Remapping of flight leg bookings

1. A method comprising:

generating, by a processor, an electronic ticket voucher having an authorization parameter and a flight leg booking based on a flight leg booking of a seat on a first flight leg of an airline flight;

closing, by the processor, a plurality of seats for the flight leg booking in a fare class for the first flight leg;

determining, by the processor based on a set of class authorization level (AU) values and for the fare class, a number of seat protects to fit to a bid-price curve;

determining, by the processor and for the fare class, even-spaced quantiles based upon the number of seat protects to fit in the fare class;

generating, by the processor, a set of expected network revenue (ENR) values based on the even-spaced quantiles associated with the seat;

remapping, by the processor and based on a network value of the flight leg booking, the flight leg booking from the fare class to a network value class corresponding to the network value;

iteratively updating, by the processor, the flight leg booking on the electronic ticket voucher based on at least one of the authorization parameter, the network value, the closing the plurality of seats for the flight leg booking in the fare class, the number of seat protects or the remapped flight leg booking;

determining, by the processor, an impact of a denied boarding on airline flights that are scheduled for departure during a time period;

adjusting, by the processor and based on updating of the impact of the denied boarding, the authorization parameter on the electronic ticket voucher to provide a different voucher amount for a passenger on the airline flight based on latest conditions during the time period; and

improving, by the processor, network performance by:

analyzing a value of at least a subset of flight leg bookings in a passenger itinerary to obtain more accurate data by avoiding under-valuation and over-valuation of the flight leg bookings; and

separating enhanced demand forecasting from fare classes to reduce errors and reduce inaccurate data that slows down the network performance.

2. The method of claim 1 , wherein the number of seat protects exceeds a number of the even-spaced quantiles, and wherein at least two seat protects are allocated to the even-spaced quantile corresponding to a 0th percentile.

3. The method of claim 1 , wherein the number of seat protects exceeds a number of even-spaced quantiles, and wherein the number of seat protects exceeding the number of even-spaced quantiles are all allocated to the even-spaced quantile corresponding to a 0th percentile.

4. The method of claim 1 , wherein if the fare class is an active class, a number of even-spaced quantiles is an achievable demand for the active class.

5. The method of claim 1 , wherein if the fare class is a non-active class, a number of even-spaced quantiles is a pickup, wherein the pickup is equal to unconstrained demand less unconstrained bookings.

6. The method of claim 1 , further comprising updating, responsive to the flight leg booking occurring in the fare class, the number of seat protects to fit to the bid-price curve for the fare class.

7. The method of claim 6 , further comprising allocating, if the fare class is the active class, the flight leg booking to lowest priced seat protect in the fare class.

8. The method of claim 6 , wherein an update of the number of seat protects is evenly distributed across the fare class by the even-spaced quantile.

9. The method of claim 1 , further comprising determining, via a non-parametric empirical distribution estimation, a distribution of revenue for the fare class.

10. The method of claim 1 , further comprising

iteratively updating, by the processor, the impact of the denied boarding during predetermined intervals.

11. The method of claim 1 , further comprising creating, by the processor, the bid-price curve using the set of ENR values.

12. The method of claim 1 , further comprising:

determining, by the processor, a change to the authorization parameter for other passengers for the denied boarding for the airline flights;

adjusting, by the processor and based on the determining of the change to the authorization parameter, the authorization parameter on the electronic ticket voucher, wherein an adjusted first authorization parameter provides for a re-accommodation on an alternate accommodation flight among the airline flights in a same directional market for the passenger, based on a plurality of forecasts for a cost of the re-accommodation for each of a plurality of denied passengers for the airline flights and based on the change to the authorization parameter for the other passengers for the denied boarding for the airline flights; and

adjusting, by the processor, the authorization parameter on the electronic ticket voucher, wherein the adjusted first authorization parameter provides access to hotel, meal and transportation (HMT) services due to a number of hours to an alternate accommodation flight, in response to the denied boarding based on revising of booking strategies and the closing the plurality of seats in the fare class.

13. The method of claim 1 , further comprising, responsive to a notice from a reservation system that one of the number of seat protects has been filled via the flight leg booking, determining, by the processor and for the fare class associated with the flight leg booking, an update of a number of the even-spaced quantiles based upon a reduction of the number of seat protects to fit in the fare class.

14. The method of claim 1 , further comprising improving, by the processor, network performance by:

analyzing a value of the first flight leg in a passenger itinerary to obtain more accurate data by avoiding under-valuation and over-valuation of the flight leg booking of the seat; and

separating enhanced demand forecasting from the fare class to reduce errors and reduce inaccurate data that slows down the network performance.

