1. AIR-TRAVEL DEMAND FORECASTING

2. In Brief Observe past and current trends in air travel demand
Inventory the variations in economic, social, demographic and technological factors affecting air travel demand
Establish relationship between travel demand and those factors found to be significant in altering travel demand
Project the values of those factors affecting travel demand into the future
Use the model and the forecasts to obtain the future air travel demand

3. PRINCIPAL ITEMS FOR WHICH DEMAND FORECASTS ARE MADE
Volumes and peaking characteristics of passengers, aircraft, vehicle, freight and mail
Number and types of aircraft needed to serve the above traffic
Performance and operating characteristics of ground access systems

4. DATA TO BE COLLECTED FOR AIR TRAVEL DEMAND FORECASTING
Airport service area
Air travel market area (i.e., Origins and Destinations of trips by both residents and nonresidents)
Demographic and population data of the area to be served by the airport
Economic character of the area
GDP, Per capita income, Employment, Retail, commercial, and wholesale trade sales, and bank deposits, Hotel registrations, Price of travel
Trends in the existing air travel demand (passenger and cargo)
Trends in national air traffic and the influencing variables
Distance, population, and industrial character of nearby areas having air service

5. FORECASTING METHODOLOGIES
Forecasting by Judgment
- Delphi Method
Trend Models
-  Linear
-  Exponential
-  Logistic
Market Share Models
Econometric Models

6. Simple Trend Models Linear Pn=Po+an Exponential Pn=Po(1+)n
Modified Pn= P - n (P- Po) Exponential
Double
Exponential
Logistic

7. Typical Trend Models

8. Comparative Models Also known as -Ratio methods -Mrket share models
Typical Comparative Models
PTA = kAPTN
PTA = kA (PTN – b)
PTA = kPBT-t

9. Trend of Proportion of traffic

10. A Top-Down Approach
Aggregate Forecast for the Nation by Econometric/Trend Model
Allocation to specific region based on historical share/economic growth
Airport choice model for allocation to airports with in the metropolis
Estimation of Induced traffic because of hub facility by market surveys/stated preference surveys
Refine forecasts based on experts’ opinion

11. Econometric Trend Models
Relate Economic and Social Factors to Aviation Activity
Trip generation Models
-Multiple Linear Regression Technique
Distribution Models
- Gravity Model

12. Explanatory Variables used in Econometric Models
Size and Traffic Potential
- Population
- GDP
-  Industrial production
- Per capita income
-  Personal expenditure
-  Leisure time
-  Interregional Linkages:
Economic and Social

13. Transportation
- Accessibility
Distance to airport, Travel time to airport
- Competition
relative cost, relative travel time,
schedule and reliability of alternate modes
-  Cost of air travel
average fare, total travel cost and
value of travel time
-   Schedule convenience
service frequency, time of departure,
necessary connections
-    Service reliability
on time performance, Cancellation history
-    Transport time
airport-to-airport time, door-to-door time

14. Typical Econometric Models
Domestic air travel at Frankfurt Airport
Yt = GDPt
(1.877) (6.637) R2 = 0.82
International air travel at Frankfurt Airport
Yt = GDPt
(-6.316) (9.765) R2 = 0.87

15. Statistical Testing Coefficient of determination
Standard Error of estimate
t-statistic
t-value = Parameter estimate/Standard error

16. Passenger Traffic in Important Airports

17. Example Problem Year Enplaned Passengers (millions)
Population of the city, millions
Per capita disposable income of the city, $
City
Region
1976
0.5 94
0.125
8500
1979
0.8
135
0.160
9500
1982
1.00
165
0.200
10500
1985
1.20
185
0.222
11000
1988
1.30
200
0.235
12000
1991
1.45
220
0.255
13500
Develop a top-down forecast for the 1997 enplaned passengers at the city airport by fitting a suitable trend model for the region.
  Develop an econometric model of enplaned passenger growth at the city airport. Also prepare a bottom-up forecast of the enplaned passengers at the city airport in 1997 if it is expected that the percapita disposable income will be $15,000 and the population will be 310,000.

18. Gravity Type Cross-sectional Model
Gravity Type Cross-sectional Model
General form
A Typical Model