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Impact of Jet Fuel Price Uncertainty on Airport Planning and Development (2011)

Chapter: Part II - Documentation for Airport Forecasting Risk Assessment Program

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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
Page 57
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Suggested Citation:"Part II - Documentation for Airport Forecasting Risk Assessment Program." National Academies of Sciences, Engineering, and Medicine. 2011. Impact of Jet Fuel Price Uncertainty on Airport Planning and Development. Washington, DC: The National Academies Press. doi: 10.17226/14506.
×
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Documentation for Airport Forecasting Risk Assessment Program P A R T I I

The Airport Forecasting Risk Assessment Program is a Microsoft® Excel spreadsheet; the user will need Microsoft Excel 2000 or later to run the software, and Excel macros must be enabled. Open the spreadsheet. Go to the SelectLOCID worksheet, and select an airport from the pull-down menu (Ex- hibit II-1). Press . The program takes the user to the OAGHistory worksheet (Exhibit II-2) where he or she can view 20-year trends for the airport including average domestic flight departures, domestic seat departures, average seat size, and number of domestic destinations served. The pull-down menu is used to focus on specific airlines at the airport or to compare the airport to others. The user should also examine the TAFHistory worksheet (Exhibit II-3), which shows how ac- curate recent TAF forecasts have been for the subject airport. Update Tables 39 Software Quick Start Exhibit II-1. SelectLOCID worksheet.

The CurrentService worksheet (Exhibit II-4) shows the air services available in individual do- mestic markets by identified airlines in 2009. The user can modify this information by adding new cities in the first two columns and new average weekly departures and average seat size in the last two columns labeled . The user can also modify existing services information in the last two columns. All of the modifications will show up in red font. To take account of these modifications in a new Baseline Forecast, press . The software will then take the user to the Baseline&Scenarios worksheet (Exhibit II-5). If modifications were made in the CurrentService worksheet, the Baseline Forecast at the top of the page will reflect those changes. If modifications were not made in the CurrentService work- sheet, the Baseline Forecast at the top of the page will be the TAF forecast. The user can further modify the forecast directly in the columns labeled by typ- ing in the numbers or using standard Excel commands. For the ACY example shown in Exhibit II-5, the results of increasing future activity by 5 percent across the board (relative to the default TAF baseline) are shown in Exhibit II-6. Changes will be shown in red font. User Updates Update Tables User Updates 40 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development Exhibit II-2. OAGHistory worksheet.

Software Quick Start 41 Exhibit II-3. TAFHistory worksheet. ACY Air Carrier + Air Taxi Operations: TAF Predicted vs. Actual 2,000 7,000 12,000 17,000 22,000 27,000 32,000 37,000 2004 2005 2006 2007 2008 2009 TAF 2003 TAF 2004 TAF 2005 TAF 2006 TAF 2007 TAF 2008 Actual Exhibit II-4. CurrentService worksheet. Exhibit II-5. Upper portion of Baseline&Scenarios worksheet.

In the lower portion of the Baseline&Scenarios worksheet (Exhibit II-7), the user can input ranges for key air service drivers, which in turn will create scenarios for the Baseline Forecast. In general, increases in these drivers will have the following impacts on air services: • Jet fuel price: (−) • Economic growth: + • Inflation:19 + • Average seats:20 + • Airport concentration: (−) • Other airport competition:21 (−) The Herfindahl–Hirschman airport concentration index (shown at the bottom left of the worksheet) is a measure of the level of market competition at the airport. It is computed as the sum of the squared seat-departure shares of all the carriers at the airport, and ranges from 0 to 10,000, with higher values reflecting less competition. If an airport were served by only a single monopoly carrier, the index would equal 10,000 (= 100 percent seat share squared). This driver has a negative impact on air services, reflecting the fact that the higher the index, the lower is the level of competition and therefore the lower the level of overall air service. The user can compute the index for a given set of market shares by using the calculator shown at the bottom of the Baseline&Scenarios worksheet. The user can also create Confidence Bands around the Baseline Forecast taking account of jet fuel price and economic uncertainty by pressing the buttons: Set Jet Fuel Scenarios based on Futures Uncertainty Set Income Scenarios based on EIA GDP Uncertainty 42 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development Exhibit II-6. Results of user updates to ACY example scenario. Domestic Operations Domestic Enplanements Domestic Operations Domestic Enplanements 2009 14,406 520,470 14,406 520,470 2010 14,548 527,543 15,275 553,920 2011 14,692 534,712 15,427 561,448 2012 14,836 541,979 15,578 569,078 2013 14,983 549,350 15,732 576,818 2014 15,133 556,821 15,890 584,662 Year User Updates (changes in red) Default Baseline Domestic Forecast 19 In the air services model, inflation is used to adjust nominal jet fuel prices to real prices; so high inflation results in lower real prices for jet fuel and thus more air service. 20 Average seat size is a proxy for the cost of producing a seat departure; larger aircraft produce lower seat costs, which in competitive markets result in lower prices and thus more air service. 21 Competition from large or medium hub airports within 50 miles tends to reduce air service.

