CHAPTER 2 - DEMAND FORECASTmfrmasterplan.com/wp-content/uploads/2019/12/DRAFT... · CHAPTER 2 - DEMAND FORECAST FORECAST SUMMARY The forecast chapter provides a 20-year projection

Chapter 2 – Demand Forecast

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CHAPTER 2 - DEMAND FORECAST

FORECAST SUMMARY

The forecast chapter provides a 20-year projection of aviation activity at the Rogue Valley International-

Medford Airport (MFR). Forecasts consist of future activity level estimates that help guide decision makers

in planning airport development and improvement. The forecasts are used to determine facility demand

requirements and the timing of demand-driven improvement projects. Table 2-1 is a summary of the

forecasts described in this chapter.

Table 2-1: MFR Forecast Summary

Fiscal Year 2010 2020 2030 2040 ‘20-'40 CAGR1

Enplanements 304,873 528,649 797,000 1,000,000 3.2%

Operations 50,235 46,768 49,142 51,015 0.4%

Air Carrier 7,248 15,780 17,882 19,030 0.9%

Air Taxi 12,016 6,488 6,060 6,060 -0.3%

Itinerant GA2 19,945 16,700 17,200 17,600 0.3%

Itinerant Military 296 500 500 500 0.0%

Local GA 10,596 7,000 7,200 7,525 0.4%

Local Military 134 300 300 300 0.0%

Based Aircraft 211 205 230 247 0.9%

Single Engine Piston 154 137 149 161 0.8%

Jet 21 25 30 36 1.8%

Multi Engine Piston 26 22 25 27 1.0%

Helicopter 10 13 16 18 1.6%

Other3 0 8 10 11 1.6%

1 CAGR: Compound Annual Growth Rate

2 GA: General Aviation

3 Other = Light sport aircraft, gliders, experimental aircraft, ultralights,

Unmanned Aircraft Systems (UAS)

Source: 2018 MFR records, U.S. DOT T-100

The aviation activity forecast considers the impact of socioeconomics and the aviation market, both

regionally and nationally. Socioeconomic data is based on the Medford metropolitan stational area (MSA),

as defined by the U.S. Office of Management and Budget, which encompasses the entirety of Jackson

County. Figure 2-1 shows the area covered by the MSA.

Jackson County is located in southern Oregon north of the border with California, and the City of Medford

is the county seat. The County has been growing both in population and in economy for the past decade

with an average annual growth rate of 0.8 percent for each. Portland State University forecasts that Jackson

County’s population will continue growing at an annual average rate of 0.9 percent. Economically, the

County has recovered from the 2007-2009 recession and has since surpassed pre-recession employment

and gross regional product (GRP) levels. County GRP is projected to grow at an average of 1.7 percent.


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Figure 2-1: Map of Jackson County


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INTRODUCTION TO FORECASTS

Aviation activity forecasts evaluate and project future demand at an airport. The MFR forecasts have a base

year of 2018 and use the Federal Aviation Administration (FAA) fiscal year (October to September). The

forecast period is 20 years, and the first year forecasted is 2020. Data are reported in five-year intervals.

Each category is evaluated using multiple forecasting methods and is compared to the 2018 FAA Terminal

Area Forecast (TAF), published in February 2019. Data from the previous ten years (2008-2018) is used

as the basis of historical trend analysis. This ten-year period includes periods of economic growth and

contraction. This enables the forecasts to account for a wide range of economic conditions and insight into

economic effects on aviation activity at MFR. This chapter is organized into the following sections:

Community Profile

Aviation Activity Profile

Scheduled Service Forecasts

General Aviation Forecasts

Peak Forecasts and Critical Aircraft

Summary

Data sources used in the forecast are described in Table 2-2. Definitions for terms used in the chapter may

be found in the Glossary (Appendix G).


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Table 2-2: Description of Data Sources

Source Description

FAA Traffic Flow

Management

System Counts

(TFMSC)

The TFMSC includes data collected from flight plans. These operations are

categorized by aircraft type and used to identify trends in the MFR fleet mix. The

advantage of the TFMSC data is its degree of detail and its insights into the itinerant

users of MFR. A disadvantage of TFMSC data is that it does not include local

operations or operations that did not file a flight plan. Thus, the utility of TFMSC

data is limited to larger aircraft, including scheduled commercial passenger, cargo,

and charter operations, and business jets.

FAA TAF

The FAA TAF, published February 2019, provides historical records and forecasts

for passenger enplanements, aircraft operations, and based aircraft at MFR. These

forecasts serve as a basis of comparison for the forecast prepared as part of the

planning effort. The TAF provides historical information on aircraft activity and is

included as Attachment 1.

FAA Aerospace

Forecast

The Aerospace Forecast (ASF) 2019-2039 is a national-level forecast examining

different segments of the aviation industry. The ASF guides local forecasts by

serving as a point of comparison between local and national trends.

U.S. Department

of Transportation

(USDOT) T-100

Database

The T-100 form is filled out every month by scheduled, charter passenger, and air

cargo airlines. The T-100 database is an online repository of data recorded on the

forms including the number of seats sold, number of seats available, freight

transported, aircraft used, and departures performed. The T-100 provides a

detailed look at the operations of passenger and cargo airlines.

Woods & Poole

Economics, Inc.

(W&P)

Socioeconomic data is provided by data vendor Woods & Poole Economics, Inc.

(W&P). W&P provides data for gap years in the U.S. Census. The W&P dataset

considers the Medford MSA. The dataset provides 124 data categories with records

from 1970 to 2016 and forecast through 2040. Data categories considered include

employment, earnings and income, and GRP.

Portland State

University (PSU)

Historical and forecasted population data was provided by PSU Population

Research Center. PSU produces annual population estimates for Oregon and its

counties and cities. The PSU population data was used for forecasting over W&P

population data since local city/county/regional agencies use PSU population

estimates for planning.

Stakeholder

Interviews

The Consultant conducted interviews with stakeholders during site visits. Interviews

included Air Traffic Control Tower (ATCT), Transportation Security Administration

(TSA), terminal tenants, fixed based operators, the City of Medford, the City of

Ashland, the City of Central Point, Jacksonville County, and Oregon Department of

Transportation. Airlines interviewed were Horizon Air (operating for Alaska), United

Express, Allegiant Airlines, and American Airlines.

OPSNET

OPSNET (Operations Network) is the source of National Airspace System (NAS)

air traffic operations and delay data. The OPSNET provides information about IFR

(instrument flight rules) and VFR (visual flight rules) operations.

Rogue Valley

International–

Medford Airport

The Airport provided operations, passenger, and cargo data.


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COMMUNITY PROFILE

Population

The PSU Population Research Center releases annual estimates and forecasts of state, county, and

municipal populations in Oregon, as required by Oregon House Bill 2253, passed in 2013. The historical

population estimate uses decennial (10-year) census counts, recent historical trends, and data records for

state and federal tax returns, Medicare disbursements, driver licenses, and birth data to estimate county

populations between census years. The population forecast is based on historic and current trends

including compiling data from the Oregon Department of Education, Center for Health Statistics, and

Employment Department. The population data produced by PSU is used by state, county, and local

agencies for revenue sharing, funds allocation, and planning purposes.

Table 2-3 shows the historical data and forecast for County. 2018 is the forecast base year while 2020 is

the first reported year for the forecast, thus the percent change between 2018 and 2020 should be noted

to be for two years rather than five years.

Table 2-3: Jackson County Population

Calendar Year Population Percent Change

2008 201,538 N/A

2013 206,310 2.4%

2018 219,270 6.3%

2020 224,980 2.6%

2025 235,066 4.5%

2030 246,611 4.9%

2035 257,256 4.3%

2040 266,910 3.8%

'08-'18 CAGR1 0.8% N/A

'20-'40 CAGR1 0.9% N/A

1 CAGR = Compound Annual Growth Rate

Source: Portland State University Population Research Center

2018 is the forecast base year while 2020 is the first reported year for the forecast, thus the percent change between

2018 and 2020 should be noted to be for two years rather than five years

Employment and Economic Development

Like most communities, Jackson County’s economy contracted during the 2007-2009 recession. However,

the employment levels returned to pre-2007 levels by 2016 and have since exceeded pre-recession levels.

W&P forecasts that employment will grow at an average annual rate of 1.2 percent in the next 20 years.

The top six non-government employers identified by the Jackson County Administrator’s Office are on the

next page:


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Amy’s Kitchen – Family owned manufacturer of organic and non-GMO convenience and frozen foods.

Asante Health System – Not-for-profit, private acute care hospital.

Harry & David – Premium food and gift producer and retailer.

Lithia Motors, Inc. – Fourth largest automotive retailer in the U.S.

Pacific Retirement Services – Not-for-profit retirement care service provider.

Providence Health System – Not-for-profit health care system.

Jackson County’s economy includes health care, retail and manufacturing, and government as the top

employment groups. Other growing industries in the County include wine production, film making, and

agriculture. The attractive quality of life in the region continues to draw new residents and continue the

population growth. However, one of the current limiting factors in the region is the supply of housing not

growing at the same pace as demand. The City of Medford has indicated that, while employment

opportunities are available, the lack of housing is a barrier to the potential growth of the region. Table 2-4

shows the historical and projected employment for the County for the next 20 years.

Table 2-4: Jackson County Employment

Calendar Year Total Employment Percent Change Employment per Capita

2008 117,042 N/A 0.58

2013 112,576 -3.8% 0.55

2018 127,207 13.0% 0.58

2020 131,044 3.0% 0.58

2025 140,934 7.5% 0.60

2030 150,579 6.8% 0.61

2035 158,400 5.2% 0.62

2040 163,983 3.5% 0.61

Compound Annual Growth Rates

2008-2018 0.8% N/A 0.0%

2020-2040 1.1% N/A 0.3%

CAGR = Compound average growth rate


Table 2-5 shows the top industries by employment and sales from 2008 to 2018 as reported by W&P. Table

2-6 shows the top industries by employment and sales from 2018 to 2040 as forecasted by W&P. The

tables show which sectors of the industry contribute the most to the County’s employment. The health care

industry in Jackson County has grown in the past decade to become the largest employer in the region with

non-store retail sales also growing to be the top industry in the County by retail sales. Non-store retail

includes sectors such as mail-order products, home delivery sales, and catalog sales. Employment and

retails sales for the next 20 years maintain the same two industries at the top. Manufacturing employment

has grown in the past five years, which indicates more companies are starting or moving in the region;

however, the forecast predicts government employment to catch up during the forecast period.


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Table 2-5: Jackson County Top 5 Industries by Employment and Sales (2008-2018)

Top 5 Industries by Employment

2008 2013 2018

Rank Industry Jobs Industry Jobs Δ Industry Jobs Δ

1 Retail Trade 16,820 Health Care 16,388 8.1% Health Care 19,104 16.6%

2 Health Care 15,167 Retail Trade 15,303 -9.0% Retail Trade 18,076 18.1%

3 State + Local Gov't 9,724 Accommodation + Food 8,905 2.5% Accommodation + Food 11,206 25.8%

4 Accommodation + Food 8,684 State + Local Gov't 8,712 -10.4% Manufacturing 9,281 14.7%

5 Construction 7,756 Manufacturing 8,090 6.4% State + Local Gov't 9,067 4.1%

Top 5 Industries by Retail Sales

2008 2013 2018

Rank Industry Sales Industry Sales Δ Industry Sales Δ

1 General Merchandise $604.70 General Merchandise $661.15 9.3% Nonstore Retailers $758.54 43.2%

2 Nonstore Retailers $576.76 Nonstore Retailers $529.75 -8.1% General Merchandise $685.57 3.7%

3 Motor Vehicles $556.35 Motor Vehicles $510.05 -8.3% Motor Vehicles $599.39 17.5%

4 Food + Bev Retail $500.74 Food + Bev Retail $491.15 -1.9% Food + Bev Retail $525.99 7.1%

5 Gas Station Retail $300.44 Eating + Driving Places $289.54 -0.5% Eating + Driving Places $349.78 20.8%


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Table 2-6: Jackson County Top 5 Industries by Employment and Sales (2018-2040)

Top 5 Industries by Employment

2018 2030 2040

Rank Industry Jobs Industry Jobs Δ Industry Jobs Δ

1 Health Care 19,104 Health Care 29,248 53.1% Health Care 33,293 13.8%

2 Retail Trade 18,076 Retail Trade 22,108 22.3% Retail Trade 25,202 14.0%

3 Accommodation + Food 11,206 Accommodation + Food 12,440 11.0% Accommodation + Food 12,800 2.9%

4 Manufacturing 9,281 State + Local Gov't 9,762 7.7% State + Local Gov't 10,051 3.0%

5 State + Local Gov't 9,067 Manufacturing 9,699 4.5% Manufacturing 10,024 3.4%

Top 5 Industries by Retail Sales

2018 2030 2040

Rank Industry Sales Industry Sales Δ Industry Sales Δ

1 Nonstore Retailers $758.54 Nonstore Retailers $993.02 30.9% Nonstore Retailers $1,204.67 21.3%

2 General Merchandise $685.57 General Merchandise $841.04 22.7% General Merchandise $939.18 11.7%

3 Motor Vehicles $599.39 Motor Vehicles $640.82 6.9% Motor Vehicles $661.69 3.3%

4 Food + Bev Retail $525.99 Food + Bev Retail $544.33 3.5% Food + Bev Retail $554.07 1.8%

5 Eating + Driving Places $349.78 Eating + Driving Places $409.57 17.1% Eating + Driving Places $466.20 13.8%


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Gross Regional Product

The GRP is the value of goods and services produced in the County and serves as an index for the health

of the overall economy. GRP grows as industries increase production of higher value goods. Jackson

County’s GRP returned to pre-2007 levels by 2013 and has continued to grow in the years that followed.

