Note: Descriptions are shown in the official language in which they were submitted.
Attorney Docket No. 017271-0011
METHOD FOR CREATING A DATA INPUT FILE FOR INCREASING THE EFFICIENCY OF THE
AVIATION
ENVIRONMENTAL DESIGN TOOL (AEDT)
FIELD
[0001] This disclosure relates to the field of noise pollution analysis and
remediation. More
particularly, this disclosure relates to determining noise contours caused by
aircraft around
airports.
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BACKGROUND
[0002] Airports and Air Navigation Service Providers ("ANSPs", such as the US
Federal
Aviation Administration (FAA)) have a regular need to calculate the noise
"contours" around
airports. These contours define the specific noise impact around an airport,
neighborhood by
neighborhood, street by street. The annual average decibel level of impact at
a given location is
a critical piece of information needed by airports and ANSPs because
government programs are
in place to pay for noise mitigation measures depending on the decibel levels
from aircraft noise
pollution in a given location. Large financial decisions are made based upon
correct calculation
of this information.
[0003] The FAA has created a software program called the Aviation
Environmental Design Tool
(AEDT) to help calculate these noise contours based on an input of "flight
track" data, which is
information about what types of aircraft have departed and arrived at an
airport and the precise
path and altitudes of each track. More specifically, flight track data is
information about a flight
(such as the aircraft/engine type, whether it was a departure or an arrival,
what runway it used)
and then each of the points of that departure or arrival (where each point
contains four
components: time/latitude/longitude/altitude).
[0004] Typically, airports and ANSPs are required to generate an annual
contour including noise
contour data which is often represented as a series of polygons surrounding an
airport indicating
the average noise exposure level across the year for areas surrounding the
airports. Different
features and complexities exist in tailoring these contours, including the
decibel ranges (e.g., 65
db, 70 db, 75 db), the type of decibel levels used (e.g., "A-weighted"
decibels to approximate
how the human ear reacts to noise), and adjustment for evening and night time
hour noise as
those have greater impact on residents. AEDT is not only used to generate the
contours of noise
based on existing operations at an airport, but also projected future contours
based on predicted
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air traffic plans, or ad-hoc contours based on proposed ideas such as adding a
runway or
changing where aircraft will fly near an airport.
[0005] Consultants use AEDT to generate contours for airports and ANSPs, but
AEDT is
cumbersome and considerably slow to use. AEDT requires special expertise plus
patience in
waiting for the software to run. Nonetheless, AEDT is the de facto standard
for accurate noise
contour results. AEDT runs slower the more "flight tracks" are fed into it.
For example, if an
airport as 200,000 flight operations a year, and a user wishes to generate a
contour representative
of the use at that airport, the user would feed 200,000 flight tracks into the
AEDT software and
wait many hours or even days for the software to run and generate contours.
[0006] What is needed, therefore, is a method to reduce the wait time when
using AEDT while at
the same time, ensuring that the resulting noise contours are accurate.
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SUMMARY
[0007] The above and other needs are met by a method to minimize the number of
flight tracks
that are input to AEDT with the AEDT result still being substantially the same
as if all flight
tracks were input to AEDT. For the example described above, the method would
select a fraction
(e.g., 10%) of the 200,000 flight tracks occurring during a year but the
selection is made to
ensure that the AEDT output is substantially the same as if all 200,000 flight
tracks were input to
AEDT. The method reduces data not merely via random sample, but by several key
processes
that allow the method to generate a sample that accurately represents the
data. This is based on
knowledge of AEDT built into the method that will ultimately provide a random
sample that
AEDT will consider equivalent to the full data set.
[0008] The method described herein includes the ability for a user to specify
many parameters to
assist in creating the most accurate reduced data set. This flexibility is
necessary to account for
differences at different airports, and differences in the types of aircraft
that operate at each
airport (and what times of day they fly). Times of day are important because
the results that
come out of AEDT are sensitive to aircraft flying at different times of day.
Optimization
combined with the ability to automate intelligent customization could save
untold amounts of
time and money.
