Canadian Forecasting WorkshopSession 1:
Introductory Remarks on the Science and Art Forecasting
Dr. Mike TrethewayInterVISTAS Consulting Chief Economist
Realizing the vision together1
Today’s Workshop
1. Introductory Remarks on Forecasting2. Transport Canada PODM/PTAM models3. Alternative Approaches-
Single Airport Forecasts4. Incorporating Uncertainty into
Air Traffic Forecasts5. Current Outlook
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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
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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
3
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Airport Forecasting Record.
• Forecasting is an essential tool for airports
• Medium to long term master planning
• Financial forecasts
• Operational Forecasts
4
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Airport Forecasting Record.
• But the track record has not always been good
5
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Atlanta
6
35
37
39
41
43
45
47
49
51
53
55
2000 2002 2004 2006 2008 2010 2012 2014
Pas
seng
er E
npla
nem
ents
(Mill
ions
)
Actual TrafficTAF 2001TAF 2003TAF 2005TAF 2007TAF 2009
Actual and Forecast Total Passenger Enplanements at Hartsfield-Jackson Atlanta International Airport
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Washington Dulles
7
0
5
10
15
20
25
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
Pass
enge
r Enp
lane
men
ts (M
illio
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
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Airport Forecasting Record.
• But the track record has not always been good
• Albeit with some learning
8
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St. Louis
9
0
5
10
15
20
25
30
1985 1990 1995 2000 2005 2010 2015
Pass
enge
r Enp
lane
men
ts (M
illio
ns)
Actual TrafficTAF 1998TAF 2001TAF 2002TAF 2003TAF 2009
TWA declaresbankruptcy
TWA declaresbankruptcy for
the second time
TWA declaresbankruptcy for
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
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St. Louis
10
0
5
10
15
20
25
30
1985 1990 1995 2000 2005 2010 2015
Pass
enge
r Enp
lane
men
ts (M
illio
ns)
Actual TrafficTAF 1998TAF 2001TAF 2002TAF 2003TAF 2009
TWA declaresbankruptcy
TWA declaresbankruptcy for
the second time
TWA declaresbankruptcy for
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
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Airport Forecasting Record.
• Sometimes the long run forecast has been good
• but with short term variance
• And different traffic mix than original forecast
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BWI
12
0
2
4
6
8
10
12
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
Pass
enge
r Enp
lane
men
ts (M
illio
ns)
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
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Airport Forecasting Record.
• Sometimes unanticipated events dramatically change a market
13
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New Orleans
14
0
1
2
3
4
5
6
7
8
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
Pass
enge
r Enp
lane
men
ts (M
illio
ns)
Actual TrafficTAF 2001TAF 2004TAF 2005TAF 2009
Hurricane Katrina
Actual and Forecast Total Passenger Enplanements atLouis Armstrong New Orleans International Airport
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Airport Forecasting Record.
• The forecasting record can also be one of underforecasting
15
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Bellingham WA
16
0
50
100
150
200
250
300
350
400
450
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
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Guessing vs. Analysis
Midway (1976)
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• 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)
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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
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Types of Airport Forecasts.
• Passenger traffic• Total pax
• Enplaned/deplaned
• O/D vs Connecting
• Cargo tons• E/D vs on-board
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Types of Airport Forecasts.
• 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 stop21
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Total GA Movements for Canada
23
Source: Statistics Canada, Table 401-007, 401-0015 and 401-0021.
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Types of Airport Forecasts.
• 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
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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?
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Thank You
Subscribe to Monthly Aviation Intelligence Reportwww.InterVISTAS.com
Transport Canada Forecasts
InterVISTAS Consulting Group
10 April 2013
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Outline
28
•Overview of the Transport Canada Forecasts:• Methodology
• Data
• Pros and cons
•Alternative Approaches:• Single airport methodologies
• Methodology
• Addressing uncertainty
Transport Canada Forecasts
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Transport Canada Forecasts
30
•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
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Transport Canada Forecasts
31
•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.
