InterVISTAS Airport Traffic Forecasting Workshop - Presentations, 10April2013

Canadian Forecasting WorkshopSession 1:

Introductory Remarks on the Science and Art Forecasting

Dr. Mike TrethewayInterVISTAS Consulting Chief Economist

Realizing the vision together2

Today’s Workshop

1. Introductory Remarks on Forecasting

2. Transport Canada PODM/PTAM models

3. Alternative Approaches- Single Airport Forecasts

4. Incorporating Uncertainty into Air Traffic Forecasts

5. Current Outlook


Today’s Workshop

• Dr. Mike Tretheway• Chief Economist, InterVISTAS Consulting Group• Technical Director, Business Line Aviation

• Ian Kincaid• Vice President, Economic Analysis

• Head of Forecasting Practice

Realizing the vision together

Background Report ACRP 76

Addressing Uncertainty about Future Airport Activity Levels in Airport decision making

Undertaken by

•InterVISTAS Consulting Inc.

• Mike TrethewayIan Kincaid

•HDR Inc.

• David LewisStéphane Gros

4


Airport Forecasting Record.

• Forecasting is an essential tool for airports

• Medium to long term master planning

• Financial forecasts

• Operational Forecasts

5



• But the track record has not always been good

6


Atlanta

7

35

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55

2000 2002 2004 2006 2008 2010 2012 2014

Pass

enge

r Enp

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illio

ns)

Actual TrafficTAF 2001TAF 2003TAF 2005TAF 2007TAF 2009

Actual and Forecast Total Passenger Enplanements at Hartsfield-Jackson Atlanta International Airport


Washington Dulles

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0

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1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

Pass

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r Enp

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men

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ns)

Actual Traffic

TAF 2000

TAF 2001

TAF 2003

TAF 2004

TAF 2005

TAF 2009

Actual and Forecast Total Passenger Enplanements at Washington Dulles International



• But the track record has not always been good

• Albeit with some learning

9


St. Louis

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1985 1990 1995 2000 2005 2010 2015

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Actual Traffic

TAF 1998

TAF 2001

TAF 2002

TAF 2003

TAF 2009

TWA declaresbankruptcy

TWA declaresbankruptcy for

the second time


the third time andAA buys TWA.Construction of

new runway beginsAA reduces

services at STL

AA terminates itsfocus city at STL

Actual and Forecast Total Passenger Enplanements at Lambert-St. Louis International Airport


St. Louis

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0

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1985 1990 1995 2000 2005 2010 2015

Pass

enge

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lane

men

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ns)

Actual Traffic

TAF 1998

TAF 2001

TAF 2002

TAF 2003

TAF 2009



the second time




services at STL


Actual and Forecast Total Passenger Enplanements at Lambert-St. Louis International Airport



• Sometimes the long run forecast has been good

• but with short term variance

• And different traffic mix than original forecast

12


BWI

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0

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Actual Traffic

1987 Master Plan Forecast (Baseline)

Piedmont announces hub

First Gulf Warand recession

Southwest Airlines launches services

9/11 andrecession

Recession

U.S. Airways "de-hubs"

Actual and Forecast Total Passenger Enplanements atBaltimore/Washington International Thurgood Marshall Airport



• Sometimes unanticipated events dramatically change a market

14


New Orleans

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1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

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Actual Traffic

TAF 2001

TAF 2004

TAF 2005

TAF 2009

Hurricane Katrina

Actual and Forecast Total Passenger Enplanements atLouis Armstrong New Orleans International Airport



• The forecasting record can also be one of underforecasting

16


Bellingham WA

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0

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1985 1990 1995 2000 2005 2010 2015 2020

Pass

enge

r Enp

lane

men

ts (T

hous

ands

)

Actual Traffic

TAF 2000

TAF 2003

Master Plan Forecast

United Express / SkyWest exits in 2001

Allegiant enters in August 2004 and rapidly develops service

Allegiant opens base at BLI in January 2008

Actual and Forecast Total Passenger Enplanements at Bellingham International Airport


Guessing vs. Analysis

Midway (1976)