Table 2-7 shows the Jackson County GRP from 2008 to 2040. The GRP per capita did not decrease during

2007-2009 recession. This may be due to the higher relative growth rate of the retirement age population

(65 and up) and the stable economy of the area. The growth in retire age population may be due to how

attractive the area is to retirees moving from out of the area. W&P projections show the GRP increasing at

a faster rate than the County population. This can be explained by the projected increase in the production

of high value goods and services. Health care and professional services produce higher value goods per

capita relative to industries like agriculture or forestry.

Table 2-7: Jackson County Gross Regional Product

Calendar Year GRP ($M) Percent Change GRP ($M) per Capita

2008 $6,047 N/A $0.030

2013 $6,527 7.9% $0.032

2018 $7,609 16.6% $0.035

2020 $7,919 4.1% $0.035

2025 $8,746 10.4% $0.037

2030 $9,577 9.5% $0.039

2035 $10,273 7.3% $0.040

2040 $11,081 7.9% $0.042

Compound Annual Growth Rates

2008-2018 2.3% N/A 1.5%

2020-2040 1.7% N/A 0.8%

CAGR = Compound average growth rate


Regional Airports

Regional airports are considered in the demand forecasts because they affect to what level an airport’s

growth reflects the growth of the surrounding community. Communities with many airports will see demand

spread across facilities, whereas communities with few airports will see demand concentrated. The airport

catchment area is the area from which the airport draws passengers and users. It represents the local

market. Figure 2-2 shows the MFR catchment area for general aviation users. The catchment area for

commercial airline passengers is larger and included in the Passenger Demand Analysis Report. The

needs of general aviation users vary greatly, and aircraft owners tend to store their aircraft at the airport

closest to their home or business provide it has adequate facilities. However, MFR has seen an influx in

based jet aircraft from northern California, reflecting financial and operational advantages to basing aircraft

outside of the congested San Francisco Bay area.


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Figure 2-2: MFR Catchment Area


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The primary market of an airport reflects the availability of facilities and services that meet the needs of a

specific market. For example, piston aircraft owners typically have fewer requirements compared to

business jet owners. Business jets typically require longer runways to operate at full payload and need

navigational aids and instrument flight procedures to operate regardless of weather conditions. In contrast,

piston aircraft can operate on shorter runways, generally do not operate during low visibility conditions, and

do not need Jet A fuel. MFR’s catchment area covers parts of both Southern Oregon and Northern

California; state specific factors such as taxes and fees influence how users may choose between general

aviation airports. Table 2-8 describes neighboring airports in the catchment area and their primary markets

and key facilities.

Table 2-8: Regional General Aviation Airports

Airport

Characteristics Primary Markets

Runway Length1

IAP2 Jet A Large Jets

Small Jets

Turboprops

Piston

MFR Rogue Valley International - Medford

8,800’ (14/32)

Precision Yes Yes Yes Yes Yes

3S8 Grants Pass Airport 4,001’ (13/31)

Non- Precision Yes No No Yes Yes

SIY Siskiyou County Airport

7,490' (17/35)

Circling Yes No Yes Yes Yes

LMT Crater Lake - Klamath Regional Airport

10,301' (14/32)

Precision Yes Yes Yes Yes Yes

RBG Roseburg Regional Airport

5,003' (16/34)

Circling Yes No Yes Yes Yes

O46 Weed Airport 5,000' (14/32)

Visual Yes No Yes Yes Yes

1) The longest runway is listed for airports with multiple runways. 2) IAP = Instrument Approach Procedure

Source: FAA Airport Facilities Directory. Market determination based on instrumentation, runway length, and fuel availability.

MFR and Crater Lake – Klamath Regional Airport (LMT) are the only two airports that have facilities to

accommodate large jets and have precision approach procedures. This makes both airports appeal to users

who require instrumentation to operate. MFR, relative to LMT, has a larger population and a stronger local

economy. MFR does not have based military aircraft so it does not experience as many military operations

as LMT with its Oregon Air National Guard base. Both airports host the U.S. Forest Service.


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AVIATION ACTIVITY PROFILE

The aviation activity profile provides context for historical airport activity trends and helps explain the

changes that have occurred. The profile is the baseline for forecasts and includes information on passenger

and air cargo airline service, general aviation, and military aviation activity.

The MFR Air Traffic Control Tower (ATCT) operates from 6:00 a.m. to 9:00 p.m. Thus, some operations

that occur outside these hours are not included in records submitted to the FAA. Commercial airline

operations are reported to the U.S. Department of Transportation (USDOT), which includes operations

occurring outside of ATCT operating hours. General aviation operations that occur outside of ATCT

operating hours are captured from flight plans.

Air Carrier Service

The air carrier service section covers scheduled passenger flights, cargo flights, and non-scheduled charter

flights. The following section describe the air carrier profile, opportunities for additional air service,

passenger enplanements, commercial operations, and air cargo service at MFR.

Air Carrier Profile

MFR had service from five scheduled passenger air carriers in 2018: Alaska Airlines, Allegiant Airlines,

American Airlines, Delta Air Lines, and United Airlines. Regional carriers, like SkyWest, Compass, and

Horizon Air, operate some flights on behalf of the air carriers. These airlines provide non-stop service to

Seattle (SEA), Portland (PDX), Salt Lake City (SLC), Denver (DEN), San Francisco (SFO), Las Vegas

(LAS), Los Angeles (LAX), Phoenix Sky Harbor (PHX), and Phoenix-Mesa (AZA). Figure 2-3 provides a

map of non-stop air carrier routes as of September 2019.

Scheduled air cargo service includes Empire Airlines operating on behalf of Federal Express (FedEx) and

Ameriflight operating on behalf of United Parcel Service (UPS). Alaska and United transfer some cargo on

passenger flights.


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Figure 2-3: MFR Non-Stop Routes


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New Air Service Opportunities

New air service opportunities at MFR are based on the top destinations without non-stop service. Demand

for flights to these cities is met via connections on existing service. For example, a passenger traveling from

MFR to San Diego may connect in PDX, SEA, SLC, PHX, SFO, and LAX, or they may drive to another

airport that offers non-stop service. Table 2-9 describes the market share of domestic traffic in the MFR

compared with PDX and other nearby commercial service airports including SFO, Sacramento International

(SMF), Eugene (EUG), and Redmond Municipal (RDM). The table only includes destinations that do not

have non-stop service from MFR. Based on Airline Reporting Corporation ticketing data, San Diego and

Minneapolis are the only two destinations without non-stop service in the top 10 markets from MFR.

Chicago is within the top twenty destinations without non-stop service that is likely to see non-stop service

to serve passengers looking to travel to the east past the Mississippi River.

Table 2-9: MFR Top 10 Domestic Destinations Without Non-Stop Service by Originating Airport

Destination Total

Passengers

Origin Airport %

MFR PDX Other

San Diego, CA 30,778 89% 4% 7%

Minneapolis, MN 20,554 84% 11% 5%

Chicago, IL (ORD) 20,292 82% 11% 6%

Orange County, CA 19,519 92% 5% 3%

Dallas, TX (DFW) 16,146 81% 11% 8%

Kahului, HI 15,247 57% 24% 19%

Anchorage, AK 14,840 76% 19% 5%

Orlando, FL (MCO) 13,935 82% 16% 1%

New York, NY (JFK) 13,871 65% 24% 11%

Boston, MA 13,642 82% 7% 11%

Source: Rogue Valley International – Medford Airport Passenger Demand Analysis

The change over time in the types of aircraft that airlines use also presents new air service opportunities.

As airlines transition from smaller 50-seat aircraft (like the Bombardier CRJ-200) to larger regional and

narrow-body jets (like the Embraer 175, Airbus A220/319/320, and Boeing 737), longer routes become

more feasible. This presents an opportunity to establish new service to new destinations provided the

market has demand to fill the additional seats. Switching to larger aircraft also allows airlines to serve more

passengers without having to increase flight frequency.

Based on airline order books, many airlines are currently replacing older aircraft with new, often larger ones.

Skywest has the 76-seat Embraer 175-E2 and Mitsubishi SpaceJet (formerly the MRJ) aircraft on order to

replace smaller CRJ200s. Horizon Air only has 76-seat aircraft in its fleet and is currently replacing the

Bombardier Q400 with the Embraer 175, which is faster and has longer range. This reduces operating costs

for longer routes. Delta, Allegiant, and United currently operate the 125- to 150-seat Airbus A319 with

United having more orders pending delivery. Delta has the 109- to 130-seat Airbus A220 ordered along

with 191- to 194-seat A321ceo and A321neo planned to replace the older 157-seat A320.


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Passenger Enplanements and Airline Operations

The TAF classifies a passenger enplanement as a passenger who boards a scheduled commercial or

chartered aircraft with more than nine seats for turboprops (or any number of seats for jet aircraft). The

aircraft must operate under Title 14 Code of Federal Regulations (CFR) Part 121 that applies to air carriers

and commercial operators. Passenger enplanements include revenue and non-revenue passengers who

paid taxes and passenger facility charges (PFC) for their carrier. Passenger enplanements do not include

pilots, flight attendants, and non-revenue airline crew members.

The FAA has two classifications for passenger enplanements based on the type of carrier operating the

route:

Air carrier enplanement: Passengers on flight operated by a mainline carrier. These are typically

the marketing airline, or the airline that sells the ticket. Examples include Alaska Airlines, Delta Air

Lines, American Airlines, United Airlines, and Allegiant Airlines.

Air taxi/Commuter enplanement: Passengers on flight operated by a regional carrier. These are

typically an airline that feeds passengers from a smaller market to a hub airport on behalf of an air

carrier. Examples includes Compass Airlines, Horizon Air, and SkyWest Airlines.

Passengers on a United Airlines Boeing 737-700 operated by United Airlines pilots and crew are

categorized as air carrier enplanements. In comparison, passengers on an Embraer 175 operated by

SkyWest pilots and crew on behalf of United Airlines are categorized as air taxi enplanements.

The FAA splits commercial operations into two categories; however, it is based on capacity rather than

operator type:

Air carrier operations: Takeoffs or landings of commercial aircraft with more than 60 seats and air

cargo operations with a maximum payload of 18,000 pounds and more.

Air taxi operations: Takeoffs and landings by commercial aircraft with 59 and fewer seats, and air

cargo operations with a maximum payload of less than 18,000 pounds.

Enplanements from 2008 to 2018 are shown in Table 2-10 MFR enplanements have increased an average

of 4.8 percent annually over the past decade. While both air taxi and air carrier enplanements have

increased during this period, air carrier enplanements have increased at a Compound Annual Growth Rate

(CAGR) of 16.4 percent compared to a CAGR of 3.5 percent for air taxi enplanements. This reflects the

overall decrease in the number of air taxi operations as aircraft with less than 60 seats are being replaced

by larger aircraft.


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Table 2-10: Passenger Enplanements

Fiscal year Air Carrier Air Taxi/Commuter Total Percent Change

2008 13,654 286,716 300,370 N/A

2009 31,070 248,564 279,634 -6.9%

2010 46,387 258,486 304,873 9.0%

2011 46,002 261,397 307,399 0.8%

2012 40,186 267,628 307,814 0.1%

2013 33,827 277,105 310,932 1.0%

2014 34,819 277,813 312,632 0.5%

2015 38,715 320,492 359,207 14.9%

2016 40,416 355,302 395,718 10.2%

2017 78,751 343,725 422,476 6.8%

2018 112,464 367,807 480,271 13.7%

CAGR 23.5% 2.5% 4.8% N/A

CAGR = Compound Annual Growth Rate; Source: MFR Records

Scheduled Passenger Airline Load Factor

Load factor is a metric that airlines use to determine performance. It is a method for showing the difference

between supply and demand. The load factor is calculated by dividing the number of passengers (demand)

by the number of available seats (supply). Load factor grows as demand approaches supply and declines

when supply increases faster than demand.

