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Civil Aviation & Meteorology Authority (Yemen) January - March 2013, issue 18
DubaiAirport Show 2013
A320 Family VS B737NG
Ajurney to Wadi Do’an
www.camamagazine.com

Fair – Poor Forecasting Matrix
Accuracy of Forecasting Model
“Nothing limits achievement
like small thinking; nothing
expands possibilities like
unleashed imagination.”
William Arthur Ward
24 study
U S Carriers Case Study
One of the major challenges in
the forecasting, is ACCURACY,
how far we can except the results,
is it reliable and practical or it
might mislead us in undesirable
direction, how we can set a
reasonable targets, that can be
achieved , is it good to forecast
with a negative trends or not, and
when we can to do that and how
to adjust it. How we can interpret
the trend analysis with seasonality
model. All these issues have
their own impact on the accuracy
formula. So what is the best
method to define and measure the
accuracy of forecasting model.
In this article we will address one
of the new creative methodology,
we will called it Fair – Poor
Forecasting Matrix. It basically
developed based on two
main estimated mathematical
parameters, Displacement and
Directional factors which has a
consequence impacts on R and
Signal Tracking.
Setting Boundary Accuracy:
For Fair forecasting, the model
should fulfill these criteria
R2 ≥ 80 and Signal Tracking
should be - 4 ≤ S.T. ≤ + 4 Then
to developed
Fair – Poor Forecasting Matrix
the following outcomes will be
concluded;
1- Fair Forecast – when R2
and Signal Tracking are in
bond.
2- Mislead – Displacement
Issue. This case when R2
is in
bond and Signal Tracking is out
bond. we can adjusted signal
tracking to be in bond when
there is a room for R2
in the
same analysis so that it can be
consider as a fair forecast.
3- Unrelated – Directional
Issue. This case when R2
is
out of the bond and Signal
Tracking in the bond. i.e. the
balance of accumulated error
without any correlation
4- Poor Forecast – when both
R2 and Signal Tracking are out
of the bond (Total Mess).
This matrix manipulate the four
decision regions to develop
the right and best picture of the
accuracy of forecasting. And to
enhance the process of decision
making for airline data analysis
especially traffic forecasting,
that maps the overall forecasting
accuracy of US Carriers.
Fair
Unrelated
Mislead
Poor
CAMA Magazine | issue 18 | March, 2013
Mohammed S. Awad
Research Scholar Aviation Management

25study
R2
=Coef.ofDetermination T.S.=TrackingSignal
ForecastingEvaluation
dataavailable
modelestimationperiod expostforecasts
Y1 Yn YN
expostforecasts
Case Study: Traffic Forecasting of Major US carriers
Data Collections:
Based on the data published in RITA website, concerning
traffic passengers of US major carriers, for the period of three
years data base on a monthly bases, started from Aug- 2009
to July 2012 except Jetblue Airways which input reported
from Jan – 2009 to Dec - 2011. While unavailable data for
Atlas Air.
.
Forecasting Model:
The basic data span is 36 months (Input) with 12 months
forecasting, the fair bond restricted by the preset design
values of R2 and Signal Tracking. 12 US carriers are
addressed. The forecasting process has two stages,
Evaluation, and Forecasting. In the evaluation stage we try
to analysis the input data, and align the practical data with a
mathematical model, we use state of art forecasting program
to fit data. Two control factors have a great impact on the
model, First displacement factor (Displacement Issue), this
factor acts to shift the whole data from it running bath to a
new one but keeping the trend and direction of the analysis.
While the second factor is Directional factor, definitely if we
manipulate this factor and try to use many trail values (positive
and negative value), the model will position itself accordingly
as a clock about the origin.
As in the below airlines forecasting graphs.
Forecasting of US Carries – Graphs:8

Analysis:
About (12) US carries are
analysis-ed, based on a RITA input
data, the analysis are varied from
Fair Category to Poor one.
The blue line represent the
actual Input Data, for 36 input
points (Months) while the red
line represents the tracked and
forecast points, tracked are
36 points for the purpose of
monitoring and evaluation, while
the forecast points for purpose of
planning and setting goals and
targets of these airlines for 2013
and 2014 respectively.
