF THE COMPONENT FACTORS Impact of new direct flights, by national region FIGURE 1-7. INCREASE IN AIR TRIPS FROM INCREASED DIRECT SERVICE, BY NATIONAL REGION Significant variation is revealed in terms of the impact of the hypothetical increase in direct flights between and among the smaller airports. Figure 1-7 shows that the smallest change in travel pattern is predicted for the Pacific region, where, presumably, a good selection of direct flights already exists â with less than 1% increase in tripmaking. By contrast, the unmet need for direct short flights is revealed for the Southeast Central census division, where an increase 8% in air trips is predicted by the new model under the assumption of increased direct flights. Attitudinal variation by age As analyzed in Chapter 5 of this report, there are significant variation on travel preferences based on the age of the traveler. Those under 35 have a greater distaste for the multi-day auto trip than 8.1% 5.1% 3.7% 3.4% 3.1% 2.4% 1.6% 1.6% 0.7% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% SEÂ CENT NWÂ CENT NEÂ CENT SOUÂ ATL SWÂ CENT MIDÂ ATL MOUNTNÂ NEWÂ ENG PACIFIC IncreaseÂ inÂ AirÂ TripsÂ fromÂ HypothesizedÂ IncreaseÂ inÂ DirectÂ Flights,Â byÂ RegionÂ
17 do those over 35. No one knows how their attitudes and preferences will change as they, too, later occupy the older age categories in the future. A scenario was built to reflect the idea that, as they age, they will retain the preferences they express in their youth. Figure 1-8 shows how air travel would increase if all age groups were to adopt such attitudes; implicit here is that if these young people were, as they grow older, to retain their present negative attitudes and behavior towards the multi-day auto trip, air choice in the future would increase, most dramatically for the lower distance trips. FIGURE 1-8. AIR TRIP MARKET GROWTH IF ALL POPULATION HAD ATTITUDES OF THE MILLENNIALS In a similar manner, those under 35 report a much higher level of dislike for the multi-day auto trip than do those over 35; tolerance of the long auto trip increases by age group at this point. If all the population had this distaste for the long auto trip, air use would increase by about 4%. Perhaps surprisingly, if all the population had the same lessened reliance on the car as the young, no significant change in air trips is simulated in the model. In a parallel result, if all the population had the same desire to remain electronically connected during long-distance as the under 35 age group, no significant change in air use is predicted. Perceptions of auto travel As explored in Chapter 5, some groups feel that the long-distance auto trip is a cause of more stress than other groups. A forecast was made of the implications of all market groups becoming less dissatisfied with the amount of stress perceived in the auto trip. Figure 1-9 shows that changing only this one attribute of the comparative experience could result in a 10% decline in air trip making in the highly vulnerable 200-400 mile range. This is 9.9% 7.6% 5.8% 4.6% 3.9% 3.1% 2.3% 1.2% 200â400 400â600 600â800 800â1000 1000â1200 1200â1400 1400â1600 1600â3200 IncreaseÂ inÂ airÂ tripsÂ ifÂ allÂ hadÂ theÂ attitudesÂ ofÂ theÂ young,Â byÂ tripÂ distanceÂ Â Perception that the auto becomes less stressful would result in a 10% decline in air trips between 200 and 400 miles in length.
18 particularly important as the automobile manufacturers make incremental improvements leading to the ultimate acceptance of autonomous vehicles, perhaps somewhat later. Such advanced concepts as the âconnected vehicle technology,â combined with the present pattern of providing higher and higher levels of comfort and entertainment in the vehicles could have significant impacts on the choice between the car and the air trip. Consistent with the major conclusions of this study, driving even the most âstress-freeâ auto would not greatly impact the modal decision for the trips longer than 1,600 miles, however. FIGURE 1-9. DECREASE IN AIR TRIPS IF THE CAR IS PERCEIVED AS LESS STRESSFUL THAN PRESENT At the other end of the spectrum of possible changes in attitudes towards the auto, if, over time the population has a decrease in its belief that auto ownership is essential for personal mobility, then auto trips of under 1,000 miles might divert to air, while trips above 1,000 are less volatile. Such a shift away from the car would impact the smaller airports more than the larger airports (not pictured.) Figure 1-9 shows that less than a 4% change is forecast for trips of 1,000 miles and longer. 200â400 400â600 600â800 800â1000 1000â1200 1200â 1400 1400â 1600 1600â 3200 Series1 â10.00% â7.40% â5.60% â4.70% â3.70% â2.90% â2.20% â1.20% â10.00% â7.40% â5.60% â4.70% â3.70% â2.90% â2.20% â1.20% DecreaseÂ inÂ AirÂ ifÂ CarÂ LessÂ StressfulÂ
19 FIGURE 1-10 INCREASE IN AIR TRIPS WITH DECREASE IN PRIVATE CAR ORIENTATION, BY DISTANCE 1(H) IMPLICATIONS FROM THE SCENARIO TESTING EXERCISE This chapter has focused primarily on the results of the scenario testing process developed in this ACRP study. While the scenario testing process was but one aspect of the larger research effort, several policy implications can be drawn from this element of the work program. What travel patterns are most vulnerable? It is clear from the results of several parallel research approaches that the impact of changes in the automobile system will have greater impact on overall air travel patterns than any other isolatable phenomenon examined in this study. The competition from the auto of the future will have more impact on the smaller airports than on the larger ones, and on the shorter distance trips more than on the longer distance trips. THE IMPORTANCE OF âLEAKAGEâ It is also clear that the issue of âleakageâ as perceived by the smaller airport managers will not go away. As discussed in Chapter 4, our exploration of âwillingness to payâ reveals that the average traveler would spend an extra hour in a car to avoid an extra half hour in the air trip. Travelers on average are willing to pay an additional $17 on their airfare to save an hour of access time but are willing to spent about double that amount to avoid an hour of in-flight time. This confirms results from many past studies that flight time is considered to be more onerous than ground access time. At the same time, the level of hedonistic comfort in the private car or van is increasing sharply, (e.g., offering separate electronic screens to each seat). In short, longer trips in the auto may become less stressful, and increasing time spent to divert to a more distant airport will become less onerous. The extent to which development of aircraft designed to provide cheaper service over short distances âthereby improving the fortunes of the smaller airportsâremains to be answered over time. 17.8% 9.9% 6.8% 5.2% 3.4% 2.5% 1.8% 1.0% 200â400 400â600 600â800 800â1000 1000â1200 1200â1400 1400â1600 1600â3200 IncreaseÂ inÂ AirÂ TripsÂ withÂ DecreaseÂ InÂ OrientationÂ toÂ theÂ PrivateÂ CarÂ (byÂ tripÂ distance)Â
20 WHAT IS NEXT? This chapter has focused on the overall impact of various factors, both individually and assembled into larger scenarios, on the possible futures for the relationship between auto and air markets for the long-distance trip. The purpose of the chapter was to establish an overall sense of scale for the range of possible futures reasonable under a variety of assumptions. But, it is important to examine how each of these factors separately influences long-distance travel behavior. Chapter 2 will document the history of the two modes, both separately and interacting together. Chapter 3 will examine the nature of the choice between modes, as dominated by trip length in interaction with a set of independent variables. Chapter 4 focuses policy attention on the needs of the smaller airport, and explores the choice of the departure airport, often involving market leakage from the more local to the more national airport. Chapter 5 explores variation in attitudes and preferences towards the modes, and towards the choice of modes. Chapter 6 briefly reviews the methods used in the project, with reference to the Technical Appendix for those seeking more detail. The report concludes with a discussion of policy conclusions from the study in Chapter 7, including some early topics for further research.