O&D Demand Forecasting: Dealing with Real-World Complexities

1. O&D Demand Forecasting:Dealing with Real-World Complexities Greg Campbell and Loren Williams

2. Outline • Benefits of O&D forecasting • Definition of an O&D forecast • Some real-world complexities • Schedule changes • Small markets • Summary and questions

3. Benefits of O&D Forecasting • Prerequisite for network optimization • Increased forecast accuracy • Helps revenue managers understand traffic flows • Allows for more targeted forecast adjustments • Produces highly valuable data for reporting and analysis

4. Definition of an O&D Forecast • Market entities: virtual route/passenger type • Virtual route: departure date and time, airport sequence, and connection quality • Passenger type: cabin, class, market segment, POS country, in or outbound • Market entity forecast • For all virtual market entities with enough actual observations • Forecast for all future departure dates • Matched to the operational schedule in the future

5. Market Entity Demand Forecasts • Uses Winter’s/Holt time series model • AirRMS computes statistics to reflect • Deseasonalized demand levels • Seasonal factors • Booking fractions • Materialization (cancellation) rates • The the forecast computation is

6. Some Real-World Complexities • Schedule changes • Small O&D markets • Reconciling PNR and leg inventory data • Passenger segmentation • Reaccomodation • Seasonal markets • Midnight flights • Frequent flyers • Differences in daylight savings rules

7. Solutions to the First Two Issues • Schedule changes • Schedule and route matching • Small O&D markets • Aggregation of scale-free statistics • Direct vs. pseudo-local classification of market entities

8. Schedule and Route Matching • AirRMS creates a virtual schedule for which bookings and no-show forecasts are computed • “Ideal” schedule • Connection Quality Code • Produces virtual key, e.g. ATLJFKFRA/vfid/CQC • Schedule Match Processor matches operational schedules to the virtual schedule • Route Match Processor matches PNRs to virtual route

9. Virtual Routes • A virtual route is a means to define a route that is independent of operational schedule details. • A virtual route is defined in terms of the airports that are visited, the departure time of the first leg, and the “quality” of connections at each connecting point. • In AirRMS, • Historical data are aggregated to common virtual routes. • Forecasts are computed for virtual routes. • Virtual route forecasts are “assigned” to operational schedules in the future.

10. Virtual Route Composition • Airport list • Simply an ordered list of the airports visited on the route • Virtual flight leg id for the first leg • VFID’s define the ideal schedule, that is the most common schedule, that will be operated during the forecast horizon • VFID’s are matched to all historical and future schedules on that leg • CQC list • A means to rank each connection, relative to other connections serving the same two airports

11. Ideal Week Schedule Generator Flt0023/09:45 Flt0107/11:05 Flt1614/21:30 VFID623/Flt0023/09:45 Ideal Week Schedule VFID624/Flt0107/11:05 Future Operational Schedules VFID625/Flt1614/21:30 Schedule Match Flt0032/09:30 Flt0023/09:45 Flt0110/11:00 Flt0107/11:05 Flt1705/20:30 Flt1614/21:30 Past Operational Schedules Future Operational Schedules Virtual Flight Match Construction and Use of Virtual Flight Legs

12. Schedule Match • Minimizes a cost function for each match between a flight in the operational schedule and a flight in the ideal schedule • Cost Function: where the criterion are differences in departure times, flight number differences, equipment type differences; each with its own weight

13. ATL LAX PHX Routes and Flight Routes • Route = ATL-PHX-LAX • Flight Route = 2 Flight Routes. ATL-PHX-LAX (FLT0123 + Flt0237) ATL-PHX-LAX (FLT0123 + Flt0238) You have two flights PHX-LAX FLT: 237 and 238 You have one flight ATL-PHX FLT: 123

14. Connection Quality Codes • CQC purpose: • Characterize the connection quality of a particular itinerary • CQC criteria: • Must be a “legal” connection • CQC codes: • 00 = best possible connection • 10 = one later inbound flight could have connected • 20 = two later inbound flights could have connected

15. Route Match LAX ATL PHX Airport: Flt 123 Flt 125 Flt Time 237 Flt 238 Flt 239 Connection Quality Codes Flight Route: ATL-PHX-LAX/Flt0123 - Flt0237 has a CQC of 00 Flight Route: ATL-PHX-LAX/Flt0123 - Flt0238 has a CQC of 10 Flight Route: ATL-PHX-LAX/Flt0123 - Flt0239 has a CQC of 20 Flight Route: ATL-PHX-LAX/Flt0125 - Flt0238 has a CQC of 00

16. Schedule Change Process • Each OD booking is assigned to a virtual route, based on its actual path, first flight leg, and connection quality. • Forecasts are constructed for virtual routes. • The operational schedules for all future departure dates are inspected and dated, operational routes are “created” and the virtual route forecasts are assigned to them. • If there have already been bookings on an operational route for a future departure date, there is no need for the forecaster to create that route.

17. Small O&D Markets • Problem • Small numbers are difficult to forecast. • Potentially very large number of forecasts require long run times and large data storage. • Solutions • Aggregation of scale-free statistics • Direct vs. Pseudo-local forecasts

18. ATL Origin FRA LGW Destination Ind Grp Ind Grp Y Q Y Q Y Q Y Q Class etc. Aggregation of Scale-Free Statistics Data Mapper is a Manugistics-proprietary data aggregation component. It is used in AirRMS to ensure that the forecast statistics are computed at the best level of aggregation. Market Type

19. Direct Vs. Psuedo-local Forecasts • AirRMS aggregates low frequency market entities to “pseudo-locals” for forecasting purposes. • Forecast statistics and computations are performed independently for direct and pseudo-locals. • The pseudo-local threshold is determined by an trading off forecast accuracy against problem size and run time.

20. Setting the Small Market Threshold • Build a production database from historical and current data • Make forecasts with a post date in the past • Compare forecasts with actuals using forecast accuracy measures • Measure the size and run time of the market entity forecast • Trade-off accuracy with size and run time

21. Small Market Results • Forecast accuracy at the leg-class level increases slightly with raising the small market threshold but is fairly insensitive to broad changes in threshold. • The market entity forecast is large compared to a leg-class forecast, but the size and run time are manageable with modern computer equipment.

22. Summary • Benefits of O&D forecasting • Definition of an O&D forecast • Some real-world complexities • Schedule changes • Small markets

23. Questions