The number of available seats at MFR for the past 10 years has been increasing by an average of 3.3

percent annually with additional routes, increasing flight frequency, and growing aircraft size. The increase

in seats along with the growing passenger enplanements has kept load factors at an average of 79 percent

in the past decade. Overall, load factors at MFR have grown at a CAGR of 1.1 percent from 2008 to 2018.

This shows that passengers are consistently filling seats even as additional seats are added.

The 2019 FAA Aerospace Forecast reports an average domestic load factor of 79.7 percent for U.S.

regional air carriers and 85.3 percent for U.S. mainline air carriers in 2018. Performance consistent with or

exceeding industry averages helps MFR market itself to airlines to consider additional routes.


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Figure 2-4: MFR Outbound Seats, Enplanements, and Average Load Factor

Source: USDOT T-100. Data presented includes passengers, seats, and load factors for outbound travel.

FY19 is outside the range for this forecast but MFR reported an average 81 percent year-to-date load factor in August 2019

Scheduled Air Cargo

The 2008 to 2018 scheduled air cargo volume data is provided by MFR records and includes Empire Airlines

(operates for FedEx) and Ameriflight (operates for UPS). However, operations data are obtained from the

USDOT T-100, which only includes Empire Airlines operations data. Ameriflight, does not report to the

USDOT due to their operating certificate. Table 2-11 shows historic air cargo operations and volume at

MFR with U.S. Domestic Market revenue ton miles for comparison.

MFR air cargo volume (in tons) has increased at an annual average of 0.7 percent. In contrast, air cargo

operations have decreased an average 4.9 percent annually in the same period. This indicates that air

cargo aircraft are not operating at full capacity and are able to carry more volume with fewer operations. In

the same period the U.S. Domestic market has experienced a 1.9 percent average annual increase of

revenue ton miles. Revenue ton miles are an industry-specific metric that equates to 1 ton of air cargo flown

for 1 mile. The 2019 FAA Aerospace Forecast indicates that increased security screening for air cargo and

competition from ground cargo transportation are factors that depress air cargo volumes.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-

100,000

200,000

300,000

400,000

500,000

600,000

700,000

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Load F

acto

r (%

)

Seats

and P

assengers

Fiscal Year

Seats Enplanements Average Load Factor


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Table 2-11: Cargo Airline Operations and Activity

Fiscal Year

MFR U.S. Domestic Market

Operations2 Total Cargo

(Tons) Percent Change

Operations

Percent Change Cargo

Revenue Ton Miles

(Millions)

Percent Change

2008 1,849 3,299 N/A N/A 12,261 N/A

2009 1,638 2,629 -11.4% -20.3% 10,275 -16.2%

2010 1,800 2,556 9.9% -2.8% 11,243 9.4%

2011 1,353 2,716 -24.8% 6.2% 10,601 -5.7%

2012 1,102 2,424 -18.6% -10.8% 10,886 2.7%

2013 1,118 2,546 1.5% 5.0% 10,996 1.0%

2014 1,165 2,790 4.2% 9.6% 11,226 2.1%

2015 1,139 2,996 -2.2% 7.4% 11,636 3.7%

2016 1,126 3,070 -1.1% 2.5% 11,851 1.8%

2017 1,133 3,403 0.6% 10.8% 13,031 10.0%

2018 1,124 3,527 -0.8% 3.7% 14,829 13.8%

CAGR1 -4.9% 0.7% N/A N/A 1.9% N/A 1 CAGR: Compound Annual Growth Rate

2 Operations data only accounts for Empire Airlines. UPS Operations are not reported to U.S. DOT T-100 and are instead

included as air taxi operations.

Sources: Operations data from U.S.DOT T-100; MFR cargo volume data from MFR 2018 Records; National revenue ton miles

data from FAA 2019 Aerospace Forecast.

General Aviation

General aviation encompasses flight activities that do not include passenger operations, cargo operations,

and military operations, general aviation activities include, but are not limited to, emergency response, law

enforcement, flight training, recreational flying, private and corporate air transportation, and flight testing.

Itinerant Operations

Itinerant operations originate and terminate at different airports. In 2018, itinerant operations made up 75

percent of overall general aviation operations at MFR. Itinerant general aviation operations at MFR have

been declining at an average 1.0 percent annually from 2008 to 2018. However, from 2013 to 2018, itinerant

general aviation has increased 3.2 percent annually. This indicates that MFR experienced a decline in

itinerant general aviation over the 2007-2009 recession and has been recovering in recent years but has

not reached pre-recession numbers.

Relative to national trends as reported by the 2019 FAA Aerospace Forecast, the decline of itinerant general

aviation at MFR is lower than the national average 2.1 percent annual decrease. Table 2-12 and Figure 2-

5 compare 2008 to 2018 itinerant general aviation operations for at MFR with national numbers provided

by the 2019 FAA Aerospace Forecast.


19

Table 2-12: Itinerant General Aviation Operations

Fiscal year MFR Percent Change National Percent Change

2008 18,341 N/A 17,492,653 N/A

2009 18,706 2.0% 15,571,066 -11.0%

2010 19,945 6.6% 14,863,856 -4.5%

2011 18,210 -8.7% 14,527,903 -2.3%

2012 17,945 -1.5% 14,521,656 0.0%

2013 16,863 -6.0% 14,117,424 -2.8%

2014 15,996 -5.1% 13,978,996 -1.0%

2015 15,517 -3.0% 13,886,711 -0.7%

2016 16,405 5.7% 13,904,397 0.1%

2017 15,153 -7.6% 13,838,000 -0.5%

2018 16,655 9.9% 14,130,000 2.1%

CAGR -1.0% N/A -2.1% N/A CAGR: Compound Annual Growth Rate

Source: 2018 MFR records; 2019 FAA Aerospace Forecast for national data

Figure 2-5: Itinerant General Aviation Operations

Source: 2018 MFR records; 2019 FAA Aerospace Forecast for National.

Nationally, barring some sectors experiencing growth such as Jets and helicopters, the overall general

aviation market has been declining. The 2019 FAA Aerospace Forecast projects growth in turbine,

experimental, and light sport fleets, which will offset the decline in the fixed-wing piston fleet. While both

MFR and the country’s itinerant general aviation operations in 2018 have declined relative to 2008,

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

0

5,000

10,000

15,000

20,000

25,000

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018N

ational Itin

era

nt

Opera

tions

MF

R I

tinera

nt

Opera

tions

Fiscal YearMFR National


20

MFR’s operations are not strongly correlated with national numbers with a correlation coefficient under 0.8.

The correlation coefficient is used to measure the strength of the linear relationship between variables with

1.0 meaning a very strong relationship while 0.0 means no relationship. The weak correlation may be due

to the strong socioeconomic growth near MFR compared to a slight decline in national general aviation

activity. Between 2013 to 2018, Jackson County’s GRP has grown an average 3.1 percent annually while

national GDP has grown 2.4 percent annually. This means Jackson County’s economy has grown at a

faster rate than the rest of the country. Based operators U.S. Forest Service and Erickson Inc. are a source

of regular aircraft operations. Further itinerant operations come from a stream of based jets moving to MFR.

One of the FBOs has recently constructed a large box hangar, with plans to build more, to accommodate

the jets relocating from California.

Local General Aviation Operations

Local general aviation operations are those that originate and terminate at the same airport. These

operations are generally performed by pilots practicing takeoffs and landings, and aircraft being flown for

flight testing after a repair. Touch-and-go operations, where aircraft land, slow, and then accelerate to take

off without leaving the runway, count as two operations and are included in local operations counts. Local

general aviation operations are highly sensitive to the amount of flight training occurring at an airport. An

aircraft can perform more than six operations in an hour while practicing touch-and goes depending on the

traffic pattern. MFR does not have a flight school, but there is a flight club present at the Airport.

Table 2-13 and Figure 2-6 compare 2008 to 2018 local general aviation operations at MFR with national

numbers provided by the 2019 FAA Aerospace Forecast.

Table 2-13: Local General Aviation Operations

Fiscal year MFR Percent Change National Percent Change

2008 6,874 N/A 14,081,157 N/A

2009 6,814 -0.9% 12,447,957 -11.6%

2010 10,596 55.5% 11,716,274 -5.9%

2011 8,390 -20.8% 11,437,028 -2.4%

2012 7,390 -11.9% 11,608,306 1.5%

2013 7,220 -2.3% 11,688,301 0.7%

2014 5,336 -26.1% 11,675,040 -0.1%

2015 5,424 1.6% 11,691,338 0.1%

2016 5,887 8.5% 11,632,078 -0.5%

2017 3,698 -37.2% 11,732,000 0.9%

2018 5,500 48.7% 12,354,000 5.3%

CAGR -2.2% N/A -1.3% N/A CAGR: Compound Annual Growth Rate

Source: 2018 MFR records; 2019 FAA Aerospace Forecast for National


21

Figure 2-6: Local General Aviation Operations


Local general aviation operations at MFR have declined at a faster rate than the rest of the country. MFR

local operations do not correlate with national general aviation trends (correlation coefficient of -0.4). This

is due to the relative volatility of local operations at MFR compared to the relatively stable national

operations count. The volatility is likely due to the smaller sample size when comparing MFR operations to

the entire country. Overall, local operations at MFR have declined since 2008; however, they remain within

1,500 operations of 2008 levels.

Based Aircraft

The FAA categorizes aircraft by the propulsion system, engine configuration, and weight, with the main

categories being Single-Engine Piston (SEP), Multi-Engine Piston (MEP), Jets (includes turboprops and

turbojets), Helicopters, and Other, which includes experimental, light sport, glider, and ultralight aircraft.

More details on each category can be found in the Glossary (Appendix G).

Based aircraft are those stored at MFR and do not include itinerant aircraft. The FAA classifies based

aircraft by the propulsion system, engine configuration, and weight. Data for MFR based aircraft are from

the TAF and MFR records. Table 2-14 and Figure 2-7 show the based aircraft at MFR from 2008 to 2018

by aircraft category. In 2018, SEP at 64 percent made up the majority of the based aircraft at MFR, while

Other aircraft make up just under 4 percent.

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

National Local O

pera

tions

MF

R L

ocal O

pera

tions

Fiscal YearMFR National


22

Table 2-14: MFR Based Aircraft

Fiscal year SEP Jet MEP Helicopter Other Total Percent Change

2008 149 19 17 8 1 194 N/A

2009 155 24 24 10 6 219 12.9%

2010 154 26 21 10 0 211 -3.7%

2011 148 30 22 10 9 219 3.8%

2012 145 31 22 8 0 206 -5.9%

2013 141 23 23 7 0 194 -5.8%

2014 139 27 25 10 9 210 8.2%

2015 134 27 28 10 8 207 -1.4%

2016 131 26 23 14 8 202 -2.4%

2017 130 24 25 14 7 200 -1.0%

2018 128 24 27 13 7 199 -0.5%

CAGR -1.5% 2.4% 4.7% 5.0% 21.5% 0.3% N/A CAGR: Compound Annual Growth Rate

Source: 2018 MFR records; 2019 FAA Aerospace Forecast for National

Figure 2-7: MFR Based Aircraft


0

50,000

100,000

150,000

200,000

250,000

300,000

0

50

100

150

200

250

300

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

National G

enera

l A

via

tion F

leet

MF

R B

ased A

ircra

ft

Fiscal Year

MFR National


23

The total number of based aircraft at MFR has remained relatively steady in the last decade. The decline

in SEP has been offset by the growth in all other aircraft types. In contrast to the national general aviation

fleet, MEP based at MFR have increased, while nationally MEP have decreased at an average rate of 2.9

percent annually throughout the same period. Similarly, Other based aircraft have declined nationally as

demonstrated by a CAGR of -0.8 percent, while their numbers increased at MFR. The high average annual

growth rate of MFR Other aircraft is due to the low number of such aircraft: with only one Other aircraft at

MFR in 2008, the addition of one Other aircraft would in the next year would have resulted in a 100%

increase in the category. Helicopters at MFR have increased at a higher rate relative to the rest of the

country (CAGR of 0.8 percent), while SEP have decreased at a faster rate with the national general aviation

SEP fleet declining 1.1 percent in the last 10 years.

Military

No military aircraft are based at MFR. Historically, military aircraft have operated at MFR, primarily for

training purposes. Military activity is based on the demands of the U.S. Department of Defense rather than

socioeconomic drivers; therefore, for planning purposes, military operations are projected to remain flat

throughout the forecast period. Historical military operations are provided in Table 2-15.

Table 2-15: MFR Military Operations

Fiscal year Itinerant Local Total Percent Change

2008 22,136 8,773 30,909 N/A

2009 18,610 6,840 25,450 -17.7%

2010 19,950 10,596 30,546 20.0%

2011 18,113 8,359 26,472 -13.3%

2012 17,948 7,409 25,357 -4.2%

2013 16,718 7,216 23,934 -5.6%

2014 16,007 5,360 21,367 -10.7%

2015 15,437 5,430 20,867 -2.3%

2016 16,398 5,887 22,285 6.8%

2017 15,024 3,701 18,725 -16.0%

2018 16,606 5,502 22,108 18.1%

CAGR -2.8% -4.6% -3.3% N/A CAGR: Compound Annual Growth Rate

Source: 2018 MFR records

FAA TAF

The TAF is the official forecast that FAA Headquarters prepares annually for each airport in the FAA

National Plan of Integrated Airport Systems (NPIAS). The TAF uses the FAA fiscal year (October to

September). TAF data comes from the USDOT T-100 database, ATCT records, and FAA Form 5010, which

airports submit annually to the FAA.