Software Quick Start 43 Exhibit II-7. Lower portion of Baseline&Scenarios worksheet. Year Baseline Price of Jet Fuel (Current Yr $/gal) Scenario 1 Scenario 2 Year Baseline Inflation Rate Scenario 1 Scenario 2 2005 $1.622 2005 3.34% 2006 $1.906 2006 3.25% 2007 $2.025 2007 2.87% 2008 $2.938 2008 2.14% 2009 $1.844 $1.199 $3.227 2009 1.18% 2010 $2.174 $1.500 $4.000 2010 1.47% 0.00% 0.00% 2011 $2.258 $2.258 $2.258 2011 1.31% 1.31% 1.31% 2012 $2.499 $2.499 $2.499 2012 1.43% 1.43% 1.43% 2013 $2.719 $2.719 $2.719 2013 1.76% 1.76% 1.76% 2014 $2.888 $2.888 $2.888 2014 1.73% 1.73% 1.73% RealIncomeValu SeatsizeValues Year Baseline Local Real Income Growth Scenario 1 Scenario 2 Year Baseline Airport Avg Seatsize Scenario 1 Scenario 2 2005 0.43% 2005 119.9 2006 0.73% 2006 126.7 2007 0.27% 2007 130.1 2008 0.34% 2008 140.2 140.2 140.2 2009 -2.83% #N/A #N/A 2009 141.9 141.9 141.9 2010 1.07% 4.00% -1.00% 2010 141.9 141.9 141.9 2011 3.52% 3.52% 3.52% 2011 141.9 141.9 141.9 2012 3.64% 3.64% 3.64% 2012 141.9 141.9 141.9 2013 2.80% 2.80% 2.80% 2013 141.9 141.9 141.9 2014 2.46% 2.46% 2.46% 2014 141.9 141.9 141.9 HHIValues Set50Values Year Baseline Airport Concentration Index - HHI (0-10,000) Scenario 1 Scenario 2 Year Baseline Domestic Daily Seat- Departures at Lrg/Med Hubs within 50 Miles Scenario 1 Scenario 2 2005 8,189 2005 0 2006 8,450 2006 0 2007 8,804 2007 0 2008 9,715 9,715 9,715 2008 0 0 0 2009 8,333 8,333 8,333 2009 0 0 0 2010 8,333 8,333 8,333 2010 0 2011 8,333 8,333 8,333 2011 0 2012 8,333 8,333 8,333 2012 0 2013 8,333 8,333 8,333 2013 0 2014 8,333 8,333 8,333 2014 0 0 0 0 0 0 0 0 0 0 0 (Default baseline from 2010 forward equal to 2009 value.) (Default baseline from 2010 forward derived from TAF.) (Default baseline from 2010 forward based on change in projected price of jet fuel from EIA Annual Energy Outlook 2010.) (Default baseline from 2010 forward based on projected GDP Implicit Price Deflator from EIA Annual Energy Outlook 2010.) (Default baseline from 2010 forward based on projected US GDP from EIA Annual Energy Outlook 2010; 2009 value equal to US GDP growth.) (Default baseline from 2010 forward equal to 2009 value.) Not relevant for Small Hubs Forecast Drivers for Domestic Scenarios (International Forecast is Fixed) Not relevant for Small Hubs 2005-2009 data are fixed; you may change the Baseline and/or Scenario assumptions below for 2010-2014. If you entered updates to the Baseline Domestic Forecast above, you should ensure that the Baseline assumptions below are consistent with those updates. View the latest Heating Oil futures prices by clicking here JetFuelValues Set Jet Fuel Scenarios based on Futures Uncertainty Set Income Scenarios based on EIA GDP Uncertainty

44 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development Exhibit II-8. Projections worksheet. Projected Annual Operations for ACY 10,000 12,000 14,000 16,000 18,000 20,000 22,000 Baseline 17,962 14,406 15,275 15,427 15,578 15,732 15,890 Scenario 1 17,962 14,406 15,275 15,940 15,861 15,891 15,980 Scenario 2 17,962 14,406 15,275 14,589 15,076 15,418 15,679 2008 Act 2009 2010 2011 2012 2013 2014 Projected Annual Enplanements for ACY 450,000 500,000 550,000 600,000 650,000 Baseline 553,177 520,470 553,920 561,448 569,078 576,818 584,662 Scenario 1 553,177 520,470 553,920 580,140 579,441 582,641 588,000 Scenario 2 553,177 520,470 553,920 530,975 550,752 565,295 576,911 2008 Act 2009 2010 2011 2012 2013 2014 Projected Annual Revenues for ACY $5,000,000 $10,000,000 $15,000,000 $20,000,000 $25,000,000 $30,000,000 Baseline $21,809,615 $13,886,975 $14,587,270 $14,731,777 $14,877,321 $15,024,827 $15,174,229 Scenario 1 $21,809,615 $13,886,975 $14,587,270 $15,126,114 $15,095,449 $15,147,055 $15,244,057 Scenario 2 $21,809,615 $13,886,975 $14,587,270 $14,083,422 $14,489,700 $14,782,287 $15,011,764 2008 Act 2009 2010 2011 2012 2013 2014 The Baseline and Sensitivity Cases will be shown in the Projections and One-Page Report worksheets (Exhibits II-8 and II-9).

Software Quick Start 45 Exhibit II-9. One-Page Report worksheet.