18


• Setting:Station Hypo• Code breaking centre, Pearl Harbor Naval Base

• Early April 1942 (US fleet crippled after 7Dec1941)

• Issue: forecasting Japanese naval fleet intentions for coming 2 months

• MG is fictional character (Matt Garth)• JR is historical character (Joe Roquefort,

head of Honolulu code breaking unit)

19


o MG: Now the boss is afraid Yamamoto's going to jump back at us. But where? JR: We got the latest intercepts here. Here's a list of Japanese ships we suspect will be assigned to amphib operations south of Rabaul. The Coral Sea! That's where we think they'll strike next. But something else is stirring, something out our way.

o MG: We need facts, not guesswork. o JR: Matt, we cracked Yamamoto's code, but we can't just reel it off. We get a flicker here

and a glimmer there. o MG: How much can you decipher? o JR: Hell, maybe... o MG: Really decipher. o JR: Ten percent. o MG: That's one word in . For Christ's sake, you're guessing! o JR: We like to call it analysis

20


Types of Airport Forecasts.

• Passenger traffic• Total pax

• Enplaned/deplaned

• O/D vs Connecting

• Cargo tons• E/D vs on-board

21



• Aircraft Movements• Commercial

• Heavy jet, turboprops, piston

• Typically driven by

• base pax/cargo-freighter forecast

• Pax/aircraft (cargo/aircraft)

• General Aviation

• See recent trend

• Other

• Military (Busan, new Beijing examples),

• rescue, government, tech stop22


Total GA Movements for Canada

24

Source: Statistics Canada, Table 401-007, 401-0015 and 401-0021.



• Operational Forecasts• Day by day, Hour by hour forecasts

of a specific traffic type

• E.g., volumes through a security pointborder process

• Ex- Blackcomb Ski Corp.• Recognize effects of

• Annual volume drivers• weather, • Interaction between the sessions

• Increased local skiing today means less in 2 weeks

25


Forecast Probability and Risk.

• Low-Central-High• What are the probabilities of each scenario?

• Threshold

• What is probability that traffic in each year will fall below 19mn pax?

• Risk

• What is the 20/80% range of the forecast 10 years from now?

26


Thank You

Subscribe to Monthly Aviation Intelligence Reportwww.InterVISTAS.com

Transport Canada ForecastsInterVISTAS Consulting Group

10 April 2013


Outline

29

• Overview of the Transport Canada Forecasts:• Methodology• Data• Pros and cons

• Alternative Approaches:• Single airport methodologies• Methodology• Addressing uncertainty

Transport Canada Forecasts



31

• Each year, Transport Canada generated medium and long term forecasts of Canadian air traffic

• Up to 14 years in the future• Forecasts of

• E/D passengers and passenger-kms• Air cargo tonnage• Aircraft movements (commercial and GA)

• Breakdowns into domestic, transborder and international• Published forecasts provided national and regional forecasts

(Atlantic, Ontario, Quebec, Prairies/Northern, Pacific)• Most recent document was 2007

• Forecasts for individual airports available for purchase



32

• Transport Canada forecasts based on two inter-connected models

• Originally developed in 1976

• Generation of traffic and allocation of traffic:• Generation: PODM-V2 (Passenger Origin-Destination Model)

• Forecasted Origin-Destination traffic

• E.g., Vancouver-Montreal; Toronto – Los Angeles, etc.

• Allocation: PTAM (Passenger Traffic Allocation Model)• Allocates forecasted passenger traffic to air carrier operations

• E.g., Forecast Vancouver-Montreal traffic allocated to direct service and to connections via Calgary, Toronto, etc.



33

• The Generation-Allocation approach is similarapproach to that used in urban transport modeling

• Road systems

• Toll roads

• Public transport

• It allows changes in the network to affect traffic flows

• However, the Transport Canada modeldoes not address congestion or other constraints

• This is often a major factor in urban models


PODM (Passenger Origin-Destination Model)

34

• PODM:



35

• PODM (Translated):• Based on a zonal system• PODM forecasts traffic between zones• Domestic zones:

• Approx i mately 36 zones based around major airports

• E.g., Toronto - Pearson, City Centre, Hamil ton, Oshawa, Buttonv i ll e, Ki tchener)Vancouver – Vancouver, Abbots ford, Vancouver Harbour

• Transborder zones:• Approx i mately 20 zones

• E.g., Los Angel es , New York , etc .