The TAF contains forecasts for passenger enplanements, operations, and based aircraft. It does not provide

forecasts for operations by aircraft type, peak activity level, critical aircraft, or air cargo. The TAF used for

this forecast was published in February 2019. Table 2-16 summarizes the TAF prepared for MFR.


24

Table 2-16: FAA TAF Summary

Fiscal Year 2018 2020 2025 2030 2035 2040 ‘20-'40 CAGR

Enplanements 483,867 570,745 635,881 700,246 775,922 858,765 2.1%

Operations 42,312 46,558 48,130 50,391 52,962 55,748 0.9%

Air Carrier 11,312 13,193 15,682 17,269 19,133 21,175 2.4%

Air Taxi 8,073 9,487 8,410 8,924 9,471 10,054 0.3%

Itinerant general aviation 16,606 16,833 16,918 17,003 17,088 17,173 0.1%

Itinerant Military 487 487 487 487 487 487 0.0%

Local general aviation 5,502 6,226 6,301 6,376 6,451 6,527 0.2%

Local Military 332 332 332 332 332 332 0.0%

Based Aircraft 180 180 180 180 180 180 0.0%

Single Engine Piston 130 130 130 130 130 130 0.0%

Jet 4 4 4 4 4 4 0.0%

Multi Engine Piston 25 25 25 25 25 25 0.0%

Helicopter 14 14 14 14 14 14 0.0%

Other 7 7 7 7 7 7 0.0% CAGR: Compound Annual Growth Rate

Source: 2019 TAF

The FAA reviews master plan forecasts by comparing them to the TAF. Forecasts within 10 percent of the

TAF over the five-year period, and within 15 percent within the ten-year period, can be approved by the

Airports District offices. Forecasts outside of these tolerances go to FAA Headquarters for review.

While the TAF is a generally reliable source of information, the most recent data trends may lag a year

behind airport records. Compared to data collected by MFR, the 2019 TAF does not match in several

categories for fiscal year 2018. These differences are quantified in Table 2-17.

Table 2-17: Airport Management Records and TAF Comparison – FAA Fiscal Year 2018

Category Airport Records TAF Difference % Difference

Enplanements 480,271 483,867 -3,596 -0.7%

Operations 43,877 42,312 1,565 3.7%

Air Carrier 12,826 11,312 1,514 13.4%

Air Taxi 8,073 8,073 0 0.0%

Itinerant GA 16,655 16,606 49 0.3%

Itinerant Military 489 487 2 0.4%

Local GA 5,500 5,502 -2 0.0%

Local Military 334 332 2 0.6%

Based Aircraft 199 180 19 10.6%

Single Engine Piston 128 130 -2 -1.5%

Jet 27 4 23 575.0%

Multi Engine Piston 24 25 -1 -4.0%

Helicopter 13 14 -1 -7.1%

Other 7 7 0 0.0% Difference = Airport Records minus TAF



25

A contributing factor in the difference between TAF data and airport records are the operations that occur

after the ATCT is closed. The MFR ATCT is open from 6 a.m. to 9 p.m., so operations that occur outside

of these hours are not reported to the FAA. Airport management, in contrast, receives information from the

airlines, which includes even the flights that operate when the ATCT is closed. Airport management records

were checked against the T-100 database, which also receives information about operations from airlines.

The demand forecasts in this chapter are based on airport management records and T-100 data rather

than TAF records due to the noted difference of over 1,500 operations and an unexplained addition of over

3,500 enplanements when compared to airport records. Reliance on TAF numbers would result in

inaccurate forecast analysis with overestimated load factors as there would be more passengers on fewer

flights. Basing forecasts on airport records accounts for the after-hours operations and enplanements.

COMMERCIAL SERVICE FORECASTS

This section discusses the methods, assumptions, risk, and uncertainty of the enplanement, air cargo

volume, and commercial operation forecasts. A preferred method is selected for each forecast and is then

compared with the FAA TAF. The selection of the preferred method is based on factors including feasibility,

past trends, and known airport conditions. The forecasts help set the parameters around which future facility

requirements at MFR are determined.

Passenger Enplanements

Methods

The passenger enplanement forecast looked at historical trends and multi-variable regression methods to

project passenger enplanements. Variables highly correlated (correlation coefficient (r) greater than 0.8)

with passenger enplanements in the past 10 years are applied in the regression models. Correlation

describes how strongly related the rates of change between two variables are to each other. The stronger

the correlation, the more linear their relationship is – a positive correlation means two variables increase

together while a negative correlation means one variable decreases while the other increases. The stronger

the positive correlation, the closer the correlation coefficient approaches the value of 1.0. Strong negative

correlations are closer to -1.0 while having no correlation equals a correlation coefficient of 0.

The four most highly correlated variables with MFR passenger enplanements in the past 10 years are listed

in Table 2-18. These four variables were tested against passenger enplanements using regression

analysis. The validity of each test is measured by the R-squared (R2) value, which describes how well the

variables explains variance in the dependent variable (enplanements). R2 is the percent of variance

explained by the model. The closer the R2 value is to 1.0 (100% of variance explained), the more confidence

can be placed on the model’s ability to explain historical variability.


26

Table 2-18: 2008-2018 MFR Enplanement Correlation Analysis

Variable Correlation Coefficient

U.S. Gross Domestic Product (GDP)1 0.91

County Employment2 0.92

County Population2 0.96

National Commercial Passengers3 0.97

Source:

1 Organization for Economic Co-operation and Development (OCED)

2 Portland State University

3 2019 FAA Aerospace Forecast

In order to account for the effects of the different but strongly correlated variables, multi-variable regression

models were tested against historical enplanements. Multi-variable models allow the forecast to account

for local (county employment and population) and national (GDP and national commercial passengers)

forces. In the case of multi-variable regression, the adjusted R2 is used to decide the level of confidence

each model has. Every variable added to a model increases the R2 and never decreases it, which can lead

to an incorrectly high R2 value. The adjusted R2 value accounts for this effect and avoids the issue of not

knowing if the R2 value is high due to the model being better or because it has more predictor variables.

Table 2-19 shows the adjusted R2 value of different variable combinations tested.

Figure 2-8 provides an index of the tested variables against MFR enplanements with 2008 as the baseline.

Index charts show changes of variables relative to the baseline, which is equal to 1.0. An index greater than

1.0 indicates that the variable is above its 2008 level, and an index below 1.0 indicates that the variable is

below its 2008 level.

Table 2-19: Multi-Variable Regression Analyses

Variable Adjusted R-Squared Value

GDP1, Population2, Employment2 0.932

Population2, Employment2 0.926

Population2, GRP3 0.932 Source:

1 Organization for Economic Co-operation and Development (OCED)

2 Portland State University

3 Woods & Poole


27

Figure 2-8: Multi-Regression Variable Index (2008 Baseline)

Forecasts for each variable were considered throughout the forecast period to determine the preferred

regression model. Through further testing, the national commercial passenger variable was excluded due

to the FAA Aerospace Forecast not having forecasted 2040 passenger numbers and the very high growth

rate generated when including it in models. The Jackson County population forecast is sourced from the

Portland State University Population Research Center, which is the forecast that planning organizations at

the state and local levels use. The County employment forecast is from the W&P projections, and the U.S.

GDP forecast comes from the Organization of Economic Cooperation and Development. The forecast of

each variable is used to produce the passenger enplanement forecast for the next 20 years.

Based on the results of the regressions analyses, the equation accounting for population, employment, and

GDP was selected to model forecasted passenger enplanement at MFR. This combination of variables has

strong adjusted R2 values and accounts for both local and national factors that have historically correlated

with enplanements at MFR:

Passenger Enplanement Regression Equation: y = m1(x1) + m2(x2) + m3(x3) + b

y = Passenger Enplanements, b = Intercept from Regression Analysis

𝑦 = (12.29 × 𝐶𝑜𝑢𝑛𝑡𝑦 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛) + (2.81 × 𝐶𝑜𝑢𝑛𝑡𝑦 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡) + (−20.88 × 𝐺𝐷𝑃) − 2,149,358.67

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

2008 2010 2012 2014 2016 2018

Rate

of

Change (

2008 =

1.0

0)

Year

Enplanements Population Employment

Gross Domestic Product MSA GRP 2008 Baseline


28

Addressing Risk and Uncertainty

The regression-based method of forecasting incorporates a statistical analysis to give confidence that the

variables chosen for forecasting have exhibited a degree of correlation with passenger enplanements in

the past. However, this method is that future forecasts are ultimately based on one set of external

projections and assumes the projections to be true at the time of consideration. This assumption is

inherently risky as forecasted values may not be met. Forecasts grow increasingly uncertain as projections

venture further into the future as the likelihood of unforeseen events that can impact aviation activity at MFR

are more likely to occur. To address this, the passenger enplanement regression model uses the Monte

Carlo simulation process to account for future uncertainty.

The Monte Carlo simulation attempts to mitigate the uncertainty by incorporating a range for each variable’s

forecast. This is accomplished by evaluating historical volatility of each of the variables in the model and

assuming future values may deviate from the forecast accordingly. This process is established for each

variable, and then trials are run for each forecast year. Each variable independently and randomly fluctuates

within the defined range for thousands of trials. This results in trials considering situations where some or

all variables grow and decline. However, it is important to note that the Monte Carlo simulation requires

analysts performing the forecast to define the range within which the variables can fluctuate before the trials

begin. Once established, the model will randomly pick the values of each variable.

As an example, the Organization of Economic Cooperation and Development forecasts U.S. GDP in 2025

being just under $21 trillion dollars. Historical volatility shows that U.S. GDP could sway by plus or minus

$3 trillion dollars, which means that the actual value for 2025 could be as low as $18 trillion (an economic

recession), or as high as $24 trillion (a period of strong growth). Since the value of U.S. GDP is one of the

drivers of the enplanement forecasts, it makes sense to account for this volatility in the future and not

assume that the U.S. GDP is guaranteed to grow as it has exhibited contraction in the past.

To reduce the impact of outliers (e.g., scenarios where all the variables are at their maximum or minimum

values at the same time), the Monte Carlo simulation is run multiple times. The results are interpreted using

percentiles. Percentiles measure the probability of a value being higher or lower than the given value. To

illustrate, if the 25th percentile value for passenger enplanements for 2025 is 559,000, then out of the

thousands of trials run for 2025, 25 percent of the results were below 559,000 and 75 percent were above.

This can also be expressed as a 25 percent probability that the 2025 passenger enplanement will be

559,000 or below.

The Monte Carlo simulation was run for 6,000 trials to reduce the effect of outliers. Multiple trials result in

the results converging around the mean. The law of diminishing returns applies as the results differ less

and less beyond 1,000 trials. This effect is shown in Figure 2-9.


29

Figure 2-9: Effects of Multiple Trials on Monte Carlo Standard Deviation

Figure 2-10 shows the MFR enplanement forecast Monte Carlo simulation results presented in minimum,

25th, 50th, 75th, and maximum percentiles with the 2019 TAF plotted for comparison.

Figure 2-10: MFR Passenger Enplanement Forecast - Monte Carlo Results by Percentile

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

10 100 1000 1500 2500 3000 5000 6000

Sta

ndard

Devia

tion

Number of Trials Run

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

Passenger

Enpla

nem

ents

FAA Fiscal Year

FAA TAF Records Min 25th 50th 75th Max TAF


30

Preferred Passenger Enplanement Method and TAF Comparison

The variables used in the regression model represent local and national socioeconomics. The PSU

population forecast is the same used for local planning, which local stakeholders find reasonable.

Employment represents a local economic indicator, and the GDP represents the rest of the country, both

of which account for the effect economic changes have on enplanements at MFR. The Monte Carlo

simulation provides a sensitivity analysis for future passenger enplanements should Jackson County grow

at a faster or slow rate than expected. The known future routes and fleet plans of the airlines currently

operating at MFR also support these forecasts. The preferred passenger enplanement forecast derived

from the Monte Carlo simulation is used to derive the scheduled commercial operations and peak

enplanement numbers.

The preferred passenger enplanement forecast method is the 75th percentile Monte Carlo results. The

selection of the 75th percentile results as the preferred forecast is based on the available information about

airlines’ historical performance and planned changes in airline operations at MFR. This model projects a

20-year (2020-2040) CAGR of 3.2 percent, which is faster growth than the 2019 TAF. This is due to MFR

being in a market with population and economic growth with increasing passenger demand. The TAF and

Aerospace Forecast projections are less specific to the MFR, driven by more mature markets with slower

growth.