All airlines shows a seasonality
patterns except united airline, it
suddenly shocked to double level
at the beginning of 2012, and may
explain by a merger with other
airline, and that why there will be a
negative trends if we use the data
of 2011 for united airline alone.
But for other US carriers the
picture is completely different, Now
it is easy to set goals and targets
for each month (2013 and 2014)
and consequently developed a
KPI system to enhance the airline
performance program.
Really it is a normal arguments
US carriers are out of the bond
i.e. Delta Airlines, US Air and
FRONTIER Airlines, (POOR
region) while the remaining US
carriers are positioning them
self in a out-bond signal tracking
values in spite of the higher values
of R2. This lead us to (MISLEAD
region). To solve this issue we
have to repeat the analysis and
adjust the signal Tracking when
there is a room for change R2 in
the bond at acceptable level.
2- Final (Adjusted):
Since most of US carriers are
in MISLEAD region, as Signals
Tracking (S. T.) are out-bond, we
have to adjust S. T. and repeat
the analysis to direct these US
carriers to FAIR region. Some
of US carriers we cannot do
anything for them, as R2 and S.
T. are out of the limits. While other
get improved (as FRONTIER
Airlines) which moved from
POOR to FAIR region. In terms
of Forecasting of 2013, There is
a significant improve in figures
when we adjusted S. T. unless it
will MISLEAD us if we rely on a
First Trail.
for airline analyst to say “yes it
is a summer season we have to
adjust our fare and increase the
frequency to serve this demand or
to change the aircraft type to cover
these demand”, but to what level.
These graphs shows clearly
how airline to react, when the
should response and what level
they have to do that, off course
the error will be there, but it will an
acceptable error.
Its also define its trends either
it is positive or negative one, and
how to align a minor negative
trend to be a positive one in an
acceptable preset boundary.
Results:
The result can be explore in two
steps;
1- First Trail:
Based on first analysis, three
First TrailFinal (Adjusted)
Summary:
This paper address the relation between R2 and S. T. , we
target US carries traffic data from RITA for 3 year data base.
Really we will Mislead if we rely on the classical monitoring
approach to assign R2 as the only factor for Goodness of
Fit to the data, really it is clear with the value of R2 in the
analysis and undesirables level of S. T. but if we reflect S. T.
to be adjusted in the required bond. Then the picture will be
different as shown in the last columns ( Final Adjusted), and
that will have a significant impact on 2013 forecasting figures
(please compare the results).
Finally Fair – Poor Forecasting Matrix it is a unique
method that reflects the impacts of R2 and Signal Tracking
by manipulating the mathematical factor (Displacement and
Directional) to align for best scenario of actual data (US
carries data).■
26 study
Relative Signal Tracking
RelativeR
2
Fair
Unrelated
Mislead
Poor

Introduction:
Generally most of airlines companies in third
world countries found too hard to evaluate
commercial aircraft’s, due to the complexity
of aviation industry and its optimizations
programs especially linear program which
implemented in the network of the airline also
the high techniques that possess by aircraft
manufacture and modern sales approach that
deliver by their sale forces and their pretend of
manufacturing the best commercial aircraft’s –
products in the world either Airbus Industries
or Boeing Group, as both aircraft’s of these
parties have an excellent characteristics that
cannot be differentiated and consequently most
of small airlines refer to third party to evaluate
their fleets, which normally a consultant
company specialize in aviation as Sabre
Solutions that implemented statistical tools
and analysis in their approaches of evaluation
as Value Analysis, Cost per Trip vs Cost per
Seat Matrix, and Profit vs Market Opportunity
Matrix. While the issue of selecting the right
aircraft is a main effective parameter for
successful airline, as each market has its own
characteristics that reflects/ impact by positive
or negative signals on airline network study
for the aircraft specification that have different
business model as corporate airline, private
and taxi air, legacy airline, low cost airlines,
and other Mega air carriers and accordingly the
aircraft’s are diversified and differentiated to be
short range and long range, some has small
capacity while the others have larger one.