• International zones:• Country (e.g. , UK) or cont inental (e.g., Africa)



36

• PODM (Translated):• Gravity Model:

• Traffic between two zones is a function of:

•

• Model was di rectional

Country A

Country B

Zone A

Zone B

Attractors:PopulationGDPLinguistic similaritiesOther factors

Impeders:Air fareLevel of direct serviceTravel time by car



37

• Calibrat ion/estimat ion required a lot of data:• O/D passenger Data

• Direc ti onal Ori gin-Des ti nat ion Database

• Based on 10% sample of al l air t ickets

• Espec ial ly devel oped for the model by Stat is ti cs Canada

• Also used U.S. data for transborder

• Air Fare Data• Airfare Bas is Survey

• Quarterl y survey of domesti c, transborder and internat ional air passengers

• OAG schedule data• For determi ning direc t serv ices

• Socio-Economic Data• Populat ion, GDP, etc .



38

• Separate models for full economy and discount economy

• Proxies for trip purpose but also included cross-elasticities

(switching between full and discount)

• Based on data from several years – 1995 to 2001

• Panel data: based on variation over time and between routes

• Calibrated to ensure that the model reasonably

matches historical data – backcasting



39

• Forecasting Traffic• Requires forecasts of input variables

• GDP, population:• Based on Conference Board of Canada and other sources

• Air Fare:• Required separate model (Cost and Fare Model)

• Air fare based on input costs:

fuel, labour, aircraft equipment, other

plus productivity improvements


PTAM (Passenger Traffic Allocation Model)

40

• Having established the O/D flows, these need to be allocated to airlines

• Moving from O/D to E/D

• Also attempts to address the impact of O/D traffic on airline services

• Development of direct services• Incremental frequencies

• Incorporates assumptions about future airline fleets, aircraft technology and load factors

• Could require iteration of the PDOM model:• Introduction of direct service could stimulate O/D traffic



41

• Overall assumption of greater allocation of traffic to direct services:

Source: Transport Canada Assumptions Report (2006-20)



42

• Contained specific assumptions about the development of new direct services:

Source: Transport Canada Assumptions Report (2006-20)


Pros and Cons

43

• Positives• Arguably the most sophisticated air traffic forecasting system in the world

• The FAA approach is much more basic:National traffic model based on econometric analysisPlus separate forecasts for some individual airports(Terminal Area Forecasts)

• Could model in a complex way the implication of changes to the airline network

• E.g., O/D – Edmonton to Europe• Starts connecting through Toronto (and other hubs)

• Direct start-ups which impacts on traffic flows through Toronto

• Similarly, the gateway impacts on Vancouver:E.g., YOW-YVR-HK now moves YOW-YYZ-HKG

• no zonal system or integrated approach to forecasting


Pros and Cons

44

• Negatives• Data!

• O/D Passenger and Air Fare Basis data generally not available due to confidentiality concerns

• Used for national accounts requirements and other purposes

• Raw data needs processing – Transport Canada had a bespoke data pulls to suit forecasting requirements

• More restrictive than U.S. equivalents (available to U.S. citizens)

• Alternative commercial sources are available – MIDT, DIIO, etc.

• But costs are high:• Tens of thousands of dollars per airport

• Prohibitive for individual airports but might still be useful for system-wide purposes (e.g., Nav Canada, CATSA)

• no zonal system or integrated approach to forecasting


Pros and Cons

45

• Negatives

• Complexity

• Resources

• Required a small full-time team to maintain

• Costs of model maintenance, calibration and result production

development beyond the capabilities of individual airports and most

other organisations

Alternative Approaches


Single Airport Forecasts

47

• Range of methodologies available:

• Time series / trend analysis

• Bottom-up / schedule based

• Econometric models

• Market share models

• A combination of these approaches can be used



48

• Time series / trend• Based on historical traffic growth rates

• Statistical techniques (ARIMA)

• “Historically has grown at 3.5% per annum so will grow at similar rates in the future”

• Can also reference global forecasts by Boeing, Airbus, FAA, IATA, etc.