[0009] In a preferred embodiment, a method of generating an input file in a
computable readable
format for use with the Aviation Environmental Design Tool (AEDT) to increase
the efficiency
of the AEDT includes the steps of: (a) running a software program on a
computer wherein the
software program is stored on a computer readable medium in communication with
the computer
and wherein the software program includes an algorithm for performing specific
steps; (b)
inputting user input data to the computer using a graphical user interface in
communication with
the computer, wherein the user input data comprises information in the form of
values related to
specific variables associated with flights at a specific airport; (c)
determining a plurality of
unique factors using the computer based on the user input data, wherein each
unique factor
comprises a unique combination of variables from the user input data; (d)
accessing a database in
communication with the computer wherein the database includes flight track
data stored thereon,
such flight track data including data related to flight tracks of aircraft
arriving and departing at a
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specific airport for a specific period of time; (e) querying the database
using the computer to
determine how many aircraft arrivals or departures meet each unique factor
combination of
variables for each unique factor; (f) running the algorithm to perform the
steps of: (1) calculating
an exact number of flight tracks to query the database for each unique factor;
and (2) determining
weight values to assign to the flight tracks for each unique factor; (g)
querying a random sample
of flight tracks in the database for each unique factor based on the
calculated exact number of
flight tracks and the determined weight values; and (h) generating an input
file comprising all
flight track data associated with the random sample of flight tracks queried
by the computer.
[0010] The method preferably further includes an initial step of collecting
flight track data and
storing such flight track data in a database in communication with the
computer.
[0011] The step of inputting user input data may further include inputting
user input data in the
form of weight values for specific variables wherein more weight is given to
queried data that
meets specific criteria.
[0012] The step of accessing the database may further include detecting and
adjusting anomalies
in the flight track data.
[0013] The method may further include the step of determining an ideal number
of flight tracks
for selection based on the user input data including a variable of average
daily flight operations
multiplied by a variable of number of days for such flight operations.
[0014] The method may further include the steps of: querying the database to
determine the total
number of aircraft departures and arrivals by aircraft type for a specified
time period; and
creating a departure weight value based on the total number of aircraft
departures and arrivals
equaling total number of arrivals divided by the total number of departures
during the specific
time period; creating an arrival weight value based on the total number of
aircraft departures and
arrivals equaling total number of departures divided by the total number of
arrivals during the
specific time period; and adjusting a percentage of aircraft arrivals versus
departures using the
algorithm by factoring in the departure weight and the arrival weight.
[0015] In another aspect, embodiments of the disclosure provide a method of
generating an input
file in a computable readable format for use with the Aviation Environmental
Design Tool
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Attorney Docket No. 017271-0011
(AEDT) to increase the efficiency of the AEDT, the method comprising the steps
of: (a)
receiving user input data on a computer, the user input data including values
related to specific
variables associated with flights at a specific airport; (b) determining a
plurality of unique factors
using the computer based on the user input data, wherein each unique factor
comprises a unique
combination of variables from the user input data; (c) accessing a database in
communication
with the computer wherein the database includes flight track data stored
thereon, such flight
track data including data related to flight tracks of aircraft arriving and
departing at a specific
airport for a specific period of time; (d) querying the database using the
computer to determine
how many aircraft arrivals or departures meet each unique factor combination
of variables for
each unique factor; (e) calculating an exact number of flight tracks to query
the database for each
unique factor; (f) determining weight values to assign to the flight tracks
for each unique factor;
(g) querying a random sample of flight tracks in the database for each unique
factor based on the
calculated exact number of flight tracks and the determined weight values; and
(h) generating a
file comprising all flight track data associated with the random sample of
flight tracks queried by
the computer.
