Note: Descriptions are shown in the official language in which they were submitted.
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BACKGROUND OF THS INVENTION
1. Field of the Invention
The present invention relates in general to the field
of modeling large volume purchasing of services and
predicting shifts in service supplier market shares that
would result if particular changes in the volumes of
purchases were to be made, and, more particularly, to com-
puter systems and methods usable by corporate travel
department managers for predicting effects on separate
airline market shares caused by shifts between airlines in
the volume of airline tickets purchased by the corporation.
2. Discussion of the Prior Art
Travel management is a discipline practiced over a very
broad range of sophistication. At the lower end is the
individual planning a trip who needs to have airline
schedules searched to learn which flights might be taken to
make a trip, and who has to inquire as to the prices for
airline tickets for flights that might be taken. On the
basis of such information a trip can be planned, tickets
purchased and the trip taken. At the upper end are large
groups, including businesses, that employ managers to
project, recommend and implement multi-trip travel cost
budgets. Such groups can negotiate with airlines for
contracts including provisions granting them ticket price
discounts, some of which discounts being tied to the numbers
of tickets the group buys. On the basis of projections the
travel managers of such groups, e.g., corporations, are
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supposed to develop, recommend and then implement long term
plans including strategies for negotiation of contracts with
airlines.
In recent years a range of computer-based systems and
methods have been developed and used to assist in travel
management. Those that are relevant here are intended for
use by travel managers for large groups.
Among these computer-based systems and methods are a
group of systems intended for trip planning that select
flight itineraries from published airline scheduled flights
for a trip which comply with input corporate travel policies
and traveler preferences, and further select and identify
those flight itineraries for trips with the lowest fares.
Examples of such computer systems are described in U.S.
Patent Nos. 5,021,953 and 5,331,546, which issued from a
continuation application of the application from which
5,021,953 issued. A variation of the computer system and
method from that described in the preceding two identified
patents is one described in U.S. Patent No. 5,237,499
whereby using the described computer system and method an
individual business traveler may additionally book an itin-
erary, including airline flights, hotel reservations and, if
necessary, ground transportation. According to U.S. Patent
No. 5,237,499, tickets for the selected itinerary are
purchased at fares that were previously negotiated between
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airlines and the group employing the individual business
traveler.
These described computer systems and methods all take
the current airline flight schedules, current fares and
current corporate travel policies as inputs and process that
data to select flight itineraries that can be purchased at
the then existing fares which are lowest. No predictions
are calculated as to effects on sales of airline tickets
that could result from multiple travelers being offered
different flight schedules or discount fare rates.
Multiple variations of such computer systems and
methods that essentially process large volumes of existing
flight schedule and fare data, including fare discount
information, by sorting and scoring flight itineraries and
fares have been developed. Further examples include the
computer system and method described in U.S. Patent No.
4,862,357 which is supposed to be able to screen out
unacceptable and unavailable flights while displaying for an
operator flights scored in accordance with preloaded travel
policies, airline preferences and layover restric-
tions/requirements. One computer system and method
described in U.S. Patent No. 5,191,523 determines the number
of connecting segments flown, the number of miles flown, the
anticipated amount of time from departure to arrival, and
the costs on per-hour and per-mile bases for the selected
itineraries.
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A more recent computer system and method described in
U.S. Patent No. 5,832,453 is supposed to be usable to
develop a model to represent a group's travel requirements
in order to optimize selection of multiple itineraries
purchased from airlines. Inputs for this computer system
and method include existing airline flight schedules, fares,
the discounts that the group has available from airline
databases, and the trips that the group's members have to
make. Using such data the described computer system and
method is supposed to construct an objective function that
represents a travel cost to the group to purchase travel
trips for a plurality of travelers who would take various
specified trips, and the computer system and method also is
supposed to construct constraints from input trip demand and
airline flight data, including any airline utilization goal
data for the group. Then the constraints are applied to the
objective function, according to the description set out in
the 5,832,453 patent using linear programming, to determine
a solution for the objective function that satisfies the
constraints and thereby identifies minimized travel costs
for the group.
All of these prior computer systems and methods are
supposed to take existing airline flight schedules, current
fares and current group travel policies as inputs, and
process that data to identify those flight itineraries which
can be purchased at the lowest then existing fares. No pre-
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dictions are calculated as to effects on sales of airline
tickets that could result from multiple travelers of a group
being offered different fight schedules or discount fare
rates.
SUMMARY OF TH$ INVENTION
In a preferred embodiment, the present invention
provides a method and apparatus for corporate travel
managers to use computers to develop models of the airline
markets in which their corporations buy tickets. Then,
using the present invention, a corporate travel manager can
vary the model defining parameters so as to predict shifts
in separate airline market shares. Developing air travel
models and calculating predictions using the present
invention enables corporate travel managers to investigate
different situations and forecast how best to negotiate
contracts with airlines for the purchase of tickets,
including how to evaluate offered airline contract terms,
how to more accurately develop travel budgets and manage
travel expenditures.