49

• Bottom-up• Tend to be supply-side:

• Development of new routes

• Increase in frequencies and changes in gauge

• Route-by-route forecasts of air service and passenger volumes

• Can be based on announced schedules in the short term

• Guided by fleet acquisitions in the medium term

• Harder for long term forecasts



50

• Econometric models• Traffic as a function of:

• GDP (or GDP per capita)

• Personal income

• Population

• Air fare

• One-off factors: SARS, air failure, 9/11 (historical events)

• Separate models can sometime be developed for individual markets (Domestic, transborder, international)

• Can be seen as simplified version of the Transport Canada model:• One zone (airport) to small number of destination zones

• Dependent on the data available for airport



51

• Econometric models• Requires forecasts of explanatory variables

• Generally good sources available for GDP, population, etc.

• Inclusion of air fare can be problematic:• Hard to obtain historical data especially for a long time series

(need 10 years at least)

• Technical issues – fares are an endogenous variable, affected by demand and supply conditions

• Requires use of advanced statistical techniques(Two stage least squared regressions)

• Air fares also need to be forecast, e.g., using a airline cost model

• As a result, air fares are often not included in the analysis



52

• Yield Trend (U.S.)

0

0.05

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1945

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Pass

enge

r Yie

ld (U

S$ P

er R

PKM

)

Inflation Adjusted 2008 Dollars

Nominal Dollars

1958Boeing 707

enters service

1970Boeing 747

enters service

1978U.S. Airline Deregulation Act

2001September 11th terrorist attacks



53

• Market Share Model• Generally involves forecasting the airport’s share of some aggregate

measure of air traffic (national traffic, regional traffic)

• Generally used where there are a number of airports realistically competing for the same traffic

• E.g., UK – five airports compete for the same traffic in London alone(Heathrow, Gatwick, Luton, Stansted, London City)

• New York market

• San Francisco / Oakland

• Few examples in Canada

Concluding Comments - Uncertainty


Addressing Uncertainty

55

• Standard approach to uncertainty in both the Transport Canada forecasts and single airport forecasts

• Base case with low and high forecasts



56



57

• High-Base-Low:• Everything is bad or everything is good all at once• Variation tends to be arbitrary – why are these the

outer bounds?• No information or assessment of likelihood• Often the range is not that large (+/- 25%) –

history has shown us that bigger deviations are possible

• Has little input into the planning process• Low can be of interest for financing



58

• Other approaches:• “What-ifs”• Sensitivity tests

• These provide some information on the impact of specific factors or the outcome of certain events

• Again, can be arbitrary without reference to the likelihood of such an outcome

• More on advanced approaches to uncertaintythis afternoon…

Thank You!www.intervistas.com

Addressing Uncertainty in Air Traffic forecasting

InterVISTAS Consulting Group

10 April 2013


Outline

61

• Consequences of uncertainty

• Causes of uncertainty• Identifying and evaluating

risk• Incorporating risk into

forecasting

Consequences of Uncertainty


Washington Dulles International Airport

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Pass

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Actual Traffic

TAF 2000

TAF 2001

TAF 2003

TAF 2004

TAF 2005

TAF 2009


Lambert-St. Louis International Airport

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0

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1985 1990 1995 2000 2005 2010 2015

Pass

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Actual Traffic

TAF 1998

TAF 2001

TAF 2002

TAF 2003

TAF 2009



the second time




services at STL



Louis Armstrong New Orleans International Airport

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0

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1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

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Actual Traffic

TAF 2001

TAF 2004

TAF 2005

TAF 2009

Hurricane Katrina


Bellingham International Airport

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1985 1990 1995 2000 2005 2010 2015 2020

Pass

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ands

)

