Understanding Impacts to Airports From Temporary Flight Restrictions (2020)

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39 Models to Assess Quantitative and Qualitative Impacts of TFRs 4.1 Impact of Restrictions on Flight Counts One way to directly measure the impact of TFRs with long duration (roughly 12 hours or longer) is by estimating the number of flights that did not operate while the TFR was in place. To do this, researchers used operational data from the FAA to compare traffic at affected airports on days with and without a TFR. That data is shown in Sections 4.1.1 and 4.1.2. For shorter-duration VIP TFRs, the number of flights affected is often hard to discern, espe- cially at large airports, because flights are simply delayed by 15 minutes or so while the president lands or departs. There will be gaps in operations of 2–3 hours at smaller airports, but these could be offset by increased traffic in the times just prior to and just after a TFR is in effect. The data source was an FAA database called Traffic Flow Management System Counts (TFMSC), also known as CountOps. This database includes data for flights that fly under instrument flight rules (IFR) and are captured by the FAA’s en route computers. Most visual flight rules (VFR) traffic and some non-en-route IFR flights are excluded from CountOps, so the data includes most flights but not every one. TFMSC includes information about commercial traffic (air carriers and air taxis), GA, and military flights to and from every landing facility in the United States and in nearby countries that participate in the Traffic Flow Management System (TFMS). Because this detailed flight data was only available for the most recent year, researchers used a representative sample of overnight presidential trips in 2018. (The full list of trips is shown in Table A2 in Appendix A.) On most trips, the president leaves and returns to Washington in the same day, but on a few occasions stayed overnight in a different U.S. city. Because of the potential for greater disruption from these longer TFRs, researchers focused on these overnight trips. Researchers gathered records of individual flights at airports surrounding these cities on the dates of the TFRs, and on the corresponding days of the week both one week prior to and one week following the presidential visit. 4.1.1 Recurring Overnight Trips The first example looks at eight TFRs in New Jersey over the spring and summer of 2018. Researchers obtained CountOps data for airports inside the New Jersey TFR on weekends during May through September 2018. Researchers compared traffic during TFRs with traffic on other weekends when no TFR was in place. The CountOps database includes a caveat that counts might be incomplete, especially at smaller airports that are not official CountOps facilities. None of the small airports inside the New Jersey VIP TFRs are official CountOps facilities. C H A P T E R 4

40 Understanding Impacts to Airports From Temporary Flight Restrictions The two airports inside the TFR’s inner ring are Somerset (SMQ) and Solberg-Hunterdon (N51). Flight data from those airports is shown in Table 6. For weekends when there was no TFR, researchers counted operations between 5:00 pm local time on Friday and 5:00 pm local time on Sunday, which is close to when the TFRs typically begin and end. For weekends when there was a TFR, researchers counted operations during the actual TFR times. Researchers also counted the number of flights that were military or security aircraft associated with the president’s travel party. Apart from those flights, the traffic was zero or nearly zero, as expected. The average number of operations at Somerset during non-TFR weekends was 20.7 and at Solberg-Hunterdon it was 7.8. The absence of these flights during TFRs can be interpreted as a direct impact to these airports and associated businesses. This analysis was extended to Morristown Airport (MMU), because MMU is a much busier airport and lies in the outer ring of the TFR. Also, Air Force One often lands at MMU. Table 7 shows MMU flight counts for each Saturday from June through September in 2018. Researchers counted operations during a full 24-hour period (Saturday) each week to avoid the varying start and end times of the TFRs. The data is from CountOps, which was used before, but which does not include VFR flights. The average number of operations at Morristown on non-TFR Saturdays was 104 and on Saturdays with TFRs the average number of operations was 141. This is the opposite of the effect Weekend (Fri) TFR? Somerset (SMQ) Solberg (N51) # of operations # military / security # of operations # military / security June 1 No 12 0 4 0 June 8 No 31 0 10 0 June 15 No 26 0 6 0 June 22 No 16 0 12 0 June 29 Yes 17 15 0 0 July 6 Yes 20 20 2 1 July 13 No 28 0 5 0 July 20 Yes 10 10 2 2 July 27 Yes 18 17 2 0 Aug 3 Yes 16 16 4 2 Aug 10 Yes 12 12 1 1 Aug 17 Yes 12 11 2 2 Aug 24 No 29 0 11 0 Aug 31 No 17 0 13 0 Sep 7 No 16 0 2 0 Sep 14 No 9 0 5 0 Sep 21 Yes 16 16 1 1 Sep 28 No 23 0 10 0 Table 6. Somerset and Solberg airport traffic, June through September 2018 weekends.