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Transport Canada Forecasts
32
•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
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PODM (Passenger Origin-Destination Model)
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•PODM:
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PODM (Passenger Origin-Destination Model)
34
•PODM (Translated):• Based on a zonal system
• PODM forecasts traffic between zones
• Domestic zones:• Approximately 36 zones based around major airports
• E.g., Toronto - Pearson, City Centre, Hamilton, Oshawa, Buttonville, Kitchener)Vancouver – Vancouver, Abbotsford, Vancouver Harbour
• Transborder zones:• Approximately 20 zones
• E.g., Los Angeles, New York, etc.
• International zones:• Country (e.g., UK) or continental (e.g., Africa)
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PODM (Passenger Origin-Destination Model)
35
•PODM (Translated):• Gravity Model:
• Traffic between two zones is a function of:
•
• Model was directional
Zone A
Zone B
Attractors:PopulationGDPLinguistic similaritiesOther factors
Impeders:Air fareLevel of direct serviceTravel time by car
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PODM (Passenger Origin-Destination Model)
36
•Calibration/estimation required a lot of data:• O/D passenger Data
• Directional Origin-Destination Database
• Based on 10% sample of all air tickets
• Especially developed for the model by Statistics Canada
• Also used U.S. data for transborder
• Air Fare Data• Airfare Basis Survey
• Quarterly survey of domestic, transborder and international air passengers
• OAG schedule data• For determining direct services
• Socio-Economic Data• Population, GDP, etc.
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PODM (Passenger Origin-Destination Model)
37
•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
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PODM (Passenger Origin-Destination Model)
38
•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
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PTAM (Passenger Traffic Allocation Model)
39
•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
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PTAM (Passenger Traffic Allocation Model)
40
•Overall assumption of greater allocation of traffic to direct services:
Source: Transport Canada Assumptions Report (2006-20)
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PTAM (Passenger Traffic Allocation Model)
41
•Contained specific assumptions about the development of new direct services:
Source: Transport Canada Assumptions Report (2006-20)
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Pros and Cons
42
•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
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Pros and Cons
43
•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)
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Pros and Cons
44
•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
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Single Airport Forecasts
46
•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
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Single Airport Forecasts
47
•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.
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Single Airport Forecasts
48
•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
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Single Airport Forecasts
49
•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
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Single Airport Forecasts
50
•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
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Single Airport Forecasts
51
•Yield Trend (U.S.)
0
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
1945
1948
1951
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1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
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
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Single Airport Forecasts
52
•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
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Addressing Uncertainty
54
•Standard approach to uncertainty in both the Transport Canada forecasts and single airport forecasts
• Base case with low and high forecasts
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Addressing Uncertainty
55
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Addressing Uncertainty
56
•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
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Addressing Uncertainty
57
•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
Realizing the vision together
Outline
60
•Consequences of uncertainty•Causes of uncertainty•Identifying and evaluatingrisk•Incorporating risk intoforecasting
Consequences of Uncertainty
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Washington Dulles International Airport
62
0
5
10
15
20
25
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
Pass
enge
r Enp
lane
men
ts (M
illio
ns)
Actual Traffic
TAF 2000
TAF 2001
TAF 2003
TAF 2004
TAF 2005
TAF 2009
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Lambert-St. Louis International Airport
63
0
5
10
15
20
25
30
1985 1990 1995 2000 2005 2010 2015
Pass
enge
r Enp
lane
men
ts (M
illio
ns)
Actual TrafficTAF 1998TAF 2001TAF 2002TAF 2003TAF 2009
TWA declaresbankruptcy
TWA declaresbankruptcy for
the second time
TWA declaresbankruptcy for
the third time andAA buys TWA.Construction of
new runway beginsAA reduces
services at STL
AA terminates itsfocus city at STL
Realizing the vision together
Louis Armstrong New Orleans International Airport
64
0
1
2
3
4
5
6
7
8
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
Pass
enge
r Enp
lane
men
ts (M
illio
ns)
Actual TrafficTAF 2001TAF 2004TAF 2005TAF 2009
Hurricane Katrina
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Bellingham International Airport
65
0
50
100
150
200
250
300
350
400
450
1985 1990 1995 2000 2005 2010 2015 2020
Pass
enge
r Enp
lanem
ents
(Tho
usan
ds)
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
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Baltimore/Washington International Thurgood Marshall Airport
66
0
2
4
6
8
10
12
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
Pass
enge
r Enp
lane
men
ts (M
illio
ns)
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
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Causes of Uncertainty
68
•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
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Causes of Uncertainty
69
•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
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Identifying and Evaluating Risk
71
•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?