Actual Traffic

TAF 2000

TAF 2003

Master Plan Forecast

United Express / SkyWest exits in 2001

Allegiant enters in August 2004 and rapidly develops service

Allegiant opens base at BLI in January 2008


Baltimore/Washington International Thurgood Marshall Airport

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2010

Pass

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men

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Actual Traffic

1987 Master Plan Forecast (Baseline)

Piedmont announces hub

First Gulf Warand recession

Southwest Airlines launches services

9/11 andrecession

Recession

U.S. Airways "de-hubs"

Causes of Uncertainty



69

• Economics• Recessions and booms• Regional conditions (closure of a local business)• Fuel prices

• Airline Strategy• Start, expand, contract or shut service• Transit traffic is fungible

• Airline failure/collapse• Canadian, TWA, Swiss, Sabena

• Regulatory / policy• Deregulation contributed to hubbing, changes in aircraft size, LCCs• Also taxes, security, bilaterals



70

• Technology• More economical aircraft make new routes possible• Implications for cargo – smaller aircraft reduce bellyhold;

A380 has relatively small cargo space

• Airport Competition• New airports emerge as competitors, e.g., on the U.S. border

• Social / cultural • Concerns about environmental impacts• Use of new communications media (+ve or –ve impact?)

• Shock events• 9/11• SARS

Identifying and Evaluating Risk



72

• Develop a risk register• Identify and evaluate various risks affecting the airport• What is the particular risk?• What is its likelihood?• What is the impact if it occurs (short and long term)

• Information can be elicited from the airport team• Strategy• Marketing• Facilities• Finance

• Can also examine historical examples• What was the impact of SAR on Toronto; how long was the recovery?



73

• Presenting the results – Heat diagramLi

kelih

ood

Very High

High

Moderate

Low

Very Low

Very Low Low Moderate High Very

High

Impact on Activity



74

Economic recession

Fuel price spikes

New FAA taxes

Terrorist attack

Loss/failure of Carrier X

Entry of new carrier(e.g., LCC)

Pandemic

Open Skies Liberalization

High Speed Rail Competition

Major tourism event

Increased security requirements

New aircraft technology

Economic boom

5%

10%

15%

20%

25%

30%

35%

-5 -4 -3 -2 -1 0 1 2 3 4 5

Prob

abilt

y

Impact Opportunity >< Threat

Macroeconomic

Market

Regulatory/Policy

Technology

Social/Cultural

Key:

Shock Event

Use of internet for meetings

Incorporating Risk into Forecasting



76

• Risk analysis augments not replaces traditional forecasting

Time

Enpl

aned

Pas

seng

ers

Original Forecast

Traffic impact of carrier exit and partial recovery



77

• A simple approach:• Scenario analysis based on the risk register• Development scenarios based on the high probability and

high impact events (both positive and negative)• Similar to high/low approach, but:

• Based on comprehensive assessment of risk• Scenario can be produced to examine extreme events – stress testing• Should be a focus of planning decisions

• However, the approach still lacks information on likelihood or probability



78

68

242

280

329

401

110

123

152

114151

255

354

162

136150

195

25

2840

61

0

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45019

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2009

2011

2013

2015

2017

2019

2021

Pass

enge

rs E

npla

nem

ents

(Tho

usan

ds)

Actual Traffic 1980-2000

Post-Masterplan Traffic (2000-2010)

Masterplan Forecast

Extreme Upside Scenario

Extreme Upside with New Carrier Exit

Extreme Downside Scenario



79

• A more advanced approach:• Monte Carlo simulations• Uses randomisation / probabilities to explore uncertainty• Model inputs are probability distributions rather than fixed numbers• Using computers, the models can be run multiple times, each time with the

inputs randomly generated• With enough iterations, the range of outcomes can be determined and

probabilities applied to them• Historical note: first used at Los Alamos in design of shiled for nuclear reactors• Has also been used in finance, project planning,

telecoms design, medicine,…



80

• Normal distribution

-8% -7% -6% -5% -4% -3% -2% -1% 0% 1% 2% 3% 4% 5% 6% 7% 8%

Deviation from Long-Term Economic Growth Rate

10% to 90% Range

Pro

babi

lity



81

• Pert Distribution

0 500 1,000 1,500 2,000 2,500

Loss of Enplaned Passengers (Thousands)

10% to 90% Range

Pro

babi

lity



82

Density Function Description

UNIFORM – a distribution where all values within a range of potential outcomes have the same probability. For example, a uniform distribution should be used to characterize the impacts of a threat that may lead to a 10 percent reduction in airport activity, a 20 percent reduction, or any value in between, with the same probability. The uniform distribution is fully specified with a minimum value and a maximum value.