[0016] In another aspect, embodiments of the disclosure provide a method of
processing an input
file in a computable readable format for use with the Aviation Environmental
Design Tool
(AEDT), the method comprising the steps of: (a) running AEDT software on a
first computer;
and (b) processing an input file using the first computer running the AEDT
software wherein the
input file is in a computable readable format which is compatible with the
AEDT software and
wherein the input file was created using the steps of: (1) determining a
plurality of unique factors
using a second computer based on user input data, wherein each unique factor
comprises a
unique combination of variables from the user input data; (2) accessing a
database in
communication with the second computer wherein the database includes flight
track data stored
thereon, such flight track data including data related to flight tracks of
aircraft arriving and
departing at a specific airport for a specific period of time; (3) querying
the database using the
second computer to determine how many aircraft arrivals or departures meet
each unique factor
combination of variables for each unique factor; (4) calculating an exact
number of flight tracks
to query the database for each unique factor; and (5) determining weight
values to assign to the
flight tracks for each unique factor. The input file may comprise all flight
track data associated
with the random sample of flight tracks queried by the second computer.
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[0017] In some embodiments, the first computer comprises the second computer.
[0018] The method preferably further includes a step of producing a noise
contour file based on
the processed input file wherein the noise contour file includes noise contour
data that is
substantially the same as noise contour data that would have been produced
using the AEDT
software if all flight tracks from the database had been included in the input
file.
[0019] The summary provided herein is intended to provide examples of
particular disclosed
embodiments and is not intended to cover all potential embodiments or
combinations of
embodiments.
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BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Further features, aspects, and advantages of the present disclosure
will become better
understood by reference to the following detailed description, appended
claims, and
accompanying figures, wherein elements are not to scale so as to more clearly
show the details,
wherein like reference numbers indicate like elements throughout the several
views, and
wherein:
[0021] FIG. 1 shows a schematic of a system for accomplishing embodiments of
the methods
described herein;
[0022] FIG. 2 shows a schematic of a computer system on which the present
invention may be
implemented;
[0023] FIG. 3 shows a flow chart including method steps used for accomplishing
embodiments
of the method described herein; and
[0024] FIG. 4 shows a flow chart of an algorithm used in the method steps
listed in FIG. 3.
[0025] The figures are provided to illustrate concepts of the invention
disclosure and are not
intended to embody all potential embodiments of the invention. Therefore, the
figures are not
intended to limit the scope of the invention disclosure in any way, a function
which is reserved
for the appended claims.
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DETAILED DESCRIPTION
[0026] Various terms used herein are intended to have particular meanings.
Some of these terms
are defined below for the purpose of clarity. The definitions given below are
meant to cover all
forms of the words being defined (e.g., singular, plural, present tense, past
tense). If the
definition of any term below diverges from the commonly understood and/or
dictionary
definition of such term, the definitions below control.
[0027] FIG. 1 shows a basic embodiment of a system 100 for creating a data
input file for
increasing the efficiency of the Aviation Environmental Design Tool (AEDT).
The system 100
includes a computer 200 which is shown in more detail in FIG. 2. The system
100 including the
associated computer 200 follow method steps described herein and shown in FIG.
3 and FIG. 4
to prepare an input data file that can be fed to the AEDT to increase the
efficiency of the AEDT
by reducing the amount of data fed to a computer running AEDT software.
However, the method
is accomplished in such a way that the resultant reduced data set causes the
AEDT to output
substantially the same output data as if a full (non-reduced) data set had
been input to the
computer running the AEDT software. Because less data is input to the AEDT, it
runs faster and,
therefore, more efficiently. (When referring to "the AEDT", it should be
understood that AEDT
is a program stored on a computer readable medium and run on a computer.)
[0028] In order to obtain a reduced dataset that will cause the AEDT to output
accurate results, a
software program 102 stored on a computer readable medium in communication
with or
otherwise stored on the computer 200 is provided. In order to run the software
program 102,
flight track data must be gathered from one or more sources. Such data is then
saved to one or
more databases 104A on a local network in communication with the computer 200,
one or more
databases 104B on the cloud or generally on the Internet in communication with
the computer
200, one or more databases 104C on the computer 200 itself, or a mobile data
storage device 106
(e.g., a USB stick) (hereinafter, collectively, "the database 104"). Data can
also be entered
through a graphical user interface (GUI) 108 in communication with the
computer 200. The
gathered flight track data is usually in the form of radar data feeds that
provide, for each flight
track, the latitude, longitude, and altitude of every point in the flight
track along with the exact
time of that point. The gathered data also preferably contains information
about the aircraft type
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that was flown for each track, whether the flight was an arrival or departure,
and the
origin/destination airport. After further method steps, a resultant reduced
data set is created
which can be stored on the computer 200 or sent to be stored on the cloud 110,
a mobile storage
device 112 (e.g., a USB stick), or another computer or database 114.