46 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development Exhibit II-10. Impact of drivers on air services. Effect on Air Service if Driver is Driver Higher Lower Explanation Jet Fuel Prices + If nominal fuel prices rise, air services decline, and vice versa. Real Local Income + If real local income increases, air services increase, and vice versa. Inflation + If inflation increases, it reduces real jet fuel prices and air services rise, and vice versa. Average Seat Size at Airport + If average seat size increases, airline costs fall and air services rise, and vice versa. Airport Concentration Index + If one or a few carriers dominate seat departures, air services decline, and vice versa. Competition from Large/Medium Hubs + If average daily seat departures from an FAA large or medium hub airport within 50 miles grow, air services decline and vice versa. In creating the sensitivity cases, the user should keep in mind how the drivers affect air ser- vices at an airport. Exhibit II-10 summarizes these impacts. As with any forecasting process, the user is ultimately responsible for the assumptions used in the analysis. The software provides a structured way to improve airport forecasts and create sen- sitivity cases, but it is not a substitute for a well-thought-out analysis.

47 This user manual is presented in the form of a guided tour of the software, using Atlantic City International Airport (ACY) as an example. The steps to running the program are in bold and . SelectLOCID Worksheet The first worksheet shown in the software (Exhibit II-11) asks to the user to select from a list of 271 commercial service airports in the United States (excluding Hawaii and Alaska). In this example, select . To run the program, press the button. This erases all previous information run through the model and loads data for the selected airport. The user can also get access to information on the program; to do so, press .Information Help and Program Update TablesACY highlighted Software User Manual Exhibit II-11. Selecting an airport of interest in the SelectLOCID worksheet.

48 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development OAGHistory Worksheet Once the button is pushed, the software sends the user to the OAGHistory worksheet. At the top of the worksheet, the user can select: • Air service by individual carriers at the subject airport • Air service history at comparison airports (including by individual carriers) This information may be helpful in creating a customized forecast and in reviewing the reason- ableness of any forecast relative to history. In this example, select from the pull-down boxes for ACY (shown in Exhibit II-12). Select for a comparison airport. When an air- port is first selected, airport totals are shown, but the user may select individual carriers in any or all of the three carrier selection boxes. The graphics (shown in Exhibit II-13) provide an interesting history of air service at the subject airports. A user might test his or her own customized forecast against this history, or use a comparison airport to examine the possible future for the subject airport. In the follow- ing discussion, sample observations that might be drawn from the data are provided for illustrative purposes. These observations do not represent any formal conclusions about the airports shown. ABE (Allentown, PA) NK (Spirit) and FL (Airtran) Update Tables Locid Carrier #1 Carrier #2 Carrier #3 ACY - ATLANTIC CITY-INTL, NEW JERSEY (Small Hub) TOTAL NK - SPIRIT AIRLINES FL - AIRTRAN AIRWAYS ABE - ALLENTOWN, PENNSYLVANIA (Small Hub) (blank) TOTAL (blank) (blank) (blank) (blank) (blank) Exhibit II-12. Selecting airlines and comparison airports in the OAGHistory worksheet. Daily Domestic Flight Departures (Based on Feb and July Schedules) 0 10 20 30 40 50 60 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 ACY-TOTAL ACY-NK ACY-FL ABE-TOTAL Daily Domestic Seat Departures (Based on Feb and July Schedules) 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 ACY-TOTAL ACY-NK ACY-FL ABE-TOTAL Seat departures at ABE have fallen dramatically since the early 1990’s, and accelerated after 9/11. ACY is on an upward trend, again due to NK. Flight departures at ABE have fallen off dramatically, while they have been more stable at ACY over the past 8 years. 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 Average seats per departure at ABE has varied between 80 and 45 seats. Average seats at ACY are on an upward trend. Average Seat Size (Based on Feb and July Schedules) 0 20 40 60 80 100 120 140 160 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 ACY-TOTAL ACY-NK ACY-FL ABE-TOTAL Number of points served has been trending down at both airports, with ACY very dependent on NK. # Domestic Destinations Served (Based on Feb and July Schedules) 0 2 4 6 8 10 12 14 16 18 20 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 ACY-TOTAL ACY-NK ACY-FL ABE-TOTAL Exhibit II-13. Twenty-year air service history graphs in the OAGHistory worksheet.

Software User Manual 49 CurrentService Worksheet This worksheet shows the average weekly departures and average seat size for 2009 for each domestic market served nonstop at the airport. Exhibit II-14 is an example for ACY. This worksheet is consistent with the embedded TAF forecast, which is the default used in the model. The Baseline TAF forecast for ACY is found at the top of the Baseline&Scenarios work- sheet, and shown in Exhibit II-15. Updating the Baseline Forecast in the CurrentService Worksheet In the CurrentService worksheet, an important feature allows users to update air service information by adding service to new cities and/or changing the number of weekly departures and average seat size in existing markets (in the right two columns). Exhibit II-14. CurrentService worksheet. Exhibit II-15. Baseline forecast from the Baseline&Scenarios worksheet. Caution: It is very important to note that whatever changes are made in the CurrentService worksheet will become the Baseline Domestic Forecast in the Baseline&Scenarios worksheet. In effect the user is creating an updated Baseline using more current information.