Finally the new technology play a major role
to reduce the manufacturing cost for aircraft
which reflected by studying the technical
specifications of the aircraft and their relations
with their price and values in market which is
addressing by a so-called Value analysis for
A320 and B737-NG families.
Aircraft Performance Factors
Each aircraft characterize by its own
performance factors especially the technical
one that we can develop the evaluation studies
as:
1- Aircraft Configuration
2- Aircraft Speed
3- Mean Takeoff Weight
4- Maximum Payload Range(Kilometers).
5- Maximum Payload Range(Kilograms).
As it shown in table one, the relation
between the aircraft price and other technical
characteristics (Relative Values – multiplication
of these characteristics after filtration) are
addressed in this study.
Head To Head Analysis
A320 Family VS B737NG (Value Analysis)
Mohammed S. Awad
Research Scholar Aviation Management
RelativeValue
Aircraft Price
Attractive
Disappointment
Figure No. (1)
28 study

Summary:
Value Analysis define clearly the
effective and favorite technical
factors of aircraft’s and its relation
to its prices by splitting the area
of the graph for two main regions
according to the aircraft sampling
used and its related category so
the first region – Attractive – which
characterize by a lower price and
high effective factors while the
second region – Disappointment
– which characters by a high price
and less effective factors. That
obviously clear by the comparison
between A320 and B737-800.
Also the analysis can be improve
by giving more certain different
weights as aircraft capacity,
speed of aircraft, and MTOW. All
these weight factors will impacts
the final results to enhance the
decision to purchase the right
aircraft.■
115
113
114
112
111
110
109
17.1
R
2
= 0.9546
A 320 - 200
A 320
B 737 - 800
B 737 - 700
B 737 - 400
B 737 - 600
B 737 - 300
B 737 - 500
17.2 17.3 17.4 17.5 17.6 17.7
Better
Value
Lesser
Value
Value Analysis for B737 N G and A320
LN (Aircraft Price)
LN(RelativeValue)
130
125
120
115
110
105
15.5 16 16.5 17 17.5 18 18.5 19 19.5
Value Analysis for Boeing & Airbus Fleet
LN (Aircraft Price)
LN(RelativeValue)
B 737 - 200 B 737 - 500
Coefficient
Of
Correlation
Accepted
Rejected
No
Yes
R > 80%
Aircraft
Factor
Aircraft
Price
Value Analysis:
One of the most effective methods to select
and evaluate the aircraft’s is Value Analysis,
where it power is to link the technical
characteristics of the aircraft with it price and
consequently develop Basic Line Reference
(line fit – using regression analysis) that split
the area into two parts i.e (Attractive Region
and Disappointment Region)
=As indicated in figure (1).
As the Aircraft Characteristics by speed,
capacity, MTOW, Capability range in terms
or distance and weight should have a direct
relations with its price. But not with fuel
consumption as it will reflects a negative
impact on the analysis, as the differentiation
should be with a less fuel consumption and
not with the high one. And that relation usually
address by Coefficient of Correlation for Aircraft
Characteristics and its price according to the
following sequence:
Coefficient Of Correlation:
The importance of this coefficient is in defining
the effective factors that have impact on the
value analysis. Simply it is a mathematical
formula that explicit the relation between two
set of data in terms of percentage (%) as this
will define how far are related. While 80 %
and above the ideal level to accept that data
to included in the analysis. In the context of
the statistical analysis normally we use R
- coefficient of determination (squaring of R)
to raise the fairness of the analysis and avoid
negative values.
The Analysis:
By implementing all the previous steps and
study most of the characteristics of World
Fleet of Boeing Groups and Airbus Industries
as it it shown in figure (2), the figures shows
Three Groups but unfortunately the coefficient
of determination is to poor to accept as it is i.e
12.5%, since we includes all the world fleet
and not targeted a certain family, and if we
refer again to the the three groups, we will note
that the last group contains A320 and B737
families.
