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

Chapter: Chapter 7. Conclusions and Further Research

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Suggested Citation:"Chapter 7. Conclusions and Further Research." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
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Suggested Citation:"Chapter 7. Conclusions and Further Research." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
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Suggested Citation:"Chapter 7. Conclusions and Further Research." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
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Suggested Citation:"Chapter 7. Conclusions and Further Research." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
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Suggested Citation:"Chapter 7. Conclusions and Further Research." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
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Suggested Citation:"Chapter 7. Conclusions and Further Research." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
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Suggested Citation:"Chapter 7. Conclusions and Further Research." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
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Suggested Citation:"Chapter 7. Conclusions and Further Research." National Academies of Sciences, Engineering, and Medicine. 2019. Air Demand in a Dynamic Competitive Context with the Automobile. Washington, DC: The National Academies Press. doi: 10.17226/25448.
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102 CHAPTER 7. CONCLUSIONS AND FURTHER RESEARCH 7(A) INTRODUCTION: GOOD NEWS AND BAD NEWS It is impossible to predict the future. But, hopefully, the application of scenario analysis can help us to understand the range of possible futures for the competition between the air and the auto for the total trip, and for the access to longer distance air service. From a series of purposefully dramatic scenarios for the future, both notes of optimism and notes of pessimism can be found. Optimistic? Seen from the point of view of the airport manager, the ‘optimistic’ message is that, if for one reason or another, a new set of non-stop short and medium distance flights could be offered from the non-hub airports, there would be a market for travelers to purchase those flights. Using an ambitious model of all long-distance travel in the lower 48 states, significant market support for such new services is reported in the scenario analysis. This, in turn, supports the logic of developing new kinds of aircraft designed to go relatively short distances with lowered operating costs. Such an optimistic scenario produced increases in smaller airports use of 30-50% percent, with highest impacts for the shorter trip distances, and for the smallest airports. Pessimistic? Also, for the airport manager, the ‘pessimistic’ message is that that massive erosion in shorter distance air trip-making has already occurred throughout the country, resulting in a 30% decrease in seat miles per capita offered in short distance segments, and 30% decrease in trips taken with origin to destination distances of 500 miles or less. Beyond these empirical observations, the scenario analysis clearly sends out a warning that this erosion is by no means completed; with the incremental improvement in the level of comfort associated with the automobile, there could be incremental shift towards the automobile in the short distance air market. 7(B) OVERALL PATTERNS, INCREASE IN AUTO FOR THE MID-DISTANCE TRIP WHEN DID IT START HAPPENING? The study has concluded that the roles of the airline services and the roles of the private automobile have changed over the past two decades, particularly for trips under 1000 miles in length. This market pattern is associated with drops in total traffic at smaller airports and gains at larger airports. Arguably, this restructuring of American air services away from the shorter distance service commenced in the fall of 2001. Using 2000 as a base year, the fall in air use for the shorter trip has been dramatic. As analyzed by the regional jet manufacturer Bombardier, air trips for the market under 500 miles in distance fell by about 30% to the present time. That study noted about a 50% drop in air trips under 200 miles, 39% in trips between 200 and 300, and 14% between 300 and 400 miles in distance. Using Dallas to Houston as a case study of what the author calls “the tenuous balance of the