15. The method of claim 1 , further comprising:

identifying, by the processor, local bookings in the flight leg booking;

identifying, by the processor, flow bookings in the flight leg booking;

determining, by the processor and for a leg in a flow booking, a leg value;

inserting, by the processor, the leg value in the flow booking into a stratification table;

generating, by the processor and utilizing the stratification table, a remapped booking for the leg of the flow booking; and

writing, by the processor, the remapped booking into a forecast system history pool.

16. The method of claim 1 , wherein the determining the network value of the flight leg booking includes utilizing an equation of the form:

Legn value=(NR1/(NR1+NR2))*HP+Pricing, wherein

NR1=a first displacement cost of the seat on the first flight leg;

NR2=a second displacement cost of the seat on a second flight leg subsequent to the first flight leg;

HP=a hurdle point that is a minimum acceptable amount needing to be paid for the flight leg booking on the first flight leg in order to cover the first displacement cost;

Pricing=ODF−HP; and

ODF=an amount paid by the passenger for the flight leg booking.

17. The method of claim 1 , further comprising:

opening, by the processor, a plurality of seats for the flight leg booking in the fare class for the first flight leg of the airline flight;

providing, by the processor, the flight leg booking of the seat on the first flight leg from the plurality of seats; and

receiving, by the processor, a request for the flight leg booking of the seat on the first flight leg.

18. The method of claim 1 , further comprising determining, by the processor and in a set of flight leg bookings, that the flight leg booking in the set of flight leg bookings is a flow booking.

19. A non-transitory computer-readable storage medium having computer-executable instructions stored thereon that, in response to execution by a processor, causes the processor to perform operations comprising:

generating, by the processor, an electronic ticket voucher having an authorization parameter and a flight leg booking based on a flight leg booking of a seat on a first flight leg of an airline flight;

closing, by the processor, a plurality of seats for the flight leg booking in a fare class for the first flight leg;

determining, by the processor based on a set of class authorization level (AU) values and for the fare class, a number of seat protects to fit to a bid-price curve;

determining, by the processor and for the fare class, even-spaced quantiles based upon the number of seat protects to fit in the fare class;

generating, by the processor, a set of expected network revenue (ENR) values based on the even-spaced quantiles associated with the seat;

remapping, by the processor and based on a network value of the flight leg booking, the flight leg booking from the fare class to a network value class corresponding to the network value;

iteratively updating, by the processor, the flight leg booking on the electronic ticket voucher based on at least one of the authorization parameter, the network value, the closing the plurality of seats for the flight leg booking in the fare class, the number of seat protects or the remapped flight leg booking;

determining, by the processor, an impact of a denied boarding on airline flights that are scheduled for departure during a time period;

adjusting, by the processor and based on updating of the impact of the denied boarding, the authorization parameter on the electronic ticket voucher to provide a different voucher amount for a passenger on the airline flight based on latest conditions during the time period; and

improving, by the processor, network performance by:

analyzing a value of at least a subset of flight leg bookings in a passenger itinerary to obtain more accurate data by avoiding under-valuation and over-valuation of the flight leg bookings; and

separating enhanced demand forecasting from fare classes to reduce errors and reduce inaccurate data that slows down the network performance.

20. A system comprising:

a processor; and

a tangible, non-transitory memory configured to communicate with the processor,

the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations comprising:

generating, by the processor, an electronic ticket voucher having an authorization parameter and a flight leg booking based on a flight leg booking of a seat on a first flight leg of an airline flight;

closing, by the processor, a plurality of seats for the flight leg booking in a fare class for the first flight leg;

determining, by the processor based on a set of class authorization level (AU) values and for the fare class, a number of seat protects to fit to a bid-price curve;

determining, by the processor and for the fare class, even-spaced quantiles based upon the number of seat protects to fit in the fare class;

generating, by the processor, a set of expected network revenue (ENR) values based on the even-spaced quantiles associated with the seat;

remapping, by the processor and based on a network value of the flight leg booking, the flight leg booking from the fare class to a network value class corresponding to the network value;

iteratively updating, by the processor, the flight leg booking on the electronic ticket voucher based on at least one of the authorization parameter, the network value, the closing the plurality of seats for the flight leg booking in the fare class, the number of seat protects or the remapped flight leg booking;

determining, by the processor, an impact of a denied boarding on airline flights that are scheduled for departure during a time period;

adjusting, by the processor and based on updating of the impact of the denied boarding, the authorization parameter on the electronic ticket voucher to provide a different voucher amount for a passenger on the airline flight based on latest conditions during the time period; and

improving, by the processor, network performance by:

analyzing a value of at least a subset of flight leg bookings in a passenger itinerary to obtain more accurate data by avoiding under-valuation and over-valuation of the flight leg bookings; and

separating enhanced demand forecasting from fare classes to reduce errors and reduce inaccurate data that slows down the network performance.