By being able to forecast future air travel market
situations with calculated predictions, the present
invention provides corporate travel managers with
information not previously available. Instead of only
calculating a single optimal solution, that may or may not
realistically be achievable, the present invention permits
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calculations of predictions for multiple models controlled
by the corporate travel manager so that a preferred option
or a range of options can be evaluated for implementation.
In the past, automated systems were developed to select
trip itineraries from existing airline schedules and fares.
As such, they do not support the corporate travel managers
in the tasks of projecting, evaluating and then dynamically
monitoring travel programs.
The present invention is provided with airline
schedules, airline ticket fares, ticket discount rates,
corporate travel projections by city and airport pairs from
and to which trips are expected to be made, and other data
described below. Utilizing this data under a set of rules
described below, the present invention calculates by a
series of multiplications quantities labeled quality of
service indices that are percentage numbers representative
of airline service between cities and airports. Then,
inputting additional data such as airline contract data,
preference data for airlines, and other travel specific
data, the corporate travel manager using the present
invention constructs travel scenarios. These defined travel
scenarios in combination with the calculated quality of
service index values are used to determine predicted market
shares for specific airlines providing flight services
between cities and airports.
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It is the sensitivity of the determined airline market
share values to variations in input parameter values such as
corporate preference levels for specific airlines that
corporate travel managers can use to forecast what travel
management plans would or would not be best for their
corporations.
Software to implement the present invention can be
loaded on a server connected to the Internet so that
corporate travel managers, for a license fee, can access the
software using their corporate personal computers to run
computerized simulations of travel scenarios and predict
resulting air travel scenario details. Alternatively, the
software can be licensed or sold to corporate customers so
that corporate travel managers can load the software on
corporate computers for use.
BRIEF DESCRIPTION OF THE DRAWINGS
Fig. 1 illustrates a schematic view of one apparatus
embodiment of the present invention; and
Figs. 2 through 6 show block diagrams for an embodiment
of the present invention in serial order with data input
steps, databases, calculation steps and outputs identified.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENT
Referring to the drawings, a preferred embodiment for
an apparatus of the present invention, which is implemented
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as an Internet based system, is shown in Figure 1, and this
Internet-based apparatus is generally designated by
reference numeral 10. More specifically, the apparatus 10
includes a modeling server 12, the Internet 14, and multiple
individual customer personal computers 16. This embodiment
for the present invention uses unmodified commercially
available equipment for all of the modeling server 12 and
customer personal computers 16. Software to execute the
method of the present invention, which is described in
detail below, is loaded in the modeling server 12, and the
Internet-based apparatus 10 shown in Figure 1 is preferably
implemented to operate compatibly using 3.X browsers, e.g.,
Microsoft Internet Explorer° and Netscape Navigator°
software loaded in the customer personal computers 16. For
such usages, software to execute the method of the present
invention is Hyper Text Markup Language (HTML) based. Using
this embodiment, individual corporate travel executives, for
license fees, can with corporate personal computers 16
connect via modems through the Internet 14 to address and
access, on secure bases (again implemented using techniques
known to those skilled in the art) the modeling server 12 to
run computerized simulations for predicting resulting travel
scenario parameter values.
Other apparatus embodiments for the present invention
will be recognized by those skilled in the art upon study of
the method disclosed below including directly loading
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software to execute the method of the present invention on
customer computers.
Operation of the apparatus 10, to effect travel
scenario constructions and predictions in accordance with a
preferred embodiment of the present invention is illustrated
using input steps, database construction, calculation steps,
and output steps form, i.e., flow chart form, in Figs. 2
through 6 for a preferred embodiment as now described.
Data identified below is collected in order to make
calculations, using the method of the present invention,
whereby values for travel scenario parameters resulting from
input data are calculated. Additionally, some of the
collected data is input to calculate intermediate parameter
values that are then used for making further calculations
according to the method of the present invention to predict
final parameter values for resulting travel scenarios.
Referring to Fig. 2, geographic, airline, and airline
commission rate data, including updates for such data, is
input at a Maintain System Reference Tables 200 step. The
geographic and airline data is then input into a System
Reference Tables 210 database. The geographic data includes
specific identifications of continents, countries or
regions, through the levels of states, cities and specific
airports, e.g., North America and Europe, and the included
countries, cities and airports that are relevant to
corporate clients who use the present invention to analyze
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travel scenarios. The input airline data includes (a) the
names of airlines or carriers providing service between
airports in the selected geographic database, and (b) the
classes of service provided by each included airline on its
specific flights, e.g., first, business or coach classes
which are usually respectively designated as F, C, Y, as
well as additional classes including various discounted
classes, such as B, M, Q, K, etc. The data in the System
Reference Tables 210 database is updated as changes in facts
warrant (e.g., opening of a new airport, introduction of a
new airline service, or termination of a prior airline
service).