Table 2-20 and Figure 2-11 show the preferred enplanement forecast along with the 25th and 50th percentile

Monte Carlo results. Table 2-21 provides a side-by-side comparison of the preferred forecast with the 2019

TAF.

Table 2-20: Preferred Passenger Enplanement Forecast

Fiscal year Monte Carlo

(25th) Monte Carlo

(50th) Monte Carlo

(75th) 2019 TAF

2018 480,271 480,271 480,271 480,271

2020 501,559 515,429 528,649 570,745

2025 559,000 615,000 672,000 635,881

2030 685,000 741,000 797,000 700,246

2035 801,000 859,000 915,000 775,922

2040 886,000 943,000 1,000,000 858,765

20-'40 CAGR 2.9% 3.1% 3.2% 2.1%

CAGR: Compound Annual Growth Rate



31

Figure 2-11: Preferred Passenger Enplanement Forecast

Table 2-21: Passenger Enplanement Forecast – TAF Comparison

Fiscal year Preferred Forecast 2019 TAF Total Difference Percent Difference

2018 480,271 480,271 0 0.0%

2020 528,649 570,745 -42,096 -7.4%

2025 672,000 635,881 36,119 5.7%

2030 797,000 700,246 96,754 13.8%

2035 915,000 775,922 139,078 17.9%

2040 1,000,000 858,765 141,235 16.4%

‘20-'40 CAGR 3.2% 2.1% N/A N/A CAGR: Compound Annual Growth Rate


200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

Passenger

Enpla

nem

ents

FAA Fiscal YearFAA 2018 TAF Monte Carlo (25th) Monte Carlo (50th)

Monte Carlo (75th) Airport Records


32

Air Cargo

Methods

Air cargo operations are expected to remain flat for the forecast period because cargo load from 2008 to

2018 averaged 39 percent. This means air cargo carriers can accommodate increasing volume without

increasing operations by having higher cargo load factors or increasing aircraft size.

MFR air cargo volume exhibited strong correlation with national air cargo revenue (r = 0.90), county

employment (r = 0.92), and national commercial passengers (r =0.89). Based on these strong relationships,

multivariable analysis was performed for air cargo volume. Three models were considered for the air cargo

forecast:

Multivariable regression using county employment and national air cargo revenue

Trend forecast carrying forward air cargo volume trends from the past 10 years into the future

Single variable regression using national air cargo revenue.

Forecast

The shift from air to ground cargo transportation has already occurred, and thus, the effect of ground

transportation is unlikely to heavily impact air cargo volumes in the future. Because of this along with

commercial air cargo aircraft not flying at 100 percent cargo load, cargo carriers will not have to increase

frequency to meet additional volume. Thus, the air cargo operations are projected to remain flat.

The multivariable regression analysis is based on county employment and national air cargo revenue.

County employment data is sourced from W&P historic and projected data. This model has an R2 value of

0.83, which indicates a strong relationship. National air cargo revenue data is from the 2019 FAA Aerospace

Forecast. The model predicts air cargo volume at MFR growing at a CAGR of 2.3 percent for forecast

period, with over 11.5 million pounds of cargo by 2040.

Analysis using historical trends has air cargo volume growing at an average 3.8 percent annually. This

historical trend method resulted in the greatest growth rate out of the three methods considered. Because

it is based on what has occurred at MFR in the past decade, the recent growth in the area is carried forward

into the future. The trend forecast is not considered a preferred method due to the growth rate likely being

unsustainable for the next two decades.

The single variable regression method is based on national air cargo revenue alone. Compared to the

multivariable regression method, the adjusted R2 value is lower at 0.79. Due to the lower confidence of this

model, the single variable regression is not preferred over the multivariable regression.

The results of the three methods and the forecasted air cargo operations are presented in Figure 2-12.


33

Figure 2-12: Preferred Cargo Volume and Operations Forecast

Preferred Method

The preferred air cargo forecast method is the multivariable regression model. This is because the model

accounts for both local and national forces that are highly correlated with historic air cargo volume at MFR.

As mentioned in the 2019 FAA Aerospace Forecast, the shift from air cargo transport to ground transport

has already occurred, which should mitigate any sharp declines in air cargo volume. Thus, future changes

in air cargo should be able to also reflect local factors that affect air cargo volume.

Commercial Aircraft Operations

Commercial aircraft operations are performed by scheduled and charter passenger airlines and cargo

aircraft, and Part 135 on-demand air taxi operations. Private business jet operations are counted as general

aviation operations rather than commercial operations. This section combines the results of passenger

enplanement and air cargo forecasts to determine the number of operations that will be occurring to meet

the needs of passengers and cargo. The components of commercial aircraft operations are presented in

Figure 2-13.

-

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

18,000,000

Opera

tions

Carg

o V

olu

me (

Pounds)

Fiscal YearHistorical Employment & National CargoTrend National CargoOperations


34

Figure 2-13: Commercial Aircraft Operations


35

Methods

Over 99.8 percent of commercial aircraft operations at MFR are scheduled passenger and cargo

operations. The remaining 0.1 percent were performed by on-demand charter airlines. Charter airline data

is sourced from USDOT T-100 records. Charter airlines that have operated at MFR in the past decade

include Sun Country, Swift Air, and XTRA Airways.

TAF classifications of scheduled operations are divided into two categories: air carrier and air taxi.

Operations by aircraft with 60 or more seats are considered air carrier operations while air taxi aircraft have

fewer than 60 seats. The commercial aircraft operations forecast is calculated with the following

assumptions:

Airlines will add service to meet the level of demand in the passenger enplanement forecast.

Air taxi aircraft will be retired by 2022 following the FAA Aerospace Forecast projection of “Carriers

[removing] 50 seat regional jets and retire older small turboprop and piston aircraft while adding 70-

90 seat jets […] after 2020.” It is expected the smaller jets will be replaced with narrow-body jets.

The average number of seats per departure will increase as smaller jets are replaced with larger

aircraft. Airlines will adjust flight frequency to keep load factors at levels similar to the past 10 years,

which has been averaging near 80 percent, with the last five years averaging over 82 percent.

However, as airlines transition to the larger aircraft, load factors are expected to decrease temporarily

with an adjustment period before rising back up to the expected 80 percent average load factor. The

growth in enplanements at MFR lead to an increase in overall operations. However, the projected

increase in operations is tempered by the upgaging of aircraft, with the number of forecast operations

otherwise being even higher.

Summary and TAF Comparison

The following three tables present information on future commercial aircraft operations. 0presents only

scheduled passenger aircraft operations. Load factors for air carrier operations are expected to increase

and stabilize as routes are established, and the market matures. The number of air carrier operations will

increase as airlines transition from regional aircraft to larger jets. Air taxi/commuter load factors will fall to

zero as 50-seat aircraft are phased out and retired. Table 2-23 shows all commercial aircraft operations

classified as scheduled, non-scheduled, and cargo operations. Air Taxi/Commuter operations are broken

into Scheduled Passenger operations (operations with aircraft with less than 60 seats) and Part 135 charter

operations. Table 2-24 compares the forecasted commercial aircraft operations with the 2019 TAF. The

TAF has historically underreported commercial aircraft operations at MFR but has a higher forecasted

number of operations than the preferred forecast. The table also shows the number of seats per departure

increasing for Air Carrier. This indicates that airlines are expected to switch to aircraft with more seats in

the future. Even with the increasing seats/departure, air carrier operations are expected to grow to

accommodate the enplanements. If the 2018 level of 86 seats/departure was maintained the number of

forecasted operations would be even higher.


36

Table 2-22: Scheduled Passenger Aircraft Operations

Year Enplanements Air Carrier Air Taxi/Commuter Total

Ops. LF S/D Ops. LF S/D Ops.

2008 300,370 5,750 64% 76 18,332 70% 36 24,082

2013 310,932 6,392 83% 81 10,796 85% 40 17,188

2018 480,271 12,826 74% 86 8,073 82% 50 20,899

2020 528,649 15,780 81% 89 6,488 80% 50 22,268

2025 672,000 17,712 81% 91 6,060 0% 0 23,772

2030 797,000 17,882 82% 106 6,060 0% 0 23,942

2035 915,000 19,058 83% 116 6,060 0% 0 25,118

2040 1,000,000 19,030 83% 127 6,060 0% 0 25,090

CAGR 3.2% 0.9% 0.1% 1.8% -0.3% -99.9% -99.9% 0.6% Ops: Operations, LF: Load Factor, S/D: Seats/Departure, CAGR: Compound Annual Growth Rate ‘20-‘40


Table 2-23: Commercial Aircraft Operations Forecast

Year Air Carrier Air Taxi/Commuter

Total Sub-Total

Scheduled Passenger

Part 135 Air Cargo Sub-Total

2008 5,750 12,842 3,641 1,849 14,691 20,441

2013 6,392 5,638 4,040 1,118 6,756 13,148

2018 12,826 3,310 3,639 1,124 4,434 17,260

2020 15,780 428 4,860 1,200 1,628 17,408

2025 17,712 0 4,860 1,200 1,200 18,912

2030 17,882 0 4,860 1,200 1,200 19,082

2035 19,058 0 4,860 1,200 1,200 20,258

2040 19,030 0 4,860 1,200 1,200 20,230

20-'40 CAGR 0.9% N/A 0.0% 0.0% -1.5% 0.8%

CAGR: Compound Annual Growth Rate Source: 2018 MFR records

Table 2-24: Commercial Aircraft Operations Forecast – TAF Comparison

Year Forecast 2019 TAF Total Difference Percent Difference

2018 20,899 19,385 1,514 7.8%

2020 22,268 22,680 -412 -1.8%

2025 23,772 24,092 -320 -1.3%

2030 23,942 26,193 -2,251 -8.6%

2035 25,118 28,604 -3,486 -12.2%

2040 25,090 31,229 -6,139 -19.7%




37

GENERAL AVIATION FORECASTS

Itinerant General Aviation Operations

Methods

Itinerant general aviation operations were forecast with the following methods:

Application of the 2019 FAA Aerospace Forecast growth rate for itinerant general aviation operations.

Multivariable regression based on national itinerant general aviation operations and SEP fleet.

Assumption that MFR will maintain the same state market share of itinerant general aviation

operations.

Applying the national itinerant general aviation operations growth rate from the 2018 FAA Aerospace

Forecast general aviation to the historic data assumes itinerant general aviation operations at MFR will

grow at the same rate as the average national rate. The result is a CAGR of 0.3 percent for the next 20

years. This result is higher than the 2019 TAF, which has CAGR at 0.1 percent. Some growth in itinerant

general aviation operations are expected as more jet owners move their aircraft from Northern California to

be based at MFR. This method is the preferred forecasting method, as explained in the next following

section.

Historically, MFR itinerant general aviation operations are not highly correlated with any of the

socioeconomic and national aviation variables tested. Two of the national aviation variables with the highest

correlation are national itinerant general aviation operations (r = 0.61) and national SEP fleet (r = 0.74).

Neither of these variables have correlation coefficients higher than 0.8 and so are not considered strongly

correlated to historical itinerant general aviation operations at MFR. The multivariable regression analysis

shows the model having an adjusted R2 of 0.49. Thus, this method is rejected due to the weak correlation.

The state market share forecast is based on the percentage of Oregon itinerant general aviation operations

occurring at MFR for the past decade, which is 2.35 percent. This method assumes MFR will maintain this

share of itinerant general aviation operations against the projected operations in the 2019 TAF for Oregon.

The market share method is not the preferred method due to the high growth rates projected. While MFR

is located in a growing market, such a high growth rate for the forecast period is unlikely to be sustainable.

0and Figure 2-14 present the three forecast methods along with the 2019 TAF for comparison.


38

Table 2-25: Itinerant General Aviation Operations Forecast

Fiscal Year Aerospace Forecast

Regression State Market

Share 2019 TAF

2018 16,655 16,655 16,655 16,606

2020 16,700 16,200 18,199 16,816

2025 16,900 14,900 19,292 16,901

2030 17,200 13,500 20,475 16,986

2035 17,400 12,300 21,765 17,071

2040 17,600 11,308 23,176 17,156

20-'40 CAGR 0.3% -1.8% 1.2% 0.1%



Figure 2-14: Itinerant General Aviation Operations Forecast

Preferred Method and TAF Comparison

The preferred forecast method for itinerant general aviation operations at MFR is the Aerospace Forecast

growth rate method. Business jet owners have been moving their aircraft from Northern California to be

based at MFR. With the aircraft stored in MFR hangars, these aircraft come and go from MFR based on

the needs of their owners who are based and traveling elsewhere. Thus, even as the market grows and the

with increase in based business jets, the number of itinerant operations is unlikely to see a dramatic

increase. Table 2-26 shows that the preferred itinerant general aviation operations forecast compared to

the 2019 TAF.