50 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development So, for example, at ACY, Spirit started once-daily service to Detroit with 145-seat aircraft after December 2009, the last month of OAG data in the model. To update air service at ACY, input the city OAG code and name in the first two columns, and weekly departures and average seat size in the last two columns. Then press . The revised CurrentService worksheet is shown in Exhibit II-16. Notice that changes in the CurrentService worksheet are in red. As noted previously, these changes in air service are automatically translated into a new Baseline Scenario in the Baseline& Scenarios worksheet. Once the button is pressed in the CurrentService worksheet, the software automatically moves to the Baseline&Scenarios worksheet shown in Exhibit II-17. Notice that the Default Baseline Forecast is now higher than it was before the new service to Detroit was added. Update Tables Update Tables Caution: When updating the CurrentService worksheet, it is important to reflect all of the changes in air service, which will then be reflected in the New Baseline Forecast. Userdomdata Arrival Name Carriers Weekly Departures Avg Seat Size Weekly Departures Avg Seat Size ATL ATLANTA, GEORGIA FL 7.79 117.0 7.79 117.0 BOS BOSTON, MASSACHUSETTS NK 4.70 145.0 4.70 145.0 FLL FT. LAUDERDALE, FLORIDA NK 19.29 145.0 19.29 145.0 MCO ORLANDO, FLORIDA FL-NK 23.49 142.3 23.49 142.3 MYR MYRTLE BEACH, SOUTH CAROLINA NK 8.00 145.0 8.00 145.0 PBI WEST PALM BEACH, FLORIDA NK 3.26 145.0 3.26 145.0 RSW FORT MYERS-REGIONAL, FLORIDA NK 8.53 145.0 8.53 145.0 TPA TAMPA/ST. PETERSBURG, FLORIDA NK 7.00 145.0 7.00 145.0 DTW Detroit 7.00 145.0 OAG Scheduled Domestic Departures from ACY for YE Dec 2009 User Updates (changes in red) Here you can review current scheduled domestic service. You may update the schedule by editing the User Update columns. You can also add service to new domestic cities by filling in the Arrival and User Update columns below the last OAG record. After you update or add service, please click the "Update Tables" button below. Reset User Updates to OAG DefaultsUpdate Tables Exhibit II-16. User-revised CurrentService worksheet. BaselineValues BaselineValues_Int Domestic Operations Domestic Enplanements Domestic Operations Domestic Enplanements International Operations International Enplanements International Operations International Enplanements 2009 15,084 545,516 15,084 545,516 2009 0 0 2010 15,233 552,930 15,233 552,930 2010 0 2011 15,384 560,444 15,384 560,444 2011 0 2012 15,535 568,060 15,535 568,060 2012 0 2013 15,689 575,786 15,689 575,786 2013 0 2014 15,846 583,617 15,846 583,617 2014 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Baseline Forecast for ACY (Based on TAF Air Carrier/Air Taxi Forecast and User Updates of Current Domestic Service) You can change the Default Baseline forecasts by entering new numbers in the User Update columns. Year Year Default Baseline International Forecast User Updates (changes in red) User Updates (changes in red) Default Baseline Domestic Forecast Reset User Updates to Baseline Defaults Exhibit II-17. User-revised Baseline&Scenarios worksheet.

Software User Manual 51 Baseline&Scenarios Worksheet In this worksheet, the user can make further changes to the Baseline Forecast and create two different sensitivity cases. Also by selecting the buttons on this worksheet, the user can create confidence bands around the Baseline Forecast that reflect the historic range of error in expec- tations for jet fuel prices and economic growth (based on national GDP). Each of these function- alities is exercised below. Creating a User-Defined Scenario In the Baseline&Scenarios worksheet, the user can create alternative forecasts by simply inputting the data at the top of the worksheet in the columns labeled User Updates. For exam- ple, assume that, starting in 2010, domestic operations and enplanements at ACY were going to grow 5 percent more than indicated by the TAF. The user can easily modify the Baseline Forecast by using simple commands. For instance, in the Domestic Operations column for 2010, the user could input: which would cause domestic operations in 2010 to be 5 percent higher than the TAF projection. The same type of command could be used in following years to increase operations by 5 percent each year. Repeating the same command in the Domestic Enplanements column will cause domestic enplanements to also increase by 5 percent compared to the TAF. The result is a new User-Defined Baseline Forecast as shown in Exhibit II-18 taken from the Baseline&Scenarios worksheet. Notice that the user updates are highlighted in red. These changes in red are now the Baseline Forecast around which sensitivity cases can be created. = ∗1 05 11. B BaselineValues BaselineValues_Int Domestic Operations Domestic Enplanements Domestic Operations Domestic Enplanements International Operations International Enplanements International Operations International Enplanements 2009 15,084 545,516 15,084 545,516 2009 0 2010 15,233 552,930 15,995 580,577 2010 0 2011 15,384 560,444 16,153 588,466 2011 0 2012 15,535 568,060 16,312 596,463 2012 0 2013 15,689 575,786 16,473 604,575 2013 0 2014 15,846 583,617 16,638 612,798 2014 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Baseline Forecast for ACY (Based on TAF Air Carrier/Air Taxi Forecast and User Updates of Current Domestic Service) You can change the Default Baseline forecasts by entering new numbers in the User Update columns. Year Year Default Baseline International Forecast User Updates (changes in red) User Updates (changes in red) Default Baseline Domestic Forecast Reset User Updates to Baseline Defaults Exhibit II-18. User updates in the Baseline&Scenarios worksheet. The user updates created in the Baseline&Scenarios worksheet are the new Baseline Forecast for the model. Any sensitivity cases created subsequently will be based on the user-defined scenario.