[0029] FIG. 2 shows an example of the computer 200 that can be used to
accomplish certain
steps of various embodiments of the method described herein. The computer
includes a central
processing unit ("CPU") 202 and memory 204 in the form of computer-readable
memory, such
as random access memory ("RAM") and read-only memory ("ROM"). Program
information or
data is stored on the memory 204 and may include operating system code for the
computer 200
and application code for applications operable on the computer 200. The
computer 200 may
further include primary and secondary storage 206, such as optical disk
storage and/or magnetic
disk storage. Program information and other data may also be stored on the
secondary storage
206.
[0030] The computer 200 includes a network connection means 208 for
communicating with a
network, such as a local area network ("LAN") or the Internet. The computer
200 further
preferably includes one or more input devices 210, such as a keyboard, mouse,
scanner,
touchscreen, voice input, and other various devices for receiving input on the
computer 200.
Input is received on the computer 200 through the one or more input devices
210, such as text,
images, graphics, and other various inputs. The computer 200 further includes
one or more
output devices such as a display 212 for receiving output from a video adapter
214 of the
computer 200. Other various output devices may include, for example, a
printer, sound output,
video output, and other various outputs.
[0031] With reference to FIG. 3, a first step 300 of a preferred embodiment of
the method
includes gathering flight track data. The flight track data is saved to the
database 104. An
optional second step 302 includes detecting and adjusting anomalies in the
flight track data. This
step 302 can be thought of as a step to "clean up" the flight track data by
detecting clearly
incorrect data points and smoothing out the data using a data smoothing
algorithm such as, for
example, a Smoothing Spline or Local Polynomial Regression. In addition, and
most importantly
for AEDT, the flight track data is sometimes incomplete in that the tracks do
not fully reach a
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runway. In these cases, an additional track segment is preferably created to
attach the particular
flight to a runway. This can be accomplished manually through the graphical
user interface 108
or automatically by the software program 102. In difficult cases (e.g., the
track data is missing
too many points and is too far from an airport), the particular flight track
data featuring the
anomaly is preferably ignored by the software program 102. For example, the
program would
consider a minimum distance from the airport for a track to be valid, and if
it is the software
would simply add a synthetic track point on the runway connecting it to the
first point, with a
time value calculated based on the computed speed of the flight. Each track
sent to AEDT in the
resultant reduced data set is "weighted". For example, if a particular flight
track has a weight of
"2", that means the resultant reduced data set sent to AEDT causes AEDT to
pretend there are
two tracks in the input that are identical to this single flight track. AEDT
can process a single
flight with a weight of "2" faster than it can process two identical tracks
each with a weight of
"1". However, in both cases, the resulting output data from AEDT will be the
same. The goal is
to reduce a number of flight tracks, setting weight values accordingly.
[0032] Another step 304 of a preferred embodiment of the method includes
collecting user input
data. This is accomplished using the graphical user interface 108. Table 1
provided below
includes a first column of variables discussed in more detail herein, a second
column of example
values, and a third column providing some description of the particular
variable.
