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Untruncation

Untruncation is a part of most revenue management systems. It is a mathematical process whereby we estimate the number of customers there would have been for a particular product, assuming we would have offered the product for sale to all comers. In the cases where we actually did offer the product to all, then there is nothing for this algorithm to do beyond counting our actual sales. However, many times our RM systems will limit the number of customers we actually accept, and our actual sales are "truncated". Untruncation is needed to approximate how many customers were lost.

In PassengerSim, untruncation is included as a step within an RM system, typically within the DCP process before any forecasting or optimization steps.

example.yaml
rm_systems:
  basic_emsr_b: #(4)!
    processes:
      DCP:
      - step_type: untruncation #(1)!
        algorithm: em #(2)!
        kind: leg #(3)!
      - step_type: forecast
        algorithm: additive_pickup
        kind: leg
      - step_type: emsr
        algorithm: b
        kind: leg

  1. The step_type must be untruncation, as this is how PassengerSim identifies what to do in this step.
  2. Several different algorithms are available for untruncation, see below for details.
  3. Untruncation can be done at the leg or path level, see below for details.
  4. This is showing that basic_emsr_b is the name of this RM system. Elsewhere in the configuration (not shown in this example snippet) you will define carriers, and each will be assigned an RM system using these names.

UntruncationStep

Bases: RmStep

algorithm instance-attribute 

algorithm: Literal[
    "em",
    "em_py",
    "none",
    "naive1",
    "naive2",
    "naive3",
    "pd",
]

Untruncation algorithm.

There are several available algorithms:

none applies no untruncation, and assumes that demand was the same as sales. Applying this algorithm is still important even if no detruncation is desired, as PassengerSim tracks historical demand separately from sales and without "none" the historical demand used in forecasting would be zero.

em is an expectation-maximization model.

em_py is an expectation-maximization model implemented in Python. It is slow but useful for educational purposes.

naive1 is not recommended for use.

naive2 is not recommended for use.

naive3 is not recommended for use.

pd is a projection-detruncation model, based on the method developed by Hopperstad and described by Weatherford & Polt.

kind class-attribute instance-attribute 

kind: Literal['leg', 'path', 'hybrid'] = 'leg'

Level of collected demand data that should be used for untruncation.