Creating a Sensitivity Case Using User-Defined Ranges for Key Air Service Drivers In the Baseline&Scenarios worksheet, the user creates sensitivity cases for a forecast. This is the central reason for the creation of the software. The models embedded in the software (described in detail in the following paragraphs) are designed to show how air service may be affected depend- ing on future values for not only key drivers like jet fuel prices and income growth, but also other drivers. The user is free to input whatever range of values for drivers that seems appropriate. 52 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development To get an up-to-the minute view of likely future jet fuel prices, press the link: View the latest Heating Oil futures prices by clicking here Prior Settle 2.2325 2.2365 2.2324 377Jan-11 2.2368 -0.0142 2.251 2.2312 2.2336 2.2039 2,962Dec-10 2.2129 -0.015 2.2279 2.1858 2.1907 2.1812 556Nov-10 2.1881 -0.0167 2.2048 2.1826 2.1826 2.1562 1,688Oct-10 2.1646 -0.0158 2.1804 2.1578 2.1597 2.1293 5,114Sep-10 2.1395 -0.0167 2.1562 2.1386 2.1386 2.1034 14,238Aug-10 2.1144 -0.019 2.1334 Jul-10 2.0909 -0.0213 2.1122 2.11 2.1148 2.0801 4,733 Open High Low VolumeMonth Last Change This link takes the user to the Chicago Mercantile Exchange (CME) page for heating oil futures prices. These prices are highly correlated with jet fuel prices. Following is a sample page taken from the site on June 28, 2010. Exhibit II-19. Link to oil futures market in the Baseline&Scenarios worksheet. Jet Fuel Prices and Local Income The spike in jet fuel prices in 2007–2008 was largely unanticipated and caused a substan- tial reduction in air services (measured by average daily seat departures); the impact was com- pounded by a severe recession that began in 2008. Creating a Low scenario that anticipates both high fuel prices and low income growth is one of the logical sensitivity tests for any air service forecast. A High scenario would have relatively low jet fuel prices and high income growth. Up-to-date information on oil market expectations is available from the Chicago Mercantile Exchange website link embedded in the software and illustrated in Exhibit II-19. When creating scenarios, care should be taken to keep assumptions internally consistent for each sensitivity case. For example, a High Case should reflect an optimistic view of the future, which usually will mean low jet fuel prices, higher income growth, less competition from nearby hubs, and lower inflation. Drivers would move in the opposite direction for a Low Case.

Software User Manual 53 Jet Fuel Prices Gallon (nominal) Time Period Average Median High Low 1Q 2000 3Q 2009 1.43$ 1.33$ 3.51$ 0.59$ 1Q 2005 3Q 2009 2.07$ 1.94$ 3.51$ 1.33$ US Real GDP 2000 2009 1.79% 2.27% 3.66% -2.40% 2005 2009 1.15% 2.03% 2.94% -2.40% Sources: ATA and US BEA; EIA Exhibit II-20. Range of values for jet fuel prices and real GDP. US GDP Deflator Time Period Average Median High Low 2000 2009 2.4% 2.9% 3.3% 1.2% 2005 2009 2.6% 2.9% 3.3% 1.2% Source: US BEA Exhibit II-21. Range of annual inflation. The model embedded in the software uses local income growth (measured as the change in real per capita income in the metro- or micropolitan area where the airport of interest is located) as a key economic driver for airport activity. For projections into the future, however, such local income measures may be difficult to obtain, so the baseline projections are based on estimates of national GDP growth. If the user has access to local projections, they can be used in place of the national GDP projections. To assist the user in defining sensitivity cases, Exhibit II-20 reports the high, low, and average values for jet fuel prices and national GDP growth over the past decade. Baseline Inflation Rate The air services model embedded in the software operates on real jet fuel prices. The model takes whatever jet fuel price assumptions are made and converts them to real dollars using the assumed Baseline Inflation Rate forecast. The higher the inflation rate, the lower the real jet fuel price will be; since a lower real price of jet fuel will cause an increase in air services, higher infla- tion is consistent with more air services in the model. Exhibit II-21 reports on the range of annual inflation reported by the GDP deflator in the past decade. Average Aircraft Size In the air services model embedded in the software, aircraft size has a positive effect on seat departures. Aircraft size is a proxy for the cost of producing air services per seat. Because larger aircraft tend to produce lower seat mile costs (all else being the same), in competitive markets these lower costs would be passed onto consumers resulting in more demand and therefore air service. In developing scenarios, users would want to input any known changes in future fleet types used by carriers. Or, it might be appropriate to input a trend in average seat size if it is likely to continue. Seat-Departures from Nearby Large or Medium Hub Airports In the air services model, this variable has a negative impact on air services. An airport operating in the shadow of a large or medium hub airport would tend to have fewer air services