Standard airline commission rate data is also collated
and input at the Maintain System Reference 200 step, and is
then input to a Standard Airline Commission Rates 220
database. Such data includes airline specified standard
commission rates credited for tickets purchased at various
locations for travel between locations included in the
System Reference Tables 210 database and also input are the
maximum commission rate amounts, i.e., segment caps. For
example, if tickets are issued in the U.S. for travel on a
carrier within the U.S., then that carrier may offer a
commission rate of 5~ but this standard commission rate may
be segment-capped at $25.00, and, in such a case, the data
for that carrier would be collated at the Maintain System
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Reference Tables 200 step and input to the Standard Airline
Commission Rates 220 database.
Travel industry standardized data setting out what
airlines serve what airports, their flight schedules and the
type of service are input to a Flight Table 230 database.
This data for a preferred embodiment can be directly loaded
into the Flight Table 230 database from compact discs
("CD's") that are sold by suppliers known in the travel
industry.
Several sets of factors labeled quality of service
indices are calculated at a Generate Quality of Service
Index 240 step and are stored at a Quality of Service Index
250 database, e.g., for a preferred embodiment, three sets
of quality of service indices would be generated; namely,
one set for each airline that operates between city pairs, a
second set of quality of service indices for each airline
that operates between specific airport pairs, and a third
set of quality of service indices associated with each pair
of airports serviced by each airline that operates between
two cities. Such sets of quality of service indices are all
calculated using data from the System Reference Tables 210,
Flight Table 230, and Airport Pairs 260 databases. Quality
of service indices are calculated percentage parameters that
are intended to be representative of available air travel
services provided between city/airport pairs by carriers.
The sum of all calculated quality of service indices for
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each of the types of quality of service indices, e.g., all
carriers providing service between a pair of cities, is
adjusted to have a fixed value of 100.
Data stored in the Airport Pairs 260 database is
provided with identifications of the actual pairs of
airports and associated cities that are to be used for
calculating predictions for travel scenarios between the
included airport pairs and associated cities.
Individual quality of service index values are
calculated at the Generate Quality of Service Index 240
step, as follows:
A. Carrier flights for which quality of service
indices are to be calculated are categorized
according to routings as follows (the examples set
out below are for airport pairs; variations
required for city pairs are direct and self-
evident extensions):
~ A category of non-stop routing flights are
identified as those flights for which the
Flight Table 230 and the Airport Pairs 260
databases specify that the locations of each
flight's origin and destination airports are
the same as those for which quality of
service indices are to be calculated.
~ A category of one-stop routing flights are
identified as those flights for which the
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Flight Table 230 and the Airport Pairs 260
databases specify that an included flight's
origin airport is the same as that for which
quality of service indices are to be
calculated and the destination airport of
this first flight is an airport other than
the second airport of the specified pair of
airports but such first destination airport
is also the origin airport of a second flight
for the routing. The location of the des-
tination airport for the second flight is the
same as the second airport in the specified
pair of airports.
~ A category of two-stop routing flights are
identified as those flights for which the
Flight Table 230 and the Airport Pairs 260
databases specify that a flight's origin
airport is that for which quality of service
indices are to be calculated. The
destination airport of this first flight must
both be different from that of the second
airport of the specified pair and also be the
origin airport of a second flight having a
destination airport that is also different
from the second airport of the specified
pair. The destination airport of the second
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flight must be the origin airport of a third
flight, but the destination airport of the
third flight must be the same airport as that
of the latter of the pair of specified
airports.
H. The one- and two-stop routings are then evaluated
by applying the following rules to determine those
that are legitimate connecting flights:
The dates for the first and last flights for
each routing must overlap, i.e., all flights
for the routing operate on the same day.
The flights for each routing operate on the
same days of the week, except when one or
more flights may arrive on a day earlier or
later than when it departed due to operating
over the International Dateline or operating
past midnight.
The flights for each routing must have
connecting times for either domestic or
international flights, depending on the
situation, that are equal to or greater than
minimum connecting times which are pre-set
for periods officially specified for the
relevant airports or are set at essentially
optimal periods of time that are determined
from previously using the method of the
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present invention, e.g., for a preferred
embodiment minimum connecting times of 1.0
hour for domestic flights and 1.5 hours for
international flights were effectively
utilized. Maximum connecting times are pre-
set for periods customary within the
industry, e.g., for a preferred embodiment
maximum connecting times of 4.0 hours for
domestic flights and 6.0 hours for
international flights, not counting the hours
between 10:00 p.m. and 6:00 a.m., were
effectively used. Routings for the preferred
embodiment are excluded if they originate and
terminate within the same country but have
connections through airports in a second
country.
A primary carrier is specified for each
routing by identifying the carrier that
operated (or code shared, as is known in the
travel industry) on the longest flight within
the routing as determined by mileage.