103 drive or fly decision,31” the analysis traced through the auto access time to the origin airport, and egress time from the destination airport. To that market analyst, the addition of hours of “hassle time” associated with earlier arrivals at the airport of origin tipped the scale over to the auto competitor. Air volumes today are only 40% of their year 2000 level in their case study corridor. The author phrases well as he summarizes the main conclusion of the article: “One thing is abundantly clear: something fundamental changed shortly after 2000 in the short-haul market.32” Shorter flights become less important Airlines have found lower levels of revenue in shorter flight segments, which they can usually only justify when serving as feeder to more profitable long-distance domestic and international flights. In the vulnerable distance band categories, all of this results in a lowering of the number trips taken entirely by air, and the number of air segments used to gain access to the longest distance segment of the trips. At the same, growth in the longer distance air markets has resulted in a level of overall enplanement per capita that is higher today than ever before, with a roughly 15% increase in the rate of trips per capita over the base year of 1995. As shown in Figure 2-4 in Chapter 2, the length of the airline trip segment has increased in a nearly-linear pattern over time, reflecting the lower participation of short flights and higher role for longer flights. As a result, the auto share of the mid-distance market soars Through a long process of data analysis, ending most recently in the 2017 Project Survey, we can observe the growth of the auto share of the long-distance market. Using only the empirical results from our recent survey, Figure 2-8 in Chapter 2 revealed an increase in auto mode share since 1995 that is most dramatic in the distance band between 700 and 1500 miles. As expected, in the final simulation dataset, the scale of auto share decreases in a relatively linear pattern as the distance of the trip increases. Auto trips under 1,000 miles seem to be associated with the fall of the air mode, with less variation for the longer trips. Using the more commonly accepted definition of the long-distance trip, our analysis concluded that the role of the auto trip above 100 miles seems to be changing. In our 1995 base year data, those trips represented 17% of yearly VMT per capita; today they may constitute 29% of our VMT per capita. 7(C) OUR SELECTION OF DEPARTURE AIRPORTS IS CHANGING CHANGE IN AUTO USE TO DISTANT AIRPORTS The pattern of increased auto reliance in the long-distance trip also includes the increased use of the automobile to access the airport of choice. This study has revealed the basic logic behind “leakage.” Our research has established that, if total travel times were the same, most travelers 31 Miller, C. “What Caused the Short Haul Traffic Decline in the US? – the $34b Question” August 2017; linkedin.com/pulse/what-caused-short-haul-traffic-decline-us-34b-question-miller 32 Ibid

104 would prefer to spend additional time in an auto to gain access to a better-served airport rather than spend that same time in a feeder flight and its attendant times. The results of the project’s Hybrid Choice model show that business travelers would be willing to spend $17 to save an hour of driving time but would be willing to spend $36 to save an hour of flying time. Leisure travelers would be willing to spend $10 to save an hour of driving time and $26 to save an hour of flight time. Thus, the traveler would drive an extra hour to save one half hour of flight time. In short, the study suggests that the question of the “closest” airport is not the key attribute when selecting the airport of departure. The question seems to turn on what airport will provide the best flight options, with the key desire to minimize time within airplanes. In short, the customer may want to “leak” in spite of whatever local sense of loyalty might be in play. Given that setting, we estimated that between 16% and 32% of proximate residents would not choose the closest airport. Breaking down by non-stop and connecting travel: the median shares of passengers captured at local airports are 83% in 2015 for direct itineraries and 42% in 2015 for connecting itineraries. As reported in Chapter 4, this means that the chances of regaining passengers with the implementation of non-stop flights are far higher than with non-direct connections. This, in turn, provides support for the development of aircraft which could provide short distance direct services, particularly relevant to smaller airports. 7(D) WHAT’S IN THE FUTURE? The major conclusion from this project is that the competition between the auto and air over the short to moderate distance trip is not over. The already observed increases in the mode share of the car may be just the tip of the iceberg. THE FULLY DEVELOPED AUTONOMOUS VEHICLE TABLE 7-1. SIMULATED DECREASE IN AIR TRIPS AS A RESULT OF AUTONOMOUS VEHICLES Source: Figure 1-2 DISTANCE ( MILES) DECREASE IN NUMBER OF AIR TRIPS PERCENT LOSS IN DISTANCE BAND 200-400 8.2 million ‐18% 400-600 8.4 million ‐15% 600-800 13.2 million ‐24% 800-1000 11.1 million ‐22% 1000-1200 9.3 million ‐18% 1200-1400 6.2 million ‐16% 1400-1600 2.7 million ‐13%