DISCRETE – a distribution where each potential outcome is represented by a single value and a corresponding probability. A discrete distribution is defined by a list of possible, discrete values and corresponding probabilities, where the sum of all probabilities is equal to 1.

GENERALIZED TRIANGULAR – a distribution that uses the median, lower percentile (such as 10%), and upper percentile (such as 90%) as input parameters. Based on these parameters, a triangular distribution is fitted to the data, and the absolute minimum and maximum are calculated as a function of the distribution. This distribution is often used for event risks, where there is equal probability of an input parameter being lower or higher than the median.



83

• Determining the distribution (data based):

0

10

20

30

40

50

60

70

-1.5% -0.5% 0.5% 1.5% 2.5% 3.5% 4.5% 5.5% 6.5% 7.0%

Freq

uenc

y

GDP Growth Rate (Mid-Point)

Histogram of GDP Growth Data

Fitted Distribution



84

• Other events based on judgement (or combination of judgement and data)

• Can involve a complex set of connected inputs

• E.g., exit of a carrier:• Probability of exit• Impact of exit – loss of traffic (can be randomised)• Extent of recovery (also can be randomised)• Time to recover (also can be randomised)



85

• Running the Monte Carlo (Sample)



86

0

5

10

15

20

25

30

35

Year 0 Year 5 Year 10 Year 15 Year 20

Annu

al E

npla

ned

Pass

enge

rs (M

illio

ns)

Most Likely Forecast

25th / 75th Percentile Range





87

0%

20%

40%

60%

80%

100%

0%

2%

4%

6%

8%

10%

12%

14%2,

500

2,75

0

3,00

0

3,25

0

3,50

0

3,75

0

4,00

0

4,25

0

4,50

0

4,75

0

5,00

0

5,25

0

5,50

0

5,75

0

6,00

0

6,25

0

6,50

0

6,75

0

7,00

0

7,25

0

7,50

0

7,75

0

Cum

ulat

ive

Prob

abili

ty

Prob

abili

ty

Passenger Enplanements (Thousands)

Probability Density (Left Hand Scale)

Cumulative Probability (Right Hand Scale)



88

• Can answer questions like:• What is the probability that passenger traffic growth will exceed 4%

per annum over the next 20 years? • What is the probability that passenger traffic will be greater than 20

million in five years time? • What is the probability that passenger traffic in 2020 will be less

than 25 million?

• Obvious applications for financial analysis

• But can also be incorporated into planning…



89

Thank You!www.intervistas.com

Canadian Forecasting WorkshopSession 5:

Current Outlook

Dr. Mike TrethewayInterVISTAS Consulting Chief Economist

Economic Update


Econo-geek VocabularyIf there is a recovery, will it be? • V-shaped

• A rapid recovery back to previous level• Most recessions are V-shaped

• U-shaped• A period of stagnation, with a slow recovery

• L-shaped• An extended period of stagnation

• Japan 1990s. Great Depression

• W-shaped• A V-shaped recovery, followed by another recession

• US, 1970s93


US Real GDP Growth (Historical)

94

Sources: Historical – Bureau of Economic Analysis; Recessions as defined by the National Bureau of Economic Research



W WW




W W W V VV



US Real GDP- recent

97

Source: NBER 27May2010, BEA 23Mar2013

Recession begins

Recession has ended


US Real GDP Growth - Forecast

Source: 2007-2012 U.S. Department of Commerce, Bureau of Economic Analysis 2013-2017 International Monetary Fund, World Economic Outlook Database, April 2011

Forecast DataA

nnua

lized

Y-O

-Y G

row

th R

ate


US Real GDP - Risk

• The recent recession had V-shaped recovery

• But there is still risk in the recovery • Managing contraction of Fed Assets

• Without 2nd recession• Without inflation

• will be a challenge• Risk: less than 50%, more than 25%

99


US Real GDP (Historical)

100Sources: Historical (1946 to 2012) – Bureau of Economic Analysis;

Recessions are wiggles in a steadily growing economy


Canada Real GDP (Historical)

101Sources: Historical Canada GDP (1961 to 2012) – Statistics Canada.