So by re-analysis the last group data (
targeting the last group ), we will find a relation
between Aircraft relative factors and Aircraft
price that may reach to 95% and accordingly
we can apply minimum least square analysis to
find the best line fit to create a Reference Line
as it is indicate by figure no. ( 3 ), Witch split
the area into two parts of these aircraft’s Airbus
and Boeing.
Consequently we can define which the best
aircraft and according to the figure no. (3) we
will find that A320 own more favorite relative
value than B737-800 and with less purchasing
value than B737-800 while if we compare
A320 with B737-700 we will find that the price
of B737-700 is less than A320 but don’t reach
to technical relative values of A320, so by
referring to the prices of both aircraft’s, we
will find the B737-700 does not reflect a good
position of A320 due the difference in prices
which may support to re-position the aircraft for
a good level.
Flow Chart (1)
29study
Figure No. (2)
Figure No. (3)

32
Prepared by: Mohammed Salem Awad
Research Scholar – Aviation ManagementForecasting by Objective
Case Study TALLINN Airport
Airports Forecasting
To carry a traffic forecast for an airport is uneasy task, As
most of the statistician rely on coefficient of determination R2
to ensure the fairness of the analysis. In this article we will
try to create many scenarios that will reflects, what the top
management thinks, are they interesting to rely R2
(Classical
approach), are they trying to minimize the errors by setting
Signal Tracking to Zero, or trying to merge long range trend
forecast to be targeted (accumulated) for the seasonality
model, or to ask to follow the most update and recent
input data (recent years). Really all the four scenarios are
addressed, in case study of TALLINN Airport.
Case Study – TALLINN Airport:
Tallinn Airport (Estonian: Lennart Meri Tallinna lennujaam)
(IATA: TLL, ICAO: EETN) or Lennart Meri it formerly
Ülemiste Airport, is the largest airport in Estonia and home
base of the national airline Estonian Air. Tallinn Airport is
open to both domestic and international flights. It is located
approximately 4 km from the centre of Tallinn on the eastern
shore of Lake Ülemiste. As Tallinn is located nearest to Asia
Pacific of all EU capitals, this gives Tallinn Airport a major
geographical advantage for establishing long-haul flights
between these two regions.
Two set of data are examined to develop a Trend and
Seasonality Models.
A- Trend Model (15 years data set)
Based on these data the a trend model is developed and with
R2
= 86.1 and Signal Tracking = -1.84, the 2013 Forecasted
is = 2,495,900.
B- Seasonality Model (36 months data set)
Four Scenarios are developed as it is shown in the following
table.
1- Maximize R2
2- Setting Signal Tracking = Zero
3- Setting Trend Target to 2,495,900
4- Reflects the latest Input Data
Results:
All scenarios shows high values of R2
(all closes value) while
Signal Tracking shows a large divergence with respect to the
bond values and the best selection decision is scenario no. 3
(why) as it is almost cover three pre-constrained parameters
in spite of slight divergence of S. T. of the bond and also it
reflects the lowest one in the results.
Forecasted by Trend Target for 2013 = 2,495,900 Pax
R2
= 92.22%
Signal Tracking: 6.662
“The only true wisdom
is in knowing you know
nothing.”
Socrates
R
2
> 80% and -4 < T.S. < 4
-
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
y = 443370e0.108x
R2
= 0.861
2,495,900
TREND
ANALYSIS
No. Scenarios
Objective
?
Coefficient of
Determination
(R2
)
Signal
Tracking
(S. T.)