54 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development than would be the case in the absence of such competition. The software provides informa- tion on the average daily seat-departures at large and medium hubs within 50 miles of the subject facility. Caution: The software is NOT designed to accommodate very large changes in seat-departures at nearby large or medium hubs. Doubling the figures for a nearby hub or setting them to zero may produce nonsensical results. Scenarios showing changes of ±10% should be easily accommodated and produce reasonable results. Exhibit II-22. HHI calculator on Baseline&Scenarios worksheet. Airport Concentration Index The Herfindahl-Hirschman Index (HHI) is a measure of the level of competition in a defined market. It takes into account the relative size of competitors as well as how many of them there are. As applied in the software, market shares measure the percentage of domestic seat-departures at the airport accounted for by marketing carriers. The HHI is the sum of squares of these shares (measured in percentage points). The equation for the HHI is: A monopoly carrier with 100 percent of the seats at an airport would have an HHI of 10,000 (= 1002). Five competitors with equal 20 percent shares would have an HHI of 2,000. As the HHI increases, air services decline. The user should input any expected or feasible changes in competitive conditions. For exam- ple, an airport with five competitors that might lose one of them may see a decline in air ser- vices. Adding one or more competitors may cause an improvement in air services. To calculate a new HHI, the user can enter carrier seat-departures in the section at the bottom left of the Baseline&Scenarios worksheet (Exhibit II-22). Exhibit II-23 reports some additional HHI calculations for hypothetical situations ranging from monopoly to equal shares among five carriers. HHI share share share . . . + share1 2 3= ( ) + ( ) + ( ) +2 2 2 n( )2 Airline Seat-Departures A 10,000 B 5,000 C 2,000 D 500 E F G H I J TOTAL 17,500 Airport Concentration Index Calculator Airport Concentration Index: 4,220 Input seat-departures by all carriers at the airport (per day, per week, etc.)

Software User Manual 55 Sample Sensitivity Case for ACY Returning to the sample runs for ACY, recall that we had input a forecast that assumes that both enplanements and operations will increase 5 percent per year for the five years following 2009. To assess the sensitivity of that new forecast to unforeseen economic circumstances, cre- ate a Low case that assumes $3.50 jet fuel and zero income growth for all five years. For the High case, assume $2.00 jet fuel and 3 percent annual income growth for all five years. Leave the other variables unchanged. . Examining the Baseline and Sensitivity Cases The new Baseline and Sensitivity cases can be viewed in the Projections worksheet and in the One-Page Report worksheet. The former shows graphs and data for operations and enplanements as well as estimates of airport operating revenues; Exhibit II-25 shows the results for the assump- tions input in Exhibit II-24. By 2014, both operations and enplanements would be about 7 per- cent lower in the Low case than in the Baseline, while the High case would be about 4 percent higher. By 2014, ACY operating revenues are estimated to be 5 percent lower in the Low case and 3 percent higher in the High case. The One-Page Report worksheet shown in Exhibit II-26 was designed to be viewed in combination with the embedded risk analysis options discussed in the following section; it shows the same enplanements and operations graphs as in Exhibit II-25, along with the im- plied High and Low cases for jet fuel and income in the pre-defined risk analysis (see the next section). Exhibit II-24 in red) into the Baseline&Scenarios worksheet To implement this sensitivity case, input the assumptions (shown in Shares of Seat Departures Airline Number of Carriers A B C D E HHI 1 100% 10,000 2 90% 10% 8,200 2 80% 20% 6,800 2 70% 30% 5,800 2 60% 40% 5,200 2 50% 50% 5,000 3 90% 5% 5% 8,150 3 80% 10% 10% 6,600 3 70% 15% 15% 5,350 3 60% 20% 20% 4,400 3 50% 25% 25% 3,750 3 40% 30% 30% 3,400 3 33% 33% 33% 3,327 4 90% 3% 3% 3% 8,133 4 80% 7% 7% 7% 6,533 4 70% 10% 10% 10% 5,200 4 60% 13% 13% 13% 4,133 4 50% 17% 17% 17% 3,333 4 40% 20% 20% 20% 2,800 4 30% 23% 23% 23% 2,533 4 25% 25% 25% 25% 2,500 5 90% 3% 3% 3% 3% 8,125 5 80% 5% 5% 5% 5% 6,500 5 70% 8% 8% 8% 8% 5,125 5 60% 10% 10% 10% 10% 4,000 5 50% 13% 13% 13% 13% 3,125 5 40% 15% 15% 15% 15% 2,500 5 30% 18% 18% 18% 18% 2,125 5 20% 20% 20% 20% 20% 2,000 Exhibit II-23. HHI hypothetical market share calculations.