Models to Assess Quantitative and Qualitative Impacts of TFRs 41 observed at the smaller airports in the inner ring of the TFR. However, the general manager of the FBO at Morristown estimates they lose about 20 flights per TFR, i.e., pilots that were going to fly but delayed, relocated, or canceled their trip. Examination of the flight-by-flight timeline showed there is a gap of 20–60 minutes surrounding the departure or arrival of Air Force One, during which regular operations are prohibited. (Military and government flights traveling with the president do come and go during these times.) These periods can account for the perceived “missing” flights, although there is no way of precisely counting operations that did not occur. 4.1.2 Non-Recurring Overnight Trips Figure 20 shows a map of the TFRs put in place for a presidential trip to Arizona in October 2018. He arrived late on a Thursday night, spent the day Friday in the area, and left Saturday morning. As he traveled around the greater Phoenix area on Friday, the TFRs shifted among three discrete points to correspond to his location. Figure 21 shows the location of the airports inside the inner (10-nm) core of one or more of the TFRs. Table 8 shows the airports affected by the Phoenix-area TFRs and the time spent in the inner core and outer ring areas. Table 9 compares flight count data during Phoenix-area TFR with flight counts during the same time periods during adjacent weeks. Saturday TFR? Morristown (MMU) June 2 No 115 June 9 No 97 June 16 No 102 June 23 No 114 June 20 Yes 126 July 7 Yes 166 July 14 No 103 July 21 Yes 185 July 28 Yes 111 Aug 4 Yes 114 Aug 11 Yes 107 Aug 18 Yes 100 Aug 25 No 77 Sep 1 No 81 Sep 8 No 118 Sep 15 No 93 Sep 22 Yes 221 Sep 29 No 136 Table 7. Morristown operations, May through September 2018 Saturdays.

Figure 20. TFR map for President’s Phoenix trip, Oct. 18–20, 2018 (source: NBAA). Figure 21. Locations of airports in Phoenix metro area.

Models to Assess Quantitative and Qualitative Impacts of TFRs 43 For airports located in the inner core (10-nm radius) of one of the TFRs, the impact was greater. Table 9 shows what happened at five of these airports (shown in Figure 21 and described in Table 8). Two of them (Deer Valley and Scottsdale) lie inside the inner cores that were in effect for both the overnight periods, 32.5 hours total. The reduction in flight operations there was substantial, 78% and 91%. This represents hundreds of flights, given how busy those two airports normally are. For the other three airports, which were inside an inner TFR ring for 2.5 hours during the day on Friday, the reduction was not as great, although some of the flights that operated appear to be helicopters associated with the president’s travel team. Table 9 also shows flight counts for Phoenix Sky Harbor Airport, which was in an outer ring for the duration of the president’s visit. Operations at PHX were affected very little. Other than a delay of a few minutes when the president arrived and departed, operations continued. This shows that GA airports in the inner core of a VIP TFR experience a sharp decrease in traffic when the TFR is in effect. For a busy airport, a long TFR can result in hundreds of flights being deferred, rescheduled, or canceled. Airports in the outer ring also experience a drop in the traffic, but not as large a drop. Researchers conducted a similar study for trips to Las Vegas and Milwaukee in 2018. Data from those events is shown next. Airport Airport Name Inner core area (Figure 20) Time in inner core (hours) Outer ring area (Figure 20) Time in outer ring (hours) CHD Chandler Municipal E/F 2.5 A/B, G/H 32.5 DVT Phoenix Deer Valley A/B, G/H 32.5 C/D 2.25 FFZ Falcon Field E/F 2.5 A/B, G/H 32.5 IWA Phoenix-Mesa Gateway E/F 2.5 A/B, G/H 32.5 SDL Scottsdale A/B, G/H 32.5 C/D, E/F 4.75 PHX Phoenix Sky Harbor none 0 A/B, C/D, E/F, G/H 37.25 Table 8. Airports affected by Phoenix-area TFRs. Airport Number of operations during TFR inner ring Avg. number of ops during same times in adjacent weeks Percent reduction during TFR Number of operations during TFR outer ring Avg. number of ops during same times in adjacent weeks Percent reduction during TFR CHD 2 2.5 20 50 55 9 DVT 34 156 78 18 28 36 FFZ 7 11 36 47 79 41 IWA 5 33 85 127 213 40 SDL 21 224 91 59 53.5 -10 PHX 0 0 0 1747 1763.5 1 Table 9. Flight count data during PHX area TFR and during same times in adjacent weeks.

