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O&D Passenger Demand Forecasting. John Blankenbaker, Wassim Chaar DT Operations Research AGIFORS Reservations and YM 2003 Conference June 2 - 5, 2003 Honolulu, Hawaii. Forecasting Methods. Bottom-Up (Direct) Methods Independent time-series Correlated time-series models Top-Down Methods
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O&D Passenger Demand Forecasting John Blankenbaker, Wassim Chaar DT Operations Research AGIFORS Reservations and YM 2003 Conference June 2 - 5, 2003 Honolulu, Hawaii
Forecasting Methods • Bottom-Up (Direct) Methods • Independent time-series • Correlated time-series models • Top-Down Methods • Forecast Enrichment • Forecast Inference (FI)
FI Method • Forecast unconstrained passenger demand at the leg cabin level. • Infer unconstrained passenger demand at O&D itinerary class level. • Aggregate O&D itinerary class level forecast to leg bucket level for use in leg optimization.
Advantages of FI • Leg cabin forecasts are more accurate and error propagation can be controlled. • Sophisticated inference engine used to derive O&D itinerary class level forecasts. • Provides “natural” O&D controls by adjusting passengers mix • No adjustment needed to produce leg bucket level forecasts.
Forecast Accuracy Experiment • Used revenue accounting data. • Usedmean absolute error (MAE) on nests that were open at departure. • Used weekly checkpoints from 63 days to 7 days before departure. • Considered the Y01-Y04 nest, the Y01-Y08 nest, the Y01-Y12 nest and the Y cabin total.
Experimental Results: Example 1 Mean Absolute Error Y04 Nest Mean Absolute Error Y08 Nest (N = 2701) (N = 2099) LEG LEG FI FI Mean Absolute Error Mean Absolute Error 063 056 049 042 035 028 021 014 007 063 056 049 042 035 028 021 014 007 Check Point Check Point Mean Absolute Error Y12 Nest Mean Absolute Error Y16 Nest (N = 1554) (N = 933) LEG LEG FI FI Mean Absolute Error Mean Absolute Error 063 056 049 042 035 028 021 014 007 063 056 049 042 035 028 021 014 007 Check Point Check Point
Experimental Results:Example 2 Mean Absolute Error Y04 Nest Mean Absolute Error Y08 Nest (N = 88) (N = 73) LEG LEG FI FI Mean Absolute Error Mean Absolute Error 063 056 049 042 035 028 021 014 007 063 056 049 042 035 028 021 014 007 Check Point Check Point Mean Absolute Error Y12 Nest Mean Absolute Error Y16 Nest (N = 65) (N = 35) LEG LEG FI FI Mean Absolute Error Mean Absolute Error 063 056 049 042 035 028 021 014 007 063 056 049 042 035 028 021 014 007 Check Point Check Point
Experimental Results: Example 3 Mean Absolute Error Y04 Nest Mean Absolute Error Y08 Nest (N = 15) (N = 15) LEG LEG FI FI Mean Absolute Error Mean Absolute Error 063 056 049 042 035 028 021 014 007 063 056 049 042 035 028 021 014 007 Check Point Check Point Mean Absolute Error Y12 Nest Mean Absolute Error Y16 Nest (N = 11) (N = 8) LEG LEG FI FI Mean Absolute Error Mean Absolute Error 063 056 049 042 035 028 021 014 007 063 056 049 042 035 028 021 014 007 Check Point Check Point
Experimental Results:Example 4 Mean Absolute Error Y04 Nest Mean Absolute Error Y08 Nest (N = 11) (N = 7) LEG LEG FI FI Mean Absolute Error Mean Absolute Error 063 056 049 042 035 028 021 014 007 063 056 049 042 035 028 021 014 007 Check Point Check Point Mean Absolute Error Y12 Nest Mean Absolute Error Y16 Nest (N = 4) (N = 4) LEG LEG FI FI Mean Absolute Error Mean Absolute Error 063 056 049 042 035 028 021 014 007 063 056 049 042 035 028 021 014 007 Check Point Check Point
Summary • Despite input data problems, FI generated O&D itinerary class forecasts which were, when aggregated to the leg bucket level, quite good. • Our prototype is available for experimentation and benchmarking purposes.