C. Initial raw quality of service index values now
are calculated for each non-stop routing and
legitimate connecting routing by calculating the
product of all the following factors:
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(a) One-half the least number of seats on each
airplane for the routing in the travel
service category, e.g., F, C, Y, etc., for
which quality of service indices are being
calculated. When a routing operates on a
code-share basis, the flight seats are
divided evenly between each of the listed
code-share flights. An exemplary value for
this factor would be 62.5 for the situation
where the number of seats on an airplane is
125, i.e., 0.5 x 125 = 62.5.
(b) Aircraft type factor determined from a
previously loaded table of pre-set values,
which for a preferred embodiment have a range
of values from 0.5 (for helicopters, and
propeller aircraft with 50 or fewer seats),
0.7 (for propeller aircraft with 70 or fewer
seats), 0.8 (for propeller aircraft with more
than 70 seats), 0.9 (for narrow bodied jet
with 70 or fewer seats), 0.95 (for narrow
bodied jets with more than 70 seats), 1.0
(for narrow bodied jets with 100 or more
seats), to 1.1 (for wide bodied jets), e.g.,
Boeing 757 could have a value of 1.0;
(c) For the situation where there are routing
connections, a connection penalty factor is
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determined from a previously loaded table of
pre-set values, which for a preferred embodi-
ment have a range of values from 0.06 to
0.75, e.g., a connecting flight in an airport
pair where the minimum elapsed time is one
hour could have a factor value of 0.06.
(d) Departure time factor determined from a
previously loaded table of pre-set values,
which for a preferred embodiment in value
from 0.5 (for 11 p.m. to 4 a.m.), 0.7 (for 8
a.m. to noon), to 1.0 (for 6 a.m. to 8 a.m.
and for 4 p.m. to 6 p.m.), e.g., 7:30 a.m.
could have a value of 1Ø
(e) Arrival time factor determined from a
previously loaded table of pre-set values,
which for a preferred embodiment range in
value from 0.5 (for 11 p.m. to 4 a.m.), 0.7
(for 8 a.m. to noon), to 1.0 (for 6 a.m. to 9
a.m. and for 4 p.m. to 6 p.m.), e.g., 6:00
p.m. could have a value of 1Ø
(f) Factor for the combination of the departure
time of a legitimate connecting flight
departure time and an alternative departure
time for the closest routing with a lower
number of connections determined from a pre-
viously loaded table of pre-set values, which
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for a preferred embodiment range in value
from 0.4 (for situation of flights having the
same departure times) to 1.0 (for situation
of flights having a 4 hour or more departure
time difference for the nearest non-stop
flight.), e.g., in the case of a one-stop
legitimate connecting routing with a 9:00
a.m. departure and an alternative non-stop
routing also with a 9:00 a.m. departure, the
pre-set value could be 0.40;
(g) Factor value for the number of days per month
that a selected flight route is made by a
carrier. For a preferred embodiment this
factor value is set at the actual number of
days per month that the airline provides such
service. This factor, for the preferred
embodiment, is adjusted in value according to
which day or days of the week the flight a.s
made. Specifically, for the preferred
embodiment, the factor is retained at its
full value if the flights are made on any of
Monday through Friday. Whereas, if the
flights are made on Saturday or Sunday, the
factor is multiplied by 0.25, and if the
flights depart on Friday and arrive on
Saturday or depart on Sunday and arrive on
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Monday, the factor is multiplied by 0.50.
Therefore, in the situation where a selected
flight route is made every day during the
month of June, the factor value for the
preferred embodiment, is calculated as
follows: (22 x 1.0 + 8 x 0.25) - 24.
The initial raw quality of service index value that would be
calculated for the above set-out exemplary values, i.e., (a)
through (h), is 62.5 x 1.0 x 0.06 x 1.0 x 1.0 x 0.4 x
24=36Ø
D. Final quality of service index factor values are
now calculated as follows:
~ A summation of all the initial raw quality of
service index values for non-stop and
legitimate connecting routings made by an
actual or code-share carrier servicing a pair
of airports is determined and the value of
that summation is then divided by the sum-
mation of all the initial raw quality of
service index values for non-stop and
legitimate connecting routings for all the
carriers servicing that airport pair.
~ In those cases where the previous sumanations
and ratio calculations are less than a pre-
set value (which is found by prior use of the
present invention to be a threshold for
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eliminating essentially meaningless values,
e.g., 1~ for a preferred embodiment), the
below threshold values are deleted to provide
an intermediate set of quality of service
index values and the intermediate quality of
service index values for the carrier servic-
ing the airport pair are redistributed so
that the sum of all the values for all the
carriers providing service between each
included airport pair is 100. Such
redistribution is effected, for a preferred
embodiment, by first calculating the ratio of
one divided by the summation of all the
intermediate quality of service index values
and then multiplying that value by the
individual quality of service index values to
calculate the final individual redistributed
quality of service index values.
The final individual calculated quality of service index
values are input to the Quality of Service Index 250
database.
Now, computerized data for the corporate client using
the present invention to calculate travel scenario parameter
values that is available from travel agencies (e. g., from
computerized databases such as those known as Global Max~,
ADS/X, Sabre Travel Base~, and others) and from the cor-
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porate client's own in-house computerized databases (e. g.,
from computerized database systems such as those known as
GEMS, ISP, VantagePoint~, and others) is directly input to a
Back Office Data 270 database.