Recessions are wiggles in a steadily growing economy

http://www.google.ca/url?sa=i&rct=j&q=canada+flag&source=images&cd=&cad=rja&docid=KqDEVqPUkEfcZM&tbnid=j_ypSyfT8ODwhM:&ved=0CAUQjRw&url=http%3A%2F%2Fwww.enchantedlearning.com%2Fschool%2FCanada%2FFlagcolor.shtml&ei=T-NkUevSDY7q0QGM_IG4BQ&bvm=bv.44990110,d.dmg&psig=AFQjCNEaAN0gmQ8S1vjMJoZJyxbQcEv6fw&ust=1365652677934271


Canada Real GDP Growth

102

Sources: Historical and Forecast Data from International Monetary Fund, World Economic Database, October 2012.

Ann

ualiz

ed Y

-O-Y

Gro

wth

Rat

e

Historical Data

Forecast Data

http://www.google.ca/url?sa=i&rct=j&q=canada+flag&source=images&cd=&cad=rja&docid=KqDEVqPUkEfcZM&tbnid=j_ypSyfT8ODwhM:&ved=0CAUQjRw&url=http%3A%2F%2Fwww.enchantedlearning.com%2Fschool%2FCanada%2FFlagcolor.shtml&ei=T-NkUevSDY7q0QGM_IG4BQ&bvm=bv.44990110,d.dmg&psig=AFQjCNEaAN0gmQ8S1vjMJoZJyxbQcEv6fw&ust=1365652677934271


Real GDP Growth - Mexico

Sources:

Historical Data: Mexico: International Monetary FundForecast Data: Mexico: International Monetary Fund

2 Recessions

Fuel Prices


Fuel Cost per Litre for Canadian Air Carriers

Source: 1980-1990- Statistics Canada, Aviation in Canada 2006-2011- Statistics Canada, 51-004-X

105


Source: 1980-1990- Statistics Canada, Aviation in Canada 2006-2011- Statistics Canada, 51-004-X

Fuel Cost Percentage of Operating Expense Canadian Air Carriers

106


Fuel Litres per RPK

Source: 1970-1990- Statistics Canada, Aviation in Canada 2006-2011- Statistics Canada, 51-004-X. Transport Canada

107


Fuel Prices - historical

108


Fuel Prices

109

2 year dramatic swingPrices rose by 250%Then crashed to 66%of original price


Crude Oil Price Futures

110


Oil Price Forecast

111


Oil Price Consensus Forecast

112

•Its not down•Its not back to $145


2 Sigma Range of Forecasts

Forecast 95% ranges

$-

$20

$40

$60

$80

$100

$120

2010 2011 2012 2013 2014 2015 2016

upper 2 sigma

average

low er 2 sigma

113


2 Sigma Range of ForecastsForecast 95% ranges

$-

$20

$40

$60

$80

$100

$120

2010 2011 2012 2013 2014 2015 2016

upper 2 sigma

average

low er 2 sigma

114

•Everyone seems to agree


Forecast 95% ranges

$-

$20

$40

$60

$80

$100

$120

2010 2011 2012 2013 2014 2015 2016

upper 2 sigma

average

low er 2 sigma

2 Sigma Range of Forecasts

115

•Everyone seems to agree

Note the scale


If History Repeats the Swing….Oil Price with Full Historical Range

$-

$50

$100

$150

$200

$250

$300

2010 2011 2012 2013 2014 2015 2016

Upper range

Base price forecast

Lower range

116

Note the scale

Forecast Components


Source: Statistics Canada Average Fare data, Cat. 51 -004-Xp = preliminaryMajor Air Carriers include Air Canada (mainline & AC Jazz), WestJet, Air Transat and Canada 3000