Forecasting
of
2013
Remarks
1 Maximize
R2
93.46% -33.31 2,907,888 -
2 Setting
S. T. = zero
92.67% 0.0000004 2,568,499 -
3 15 Years Tren
Target = 2,495,900
92,22% 6.662 2,495,900 Fair
4 Reflecting the
Latest Input Data
93.34% -26.26 2,794,463 -
TALLINN AIRPORT (Seasonlity Model)
Passengers Forecasting 2013
-
50,000
100,000
150,000
200,000
250,000
300,000
350,000
NoofPassengers
TIME (Month)
Forecast
Actual
Forecasting by Trend Target
R2 = 92.22 %
S.T.= 6.6620
Pax900,495,2(F) =2013

33Airport forecasting
Washington Dulles International Airport
(IATA: IAD, ICAO: KIAD, FAA LID: IAD) is a public
airport in Dulles, Virginia, 26 miles (41.6 km) west of
downtown Washington, D.C. The airport serves the
Baltimore-Washington-Northern Virginia metropolitan
area centered on the District of Columbia. It is
named after John Foster Dulles, Secretary of State
under Dwight D. Eisenhower. The Dulles main
terminal is a well-known landmark designed by Eero
Saarinen. Operated by the Metropolitan Washington
Airports Authority, Dulles Airport occupies 11,830
acres (47.9 km2) straddling the border of Fairfax
County and Loudoun County, Virginia.
R
2
= 96.1
S.T.= 0.0
2013 (F) = 11,641,322 Pax
Detroit Metropolitan Wayne County Airport
(IATA: DTW, ICAO: KDTW), usually called Detroit
Metro Airport, Metro Airport locally, or simply
DTW, is a major international airport in the United
States covering 7,072-acre (11.050 sq mi; 2,862
ha) in Romulus, Michigan, a suburb of Detroit. It is
Michigan’s busiest airport and one of the world’s
largest air transportation hubs.
The airport serves as Delta’s second busiest hub.
Delta, along with SkyTeam partner Air France,
occupy the McNamara Terminal.
R
2
= 92.3
S.T.= -4
2013 (F) = 16,770,937 Pax
Forecasting of US Airports:
Airport forecasting is an important issue in
Aviation industry. It becomes an integral parts of
transportation planning. It sets targets and goals
for the airports, either for long term or medium term
planning. The primary statistical methods used in
airport aviation activity forecasting are market share
approach, econometric modeling, and time series
modeling.
While we will use R and Signal Tracking Approach.
Detroit Airport (Seasonlity Model)
1,000,000
1,100,000
1,200,000
1,300,000
1,400,000
1,500,000
1,600,000
1,700,000
NoofPassengers
TIME (Month)
Forecast
Actual
R2 = 92.3 %
S.T.= - 4
2013(F) = 16,770,937 Pax
Washington Dulles Airport (Seasonlity Model)
500,000
600,000
700,000
800,000
900,000
1,000,000
1,100,000
1,200,000
NoofPassengers
TIME (Month)
Forecast
Actual
R2 = 96.1%
S.T.= 0.0
2013(F) = 11,641,322 Pax

34
International Airport
(IATA: FLL, ICAO: KFLL, FAA LID: FLL) is
an international commercial airport located in
unincorporated Broward County, Florida, three miles
(5 km) southwest of the central business district of
Fort Lauderdale. It is also located near the city of
Hollywood and is 21 miles (33.7 km) north of Miami.
R
2
= 96.8
S.T.= 0.0
2013(F) = 12,080,874 Pax
Charlotte Douglas International Airport
(IATA: CLT, ICAO: KCLT, FAA LID: CLT) is a joint
civil-military public international airport located in
Charlotte, North Carolina. Established in 1935 as
Charlotte Municipal Airport, in 1954 the airport was
renamed Douglas Municipal Airport after former
Charlotte mayor Ben Elbert Douglas, Sr. The airport
gained its current name in 1982 and is currently US
Airways’ largest hub, with service to 175 domestic
and international destinations as of 2008. In 2009,
it was the 9th busiest airport in the United States
and in 2010, the 24th busiest airport in the world by
passenger traffic.
R
2
= 85.6
S.T.= 0
2013(F) = 21,135,269 Pax
Los Angeles International Airport
(IATA: LAX, ICAO: KLAX, FAA LID: LAX) is the
primary airport serving the Greater Los Angeles
Area, the second-most populated metropolitan area
in the United States. LAX is located in southwestern
Los Angeles along the Pacific coast in the
neighborhood of Westchester, 16 miles (26 km) from
the downtown core and is the primary airport of Los
Angeles World Airports (LAWA), an agency of the
Los Angeles city government formerly known as the
Department of Airports.