44 Understanding Impacts to Airports From Temporary Flight Restrictions Table 10 shows the airports affected by the Las Vegas TFR (see map in Figure 22) and the time spent in the inner core and outer ring areas, along with flight counts during those times and during the same time periods in adjacent weeks. Operations at North Las Vegas, in the inner core, were down significantly during this period, while those at Henderson, in the outer ring, appear to have increased, suggesting that some pilots may have shifted to HND when they might normally have preferred to use VGT. Table 11 shows the airports affected by the Milwaukee TFR (see map in Figure 23) and the time spent in the inner core and outer ring areas, along with flight counts during those times Airport name Time in inner core (hrs) Number of operations Avg. number of operations during adjacent weeks Percent reduction during TFR Time in outer ring (hrs) Number of operations during TFR outer ring Percent reduction during TFR Henderson (HND) 0 0 0 0 19 153 65.5 -134 North Las Vegas (VGT) 19 12 100.5 88 0 0 0 0 McCarran Intl (LAS) 18.25 750 1149 35 0.75 71 79 10 during TFR inner core Avg. number of operations during adjacent weeks Table 10. Airports affected by Las Vegas TFR, September 20–21, 2018. Figure 22. Aeronautical chart of Las Vegas area. Henderson (HND) and North Las Vegas (VGT) were inside the TFR boundaries. (source: SkyVector)

Airport name Time in inner core (hrs) Number of operations during TFR inner core Avg. number of operations during adjacent weeks Percent reduction during TFR Time in outer ring (hrs) Number of operations during TFR outer ring Avg. number of operations during adjacent weeks Percent reduction during TFR Kenosha (ENW) 2 1 3 67 16 15 15.5 3 Timmerman (MWC) 16 3 28.5 89 1 3 5 40 Racine (RAC) 2 1 1.5 33 16 13 8 -62 Mitchell Intl (MKE) 16 159 223.5 29 1 29 39.5 27 Table 11. Airports affected by Milwaukee TFR, June 27–28, 2018. Figure 23. Aeronautical chart of Milwaukee area. Kenosha (ENW), Timmerman (MWC), and Racine (RAC) were at times within the 10-nm inner core of a TFR. (source: SkyVector)