10,000

12,500

15,000

17,500

20,000

22,500

25,000

2008 2012 2016 2020 2024 2028 2032 2036 2040

Itin

era

nt

Aircra

ft O

pera

tions

FAA Fiscal Year

Airport Records FAA 2018 TAF Aerospace Forecast

State Market Share Regression


39

Table 2-26: Itinerant General Aviation Operations Forecast – TAF Comparison

Year Preferred Forecast 2019 TAF Total Difference Percent Difference

2018 16,655 16,606 49 0.3%

2020 16,700 16,816 -116 -0.7%

2025 16,900 16,901 -1 0.0%

2030 17,200 16,986 214 1.3%

2035 17,400 17,071 329 1.9%

2040 17,600 17,156 444 2.6%



Local General Aviation Operations

Methods

Local general aviation operations are forecast using the following methods:

Assumption that historic local general aviation operation trends will carry forward into the future

Application of the 2019 Aerospace Forecast growth rate for local general aviation operations

Assumption that MFR will maintain the same national market share of local general aviation

operations.

Local general aviation operations at MFR did not show strong correlation with any of the socioeconomic

variables and national aviation variables tested. Therefore, no regression methods were used for testing.

The trend method is based on the past 10 years of local general aviation operations at MFR. It extrapolates

the number of local general aviation operations from 2008 to 2018 and assumes operations will change at

the same rate. Thus, the trend method expects local general aviation operations to continue declining at a

CAGR of 2.2 percent. This method is not preferred, as local general aviation operations are not expected

to decrease at this rate for the next 20 years.

Applying the local general aviation operations growth rate nationally from the 2019 FAA Aerospace

Forecast general aviation to the historic data assumes local general aviation operations at MFR will grow

at the same rate as the average national rate. The result is a 0.4 percent CAGR for the next 20 years. This

result is higher than the 2019 TAF, which has CAGR at 0.1 percent. This method is not preferred as it does

not account for near term growth resulting in the recent market growth that MFR has experienced.

The Aerospace Forecast applies the 2019 FAA Aerospace Forecast growth rate for local general aviation

operations for the forecast period.

Table 2-27 and Figure 2-15 present the three forecast methods along with the 2019 TAF for comparison.

Note that both the Aerospace Forecast and market share methods result in the same forecast CAGR. This

is due to the CAGR being calculated from 2020 to 2040. The CAGR for the market share method is higher


40

when calculated from 2018 to 2040 as the total number operations is higher with the market share method:

7,525 operations in 2040 compared to 5,900 with the Aerospace Forecast growth rate method.

Table 2-27: Local General Aviation Operations Forecast

Fiscal year Trend Aerospace Market Share 2019 TAF

2018 5,500 5,500 5,500 5,502

2020 5,300 5,500 7,000 6,211

2025 4,700 5,600 7,100 6,286

2030 4,200 5,700 7,200 6,361

2035 3,800 5,800 7,400 6,436

2040 3,400 5,900 7,525 6,511

‘20-'40 CAGR -2.2% 0.4% 0.4% 0.2%



Figure 2-15: Local General Aviation Operations Forecast


The national market share method is the preferred forecast method for general aviation operations at MFR.

This method accounts for recent growth in local general aviation operations at MFR and forecasts the

growth continuing until 2020 when growth tapers to a 0.4 percent CAGR. MFR has relatively unique

facilities that appeal to jets that prefer precision instrument approaches compared to the other airports in

the area. Pilots looking to train with precision instrument approach procedures in the area will practice at

MFR as other general aviation airports in the region do not have precision instrument approaches. This

method also accounts for the recent market growth in the area as the population and economy of Jackson

0

2,500

5,000

7,500

10,000

12,500

Local A

ircra

ft O

pera

tions

FAA Fiscal Year

Airport Records FAA 2018 TAF Trend Aerospace Market Share


41

County grows. The increasing demand for hangars and the current waitlist shows aircraft owners are

moving into the area along with more people from Northern California migrating to Oregon. Table 2-28

shows the preferred local general aviation operations forecast compared to the 2019 TAF.

Table 2-28: Local General Aviation Operations Forecast – TAF Comparison


2018 5,500 5,502 -2 0.0%

2020 7,000 6,211 789 12.7%

2025 7,100 6,286 814 12.9%

2030 7,200 6,361 839 13.2%

2035 7,400 6,436 964 15.0%

2040 7,525 6,511 1,014 15.6%



Based Aircraft

Methods

MFR based aircraft are forecast in the following methods:

Application of the 2019 Aerospace Forecast growth rate for the general aviation fleet

Assumption that MFR will maintain the same based aircraft market share in both California and

Oregon

A hybrid method combining both the Aerospace Forecast method and the market share method.

The Aerospace Forecast method applies the growth rate projected in the 2019 FAA Aerospace Forecast to

current and historic MFR based aircraft numbers. This is done by applying the growth rate for each aircraft

type (SEP, Jet, etc.) to individually forecast the numbers for each type.

Both Oregon and California are included in the market share analysis due to many aircraft owners in

Northern California moving their aircraft up to MFR for various reasons including lower ownership costs and

California owners retiring and moving to Oregon. This method assumes MFR will maintain this share of

based aircraft against the projected state-based aircraft numbers in the 2019 Oregon TAF and 2019

California TAF. However, this method results in a flat forecast for both Helicopter and Other aircraft, both

of which have a growing presence at MFR. Additionally, the forecasted number of Jets based on market

share is lower than current hangar demand suggests. MFR has recently seen more jets moving from

California to be based at MFR as owners see to lower costs or are moving to or retiring in Southern Oregon.

Thus, this method is not preferred.

To address the flat forecast for Helicopters and Other aircraft, the hybrid method combines the two previous

methods. The market share method is applied for the SEP, MEP, and Jet forecasts, while the Aerospace

Forecast growth rate method is used for Helicopters and Other aircraft.

Table 2-29 and Figure 2-16 present the three forecasting methods with the 2019 TAF for reference.


42

Table 2-29: Based Aircraft Forecasts

Fiscal year Aerospace CA+OR Market Share Hybrid 2019 TAF

2018 199 199 199 180

2020 198 200 205 180

2025 195 209 216 180

2030 195 218 230 180

2035 192 226 241 180

2040 192 234 253 180

‘20-'40 CAGR -0.2% 0.8% 1.1% N/A



Figure 2-16: Based Aircraft Forecasts

160

170

180

190

200

210

220

230

240

250

260

Based A

ircra

ft

Historical 2019 TAF Airport Records FAA 2019 TAF

Aerospace CA+OR Market Share Hybrid


43


The preferred method of forecasting MFR based aircraft is the hybrid method. This method accounts for

the effects of California aircraft owners moving aircraft to MFR along with the growth of Helicopters and

Other aircraft. The market share method reflects both Oregon and California aircraft trends for SEP and

MEP aircraft. However, the market share method results in flat Helicopter and Other aircraft forecasts. This

is notable as the 2019 Aerospace Forecast notes the national light sport fleet is projected to double in size

by 2039 and for rotorcraft to grow. Thus, the Aerospace Forecast growth rate method is applied for

Helicopter and Other aircraft forecasts as it includes the projected nationwide growth both types of based

aircraft. With regard to based Jets, it is expected for the growth rate to be higher than forecasted in the

Aerospace Forecast due to increasing demand from owners from California wanting to base their jets at

MFR. This is due to the lower cost of living and cost of aircraft ownership in Oregon compared to rising

costs in Northern California. Many of those moving to live in the Southern Oregon are people with families

or business ties in Northern California and so will look to maintain access to Northern California.

Table 2-30 shows the preferred forecast for each aircraft type. Table 2-31 compares the total based aircraft

forecast with the 2019 TAF. The TAF projects total based aircraft to remain flat for the forecast period, while

the preferred forecast results in more based aircraft for every forecasted year.

Table 2-30: Preferred Forecast by Aircraft Type

Fiscal year SEP Jet MEP Helicopter Other Total

2018 128 24 27 13 7 199

2020 137 25 22 13 8 205

2025 143 27 23 15 8 216

2030 149 30 25 16 10 230

2035 155 33 26 17 10 241

2040 161 36 27 18 11 253

20-'40 CAGR 0.8% 1.8% 1.0% 1.6% 1.6% 1.1% CAGR: Compound Annual Growth Rate

Table 2-31: Based Aircraft Forecast – TAF Comparison


2018 199 180 19 10.6%

2020 205 180 25 13.9%

2025 216 180 36 20.0%

2030 230 180 50 27.8%

2035 241 180 61 33.9%

2040 253 180 73 40.6%




44

PEAK FORECASTS AND CRITICAL AIRCRAFT

Peak Period Forecasts

Peak period forecasts estimate when airport facilities will be the busiest. Peak period information is used

to determine the capacity needs for airfield and terminal facilities and determine the scope of improvement

projects. Improvement projects are not typically designed for the busiest day of the year specifically, as

such a design would lead to over-building. The peak period forecasts are based on examining the average

day of the busiest month of the year, in accordance to FAA Advisory Circular 150/5070-6B Change 2. July

is the peak month used for fiscal year 2018.

The method used to forecast future peaking is based on historical record. Thus, peak forecasts should be

reevaluated if changes in user or aircraft type occur. Table 2-32 shows the forecasted peak periods for

passengers and operations for the forecast period.

Table 2-32: Peak Period Forecasts

Category Period Factor 2018 2020 2025 2030 2035 2040

Enp

lan

em

en

ts a

nd

D

ep

lan

em

en

ts Annual 100% 480,271 529,000 672,000 797,000 915,000 1,000,000

Peak Month 11% 52,000 57,000 72,000 86,000 98,000 107,000

Peak Day 3% 1,700 1,900 2,400 2,900 3,300 3,600

Peak Hour – Enplanements

22% 380 420 530 640 730 800

Peak Hour - Deplanements

13% 210 240 300 370 420 450

Tota

l P

asse

nge

rs Annual 100% 960,542 1,058,000 1,344,000 1,594,000 1,830,000 2,000,000

Peak Month 11% 103,000 114,000 144,000 171,000 196,000 215,000

Peak Day 3% 3,400 3,800 4,800 5,700 6,500 7,200

Peak Hour 11% 375 398 461 534 618 716

Air

craf

t O

per

atio

ns Annual 100% 43,877 47,000 49,000 49,000 51,000 51,000

Peak Month 10% 4,500 4,900 5,100 5,100 5,300 5,300

Peak Day 6% 280 310 320 320 340 340

Peak Hour* 7% 21 23 24 24 25 25 *Peak hour operations will vary based on touch-and-go operations. Users that introduce touch-and-go operations will increase

peak hour operations.

Projected numbers over 1,000 are rounded to nearest thousand. Numbers over 100 and under 1,000 are rounded to the nearest 10.

Sources: 2018 MFR Records, USDOT T-100 Database

The peak passenger enplanement and deplanement forecast is determined by the growth in the total

number of passengers traveling through MFR and the trends in airlines transitioning from smaller to larger

aircraft. Airline schedule records and USDOT T-100 data show July being the busiest month by passenger

numbers. This coincides with the summer school break when families tend to travel together. The daily

peak for 2018 was found to be between the 6 a.m. and 7 a.m. A second, smaller peak occurred between

12 p.m. and 1 p.m.


45

Peak aircraft operations match peak passenger enplanement occurring in the month of July. Summer

months see many operations with commercial service along with general aviation pilots enjoying fewer days

of bad weather which discourage recreational flying.

Critical Aircraft

The critical aircraft is defined as being the most demanding type or group of aircraft with more than 500

annual operations (not touch-and-go) at an airport. To determine the MFR critical aircraft, operations data

by aircraft type is provided by the Traffic Flow Management System Counts (TFMSC). The TFMSC only

captures operations with filed flight plans, so aircraft used for flight training are not represented in the

dataset.

Aircraft type is defined by the Airport Reference Code (ARC), which consists of the Aircraft Approach

Category (AAC) and the Airport Design Group (ADG). These categories are defined by the aircraft

dimensions and approach speed. Table 2-33 presents the 2018 operation count at MFR by aircraft type. A

breakdown of operations by aircraft type for each ARC is included in Attachment 3. ARC D-IV operations

consist solely of DC-10 operations which are performed by the contracted USFS air tankers during fire

season. These operations are performed under VFR, which is not recorded in TFMSC.

Table 2-33: MFR FY2018 Operations by Airport Reference Code

ARC Civilian Military Total

C-I 204 0 204

C-II 5,338 0 5,338

C-III 4,982 60 5,042

Subtotal C 10,524 60 10,584

D-I 28 44 72

D-II 46 0 46

D-III 388 18 406

D-IV* 106 0 106

D-V 2 0 2

Subtotal D 570 62 632

Total C and D 11,094 122 11,216

Source: TFMSC. Detailed breakdown by aircraft included in Attachment 3. MFR Airport records.

*DC-10 (D-IV) data provided by the airport as TFMSC does not capture VFR operations. Contracted USFS DC-10 operations at

MFR mainly operate under VFR.