Data for the corporate client is also output from the
System Reference Tables 210, Back Office Data 270 and the
Back Office Numbers 280 databases and is input to the Stan-
dardized Back Office Data 290 step where the input data for
the corporate client is transformed into a common format for
use in the method of the present invention. (Identifications
of the travel agencies, the types of data systems the
agencies use and their assigned client numbers are stored in
the Back Office Numbers 280 database.) Additionally,
specific customer identification numbers are set at unique
values and are assigned to each of the individual data sets
for the individual corporate clients. The transformed data
is then output from the Standardized Hack Office Data 290
step and is input to the Standardized Back Office Data 300
database.
Next, airline contract data for the corporate client is
input at the Corporate Travel Department 310 step, and this
data is next entered into the Airline Contracts 320 database
(see Fig. 3). This contract data is updated as is necessary
to keep the data in the system current for that corporate
client.
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Specifically entered into the Airline Contracts 320
database are (a) airline identifications, (b) contract
effective dates, (c) point-of-sale discounts (including
applicable geography --origin and destination airport, city,
state, country and/or regions), (d) applicable classes of
service, (e) types of discount (e. g., flat rate, percentage,
or class-of-service upgrade), (f) whether travel under the
contract is eligible for commission and/or override
earnings, (g) back-end discounts (including applicable
geography, type of discount (cash or barter), frequency of
payment (annual or quarterly), delay in payment (from end of
year or quarter, etc.), amount of discount, and whether
travel under the contract is eligible for commission and/or
override earnings), and (h) performance targets (including
system level targets, focused market targets and individual
airport pair targets), which may be revenue and/or segment
targets, either absolute, percentage or growth based, in all
or just served markets.
Data identifying corporate preferred carriers and the
level of corporate influence used to affect preference deci-
sions is also input at the Corporate Travel Department 310
step and is entered into the Historical Preferred Carriers
and Influence 330 database. The entered preference and
influence data is generated from interviews with the
corporate travel managers) and, as appropriate, other cor-
porate travel management executives. Carrier preferences
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are segmented historically, e.g., on a month-by-month basis,
and, therefore, those carriers, during the associated time
periods, that are identified by corporate travel management
executives as being preferred carriers are so designated,
depending on the travel management executive's preference,
to specific airports, cities, states, countries, regions or,
even, system-wide. Influence levels, which also are
segmented on a month-by-month basis, are digitized to
represent the overall level of corporate travel compliance
influence -- a combination of policy, communication, and
point of sale effectiveness (for a preferred embodiment this
data is specified on a scale of values ranging from 0 to 5,
with 0 being used for no influence, 1 being used for a mild
corporate influence, and 5 being used for a corporate
mandate with the values 2 through 4 being used for the
respective intermediate levels of influence). Optionally,
the corporate client may also enter digitized values to
represent overriding influence levels for specified carriers
serving specific airport pairs that are identified in the
Historical Preferred Carriers and Influence 330 database.
Again the overriding influence levels are assigned values
for a preferred embodiment that range from 0 to 5.
Data from a Standard Airline Commission Rates 340
database, which include information on the commission rates
and segment caps for travel between pairs of countries or
regions for tickets issued in specified countries by a
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carrier, is input to a Nonstandard Airline Commission and
Override Rates 350 step. Currently, for example, when
tickets are issued in the United States for travel within
the United States the commission rate can be 5~ with a cap
of $25.00 per segment.
The data from the Standard Airline Commission Rates 340
database is now compared at the Nonstandard Airline
Commission and Override Rates 350 step to the corporate
client's data available at the Corporate Travel Department
310 step to extract nonstandard airline commission rates and
override rates that are input to the separate Nonstandard
Airline Commission Rates 360 database and the Override Rates
370 database. Override rates are additional earnings
returned to the corporate client under contract specified
conditions for travel with the identified carriers.
The corporate client now selects an analysis strategy
using all of that client's data input from the Corporate
Travel Department 310 step or a subset of it (see Fig. 4).
For example, subsets may be selected by the corporate client
based on a range of variables, such as invoice dates (i.e.,
tickets issued between certain dates), geography (i.e.,
tickets issued in certain countries or tickets issued for
travel from, to or through certain airports, cities, coun-
tries or regions, or excepting travel from, to or through
certain airports, cities, countries or regions), or travel
characteristics (i.e., minimum or maximum distance, minimum
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or maximum fare). Specifically, the corporate client's
analysis elections are exercised at the Enter Analysis Data
Selection Parameters 400 step and the selected data is input
to the Analysis Data Selection Parameter 410 database.