46 Understanding Impacts to Airports From Temporary Flight Restrictions and during the same time periods in adjacent weeks. Timmerman (MWC) spent the most time in the inner core and experienced the largest drop in traffic. The major commercial airport (MKE) showed a decrease in traffic during this period also, and the same thing is observed at LAS during that event. This might be coincidence, although it could represent GA traffic that is not allowed to operate during TFRs. These three examples illustrate that inner core airports can see traffic drop by as much as 90%. Traffic at outer ring airports is much less predictable; it can drop by 40% or it can increase. The spreadsheet (see Section 4.3) uses this data to estimate TFR impact. Researchers could query similar data for other VIP TFRs of shorter duration and for other types of TFRs. However, the data shown in this report makes clear that the effect of TFRs on each airport is slightly different. The biggest determining factor of financial impact is the number of flights unable to operate during a TFR. Therefore, to predict the impact of a future or proposed TFR on a given airport, there are two questions for that airport operator: how many flights do you normally have during that time and what is the value to you of each flight. The product of multiplying those two quantities together is an estimate of financial impact. 4.2 Method for Estimating Impact of Airspace Restrictions The research goal is to develop a model of the economic impact of airspace restrictions. Potential economic impacts may take many forms, ranging from a loss of operation-related revenues to longer-term changes in how impacted airports operate. Based on the data available for this study, only changes in airport revenues were estimated. Because individual airports, and the associated tenants that rely on the airport, differ in terms of structure, ownership, and financial practices, it would be difficult to quantify other financial and economic impacts. Thus, changes in revenues resulting from a TFR serves as a good proxy for estimating the overall impacts to an airport. Only a portion of an airport’s revenue, or that of airport tenants, is derived directly from flight operations. For example, monthly hangar or tie-down leases are not likely to be impacted as a result of short-term interruptions in flight operations. The model attempts to quantify these variable revenues only. A possible use case for this model would be to predict the impacts of future VIP TFRs. One of the stakeholders at a New Jersey aviation business said that prior to 2017, “We didn’t see this coming,” referring to the impact on his business. Now, by using this model, whenever a new president is elected, people can begin to anticipate what will happen. Right after the election (or even before—when the field can be narrowed to a small number of candidates), they can determine places the president and vice president are likely to visit frequently, apply this model, and estimate the impacts. Affected businesses will have time to prepare and to warn all their customers. They will also have more time to work with the Secret Service to arrange for accom- modations such as cutouts near the edge of the 30-nm ring, or possibly to set up a screening or gateway procedure for pilots. It is also possible that a future president or vice president, if presented with an impact estimate of potential travel, will choose to travel to other places, or to travel less often. 4.3 Electronic Tool for Estimating TFR Impact Researchers developed a spreadsheet tool that can be used by airports to evaluate the financial and other ramifications from TFRs and other airspace restrictions. The tool implements the methodology described in Section 4.2. The tool leads the user through a series of questions to

Models to Assess Quantitative and Qualitative Impacts of TFRs 47 gather input parameters that specify the type of airport or business being affected and the nature of the airspace restriction. Then, drawing on the compiled data and case studies and the created parametric relation- ships, the tool provides a best or most likely estimate for quantitative impacts, along with extreme values (upper and lower bounds), to define a range of possible impacts, based on varying the input parameters. In some cases, the estimate will be quantitative, in dollars; in other cases, outputs will be qualitative, i.e., a list of possible impacts, not all of which may apply to a specific situation, but that should be considered by the affected stakeholders. This model describes microeconomic effects on primary stakeholders (e.g., airport opera- tions, fuel sales, parking) and related businesses (e.g., flight training, skydiving). Addressing lower-order effects, such as regional economic impacts (e.g., on nearby hotels or restaurants) would require a larger data set than one associated with any airspace restrictions. Therefore, the proposed tool will provide quantitative outputs only for these primary stakeholders. Using data on drops in air traffic demand, parametric relationships can be formed that can be codified in a spreadsheet or similar tool with standard analytic functions. The duration and frequency of recurrence of airspace restrictions are major parametric inputs. For example, the impact of the recurring TFRs in Florida and New Jersey is greater than that of one-time TFRs, even those that last over a night or a weekend. Some of the inputs to the tool can be determined only by the airport stakeholders who have access to the necessary data about their operation, such as the number of customers, or revenue from certain types of operations. Each user of the tool will be able to produce a report custom- ized to his or her specific situation. Note that in order to provide a total estimate of TFR impact at any one airport, the airport manager should ask all airport businesses to fill out the spread- sheet and then combine those impact estimates. The output of the tool may include qualitative information, such as a checklist of factors to consider. The tool will also produce suggestions for mitigation steps to take, such as asking for specific accommodations from government personnel (e.g., Secret Service), and guidelines on whom to contact to ask for these. A detailed user’s guide is included in the spreadsheet. A video was created that shows how to use the spreadsheet, with instructional voiceovers explaining its features. Another feature of the spreadsheet is that it does not require users to share their data; nothing is uploaded to the internet or stored in a central database. This addresses any concerns users might have about privacy, i.e., reluctance to divulge their financial data to a third party. 4.4 Model Inputs Inputs to the model are factors that determine the impact or ramifications of TFRs. Not all of the factors listed here will be necessary, or applicable, to all use cases of the model. In addition, other input questions are possible; this is not intended to be an exhaustive list. For airports and aviation-related businesses located near restricted airspace, the first three factors separate TFRs from more permanent restrictions, which have different properties. • The restriction is permanent, such as the Washington, D.C. Special Flight Rules Area (SFRA) or FRZ. • The restricted airspace is associated with military activity, i.e., special activity airspace (SAA). • The restriction is a TFR.