46

The critical aircraft at MFR is determined by looking at the highest AAC and ADG with more than 500

operations. While no single aircraft has an ARC of D-III and conducts more than 500 annual operations at

MFR, AAC D aircraft in total (ADGs I, II, III, IV, and V) had 570 civilian operations in FY2018. These aircraft

have the highest approach speeds, and therefore, have the most demand for the runway to accommodate

such aircraft types. In terms of aircraft with the widest wingspan and tallest tail height, AAC C/D, ADG III

had 5,370 annual operations in FY2018. Thus, the critical aircraft at MFR are aircraft with ARC D-III. Having

airport facilities meet ARC D-III design standards will help the airport accommodate the forecasted carrier

aircraft that are expected to operate in the future. The representative aircrafts for ARC D-III at MFR are the

Boeing 737-800 and Boeing 737 MAX 8.

Figure 2-17: ARC D-III Representative Aircraft: Boeing 737-800

The future fleet composition is unknown and is based on Boeing and Airbus aircraft orders that are currently

being fulfilled or yet to be delivered. The newer Boeing 737 MAX and A320neo are expected to replace

their existing counterparts while having similar physical features. Regional jets such as those operated by

Skywest and Horizon are expected to transition to the 76-seat E175 and yet-to-be-produced 70- to 90-seat

Mitsubishi SpaceJet (previously called the MRJ). These aircraft are replacing smaller 50-seat jets and the

retired Q400. Skywest currently has 100 SpaceJets on order.

The U.S. Forest Service occasionally contracts with very-large air tankers (VLATs) based on the Boeing

747-400 (D-IV) and DC-10 (D-IV). These aircraft provide critical emergency services to the region and

planning will consider their needs; however, it is not expected that MFR will see over 500 annual operations

of these aircraft unless the civilian operator that runs them moves their facility to MFR.


47

Table 2-34 shows the expected increase in C-III and D-III air carrier aircraft operating at MFR. This is due

to airlines retiring older aircraft and switching to larger aircraft with more seats and longer range such as

the Airbus A320 (ARC C-III) and Boeing 737-800 (ARC D-III).

Table 2-34: Future Air Carrier Operations by Aircraft Type

Seats Typical Aircraft ARC 2020 2025 2030 2035 2040

60-76 E175, SpaceJet C-II 12,015 13,176 10,401 8,250 6,081

100-124 A220, E195-E2 C-III 2,027 2,027 2,433 3,446 3,446

125-150 A319 C-III 1,192 1,756 3,412 4,711 6,081

> 150 A320/1, 737-800 C-III or D-III 730 963 1,849 2,879 3,649 0-59 seat aircraft are expected to be phased out by 2022. Aircraft with 77-99 seats did not operate many flights at MFR in the

past 10 years so there is insufficient data to support their inclusion in the analysis.


48

FORECAST SUMMARY

The forecast summary is presented in Figure 2-18 and Figure 2-19. Highlights of the forecast are below.

Jackson County population is expected to continue growing at an average 0.9 percent per year.

Jackson County economy is growing with GRP projected to grow an average 1.7 percent annually.

Passenger enplanements are highly correlated with GDP, county employment, and county

population. Enplanements are expected to continue growing at an average annual rate of 3.2 percent

between 2020 and 2040.

Commercial operations are expected to increase as new routes are added. Additional enplanements

are expected to be mainly accommodated with larger aircraft rather than increased frequency. Even

with the larger aircraft, air carrier operations are expected to increase to accommodate the 80 percent

or more load factors. Without upgauging, airlines would need to increase operations even more to

meet demand.

Itinerant general aviation operations are projected to increase an average 0.3 percent annually using

the Aerospace Forecast growth rate model.

Based on the national market share forecasting method, local general aviation operations at MFR

are forecasted to grow an average 0.4 percent annually.

Based aircraft counts are projected using a hybrid method with the SEP, Jet, and MEP forecasts

based on the market share MFR has compared to both the Oregon and California based aircraft

fleets. Helicopters and Other aircraft are forecasted using the 2019 FAA Aerospace Forecast growth

rate. The based aircraft fleet as a whole is expected to increase an average 0.9 percent annually.

The SEP fleet is projected to grow at an average 0.8 percent, Jet at 1.8 and MEP at 1 percent. Both

Helicopters and Other aircraft are projected to grow at an average 1.6 percent annually.

Peak operations and peak passenger enplanement and deplanement occur in July.

The forecast critical aircraft type is ARC D-III.


49

Figure 2-18: Forecast/TAF Comparison

AIRPORT NAME: ROGUE VALLEY INTL/MEDFORD AIRPORT

Airport AF/TAF

Year Forecast TAF (% Difference)


Base yr. 2020 528,649 570,745 -7.4%

Base yr. + 5yrs. 2025 672,000 635,881 5.7%

Base yr. + 10yrs. 2030 797,000 700,246 13.8%

Base yr. + 15yrs. 2035 915,000 775,922 17.9%

Commercial Operations

Base yr. 2020 22,268 22,680 -1.8%

Base yr. + 5yrs. 2025 23,772 24,092 -1.3%

Base yr. + 10yrs. 2030 23,942 26,193 -8.6%

Base yr. + 15yrs. 2035 25,118 28,604 -12.2%

Total Operations

Base yr. 2020 46,768 46,558 0.5%

Base yr. + 5yrs. 2025 48,572 48,130 0.9%

Base yr. + 10yrs. 2030 49,142 50,391 -2.5%

Base yr. + 15yrs. 2035 50,718 52,962 -4.2%

NOTES: TAF data is on a U.S. Government fiscal year basis (October through September).

Chapter 2 – Inventory

50

Figure 2-19: TAF Forecast Worksheet

A. Forecast Levels and Growth Rates

AIRPORT NAME: ROGUE VALLEY INTL/MEDFORD AIRPORT Specify base year: 2020

Average Annual Compound Growth Rates

Base Yr. Level Base Yr. + 1yr. Base Yr. + 5yrs. Base Yr. + 10yrs. Base Yr. + 15yrs. Base yr. to +1 Base yr. to +5 Base yr. to +10 Base yr. to +15


Air Carrier 194,050 199,818 271,638 479,827 670,406 3.0% 7.0% 9.5% 8.6%

Commuter 334,599 354,817 400,362 317,173 244,594 6.0% 3.7% -0.5% -2.1%

TOTAL 528,649 554,635 672,000 797,000 915,000 4.9% 4.9% 4.2% 3.7%

Operations

Itinerant

Air carrier 15,780 16,334 17,712 17,882 19,058 3.5% 2.3% 1.3% 1.3%

Commuter/air taxi 6,488 6,214 6,060 6,060 6,060 -4.2% -1.4% -0.7% -0.5%

Total Commercial Operations 22,268 22,548 23,772 23,942 25,118 1.3% 1.3% 0.7% 0.8%

General aviation 16,700 16,700 16,900 17,200 17,400 0.0% 0.2% 0.3% 0.3%

Military 500 500 500 500 500 0.0% 0.0% 0.0% 0.0%

Local

General aviation 7,000 7,000 7,100 7,200 7,400 0.0% 0.3% 0.3% 0.4%

Military 300 300 300 300 300 0.0% 0.0% 0.0% 0.0%

TOTAL OPERATIONS 46,768 47,048 48,572 49,142 50,718 0.6% 0.8% 0.5% 0.5%

Instrument Operations 26,456 26,740 28,014 28,256 29,479 1.1% 1.2% 0.7% 0.7%

Peak Hour Operations 22 22 22 22 22 -2.2% 0.0% 0.0% 0.0%

Cargo/mail (enplaned+deplaned tons) 7,731,119 7,923,139 8,577,495 9,685,445 10,672,053 2.5% 2.1% 2.3% 2.2%

Based Aircraft

Single Engine (Nonjet) 137 138 143 149 155 0.7% 0.9% 0.8% 0.8%

Multi Engine (Nonjet) 22 22 23 25 26 0.0% 0.9% 1.3% 1.1%

Jet Engine 25 25 27 30 33 0.0% 1.6% 1.8% 1.9%

Helicopter 13 14 15 16 17 7.7% 2.9% 2.1% 1.8%

Other 8 8 8 10 10 0.0% 0.0% 0.0% 0.0%

TOTAL 205 207 216 230 241 1.0% 1.1% 1.2% 1.1%

B. Operational Factors

Base Yr. Level Base Yr. + 1yr. Base Yr. + 5yrs. Base Yr. + 10yrs. Base Yr. + 15yrs.

Average aircraft size (seats)

Air carrier 129 129 134 146 146

Commuter 75 76 76 76 76

Average enplaning load factor

Air carrier 81% 81% 81% 82% 83%

Commuter 80% 80% 0% 0% 0%

GA operations per based aircraft 116 114 111 106 103


51

Attachment 1 – 2019 MFR Terminal Area Forecast


52


53


54

Attachment 2 – Peak Hour Demand Analysis


55

PEAK DEMAND ANALYSIS Peak demand analysis assesses when facilities at the Rogue Valley International – Medford Airport

(MFR) are at their busiest based on their use by passengers and aircraft takeoffs and landings

(operations). This information is used to determine facility requirements of the passenger terminal

building and apron, and airfield pavements (the runways, taxiways, and aircraft parking aprons). Peak

demand analysis considers the busiest months, days, and hours, to determine the spread of demand

across time.

Peaking trends will vary from airport to airports. Some airports have highly concentrated peaks with most

annual activity occurring during a particular time of year, day of the week, or part of the day, while other

airports have more evenly distributed demand. Peak periods are calculated for aircraft operations and

for passenger enplanements and deplanements.

The data used for the analysis include operation counts from the MFR control tower, flight records from

the FAA Traffic Flow Management Systems Counts (TFMSC) and Distributed OPSNET, and airline

schedules from data provider DiioMi. MFR records provide monthly passenger counts and TFMSC

provides seating and passenger counts down to the day.

MFR has experienced substantial growth in the past decade, exceeding one million annual

passengers in the past year. Growth in the number of destinations offered by the air carriers, and the

size of aircraft that they use to serve these destinations, places a high level of peak demand on airport

facilities. Parking lots, terminal areas, parking aprons, fuel storage, and deicing facilities feel the effect

of this growth. Demand forecasts have a base year of 2018 (FAA fiscal year) as it is the most recent

year that a complete data set is available. Complete data for FAA fiscal year 2019 is not available;

however, data for the peak month of July is. To capture growth in passengers and operations

experienced at peak periods of this year, July 2019 is used in this analysis.

Peak Period Operations

Peak Month

Peak period operations examine how busy the runway system is throughout the year. The first step is

determining the busiest month or months of activity at MFR. Based on the airport provided operation

counts from fiscal year 2013 to 2018 there were two distinctive increases in activity: a busy summer

season between May and September. This data is presented in 0. On average, 49.5 percent of annual

operations occurred during these months. The busiest month on average has been July, with 10.9

percent of annual operations, followed by August with 10.5 percent. This peak period corresponds with

school summer vacation times with fewer cloud weather days. Barring fire events, this also means pilots

have more opportunities to fly for fun under VFR rather than having to rely on IFR. The least busy month

on average has been December with 6 percent of annual operations.


56

Figure A2-1: Average Operations per Month for Fiscal Years 2013 to 2018

Note: Departures line is directly underneath arrivals line. The two are essentially 1:1 on a monthly basis.

The operations distribution at MFR indicate that the highest level of demand on average are during the

summer months. Improvement projects noted in the Facility Requirements chapter should be designed

to address any capacity constraints created by the peak in demand.

Peak Day

Peak day analysis is based on the results of peak month analysis. Operation distribution is determined

by examining operation records for every day of the peak month. The following list show general trends

that contribute to peak operation days in July 2018 at MFR:

Most operations in July occurred between Monday to Wednesday with Tuesdays having the most

operations

Allegiant increases flight frequency for some routes during peak months

General aviation operations at MFR remain consistent throughout the week, likely due to MFR not

having as much recreational general aviation compared to other airports

Peak day data is based on TFMSC data and airport records. TFMSC provides daily operations counts

which is used to determine the ratio between arrivals and departures. This ratio is applied to airport

operation records as the airport did not provide daily operation data and the TFMSC and airport

operation counts did not match exactly. Peak day analysis for July 2018 is shown in 0.

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Op

era

tion

s

Months

Average Total Operations Departures Arrivals


57

Figure A2-2: Peak Day Operations (July 2018)

Source: OPSNET Operations Prorated by TFMS Report

As there are 31 days in July, an even distribution of operations throughout the month would result in 3.2

percent of monthly operations occurring daily. The peak day in July of fiscal year 2018 had 6.2 percent

of monthly operations. There were 15 days with over 3.2 percent of monthly operations and 16 days with

fewer than 3.2 percent. Having 48 percent of days in July with more than 3.2 percent of monthly

operations indicates peak periods occurring consistently throughout the month. The 6.2 percent peak

day is used for planning purposes.