At a Backout Contracts and Summarize Data 420 step the
data stored in the Analysis Data Selection Parameters 410
database is combined with data from the Standardized Back
Office Data 300, Standard Airline Commission Rates 340,
Nonstandard Airline Commission Rates 360, and Override Rates
370 databases to:
Calculate the mileage from origin to destination
airport for each segment (A segment is the
movement of one passenger on a flight from one
airport to another. If a traveler flies from
Washington to Chicago, the trip is one segment.
If a person flies from Washington to Seattle and
connects in Chicago, the total Washington-Seattle
trip consists of two segments. If a person flies
from Washington to Chicago, stops over for a
night, and flies to Seattle the next day, the two
days of travel are two segments. A round trip
from Washington to Chicago is two segments.);
~ Eliminate (i.e., collapse) connections between
segments in the database for travel on the same
tickets using standard rules for connection times;
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~ Allocate fares to each actual or collapsed segment
using the following rules:
~ If there is fare data for each segment stored
at Standardized Back Office Data 300 and the
sum of the fares is within 10~ of the total
ticket fare, (i.e., there may be airport tax
or other miscellaneous charges that increase
the total ticket fare) the fare data is used
as is;
~ If fare data is missing for certain segments,
but the sum of the fares for the segments
having designated fares is within 10~ of the
total ticket fare, then the fares for the
segments having designated fares are allo-
Gated pro rata to the segments without
designated fares using mileage and cabin
factors, e.g., fare rates for first, business
or coach cabins; and
If the sum of the segment fares is more than
10~ different from the total ticket fare,
then fares are allocated pro rata to all of
the segments using mileage and cabin factors.
~ Determine which contracts could be applied to each
segment by comparing carrier, date of travel,
airport pair and class-of-service data with data
for contract provisions. If multiple contracts
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are found that might be applied, the one which
results in the largest discount is designated to
be applied.
~ Determine the commission and override discounts
using the contract information stored in the
Standardized Back Office Data 300, the Standard
Airline Commission Rates 340, Nonstandard Airline
Commission Rates 360, and Override Rates 370
databases.
~ If a contract with a point-of-sale discount is
identified as applicable, then, using the contract
information stored in the Standardized Back Office
Data 300 database, the fare that would be paid if
the contract had not been applied is calculated
using the algorithm: Fare That Would Be Paid =
Fare Paid/(1 - Percentage Discount Rate Applied).
~ If a contract with a point-of-sale discount is
identified as applicable, then the commission and
override discounts that would be earned on the
non-discounted fare are determined using data from
the Standard Airline Commission Rates 340, the
Nonstandard Airline Commission Rates 360, and the
Override Rates 370 databases.
~ If a contract with a back-end, (i.e., after sale)
discount is identified as applicable, the amount
of that back-end discount is determined.
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~ If multiple tickets may be issued due to airline
contract rules, connections are eliminated (i.e.,
collapsed) between segments on the separate
tickets having the same passenger name record
("PNR") for the same traveler using rules for con-
nection times that are standard in the industry.
~ The resulting data at this point is then filtered
using the information stored in the Analysis Data
Selection Parameters 410 database to identify the
data relevant to the scenario being analyzed.
~ The determined and filtered data from the previous
step is next categorized by ticketing country,
airport pair, carrier, fare category (first,
business, or full coach, discounted coach, or
"junk" fares), invoice month, month of travel and
this resulting data is stored in the Summarized
Back Office Data 430 database.
~ The calculated, filtered and categorized data from
the Backout Contracts and Summarize Data 420 step
is identified by carrier, and the so identified by
carrier data is then stored in the Summarized
Carrier Data 440 database.
~ The calculated, filtered and categorized data from
the Backout Contracts and Summarize Data 420 step
also is identified by airport pairs, and the so
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identified by airport pair data is stored in the
Summarized Airport Pair Data 450 database.
At this point the data from the Summarized Carrier Data
440 database is presented via Hyper Text Markup Language
("HTML") output, paper printout or other output to the
corporate client, who selects which carriers to include in
the analysis, i.e., see Select Carriers for Analysis 460
step. Data for all carriers included in the Summarized
Carrier Data 440 database that is not selected for use is
collectively identified as "All Other Carriers".
Similarly, the data from Summarized Airport Pair Data
450 database is presented to the corporate client, who
selects the number of airport pairs to include in the
analysis, i.e., see Select Airport Pairs for Analysis 470
step. The number of airport pairs is determined outside the
system by the corporate client, based on that corporate
client's expectation of the number of airport pairs that
will cover a sufficient portion of the travel data for the
desired travel scenario, e.g., 75~ of the corporation's
travel budget for a specified time period. The list of
selected specific airport pairs is that stored in the
Airport Pairs 260 database which served as input for
calculating quality of service indices for each of the
selected airport pairs.
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Average non-discounted fares are determined for each
ticketing country, airport pair and fare category and these
are stored in an Average Fares 490 database.
