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Air Travel Demand Forecasting and Methodologies

Explore past and present trends in air travel. Analyze economic, social, and technological factors influencing air travel demand. Learn forecasting techniques and models for future predictions in aviation. Key data and statistical methods discussed.

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Air Travel Demand Forecasting and Methodologies

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  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 inEconometric 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 = -1.5298 + 0.61GDPt (1.877) (6.637) R2 = 0.82 International air travel at Frankfurt Airport Yt = -15.754 + 2.607GDPt (-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 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 General form A Typical Model

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