Peak Hour

Peak hour operations examine the when in the day flights arrive at and depart from MFR. Peak arrivals

and departures have different impacts on airport facilities and are thus analyzed separately. For

example, peak departures affect taxiway use as aircraft wait to take off while peak arrivals affect the

capacity of the passenger terminal gate. Peak hour analysis uses data from the TFMSC Distributed

OPSNET database. 0shows the total number of operations occurring at each hour in July 2019 on a 24-

hour scale. FY2019 is used because the peak month data was available and more current than FY2018.

0

50

100

150

200

250

300

7/1 7/3 7/5 7/7 7/9 7/11 7/13 7/15 7/17 7/19 7/21 7/23 7/25 7/27 7/29 7/31

Op

era

tion

s

Date (July 2018)

Departures Arrivals Total


58

Figure A2-3: Peak Hour Operations (July 2019)

Source: OPSNET Operations Prorated by TFMS Report

An evenly distributed schedule would have 4.1 percent of operations occurring hourly for each hour in a

day. Based the data, there are 15 hours where hourly operations exceed 4.1 percent, with the remaining

9 hours with less than 4.1 percent. These nine hours include late night hours of 10pm to 4am. Arrival

operations are over 4.1 percent of daily operations 12 hours of the day, mainly between 11am and 4pm.

Departures are over 4.1 percent 12 hours of the day as well, with the bulk of the peaks occurring between

12pm and 6pm. The largest arrival peak has 9.6 percent of daily operations occurring at 2pm while the

departure peak is at 4pm with 9.2 percent of daily operations.

The FAA TSFMC presents average hourly operations per five-hour block, and the average peak hour

for operations in July 2018 had 7.5 percent of the daily total. The 7.5 percent average peak, not the

maximum of 9.6 percent, is used for planning purposes.

0

50

100

150

200

250

300

350

400O

pe

ratio

ns

Time (24 HR, Pacific Time)

Departures Arrivals Total


59

Peak Period Passengers

The peak period passenger analysis looks at the number of seats and passengers going through the

airport in the year and determines when the greatest number of seats and passengers are occurring.

Information on the aircraft seat count and number of operations per aircraft is provided by USDOT T-

100 records. Peak passenger analysis is used to determine the scale and adequacy of terminal and

parking facility requirements, which will be provided in Chapter 3.

Peak Month

0and 0 show the trends in passengers, seats, and load factors in FY2018. There is a distinct busy season

from May to September. An evenly distributed year would have 8.3 percent of annual passengers or

seats. 6 months out of the year see more than 8.3 percent with a peak high of 10.7 percent of annual

passengers in July (103,070 passengers). The number of seats in the market also peaks in July with

10.8 percent, or 137,468 seats. There were five months with more than 8.3 percent of seats. June comes

in behind July with the second highest number of passengers and seats with 10.1 percent of annual

passengers and seats.

Figure A2-4: Peak Month Passengers, Seats, and Load Factor

Source: USDOT T-100, FY2018

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

30,000

50,000

70,000

90,000

110,000

130,000

150,000

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug SepL

oa

d F

acto

r

To

tal S

eats

/Mo

nth

Month

Total Passengers Total Seats Load Factor


60

Table A2-1: Peak Month Passengers, Seats, and Load Factor

Month Total Passengers Annual % Total Seats Annual % Load Factor

Oct-17 80,953 8.4% 102,926 8.1% 78.7%

Nov-17 77,751 8.1% 100,920 7.9% 77.0%

Dec-17 75,410 7.9% 100,628 7.9% 74.9%

Jan-18 61,527 6.4% 83,883 6.6% 73.3%

Feb-18 58,224 6.1% 77,333 6.1% 75.3%

Mar-18 70,019 7.3% 91,773 7.2% 76.3%

Apr-18 73,926 7.7% 98,294 7.7% 75.2%

May-18 82,012 8.5% 107,783 8.5% 76.1%

Jun-18 96,752 10.1% 127,900 10.1% 75.6%

Jul-18 103,070 10.7% 137,468 10.8% 75.0%

Aug-18 92,478 9.6% 123,073 9.7% 75.1%

Sep-18 87,202 9.1% 120,317 9.5% 72.5% Source: USDOT T-100, FY2018

Load factors remains relatively consistent throughout the year with an average of 75.4 percent, 78.7

percent in October being the high and 72.5 percent in September being the low. The peak month of July

has an average load factor of 75 percent. The increase in passenger and seats matches in the summer

months the increase in operation, as expected with school summer vacations leading to more traveling.

While FY2019 is outside of the analysis years for this forecast, passenger load factors have been made

available by MFR. FY2019 records indicate that average load factors have increased to an annual

average of 81 percent in FY2019 compared to an annual average of 76 percent in FY2018.


61

Peak Day

Based on the peak month analysis, peak day analysis reviews operations and seating capacity records

for every day of the peak month. The data is based on TFMSC daily departure and arrival seat ratios

that are applied to airport records for the month. 0shows the available number of seats on each day in

July 2018. The busiest days in July were Tuesdays throughout the month with July 2nd specifically being

the busiest in terms of seats with 4,624 seats, 13 more seats than the other following Tuesdays.

Figure A2-5: Peak Day Seats

Diio Mi provides daily schedule records and indicates all Tuesdays in July 2018 had 56 scheduled

commercial passenger service flights. The busiest Tuesday was July 2nd, right before the July 4th holiday

and long weekend, with 4,624 seats. To negate the effects of the holiday, the more frequent peak days

with 4,611 seats were selected for design and analysis purposes. Peak passengers are determined by

applying the average July load factor to the average day seats, which equates to 3,400 total passengers,

or 2.6 percent of the peak month.

4,624 4,611 4,611 4,611 4,611

2,500

2,750

3,000

3,250

3,500

3,750

4,000

4,250

4,500

4,750

5,000

7/1 7/3 7/5 7/7 7/9 7/11 7/13 7/15 7/17 7/19 7/21 7/23 7/25 7/27 7/29 7/31

To

tal S

ea

ts/D

ay

Date (FY 2018)


62

Peak Hour

Peak hour operations are based on the MFR daily flight schedule. The daily schedule and number of

available seats for July 2019 is provided by Diio Mi data. Peak passenger enplanements and

deplanements reflect passenger terminal utilization. 0shows the number of flights arriving and departing

on a 24-hour clock. Based on the number of total seats, 6 a.m. is the peak hour on the peak day of the

year with 509 departing seats. The peak for departing seats occurs at 11 a.m. with 292 seats. Table A2-

2 shows the flight schedule that serves as a source for Figure A2-6

Figure A2-6: Peak Hour Seats

Adjusted for average load factor, peak hour enplanements are 22 percent of average daily

enplanements. Deplanements are spread more evenly throughout the day and peak hour

deplanements equals 13 percent of average daily deplanements. Peak hour passengers are 11

percent of average day passengers.

0

100

200

300

400

500

600

500 700 900 1100 1300 1500 1700 1900 2100 2300

Num

ber

of

Seats

Time (24HR, Pacific Time)

Seats In Seats Out Total


63

Table A2-2: July 2019 Peak Day Airline Flight Schedule

Operation Type Airline MFR Time Origin Destination Aircraft Seats

Arrival Alaska 0:50 SEA MFR DH4 76

Departure Alaska 5:00 MFR PDX DH4 76

Departure Delta 6:00 MFR SLC E75 76

Departure United 6:09 MFR LAX CRJ 50

Departure United 6:15 MFR SFO 319 128

Departure Alaska 6:30 MFR SEA DH4 76

Departure United 6:31 MFR DEN 739 179

Departure Delta 7:00 MFR SEA E75 76

Arrival Alaska 7:35 PDX MFR DH4 76


Arrival Allegiant 8:46 LAS MFR 319 156


Departure Allegiant 9:36 MFR LAS 319 156


Arrival United 10:22 SFO MFR CRJ 50

Departure United 10:52 MFR SFO CRJ 50

Arrival American 11:35 LAX MFR CR7 70

Arrival United 11:38 DEN MFR CR7 70

Arrival Delta 11:39 SEA MFR E75 76


Departure American 12:05 MFR PHX CR7 70

Departure Delta 12:09 MFR SLC E75 76

Arrival Delta 12:19 SLC MFR E75 76


Departure United 12:35 MFR DEN CR7 70







Arrival United 14:49 LAX MFR CRJ 50


Departure United 15:20 MFR LAX CRJ 50

Arrival United 15:29 DEN MFR ER4 50

Departure United 15:59 MFR DEN ER4 50

Arrival American 16:36 PHX MFR CR7 70

Arrival Alaska 16:55 PDX MFR DH4 76 Table continued on the next page.


64

Operation Type Airline MFR Time Origin Destination Aircraft Seats

Departure American 17:06 MFR LAX CR7 70







Arrival Allegiant 18:19 LAX MFR 320 180


Departure Allegiant 19:09 MFR LAX 320 180



Arrival United 20:35 DEN MFR 738 166


Arrival United 22:41 LAX MFR CRJ 50

Arrival United 22:47 SFO MFR 738 166


Arrival Delta 23:13 SLC MFR E75 76

Flights to large hub airports are usually scheduled to accommodate business travelers with departures

occurring early in the day and arrivals in the later afternoon to evening. Thus, the distinct departure peak

is in the morning as travelers depart to their destinations or to hubs to make connecting flights. Arrivals

are scheduled more evenly spread throughout the afternoon and evening.


65

Attachment 3 – Aircraft Approach Category C and D

Operations by Aircraft Type


66

Aircraft FY2018 Operations ARC

H25B - BAe HS 125/700-800/Hawker 800 108 C-I

LJ25 - Bombardier Learjet 25 46 C-I

LJ40 - Learjet 40; Gates Learjet 4 C-I



WW24 - IAI 1124 Westwind 10 C-I

ASTR - IAI Astra 1125 26 C-II

CL30 - Bombardier (Canadair) Challenger 300 122 C-II

CL60 - Bombardier Challenger 600/601/604 136 C-II

CRJ1 - Bombardier CRJ-100 4 C-II

CRJ2 - Bombardier CRJ-200 3,310 C-II

CRJ7 - Bombardier CRJ-700 1,698 C-II

E135 - Embraer ERJ 135/140/Legacy 8 C-II

E35L - Embraer 135 LR 2 C-II

G150 - Gulfstream G150 4 C-II

G280 - Gulfstream G280 8 C-II

GALX - IAI 1126 Galaxy/Gulfstream G200 6 C-II

GLF3 - Gulfstream III/G300 6 C-II

LJ75 - Learjet 75 8 C-II

A319 - Airbus A319 1,202 C-III

A320 - Airbus A320 All Series 454 C-III

B462 - BAe 146 -200 8 C-III

B734 - Boeing 737-400 12 C-III

B737 - Boeing 737-700 56 C-III

CRJ9 - Bombardier CRJ-900 170 C-III

E170 - Embraer 170 4 C-III

E175 - Embraer 175 2,902 C-III

GL5T - Bombardier BD-700 Global 5000 14 C-III

GLEX - Bombardier BD-700 Global Express 152 C-III

P3 - Lockheed P-3C Orion* 60 C-III

RJ85 - Avro RJ-85 8 C-III

* Aircraft operated by the U.S. Department of Defense and other Federal Agencies cannot be used to support AIP eligibility. A total of 60 of the 10,584 operations in this table were conducted by Department of Defense aircraft. Source: TFMSC for FAA Fiscal Year 2018


67

Aircraft FY2018 Operations ARC

F15 - Boeing F-15 Eagle* 14 D-I

F18S - F18 Hornet* 2 D-I

F22 - Boeing Raptor F22* 4 D-I

LJ35 - Bombardier Learjet 35/36 28 D-I

T38 - Northrop T-38 Talon* 24 D-I

GLF4 - Gulfstream IV/G400 46 D-II

B738/B739 - Boeing 737-800/900 246 D-III

GLF5 - Gulfstream V/G500 36 D-III

GLF6 - Gulfstream 6 8 D-III

MD83/MD88 - Boeing (Douglas) MD 83/88 98 D-III

P8 - Boeing P-8 Poseidon* 18 D-III

DC10 - Boeing (Douglas) DC 10-10/30/40** 106 D-IV

B744 - Boeing 747-400 2 D-V

* Aircraft operated by the U.S. Department of Defense and other Federal Agencies cannot be used to support AIP eligibility. A total of 62 of the 632 operations in this table were conducted by Department of Defense aircraft. ** Data from Airport records as the contracted USFS tankers operate under VFR. Conversation with the Airport on landings is included on the next page. Source: TFMSC for FAA Fiscal Year 2018


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CHAPTER 2 - DEMAND FORECASTmfrmasterplan.com/wp-content/uploads/2019/12/DRAFT... · CHAPTER 2 - DEMAND FORECAST FORECAST SUMMARY The forecast chapter provides a 20-year projection