R
2
= 97.7
S.T.= 4
2013(F) = 32,269,576 Pax
Fort Lauderdale–Hollywood Airport (Seasonlity Model)
Charlotte Douglas Airport (Seasonlity Model)
Los Angeles Airport (Seasonlity Model)
600,000
700,000
800,000
900,000
1,000,000
1,100,000
1,200,000
1,300,000
NoofPassengers
TIME (Month)
Forecast
Actual
R2 = 96.8 %
S.T.= 0.00
2013(F) = 12,080,874 Pax
1,800,000
2,000,000
2,200,000
2,400,000
2,600,000
2,800,000
3,000,000
3,200,000
NoofPassengers
TIME (Month)
Forecast
Actual
R2 = 85.6 %
S.T.= 0.0
2013(F) = 21,135,269 Pax
1,800,000
2,000,000
2,200,000
2,400,000
2,600,000
2,800,000
3,000,000
3,200,000
NoofPassengers
TIME (Month)
Forecast
Actual
R2 = ̂ 7.7 %
S.T.= 4
2013(F) = 32,269,576 Pax
Airport forecasting

Minneapolis–Saint Paul International Airport
(IATA: MSP, ICAO: KMSP, FAA LID: MSP) is a joint
civil-military public use airport. Located in a portion
of Hennepin County, Minnesota outside of any city
or school district, within ten miles (16 km) of both
downtown Minneapolis and downtown Saint Paul,
it is the largest and busiest airport in the five-state
upper Midwest region of Minnesota, Iowa, South
Dakota, North Dakota, and Wisconsin.
R
2
= 95.3%
S.T.= -3
2013(F) = 16,650,355 Pax
Chicago Midway International Airport
(IATA: MDW, ICAO: KMDW, FAA LID: MDW), is an
airport in Chicago, Illinois, United States, located on
the city’s southwest side, eight miles (13 km) from
Chicago’s Loop.
Dominated by low-cost carrier Southwest Airlines,
Midway is the Dallas-based carrier’s largest focus
city as of 2011. Both the Stevenson Expressway
and Chicago Transit Authority’s Orange Line provide
passengers access to downtown Chicago. Midway
Airport is the second largest passenger airport in the
Chicago metropolitan area, as well as the state of
Illinois, after Chicago O’Hare International Airport.
R
2
= 96%
S.T.= 4
2013(F) = 9,397,884 Pax
Dallas/Fort Worth International Airport
(IATA: DFW, ICAO: KDFW, FAA LID: DFW) is
located between the cities of Dallas and Fort Worth,
Texas, and is the busiest airport in the U.S. state
of Texas. It generally serves the Dallas–Fort Worth
metropolitan area.
DFW is the fourth busiest airport in the world in
terms of aircraft movements. In terms of passenger
traffic, it is the eighth busiest airport in the world. It
is the largest hub for American Airlines. DFW Airport
is considered to be an Airport City.
R2
= 97.3%
S.T.= 0.76
2013 (F) = 28,183,463 Pax
35
Dallas Airport (Seasonlity Model)
Minneapolis–Saint Paul Airport (Seasonlity Model)
Chicago Midway Airport (Seasonlity Model)
1,600,000
1,800,000
2,000,000
2,200,000
2,400,000
2,600,000
2,800,000
NoofPassengers
TIME (Month)
Forecast
Actual
R2 = ̂ 7.3 %
S.T.= 0.75
2013(F) = 28,183,463 Pax
1,000,000
1,100,000
1,200,000
1,300,000
1,400,000
1,500,000
1,600,000
1,700,000
1,800,000
NoofPassengers
TIME (Month)
Forecast
Actual
R2 = ̂ 5.˼ %
S.T.= - 3
2013(F) = 16,650,355 Pax
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
NoofPassengers
TIME (Month)
Forecast
Actual
R2 = 96.1%
S.T.= 0.0
2013(F) = 9,397,884 Pax
Airport forecasting

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