The data stored in the Summarized Back Office Data 430
database now is processed at the Create Project Baseline 480
step. Specifically, for each ticketing country, airport
pair, carrier, type of service and invoice month combination
included in the Summarized Back Office Data 430, Summarized
Data 440 and Summarized City Pair Data 450 databases, de-
terminations are made at the Create Project Baseline 480
step which identify every airport pair included in the
Summarized Airport Pair Data 450 database that is not
included in the Airport Pairs 260 database. The airport
pairs that are included in both the Airport Pairs 260 and
Summarized Airport Pair Data 450 databases are stored in the
Summarized Baseline Data 500 database. Therefore, the data
for airport pairs stored in the Summarized Baseline Data 500
database is the same as that stored in the Summarized Back
Office Data 430 database except those airport pairs not
included in the Summarized Airport Pairs 260 database have
been explicitly identified as being summarized in total.
Similarly, the data for city pairs stored in the Summarized
Baseline Data 500 database is the same as that stored in the
Summarized Back Office Data 430 database except those city
pairs not included in the Summarized City Pair Data 450
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database have been explicitly identified as being summarized
in total.
The following steps setting up a set of parameters for
making simulation runs (also referred to as scenario
calculations) are now executed.
The corporate client utilizing a preferred embodiment
for the present invention, now uses the data collected at
the Corporate Travel Department 310 step to directly enter
at the Setup Simulation Scenario 510 step of one of three
types of data sets into the Simulation Scenario Parameters
520 database (see Fig. 5). The first type, called a "blank
slate", has no airline contract, preferred carrier, minimum
influence, or other data, i.e., a blank data set. The sec-
and type, called a "current actual environment", includes
data for all existing airline contracts, preferred carriers,
and influence levels. The last type, called "existing sce-
nario", maintains the data already stored in the Simulation
Scenario Parameters 520 database without addition or dele-
tion.
If the corporate client entered a "blank slate" in the
Simulation Scenario Parameters 520 database, then the
corporate client at the Setup Contract Scenario 530 step
identifies information from the Corporate Travel Department
310 step and the Airline Contracts 320 database for a set of
airline contract data for a travel scenario to be analyzed
and inputs that set of data into the Contract Scenario 540
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database. Alternatively, if at this step the corporate
client desires to modify airline contract data previously
entered at the Setup Simulation Scenario 510 step, such
modifications are made and the revised airline contract data
is entered into the Contract Scenario 540 database.
If the corporate client entered a "blank slate" in the
Simulation Scenario Parameters 520 database, then the
corporate client at the Setup Preferred Carrier Scenario 550
step identifies information from the Corporate Travel
Department 310 step for a set of data for preferred carriers
and inputs that set of data into the Contract Preferred
Carrier 560 database. Alternatively, if at this step the
corporate client desires to modify the preferred carrier
data entered at the Setup Simulation Scenario 510 step, such
modifications are made and the altered preferred carrier
data is entered into the Preferred Carrier Scenario 560
database.
If the corporate client entered a "blank slate" in the
Simulation Scenario Parameters 520 database, then at the
Setup Airport Pair User Defined Trip Distribution 570 step,
the corporate client enters estimated share numbers for each
included carrier servicing the selected airport pairs into
the User Defined Trip Distribution 580 database. To do this
the corporate client defines trip distributions to specify
desired results for a particular airport pair. Alterna-
tively, if at the Setup Airport Pair User Defined Trip
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Distribution 570 step, the corporate client desires to make
data modifications, such modifications are made and the
altered predicted share numbers are entered into the User
Defined Distribution 580 database.
If the corporate client entered a "blank slate" in the
Simulation Scenario Parameters 520 database, then at the
Setup Airport Erosion Scenario 590 step, the corporate
client enters airport predicted erosion scores into an
Airport Erosion Scenario 600 database which represents an
estimate of the percentage of corporate travelers that would
be willing or able to move their travel departures or
arrivals to another airport. Alternatively, if at the Setup
Airport Erosion Scenario 590 step, the corporate client
desires to make data modifications, such modifications are
made and the altered airport erosion scores are entered into
the Airport Erosion Scenario 600 database.
At this point data previously entered into the
following databases is applied to the data stored in the
Summarized Baseline Data 500 database to calculate predicted
simulation parameter values at the Run Scenario Simulation
610 step for the travel scenario formulated by the collected
data: (see Fig. 6)
Simulation Scenario Parameters 520;
Contract Scenario 540;
Preferred Carrier Scenario 560;
User Defined Trip Distribution 580;
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Airport Erosion Scenario 600; and,
Quality of Service Index 250 databases.
It is the following calculations that are now executed
at the Run Scenario Simulation 610 step for the ticketing
country, airport pair, type of service and month data that
has been entered into the Summarized Baseline Data 500
database.
First, the preferred and non-preferred carriers are
identified for the specified airport pairs using the
Preferred Carrier Scenario 560 database. A raw predicted
share is determined for the preferred carriers as a group
using the sum of the quality of service index values for
each of those carriers. A predicted share also is
determined for the non-preferred carriers as a group using
the sum of the quality of service index values for each of
those carriers.