56 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development BaselineValues BaselineValues_Int Domestic Operations Domestic Enplanements Domestic Operations Domestic Enplanements International Operations International Enplanements International Operations International Enplanements 2009 15,084 545,516 15,084 545,516 2009 0 0 0 2010 15,233 552,930 15,995 580,577 2010 0 0 0 2011 15,384 560,444 16,153 588,466 2011 0 0 0 2012 15,535 568,060 16,312 596,463 2012 0 0 0 2013 15,689 575,786 16,473 604,575 2013 0 0 0 2014 15,846 583,617 16,638 612,798 2014 0 0 0 0 0 0 0 0 0 Calculating… Please wait InflationValues Year Baseline Price of Jet Fuel (Current Yr $/gal) Scenario 1 Scenario 2 Year Baseline Inflation Rate Scenario 1 Scenario 2 2005 $1.622 2005 3.34% 2006 $1.906 2006 3.25% 2007 $2.025 2007 2.87% 2008 $2.938 2008 2.14% 2009 $1.844 $1.199 $3.227 2009 1.18% 2010 $2.174 $3.500 $2.000 2010 1.47% 1.47% 1.47% 2011 $2.258 $3.500 $2.000 2011 1.31% 1.31% 1.31% 2012 $2.499 $3.500 $2.000 2012 1.43% 1.43% 1.43% 2013 $2.719 $3.500 $2.000 2013 1.76% 1.76% 1.76% 2014 $2.888 $3.500 $2.000 2014 1.73% 1.73% 1.73% RealIncomeValu SeatsizeValues Year Baseline Local Real Income Growth Scenario 1 Scenario 2 Year Baseline Airport Avg Seatsize Scenario 1 Scenario 2 2005 0.43% 2005 119.9 2006 0.73% 2006 126.7 2007 0.27% 2007 130.1 2008 0.34% 2008 140.2 140.2 140.2 2009 -2.83% #N/A #N/A 2009 141.9 141.9 141.9 2010 1.07% 0.00% 3.00% 2010 141.9 141.9 141.9 2011 3.52% 0.00% 3.00% 2011 141.9 141.9 141.9 2012 3.64% 0.00% 3.00% 2012 141.9 141.9 141.9 2013 2.80% 0.00% 3.00% 2013 141.9 141.9 141.9 2014 2.46% 0.00% 3.00% 2014 141.9 141.9 141.9 HHIValues Set50Values Year Baseline Airport Concentration Index - HHI (0-10,000) Scenario 1 Scenario 2 Year Baseline Domestic Daily Seat- Departures at Lrg/Med Hubs within 50 Miles Scenario 1 Scenario 2 2005 8,189 2005 0 2006 8,450 2006 0 2007 8,804 2007 0 2008 9,715 9,715 9,715 2008 0 0 0 2009 8,333 8,333 8,333 2009 0 0 0 2010 8,333 8,333 8,333 2010 0 0 0 2011 8,333 8,333 8,333 2011 0 0 0 2012 8,333 8,333 8,333 2012 0 0 0 2013 8,333 8,333 8,333 2013 0 0 0 2014 8,333 8,333 8,333 2014 0 0 0 (Default baseline from 2010 forward equal to 2009 value.) (Default baseline from 2010 forward derived from TAF.) (Default baseline from 2010 forward based on change in projected price of jet fuel from EIA Annual Energy Outlook 2010.) (Default baseline from 2010 forward based on projected GDP Implicit Price Deflator from EIA Annual Energy Outlook 2010.) (Default baseline from 2010 forward based on projected US GDP from EIA Annual Energy Outlook 2010; 2009 value equal to US GDP growth.) (Default baseline from 2010 forward equal to 2009 value.) Not relevant for Small Hubs Baseline Forecast for ACY (Based on TAF Air Carrier/Air Taxi Forecast and User Updates of Current Domestic Service) You can change the Default Baseline forecasts by entering new numbers in the User Update columns. Year Year Default Baseline International Forecast User Updates (changes in red) Forecast Drivers for Domestic Scenarios (International Forecast is Fixed) User Updates (changes in red) Default Baseline Domestic Forecast Not relevant for Small Hubs 2005-2009 data are fixed; you may change the Baseline and/or Scenario assumptions below for 2010-2014. If you entered updates to the Baseline Domestic Forecast above, you should ensure that the Baseline assumptions below are consistent with those updates. View the latest Heating Oil futures prices by clicking here JetFuelValues Reset User Updates to Baseline Defaults Reset All Scenarios to Baseline Defaults Set Jet Fuel Scenarios based on Futures Uncertainty Set Income Scenarios based on EIA GDP Uncertainty Exhibit II-24. Inputting sensitivity assumptions in the Baseline&Scenarios worksheet.

Software User Manual 57 Projected Annual Operations for ACY 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 Baseline 17,962 15,084 15,995 16,153 16,312 16,473 16,638 Scenario 1 17,962 15,084 15,995 15,484 15,303 15,383 15,589 Scenario 2 17,962 15,084 15,995 16,288 16,573 16,951 17,369 2008 Act 2009 2010 2011 2012 2013 2014 Projected Annual Enplanements for ACY 450,000 500,000 550,000 600,000 650,000 700,000 Baseline 553,177 545,516 580,577 588,466 596,463 604,575 612,798 Scenario 1 553,177 545,516 580,577 564,097 559,593 564,544 574,160 Scenario 2 553,177 545,516 580,577 593,376 606,014 622,098 639,704 2008 Act 2009 2010 2011 2012 2013 2014 Projected Annual Revenues for ACY $7,000,000 $12,000,000 $17,000,000 $22,000,000 $27,000,000 Baseline $21,809,615 $14,419,394 $15,146,637 $15,296,747 $15,447,923 $15,601,112 $15,756,232 Scenario 1 $21,809,615 $14,419,394 $15,146,637 $14,783,759 $14,673,023 $14,762,465 $14,949,958 Scenario 2 $21,809,615 $14,419,394 $15,146,637 $15,399,595 $15,647,118 $15,964,795 $16,311,881 2008 Act 2009 2010 2011 2012 2013 2014 Exhibit II-25. Revised baseline and sensitivity example from the Projections worksheet.

58 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development Exhibit II-26. One-Page Report worksheet showing confidence bands.