Average Fare: CanadaNominal: Not Adjusted for Inflation

118


Sources: Statistics Canada Average Fare data, Cat. 51 -004-XStatistics Canada Consumer Price Index

p = preliminary air fare dataMajor Air Carriers include Air Canada (mainline & AC Jazz), WestJet, Air Transat and Canada 3000

Real Average Fare: Canada

119


Source: Transport Canada Registered Commercial Aircraft database

Commercial Aircraft: Canada

120


Passengers per Aircraft- Canada1980-2011

121

Source: InterVISTAS Calculations with data from: Aviation in Canada (1980-1990) and Table 401-0009, Statistics Canadaand Air Carrier Traffic at Canadian Airports. Statistics Canada


Seats per Aircraft- Canada1980-2011

122

Source: InterVISTAS Calculations with data from: Aviation in Canada (1980-1990) and Table 401-0009, Statistics Canadaand Air Carrier Traffic at Canadian Airports. Statistics Canada and Transport Canada.


Total GA Movements for Canada

123

Source: Statistics Canada, Table 401-007, 401-0015 and 401-0021.


Cargo Tonnes Reporting: Statistics Canada vs. Actual Site Statistics

124

Airport

Statistics Canada (Tonnes)

Actual Airport Statistics (Tonnes)

Site to Stats Can Ratio

Calgary Intl, Alta.

83,524

116,000 1.39

Edmonton Intl, Alta.

22,955

36,112 1.57

Montréal/Mirabel Intl, Que.

66,899

95,518 1.43

Montréal/Pierre Elliott Trudeau Intl, Que.

76,623

105,113 1.37

Toronto/Lester B Pearson Intl, Ont.

339,065

492,171 1.45

Vancouver Intl, B.C.

186,385

223,878 1.20

Winnipeg/James Armstrong Richardson Intl, Man.

65,254

175,000 2.68

Source: Statistics Canada, 51-203-X. Individual Airport reports.


Cargo Tons Gap: Site Stats to Stats Can

125

Source: InterVISTAS calculations with data from: Statistics Canada, 51-203-X. Individual Airport reports.


US vs Canada Pax Traffic1990-2012

126

Source: InterVISTAS Calculations with data from: Canada-Air Carrier Traffic at Canadian Airports. Statistics Canada US- 1960-2006 ATA , 2007-2012 BTS .


Load Factor- Canada

Source: Aviation in Canada, Statistics Canada. Transport Canada

127


Commercial Aircraft Movements

Source: Aviation in Canada (1980-1990) and Table 401-0009, Statistics Canada.

128


Annual Turboprop + Regional Jet Percentages

79% 72% 74% 75%

Scheduled Flight Frequency: Domestic Canada

129

Source: Official Airline Guide Schedule Data, full year data for 1998, 2002, 2007, and 2012.


Annual Turboprop + Regional Jet Percentages

49% 37% 42% 50%

Scheduled Seat Capacity: Domestic Canada

130

Source: Official Airline Guide Schedule Data, full year data for 1998, 2002, 2007, and 2012.


Domestic Canada Scheduled Flight Frequency by Aircraft Body Type

Widebody Regional Jets TurbopropsLegend:

Domestic Canada Scheduled Seat Capacity by Aircraft Body Type

Narrowbody

131Source: Official Airline Guide Schedule Data, full year data for 1998, 2002, 2007, and 2012.


Canadian Air Carrier Total Revenue and Expenses

Source: Statistics Canada, 51-004-X

132


Return on Assets for Canadian Air Carriers

Source: Statistics Canada, 1980-1985- Aviation in Canada. 2005-2011, 51-004-X.

133


Accidents for Canadian Commercial Aircraft

Source: 2001-2011 Statistics Canada, 51-004-X. Transportation Safety Board of Canada 1970-1990 Statistics Canada, Aviation in Canada

134


Thank You

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InterVISTAS Airport Traffic Forecasting Workshop - Presentations, 10April2013

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