Airport Forecasting NOTE: for HW, draw cash flow diagram to solve and review engineering economics

errata General Airport website: http://www.bluegrassairport.com/ Master Plan Executive Summary: http://www.bluegrassairport.com/documents/AUG_21_WEB_Lexington_Airport_MP_Update_ExecSumm.pdf Master Plan Appendix A (Forecasts): http://www.bluegrassairport.com/documents/LEX_Master_Plan_Appendix_A_Website.pdf Full Master Plan: http://www.bluegrassairport.com/documents/LEX_Master_Plan_Report_Website.pdf See first two pages of Appendix A

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

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

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

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

Trend Extrapolation 375000 390000

Top-Down Model Extrapolate 1 and 2, multiply to get 3: Statistical Abstract of the US Air Carrier Fleet FAA aviation stats BTS # of Aircraft, … Top-Down Model Extrapolate 1 and 2, multiply to get 3:

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

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 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

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: Multiple 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, R2 : measures the variation in the dependent variable that is explained by the variation in the independent variables (e.g. R2 = 1.0 is perfect correlation) Equation: R2 = (Yest - Yavg)2 (Y - Yavg)2

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 perfect correlation) Equation: R = (R2)1/2

[ ] Standard Error y est = 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: (Y - Yest)2 m - (n+1) [ ] y est =

Equations for Trend Line

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 Hub and spoke system

( ) q p p q  = Elasticity  < -1, Elastic, people likely to change trip behavior E = 0, Perfectly Inelastic, no effect on trip behavior -1 < E < 0, Inelastic, less sensitive to price q p p q  = ( )

Elasticity Example The current fare is 7 with 6000 tourists and 7500 commuters daily.

( ) q p p q  = Calculations Tourists: Commuters: (-4000/2) (7/6000) = -2.33  < -1, Elastic people likely to change trip behavior Commuters: (-1000/2)(7/7500) = -0.47 -1 < E < 0, Inelastic less sensitive to price q p p q  = ( )

Engineering Economics

Engineering Economics

Available forecasts FAA