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Identifying and Evaluating Risk
72
•Presenting the results – Heat diagramLi
kelih
ood
Very High
High
Moderate
Low
Very Low
Very Low Low Moderate High Very
High
Impact on Activity
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Identifying and Evaluating Risk
73
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
abilty
Impact Opportunity >< Threat
Macroeconomic
MarketRegulatory/PolicyTechnology
Social/Cultural
Key:
Shock Event
Use of internet for meetings
Incorporating Risk into Forecasting
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Incorporating Risk into Forecasting
75
•Risk analysis augments not replaces traditional forecasting
Time
Enpl
aned
Pas
seng
ers
Original Forecast
Traffic impact of carrier exit and partial recovery
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Incorporating Risk into Forecasting
76
•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
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Incorporating Risk into Forecasting
77
68
242
280
329
401
110
123
152
114151
255
354
162
136150
195
25
2840
61
0
50
100
150
200
250
300
350
400
45019
85
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
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
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Incorporating Risk into Forecasting
78
•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,…
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Incorporating Risk into Forecasting
79
•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
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Incorporating Risk into Forecasting
80
•Pert Distribution
0 500 1,000 1,500 2,000 2,500
Loss of Enplaned Passengers (Thousands)
10% to 90% Range
Pro
babi
lity
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Incorporating Risk into Forecasting
81
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Incorporating Risk into Forecasting
82
•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
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Incorporating Risk into Forecasting
83
•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)
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Incorporating Risk into Forecasting
84
•Running the Monte Carlo (Sample)
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Incorporating Risk into Forecasting
85
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
10th / 90th Percentile Range
5th / 95th Percentile Range
Realizing the vision together
Incorporating Risk into Forecasting
86
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)
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Incorporating Risk into Forecasting
87
•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…
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Incorporating Risk into Forecasting
88
Thank You!www.intervistas.com
Canadian Forecasting WorkshopSession 5:
Current Outlook
Dr. Mike TrethewayInterVISTAS Consulting Chief Economist
Economic Update
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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, 1970s92
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US Real GDP Growth (Historical)
93
Sources: Historical – Bureau of Economic Analysis; Recessions as defined by the National Bureau of Economic Research
Realizing the vision together94
Sources: Historical – Bureau of Economic Analysis; Recessions as defined by the National Bureau of Economic Research
W WW
US Real GDP Growth (Historical)
Realizing the vision together95
Sources: Historical – Bureau of Economic Analysis; Recessions as defined by the National Bureau of Economic Research
W W W V VV
US Real GDP Growth (Historical)
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US Real GDP- recent
96
Source: NBER 27May2010, BEA 23Mar2013
Recession begins
Recession has ended
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US Real GDP Growth - Forecast
Source: 2007-2012 U.S. Department of Commerce, Bureau of Economic Analysis2013-2017 International Monetary Fund, World Economic Outlook Database, April 2011
Forecast DataA
nnua
lized
Y-O
-Y G
row
th R
ate
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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%
98
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US Real GDP (Historical)
99Sources: Historical (1946 to 2012) – Bureau of Economic Analysis;
Recessions are wiggles in a steadily growing economy
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Canada Real GDP (Historical)
100Sources: Historical Canada GDP (1961 to 2012) – Statistics Canada.
