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

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

Greg Campbell and Loren Williams

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


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


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


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

1

dl

dl

Dmd CurrBkd MatlFr RemDmd

RemDmd DeseasLvl SeasFctr BkgFr


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


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


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


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.


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


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 WeekSchedule

Future OperationalSchedules

Future OperationalSchedules

Past OperationalSchedules

Schedule Match

Virtual FlightMatch


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

, , ,i j k k i jC criterion


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

ATL LAX

PHX


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


Connection Quality CodesFlight Route: ATL-PHX-LAX/Flt0123 - Flt0237 has a CQC of 00Flight Route: ATL-PHX-LAX/Flt0123 - Flt0238 has a CQC of 10Flight Route: ATL-PHX-LAX/Flt0123 - Flt0239 has a CQC of 20Flight Route: ATL-PHX-LAX/Flt0125 - Flt0238 has a CQC of 00

Route MatchATL PHX LAXAirport:

Time

Flt123

Flt237

Flt238

Flt125

Flt239


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.


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


Aggregation of Scale-Free StatisticsData 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

Y Q

FRA

Ind

Y

Grp

Q Y Q

etc.


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.


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


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.


Summary

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


Questions

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

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