1 / 21

Passenger Recapture Estimation in Airline RM

Passenger Recapture Estimation in Airline RM. Shau-Shiang Ja, Beju Rao, Scott Chandler AGIFORS Revenue Management May 8-11 2001. Outline . Introduction Basic Methodology Preliminary Results Simulation Results Summary Questions/Answers. Introduction.

roza
Download Presentation

Passenger Recapture Estimation in Airline RM

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Passenger Recapture Estimation in Airline RM Shau-Shiang Ja, Beju Rao, Scott Chandler AGIFORS Revenue Management May 8-11 2001

  2. Outline • Introduction • Basic Methodology • Preliminary Results • Simulation Results • Summary • Questions/Answers

  3. Introduction • Demand unconstraining is necessary in order to estimate the true demand in a Revenue Management (RM) system. • RM systems do not always adequately adjust spill values to account for the proportion of estimated spill that may be recaptured by higher fare classes on the same service or by other services. • Unconstraining without recapture will result in counting the recaptured passengers twice

  4. True Demand=14 True Demand=6 M Observed Demand=10 Y Observed Demand=8 2 recaptured by Y 2 lost to OA For M: Spill+Observed Demand=14 For Y: Spill+Observed Demand=8 Unconstraining w/o Recapture • The two recaptured passengers are counted twice

  5. True Demand=14 True Demand=6 M Observed Demand=10 Y Observed Demand=8 2 recaptured by Y 2 lost Recapture Rate = 2/4 = 50% For M: Spill+Observed Demand=14 For Y: Spill+Observed Demand=8 Basic Idea • Adjusted Spill = Estimated Spill * (1-Recapture Rate)

  6. True Demand=14 True Demand=6 M Observed Demand=10 Y Observed Demand=8 2 recaptured by Y 2 lost Recapture Rate = 2/4 = 50% For M: Spill + Observed Demand = 4 * ( 1 - 0.5 ) + 10 = 12 For Y: Spill + Observed Demand=8 Basic Idea (cont.) • Adjusted Spill = Estimated Spill * (1-Recapture Rate)

  7. Related Literature in RM • Thomas Gorin (AGIFORS RM Study Group, 2000) • There is a sample bias. • Sven-Eric Anderssen (International Transactions in Operational Research, 1998) • Discrete Choice model

  8. Service#1 Y Potential Recapture M Basic Methodology • Observed DemandY = True DemandY + RecaptureM2Y – SpillY Observed DemandY + SpillY = True DemandY + RecaptureM2Y

  9. Basic Methodology (cont.) • Observed DemandY + SpillY = True DemandY + RecaptureM2Y • RecaptureM2Y = Recapture RateM2Y * SpillM Therefore, • Observed DemandY + SpillY = True DemandY + Recapture RateM2Y * SpillM • In the above equation, Observed DemandY, SpillY and SpillM are known (assuming our unconstraining method is perfect) and True DemandYand Recapture RateM2Yare unknown.

  10. Regression Approach • Observed DemandY + SpillY = True DemandY + Recapture RateM2Y * SpillM • It looks like a standard regression form such that both unknowns appear as coefficients. • This regression model can be extended to consider potential recapture from other lower fare classes within the same service and potential recapture from other itineraries as well.

  11. Service#1 (7:00 am) Service#2 (8:00 am) Service#3 (9:00 am) Y Y Y M N Regression Model - example • Observed Demand2Y + Spill2Y = b0 + b1 * Spill2M+ b2 * Spill2N + b3 * Spill1Y + b4 * Spill3Y

  12. Regression Model (cont.) • Observed Demand2Y + Spill2Y = b0 + b1 * Spill2M+ b2 * Spill2N + b3 * Spill1Y + b4 * Spill3Y • The regression parameters (b1, b2, b3, and b4) test the influence that related spill has on the targeted service/class, while b0 represents the true demand. • One equation for each service/class/reading_day. • One year of spill and observed demand data. • At least 40 data points per service/class/reading_day.

  13. Preliminary Results

  14. Preliminary Results (cont.) • High spill service/classes have higher observed recapture rates. • Observed recapture is more noticeable on single-leg services. • Clumping at origin affects regression results. • Occasionally, the regression model returned recapture rates greater than one • Good data cleaning required on the O&D data. • Good O&D spill estimation is critical.

  15. Simulation Model • Demand of Y ~ Gamma(a,b) with mean 10 • Demand of M ~ Gamma(a,b) with mean 15 • Recapture Rate is 0.3 • Spill of M = Max( Demand of M - Capacity, 0 ) • Observed Demand of Y = Demand of Y + 0.3 * Spill of M • Regression Model : Observed Demand of Y = b0 + b1 * Spill of M

  16. Simulation Result - case 1 • Demand of Y and Demand of M are independent.

  17. Simulation Result - case 2 • At half of the time (randomly), both Demand of Y and Demand of M are increased by 10.

  18. Simulation Result - case 3 • At half of the time (randomly), both Demand of Y and Demand of M are multiplied by 2.

  19. MIDT/CIDT QSI Utility Matrix Historical Closing Rates Recapture Rates Calculation QSI-Based Approach Diagram Future Plan • It is possible to integrate the result from regression-based approach and QSI-based approach.

  20. Summary • Unconstraining without recapture results in counting the recaptured passengers twice. • Recapture rates can be estimated using regression from the observed demand and estimated spill on all related service/classes. • Passenger choice model can be included in the calibration of recapture rates in the future.

  21. Questions?

More Related