105 Which trips are most vulnerable to change in mode? Table 7-1, adapted from Figure 1-2 in Chapter 1, reveals the starkness of the implications of the possible development of autonomous automobiles. And, as noted throughout this document, rates of loss for trips under 1,000 miles are greater than those for the longest trips. The Scenario Testing Model process was not designed to forecast the future; it was designed to help determine a sense of scale for what might happen. But, from Table 7-1 the implications for these mid-range air trips should be clear, and a cause for concern in the aviation industry. Incremental improvements to the car There are several components to the threat posed by the car of the future. In addition to the examination of the autonomous car in with five overarching scenarios, separate component elements of a future car scenario were examined. The concept of the auto trip being less stressful than today was tested, resulting in decreases in air use ranging from 5% for the 800-1,000 trip distance band to 10% for the shortest trips. It is important to note that these sensitivities did not result from the case of full adoption of autonomous vehicles, which was shown in Figure 1-2; lowered stress of the auto trip might come from more music, more entertainment, more perception of being connected or even a feeling of greater safety associated with modern connected vehicle technology. WHAT DID WE LEARN ABOUT THE CHOICE BETWEEN THE CAR AND THE PLANE? Does market behavior make sense? The study has concluded that the traveling public acts rather logically when faced with trip options. Over long-distances, the price per mile of air travel is lower than the price per mile by car. With an average trip party size, the plane becomes cheaper at about 1500 miles—at which distance some 70% of the population chooses the plane. By contrast, while the plane is clearly cheaper for the longer distances, the price per mile for a trip under 700 miles is more than twice the price per mile of trips over 700 miles – at which distance some 70% choose the car. Thus, those with price sensitivity seek more use of the auto for shorter trips, while getting a better bargain with the plane for the longer trips. Positive and negative market segments. In terms of overall predilection towards a given mode, our market segmentation revealed that about 52% of the population belong to market segments positive towards the plane, with only 24% in pro-auto segments. Logically enough, the segment most positive about the air trip is dominated by business trips. In short, the study concludes that, while most passengers would prefer the plane, their choice of mode accurately reflects reasonable considerations of cost. What drives the modal decision? This study has addressed the question of the competition between the car and plane by using a wide variety of research approaches, ranging from attitudinal (and subjective) to predictive (and quantitative): but there are common threads running through the various types of analyses. The

106 study has re-enforced the concept that much of the decision to choose a long-distance mode is emotional, and very much associated with attitudes and values. When faced with the positive inclination to the plane, but the reality of its additional costs, the attitudes towards the stress and unpleasantness of the car trip must come into play. Then, the choice is influenced by a somewhat polarized set of attitudes about the unpleasantness of the auto trip, where one group (including the young) finds the auto trip distasteful, and second group (including those over 35) finds it more acceptable. In sum, the SEM modeling process suggests that the choice of the long-distance mode is the result of a trade off in the mind of the traveler, in which the price of the air trip is compared with perceived level of unpleasantness of the long- distance auto trip. Compared with the explanatory power of these factors, other considerations modeled are revealed to be relatively unimportant. THE POSITIVE OUTLOOK Looking at the most positive scenario for the smaller airports (Scenario 3), Table 7-2 shows that the increase in absolute number of additional fliers would be spread rather evenly over all the distance bands under 1,000 miles, with the highest increase from the shortest trip distances. TABLE 7-2 SIMULATED INCREASE IN AIR TRIPS AS A RESULT OF NEW SHORT DISTANCE FLIGHTS DISTANCE (MILES) INCREASE IN NUMBER OF AIR TRIPS PERCENT INCREASE WITHIN DISTANCE BAND 200-400 11.7 million 25.4% 400-600 11.0 million 19.7% 600-800 9.7 million 17.7% 800-1000 9.2 million 18.6% 1000-1200 7.4 million 14.7% 1200-1400 5.0 million 12.7% 1400-1600 2.0 million 9.9% The modeling process in Scenarios 2 and 3, has answered one question: assuming that the airlines elected to operate more direct, non-hub-transfer, services, would there be a market to purchase these services? While the answer to the this carefully phrased question is, “yes,” there is no assumption by the research team that the airlines would, indeed, choose to offer this new set of short to medium distance services. This is a matter that must be examined later, in further research. 7(E) FURTHER RESEARCH This ACRP report has presented the first results from a new modeling procedure which predicts trip making behavior for all long-distance trips in the lower 48 states, in this case applied to the competition between air and auto. This report has presented early interpretations of possible futures, each of which deserves more attention on its own merit. Similarly, each of the separate

