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Airport Forecasting

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 future. 1. Airport service area 2. Origins and destinations of trips

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Airport Forecasting

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  1. Airport Forecasting

  2. 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

  3. Master Plan

  4. Data used to predict future 1. 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 nearby areas having air service 8. Geographic factors influencing transportation requirements 9. Existence and degree of competition between airlines and among other modes of traffic

  5. 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 the above 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

  6. Forecasting by Judgement • Delphi Method: A panel of experts on different subjects is assembled and asked a series of questions and projections which they take into account to determine a forecast

  7. Trend Extrapolation 375000 390000

  8. Top-Down Model Extrapolate 1, given 2, get 3:

  9. Cross Classification Model • Cross Classification: examines the behavioral characteristics 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

  10. Factors • Income • Occupation • Age • Type and location of residence • Education • etc…

  11. Market Study • Market Study method does NOT require complex mathematical relationships • uses simple equations to generate a classification table or matrix • Advantage: allows for discrimination between discretionary and non-discretionary travelers and the factors that influence both types Discretionary = vacationers • Non-discretionary = business traveler

  12. 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

  13. Explanatory Variables • Economic growth • Population growth • Market factors • Travel impedance • Intermodal competition

  14. Regression Equations • Linear Regression form: Y = mx + b • Multiple Regression form: Yest= ao + a1X1 + a2X2 + a3X3 + … + anXn

  15. Statistical Testing of Models • Tests performed to determine the validity of econometric models • The analyst needs to consider the reasonableness as well as the statisticalsignificance of the model

  16. (Yest - Yavg)2 R2 = (Y - Yavg)2 Coeff. of Mult. Determination • Coefficient of multiple determination, R2 : measures the variation in the dependant variable that is explained by the variation in the independent variables • (e.g. R2 1.0 very good relationship) • Equation:

  17. Coeff. of Mult. Correlation • Coefficient of multiple correlation, R: measures the correlation between the dependent variable and the independent variables • (e.g. R  1.0 very close correlation) • Equation: R = (R2)1/2

  18. [ ] (Y - Yest)2 y est = m - (n+1) Standard Error • Standard error of the estimate: measure of the dispersion of the data points about the regression line and is used to establish the confidence limits • Equation:

  19. Equations for Trend Line

  20. 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

  21. q ( p )  = q p Elasticity •  < -1, Elastic, people may change trip behavior • E = 0, Perfectly Inelastic, no effect on trip behavior • -1 < E < 0, Inelastic, insensitive to price

  22. Elasticity Example

  23. q ( p )  = q p 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

  24. THE END

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