48 Understanding Impacts to Airports From Temporary Flight Restrictions The remaining factors all pertain to TFRs, further defining the nature of the restriction. • The TFR has two concentric rings, with different rules for each. • The airport is inside the inner core (usually a 10-nm radius). • The airport is between the inner and outer rings (usually between 10nm and 30nm). • The airport is in a cutout near the edge of the restricted area. • The airport has GA operations that normally route through the inner core. • The airport has flights that depend on filing routes using Very High Frequency Omni- Directional Ranges (VORs) in the inner core. • The duration of the restriction (start time to end time). • The number of operations expected over the TFR time period without a restriction. • The percentage of operations represented by each of: – Scheduled commercial traffic (e.g., major airlines) – GA – Cargo flights – Military flights – Helicopters – Glider or antique aircraft – Unmanned vehicles – Model aircraft – Autonomous vehicles • The number of flight school students expected to fly here during a similar duration. • The number of skydivers expected to fly here during a similar duration. • The number of banner towing operations expected during a similar duration. • Any other special operations that usually fly here, for example, crop dusting or sightseeing. • The major sources of revenue, for example, landing fees, parking fees, fuel sales, maintenance, food sales. • The time when the restriction was announced (lead time). • How frequently a similar restriction occurs at this location. Uncertainty about when restrictions will occur. Lack of predictability, especially over an extended period of time, leads to reduced business as potential customers (e.g., flight schools, training, recreational flying) go to other airports or avoid flying. These factors determine the model input parameters of duration, lead time, frequency, and geographical scope. The model will convert these airspace restriction parameters into a change in airport volume (throughput). The model will produce a best, or most likely, estimate. To address variability, the model will alter the input parameters to yield more extreme values (upper and lower bounds). 4.5 Model Outputs Outputs from the model are a list of possible impacts, and an estimate of the size or mag- nitude of each. The model thus determines the relationships between the input and output variables. Models will output a range of financial ramifications for each potential impact, based on airport activity level and other characteristics. The impact estimate in the spreadsheet is expressed in terms of the number of operations that would have flown, if the TFR were not in place, but are unable to fly because of the TFR. Based on the average revenue per operation, this quantity of operations is converted to a dollar amount of lost revenue, or opportunity cost.

Models to Assess Quantitative and Qualitative Impacts of TFRs 49 A list of possible qualitative impacts is also displayed. For example: • No impact. • Some flights will be delayed until the TFR is over, but not more than 1–2 hours. • Flights must be rerouted around the TFR. • Flights must be rescheduled (e.g., to the following weekend) or canceled. • Flight training operations must be rescheduled or canceled. • Skydiving runs must be rescheduled or canceled. • Model aircraft or UAS operations must be rescheduled or canceled. • Business revenues are reduced. • Business revenues are increased. • Future business is deterred by uncertainty surrounding restrictions. Section 3.2 explained that each stakeholder is different and could warrant its own customized model. The spreadsheet allows users to input basic information and get an estimate based on that information. The range of the estimate might be wide, if the input data is a rough guess, but ideally the act of estimating will identify the factors in play and provide some ideas on how to mitigate any potential impact.