lecture8-1.ppt

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Airport Forecasting



Forecasting Demand

• Essential to have realistic estimates of the

future demand of an airport

• Used for developing the airport master plan

or aviation system plan



Master Plan



Data used to predict future1. Airport service area

2. Origins and destinations of trips

3. Demographics and population growth of area

4. Economic character of area

5. Trends in existing transportation activities for the

movement of people, freight, and mail by various modes

6. Trends in national traffic affecting future development

7. Distance, population, and industrial character of nearbyareas having air service

8. Geographic factors influencing transportation requirements

9. Existence and degree of competition between airlines and

among other modes of traffic



Estimates Needed

1. The volumes and peaking characteristics of passengers,

aircraft, vehicles, freight, express, and mail

2. The number and types of aircraft needed to serve theabove traffic

3. The number of based general aviation aircraft and the

number of movements generated

4. The performance and operating characteristics of ground

access systems



Forecasting by Judgement

• Delphi Method: A panel of experts on

different subjects is assembled and asked a

series of questions and projections whichthey take into account to determine a

forecast



Trend Extrapolation

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

1970 1975 1980 1985 1990 1995 2000

Year PAX

1970 198128

1975 259317

1980 295780

1985 340717

1990 360670

1995

2000

375000 390000



Top-Down ModelExtrapolate 1, given 2, get 3:



Cross Classification Model

• Cross Classification: examines the behavioralcharacteristics of travelers

• Travelers broken down into classifications based

upon these characteristics

• Based on the belief that certain socioeconomic

characteristics influence the inclination for travel

• Market study performed to determine the travel

characteristics of the individual groups

• By knowing the different groups’ travel patterns,

forecasts can be made by projecting the patterns out



Factors

• Income

• Occupation

• Age

• Type and location of residence

• Education

• etc…



Market Study

• Market Study method does NOT require complex

mathematical relationships

• uses simple equations to generate a classificationtable or matrix

• Advantage: allows for discrimination between

discretionary and non-discretionary travelers andthe factors that influence both types

Non-discretionary = business traveler

Discretionary = vacationers



Multiple Regression

• Econometric Modeling: relates measures of

aviation activity to economic and social factors

• Multiple Regression is used to determine the

relationships between dependent variables and

explanatory variables



Explanatory Variables

• Economic growth

• Population growth

• Market factors

• Travel impedance

• Intermodal competition



Regression Equations

• Linear Regression form:

Y = mx + b

• Multiple Regression form:

Yest= ao + a1X1 + a2X2 + a3X3 + … + anXn



Statistical Testing of Models

• Tests performed to determine the validity of

econometric models

• The analyst needs to consider the

reasonableness as well as the statistical

significance of the model



Coeff. of Mult. Determination

• Coefficient of multiple determination, R 2 :

measures the variation in the dependant

variable that is explained by the variation inthe independent variables

• (e.g. R 2 1.0 very good relationship)

• Equation:

R 2 =(Yest - Yavg)

2

(Y - Yavg)2



Coeff. of Mult. Correlation

• Coefficient of multiple correlation, R:

measures the correlation between the

dependent variable and the independentvariables

• (e.g. R 1.0 very close correlation)

• Equation:

R = (R 2)1/2



Standard Error

• Standard error of the estimate: measure of

the dispersion of the data points about the

regression line and is used to establish theconfidence limits

• Equation:

(Y - Yest)2

m - (n+1)[ ]y est =



Equations for Trend Line

ENP POP

Year (thousand) (thousand)

1983 469 250

1984 515 260

1985 638 272

1986 758 274

1987 935 287

1988 996 296

1989 1140 307

1990 1361 317

1991 1479 326

1992 1651 332

y = 134.59x + 253.93

R 2

= 0.9872

0

200

400

600

800

1000

1200

1400

16001800

250 260 272 274 287 296 307 317 326 332

http://localhost/var/www/apps/conversion/tmp/scratch_5/lecture8-1.xls



Elasticity

• Elasticity: the percentage change in traffic

for a 1% change in fare or travel time

• In the past, it was important

• Even greater significance today due to a

deregulated industry• fare wars

• spoke and hub system





Elasticity

• < -1, Elastic, people may change trip behavior

• E = 0, Perfectly Inelastic, no effect on trip behavior

• -1 < E < 0, Inelastic, insensitive to price

q

p pq = ( )



Elasticity Example



Calculations

• Tourists:(-4000/2) (7/6000) = -2.33 < -1, Elastic

people may change trip behavior

• Commuters:

(-1000/2)(7/7500) = -0.47 -1 < E < 0, Inelastic

insensitive to price

q

p

pq =

( )



THE END

lecture8-1.ppt

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