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

2. Agifors, Bangkok – May, 2001 Outline Benefits of O&D forecasting Definition of an O&D forecast Some real-world complexities Schedule changes Small markets Summary and questions

3. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 Construction and Use of Virtual Flight Legs Flt0023/09:45 Flt0107/11:05 Flt1614/21:30 Flt0032/09:30 Flt0110/11:00 Flt1705/20:30 Flt0023/09:45 Flt0107/11:05 Flt1614/21:30 Ideal Week Schedule Generator VFID623/Flt0023/09:45 VFID624/Flt0107/11:05 VFID625/Flt1614/21:30 Ideal Week Schedule Future Operational Schedules Future Operational Schedules Past Operational Schedules Schedule Match Virtual Flight Match

12. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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 one flight ATL-PHX FLT: 123 You have two flights PHX-LAX FLT: 237 and 238 ATLLAX PHX

14. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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 Origin Destination Class LGW Ind Y Grp Q ATL YQ FRA Ind Y Grp Q YQ etc.

19. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 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. Agifors, Bangkok – May, 2001 Summary Benefits of O&D forecasting Definition of an O&D forecast Some real-world complexities Schedule changes Small markets

23. Agifors, Bangkok – May, 2001 Questions