Software User Manual 59 Risk Analysis Features of the Software One of the most important features of the software is an embedded risk analysis, which places confidence bands around a forecast designed to capture the uncertainty of future jet fuel prices and income growth. The objective is to provide airports with a way to undertake a formal risk analysis for their forecast designed to capture the range of values for these very uncertain drivers of air service. The risk analysis answers the following question: Given my current expectations about jet fuel prices and economic growth, I have created a forecast of enplanements and commercial operations for my airport; what are the High and Low forecasts if I define a range of future jet fuel prices and income growth likely to occur about 90 percent of the time? The embedded risk analysis looks at how accurately jet fuel prices and economic growth have been forecast over the past 20 years. To create an approximate 90 percent confidence band, an analysis was undertaken that compares actual to forecast values and identifies the percentage range of error (high and low) that occurs 90 percent of the time. So for example, monthly heating oil futures prices for a period 12 months forward were com- pared to actual jet fuel prices and the percentage error was measured. A confidence band was constructed, defined as the percentage range (high and low) likely to encompass the error in fore- casting the price of jet fuel 12 months into the future about 90 percent of the time. The same type of confidence band was created for errors in forecasting GDP growth. Further discussion of these embedded analyses is contained in Chapter 4. To apply the embedded risk analysis into the forecast for ACY, recall that the baseline TAF was already modified by assuming that both operations and enplanements would grow by an additional 5 percent per year. What is the confidence band around this forecast given uncertainty of jet fuel prices and income growth? To create the confidence band, : This will overwrite any other assumptions that have been made about future jet fuel prices and income growth, and automatically create Low and High Cases that correspond to a 90 percent confidence band for these two drivers. Other drivers can be modified as well, if desired. The new Baseline and Sensitivity cases can be viewed in the Projections worksheet and in the One-Page Report worksheet. The latter combines the data elements of the implied High and Low cases for jet fuel and income with the enplanements and operations graphs so the user can see the overall results on a single page. However, the graphs also reflect any other Base case or Scenario changes the user may have entered in the Baseline&Scenarios worksheet. Exhibit II-27 shows the One-Page Report for the scenario created for ACY. Interestingly, the Low case in the risk analysis shows a downside for both operations and enplanements that is 10 percent of the Baseline by 2014. The upside is far more modest—on the order of 3.5 percent. Interpreting Results Like any modeling exercise, interpreting results from the software depends almost exclusively on the assumptions the user makes. The software is designed to be a supplement to existing fore- casts. It provides a structured way to: • Modify an existing forecast with updated information on existing air services • Modify an existing forecast with new growth assumptions Set Jet Fuel Scenarios based on Futures Uncertainty Set Income Scenarios based on EIA GDP Uncertainty two buttons go to the Baseline&Scenarios worksheet and press the

60 Impact of Jet Fuel Price Uncertainty on Airport Planning and Development Exhibit II-27. Sample risk analysis for ACY.

Software User Manual 61 • Create sensitivity cases by defining ranges for key drivers of air service including jet fuel prices, income growth, inflation, the cost of producing air service (using seat size as a proxy), competition at the airport, and competition from nearby large and medium hub airports • Utilize a more formal risk analysis of the forecast based on the likely range of error in forecast- ing two key air service drivers: jet fuel prices and income growth The software will produce useful results when reasonable and consistent assumptions are applied. In such cases, it may be a useful tool for airport sponsors to examine the downside and upside of their future air services and the implications for airport finances and future development. One way to judge the results of the analysis is to compare it to the accuracy of the TAF in recent years. On the TAFHistory worksheet, the user will find a comparison of forecast and actual operations for the TAF beginning in 2003. The summary for ACY is shown in Exhibit II-28. The example for ACY illustrates one of the key motivations for the creation of the software: unanticipated spikes in jet fuel prices accompanied by sudden and unanticipated slow-down in the economy can produce dramatic and unanticipated reductions in air services. Thus, examin- ing the potential impact of sudden changes in jet fuel prices and in income growth is prudent for those interested in the consequences of changes in air services at airports. ACY Air Carrier + Air Taxi Operations: TAF Predicted vs. Actual 2,000 7,000 12,000 17,000 22,000 27,000 32,000 37,000 2004 2005 2006 2007 2008 2009 TAF 2003 TAF 2004 TAF 2005 TAF 2006 TAF 2007 TAF 2008 Actual Exhibit II-28. TAFHistory worksheet.

Impact of Jet Fuel Price Uncertainty on Airport Planning and Development Get This Book
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TRB’s Airport Cooperative Research Program (ACRP) Report 48: Impact of Jet Fuel Price Uncertainty on Airport Planning and Development is designed to help airport operators and planners measure the impact of changes in jet fuel price on supply and demand for air service at commercial service airports.

The report includes background research; a computer model, available online and in CD ROM format attached to the printed version of the report; and a user manual. Please note that macros must be enabled in the Microsoft Excel file for the program to run.

The output of the model can ultimately be used to help evaluate the impact of uncertainty on airport development and finance. Applying specific input parameters, the model, embedded in a user-friendly program, allows airport planners and managers to assess how fuel, economic, and other uncertainties may affect their particular airport and to test the sensitivity of varying assumptions about key drivers of airport activity.

The supporting research examines historical changes in fuel prices in the context of changing economic conditions and uses this experience to assess risk in adhering to existing air traffic forecasts when planning future airport improvements or expansion. The model illustrates risk using confidence bands that indicate a range of forecasts as a function of changing jet fuel prices and other factors. The research also examines the historic link between changes in jet fuel prices in relation to periodic occurrence of recessions and how changing demand may, in turn, result in changes in fleet composition and size.

Software Disclaimer

The computer model software is offered as is, without warranty or promise of support of any kind either expressed or implied. Under no circumstance will the National Academy of Sciences or the Transportation Research Board (collectively “TRB’) be liable for any loss or damage caused by the installation or operations of this product. TRB makes no representation or warrant of any kind, expressed or implied, in fact or in law, including without limitation, the warranty of merchantability or the warranty of fitness for a particular purpose, and shall not in any case be liable for any consequential or special damages.

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