107 component factors were reviewed only briefly in this document and could be explored in more detail in further research. What about the other modes? The report has noted that for the vast majority of American city pair corridors, the effective market competition is between air and auto. But, in the Northeast and a very select few city pair markets, rail and bus together represent meaningful alternative modes of intercity travel. Further development of the Scenario Testing Model should re-incorporate those trips into the analysis process for those corridors where inclusion would improve the accuracy of the modeling process. While the subject of rail’s competition with air has been explored in the Cooperative Research Program’s work, (see Integrating Aviation and Passenger Rail Planning33, and Intercity Passenger Rail in the Context of Dynamic Travel Markets34), widening the modal coverage of the Scenario Testing Model would have obvious benefits. Better understanding of the attributes of auto travel In many parts of this study, policy attention was focused on the implications of a full and complete program of autonomous vehicles replacing the car as we know it. Such a mature program would offer an alternative to long-distance travel of all kinds. But, the next few decades will clearly see incremental improvements to the automobile, whose impacts will be more selective and more subtle. On the one hand, the next decades will see more hedonic improvements to the pleasure of the auto trip, through more entertainment and quite possibly measures to increase the ability to be connected to the Internet. Although difficult to quantify, the near future may bring improvements to the safety of travel through connected vehicle technology, with more safety associated with spacing of and interaction with other vehicles on the roadway. This might be associated with a decreased level of stress from the driving experience. On the other hand, the next decades will almost inevitably see increased levels of congestion for the portion of the long-distance auto trip within urban/metropolitan areas. Concerning its market attributes, the auto of the near future may offer more pleasurable attributes while still spending more time stuck in congestion. How this trade-off will impact the competition with the air travel is beyond our present ability to predict. 33 Coogan, M., D. Brand, M. Hansen, H. Kivett, J. Last, R. Marchi, M. S. Ryerson, M. J. Taylor, and L. Thompson. 2015. ACRP Report 118: Integrating Aviation and Passenger Rail Planning. Transportation Research Board of the National Academies, Washington, D.C. 34 RSG, M. Coogan, AECOM, I. Ajzen, C. Bhat, B. Lee, M. Ryerson, and J. Schwieterman. NCRRP Report 4: Intercity Passenger Rail in the Context of Dynamic Travel Markets. Transportation Research Board, Washington, D.C., 2016.

108 Better understanding of why airlines do and do not add new service As emphasized in this report, our Scenarios 2 and 3 both assumed that, for reasons established exogenously, the airlines would add more direct service between airports that are not major hubs. At the present time, both NASA on the public sector side, and private entrepreneurs are exploring the concept of smaller airplanes providing this kind of service. To the extent that such planes could mimic the quality of travel experience offered by larger planes today, this hypothetical service makes sense. But, to the extent that smaller planes imply lack of ability to undertake instrument-based landing in lower visibility, or the ability to fly at altitudes “above the weather” – their future role in replacing the automobile for short distances is highly questionable. This report, as noted earlier, has addressed the question of the market scale for such services, but not their economic viability. Separate from the high visibility issue of new aircraft technology is the less dramatic question of airline-based decisions about the amount of service to provide between the smaller airport and even the dominant hub of that airline. Quite simply, the more the airport manager can understand the underlying economics of service provision to the smaller airports, the better that manager can be at creating a strategy to maintain and expand those services. The modeling processes developed in this study might be modifiable to address such near-term demand issues. Better understanding of the choice of the airport of arrival Much of our analysis has focused on the way in which the traveler selects the airport of departure. This was not meant to imply that the selection of the airport at the non-home end of the trip is a symmetrical, mirror-image of the selection at the home end of the trip. At the home end of the trip, most Americans have access to an automobile, whether by owning, sharing or borrowing it. At the home end of the trip, the traveler is aware of all the details of the roadway network, and quite capable of choosing the best auto trip options. The opposite is true at the non-home end of the trip. An auto must be rented, or a taxi/TNC must be obtained at some cost. The traveler may or may not know the roadway network and may not want to add several hours of driving after a long airplane trip. We propose that additional research be undertaken to explore the hypothesis that the local airport, close to the center of the destination city, may be more relevant to the arriving visiting traveler than to the departing local traveler. Gaining supporting data for this might be difficult, in that neither the BTS/FAA T-100 database, nor the DB1B database really focus on whether the trip makers are local, (going out) or non-local (coming in). The implications for the airport manager are critical. Finding that the true strength of the small airport market is the arriving passengers, rather than departing, would result in a significantly different marketing strategy than the opposite case. The need for better data collection for the auto trip Along with significant advances undertaken by FHWA, this ACRP project has resulted in the creation of new approach to understanding national long-distance travel patterns. It addresses the issue of modal competition directly. It is critically important for the future of this effort that

109 coordination be maintained with ongoing efforts at FHWA to improve the quality of data used to describe origin-to-destination automobile trip making. Future improvements to a multi-modal approach (in this case auto vs. air) will require a significantly improved understanding of long- distance auto patterns. Ongoing efforts at FHWA concerning the future of the National Household Travel Survey, and the “NextGen” for auto trip data collection must be carefully coordinated with further improvements in understanding multi-modal and intermodal passenger travel behavior.

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

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

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

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

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