Recessions are wiggles in a steadily growing economy
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Canada Real GDP Growth
101
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
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Real GDP Growth - Mexico
Sources:
Historical Data: Mexico: International Monetary FundForecast Data: Mexico: International Monetary Fund
2 Recessions
Fuel Prices
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Fuel Cost per Litrefor Canadian Air Carriers
Source: 1980-1990- Statistics Canada, Aviation in Canada2006-2011- Statistics Canada, 51-004-X
104
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Source: 1980-1990- Statistics Canada, Aviation in Canada2006-2011- Statistics Canada, 51-004-X
Fuel Cost Percentage of Operating Expense Canadian Air Carriers
105
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Fuel Litres per RPK
Source: 1970-1990- Statistics Canada, Aviation in Canada 2006-2011- Statistics Canada, 51-004-X. Transport Canada
106
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Fuel Prices - historical
107
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Crude Oil Spot PricesJanuary 2003 to April 2013
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Fuel Prices
108
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Crude Oil Spot PricesJanuary 2003 to April 2013
2 year dramatic swingPrices rose by 250%Then crashed to 66%of original price
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Crude Oil Price Futures
109
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Crude Oil Spot Prices & Crude Oil Futures PricesJanuary 2003 to December 2019
Spot Prices
FuturesPrices
Source: Spot Prices from U.S. EnergyInformation Administration. Futures Prices from
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Oil Price Forecast
110
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Oil Price Consensus Forecast
111
•Its not down•Its not back to $145
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2 Sigma Range of Forecasts
Forecast 95% ranges
$-
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$120
2010 2011 2012 2013 2014 2015 2016
upper 2 sigma
average
low er 2 sigma
112
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2 Sigma Range of ForecastsForecast 95% ranges
$-
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2010 2011 2012 2013 2014 2015 2016
upper 2 sigma
average
low er 2 sigma
113
•Everyone seems to agree
Realizing the vision together
Forecast 95% ranges
$-
$20
$40
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$80
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$120
2010 2011 2012 2013 2014 2015 2016
upper 2 sigma
average
low er 2 sigma
2 Sigma Range of Forecasts
114
•Everyone seems to agree
Note the scale
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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 forecastLower range
115
Note the scale
Forecast Components
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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
117
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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
118
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Source: Transport Canada Registered Commercial Aircraft database
Commercial Aircraft: Canada
119
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Passengers per Aircraft- Canada1980-2011
120
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
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Seats 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 and Transport Canada.
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Total GA Movements for Canada
122
Source: Statistics Canada, Table 401-007, 401-0015 and 401-0021.
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Cargo Tonnes Reporting: Statistics Canada vs. Actual Site Statistics
123
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.
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Cargo Tons Gap: Site Stats to Stats Can
124
Source: InterVISTAS calculations with data from: Statistics Canada, 51-203-X. Individual Airport reports.
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US vs Canada Pax Traffic1990-2012
125
Source: InterVISTAS Calculations with data from: Canada-Air Carrier Traffic at Canadian Airports. Statistics CanadaUS- 1960-2006 ATA , 2007-2012 BTS .
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Load Factor- Canada
Source: Aviation in Canada, Statistics Canada. Transport Canada
126
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Commercial Aircraft Movements
Source: Aviation in Canada (1980-1990) and Table 401-0009, Statistics Canada.
127
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Annual Turboprop + Regional Jet Percentages
79% 72% 74% 75%
Scheduled Flight Frequency: Domestic Canada
128
Source: Official Airline Guide Schedule Data, full year data for 1998, 2002, 2007, and 2012.
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Annual Turboprop + Regional Jet Percentages
49% 37% 42% 50%
Scheduled Seat Capacity: Domestic Canada
129
Source: Official Airline Guide Schedule Data, full year data for 1998, 2002, 2007, and 2012.
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Domestic Canada Scheduled Flight Frequency by Aircraft Body Type
Widebody Regional Jets TurbopropsLegend:
Domestic Canada Scheduled Seat Capacity by Aircraft Body Type
Narrowbody
130Source: Official Airline Guide Schedule Data, full year data for 1998, 2002, 2007, and 2012.
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Canadian Air Carrier Total Revenue and Expenses
Source: Statistics Canada, 51-004-X
131
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Return on Assets for Canadian Air Carriers
Source: Statistics Canada, 1980-1985- Aviation in Canada. 2005-2011, 51-004-X.
132
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Accidents for Canadian Commercial Aircraft
Source: 2001-2011 Statistics Canada, 51-004-X. Transportation Safety Board of Canada1970-1990 Statistics Canada, Aviation in Canada
133
Realizing the vision together134
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