Using an input influence level that has been specified
for the scenario being run, a predicted share value is
determined from one of multiple curves defined by formulae
incorporated and utilized at the Run Scenario Simulation 610
step. For a preferred embodiment, there are eleven such
curves, each of which includes two or three straight line
segments. The two straight line curves include initial
straight line segments that linearly run from the origin
point (O~quality of service index value, 0~ predicted share
value) to initial inflection points, and second straight
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line segments that run from the first inflection points to
the final point (100 quality of service index value, 100
predicted share value). For the three straight line curves,
an initial straight line segment runs from an origin point
(0~ quality of service index value, 0~ predicted share
value) to a first inflection point, a second straight line
segment runs from the first inflection point to a second
inflection point, and a third straight line segment runs
from the second inflection point to the final point
(100~squality of service index value, 100 predicted share
value). Other embodiments of the invention can use curves
having other numbers of straight line segments, non-linear
curves, or combinations of straight line segments and non-
linear curves adjusted to fit real world or extrapolated
data .
The inflection points for the predicted share value
curves for a preferred embodiment of the present invention
are as follows:
~ In the cases of preferred carriers with influence
level values ranging from 1 through 4, the
predicted share value curves have single
inflection points, but in the case of preferred
carriers with an influence level value of 5 the
predicted share value curve has two inflection
points. The inflection point values for these
curves are set out in Table I below:
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Table 1
Preferred Carrier Inflection Point Values
Predicted Share Value
Influence Level Curve Inflection Poiats
Value At:
1 (60~ quality of service
index value, 66~
predicted share value)
2 (60$ quality of service
index value, 72~
predicted share value)
3 (60~ quality of service
index value, 84~
predicted share value)
4 (45~ quality of service
index value, 84~
predicted share value)
5 (10~ quality of service
index value, 17~
predicted share value)
and (25$ quality of
service index value,
84~ predicted share
value)
~ In the case of non-preferred carriers with
influence level values ranging from 1 through 4,
the predicted share value curves have single
inflection points, but in the case of a non-
preferred carrier with an influence level value of
5 the predicted share value curve has two
inflection points. The inflection point values
for these curves are set out in Table II below:
Table II
Non-Preferred Carrier Inflection Point Values
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Predicted Share Value
Influence Level G~rve Inflection Points
Value At:
1 (40~ quality of service
index value, 34~
predicted share value)
2 (40~ quality of service
index value, 28~
predicted share value)
3 (40~ quality of service
index value, 16~
predicted share value)
4 (555 quality of service
index value, 16~
predicted share value)
5 (75~ quality of service
index value, 16~
predicted share value)
and (90~ quality of
service index value,
83~ predicted share
value )
These predicted share values for the group of preferred
and non-preferred carriers are next distributed to the
respective individual carriers in proportion to each
carrier's share of the total quality of service index values
for the group.
After all of the predicted share values have been
calculated for the input ticketing countries, airport pairs,
carriers and months, the data excluded up to this point on
the basis of airport erosion values is introduced and
predicted share values are now calculated for the input
airport pairs in accordance with the above-described
processes.
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The predicted share values for each carrier that have
quality of service index scores above a threshold level that
is set by the corporate client (e.g., the U.S. Department of
Transportation recommends 10$ and for a preferred embodiment
a value of 5~ was effectively used) are calculated for the
input airport pairs and months.
The predicted share values are next multiplied by the
corresponding segment total values and average fare amounts
(incorporating point-of-sale discounts, contract discounts,
commission discounts and override amounts) stored in the
Summarized Baseline Data 500 database.
The calculated predicted share output values are
further processed at the Run Scenario Simulation 610 stage
so as to be categorized by airport pair, carrier, type of
service and month for input to the Scenario Output 620
database from which the corporate client can read the
results of the simulation run.
For each ticketing country, airport pair, type of
service and month, the average fare from the Average Fare
490 database is multiplied by the number of segments to
determine an estimated non-discounted fare that would be
charged. The appropriate airline contract, if any, is
determined from the Contract Scenario 540 database, and the
point of sale discount, if any, is then directly calculated.
Commission and override amounts are determined from,appro-
priate airline contract, standard and company specific
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airline commission and override rates. The back end
discount, if any, is calculated. Each of these -- the point
of sale discount, commission, override amount and back end
discount -- are multiplied by the number of segments and
output with the ticketing country, airline pair, type of
service and month from the Scenario Output 620 database.
The above described preferred embodiment of the present
invention can repeatedly be used to construct scenarios
having input defining parameter values corresponding to
those of interest to a corporate travel manager, and to
calculate predicted travel parameter values for the
different scenarios. Such determinations can then be used
by the corporate travel manager to evaluate the feasibility
and desirability of implementing one or more of the studied
travel scenarios.
Those skilled in the art will recognize that the method
and apparatus of the present invention has many
applications, and that the present invention is not limited
to the representative examples disclosed herein. Moreover,
the scope of the present invention covers variations and
modifications to the systems and methods described herein,
as would be known by those skilled in the art.
39