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TABLE 1
Required Variables Example Values Description
Name JFKO1 Arbitrary name for this study
Database JFKAirport Database name, typically the airport
StartDate Jan 1, 2017 First Date of data
EndDate Dec 31, 2017 Last Date of data (typically a year after
Start)
How may flight operations (arrivals or departures) this airport
AvgDaily0ps 300 typically has in a day
Number of days to reduce data to. E.g., 36.5 days means approx
Nunn0fDays 10 1/10th the data queried than a full 365 days
Minimum number of tracks to query for each unique combination
MinTracksPerFactor 5 .. of factors, to make sure we have an
accurate representation
Maximum number of tracks to query for each unique combination
MaxTracksPerFactor 300 .. of factors, so we don't waste time
querying more than we need
Force departure and arrival counts to be equal for each unique
DepArrEqual Yes aircraft type
Optional Variables Example Values Description
FactorArrDep Yes Factor based on Arrival/Departure
FactorAirport No Factor based on Airport
FactorRunway Yes Factor based on Runway name
FactorDEN Yes Factor based on DEN (Day/Evening/Night)
FactorAirlineClass No Factor based on Airline Class
FactorAircraftType No Factor based on Aircraft Type
FactorAircraftClass No Factor based on Aircraft Class
FactorAircraftClass2 No Factor based on Aircraft Class2
FactorProcedure No Factor based on Procedure
FactorINMTrack No Factor based on INM Track
FactorINMType Yes Factor based on INM Type (Exact model of
aircraft)
ExtraWeight1 DEN,N,2 Given additional weight to flights that match
this field and value.
ExtraWeight2 AircraftClass2,J,2 Given additional weight to flights that
match this field and value
ExtraWeightN Given additional weight to flights that match
this field and value
FilterArrDep Restrict to just data to just Arrivals or
Departures
FilterAirport JFK Restrict to just this airport
FilterRunway 4L,4R,13L,13R Restrict to one or more runways
FilterDEN Restrict to Day, Evening or Night
FilterAirlineClass Restrict to a particular Airline Class
FilterAircraftType Restrict to a particular Aircraft Type
FilterAircraftClass Restrict to a particular "Aircraft Class"
FilterAircraftClass2 Restrict to a particular "Aircraft Class2"
FilterProcedure Restrict to a particular flight procedure
FilterINMTrack Restrict to a particular INM Track association
FilterINMType Restrict to a particular INM Type (Exact model
of aircraft)
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[0033] A user manually inputs the user input data to the computer 200. Of
special note is the
ExtraWeightl, ExtraWeight2,
ExtraWeightN values (there can be as many ExtraWeight
values as the user wishes to specify). This feature is important because it
allows a user to place
more emphasis or "weight" on a particular variable. For example, the
ExtraWeightl variable in
Table 1 is set so that the subcategory of "N" (Night) from the category of
"DEN" (Day-Evening-
Night) is given a weighted value of "2" instead of 1. As such, more emphasis
will be placed on
flights that fit this category (i.e., flights that arrive or depart during the
"N" (Night) period). Note
that this added emphasis does not mean that the AEDT results will be skewed
via consideration
of more "N" (Night) flights than is proportionally represented in the flight
track data, but instead
it is used to ensure that enough of a statistical sample is gathered for this
category so that there is
a sufficient representational set of track points for this category.
[0034] Step 306 includes balancing the number of arrivals and departures per
aircraft type. The
variable "INM Type" represents the unique aircraft type and model. In theory,
each INM Type
should have the same number of arrivals and departures for the selected
timeframe (e.g., a year)
at a given airport. But the flight track data may be missing information, and
such information
needs to be balanced out if information is missing. This is accomplished by
selecting (e.g., via
SQL query to the database 104) the count of unique "Departure or Arrival" and
INM Type
combinations over a selected period of time (e.g., a year). The results of the
search will provide
two new variables per INM Type including "depWeight" and "arrWeight".
"depWeight" is the
departure weight and is set to "1" unless there are less departures than
arrivals for this INM
Type, in which case it is set to #arrivals / #departures for this INM Type.
"arrWeight" is the
arrival weight and is set to "1" unless there are less arrivals than
departures for this INM Type, in
which case it is set to #departures / #arrivals for this INM Type. For
example, if for a given INM
Type, there are 10 departures and 7 arrivals, then depWeight will be "1" and
arrival weight will
be 10/7 = 1.42857. The depWeight and arrWeight values are used later in
further calculations.
[0035] Step 308 includes determining unique factors and row counts per unique
factor. A key to
reducing the amount of data sent to the AEDT is to query "counts" of
information across the
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entire selected time period (e.g., a year) for "unique factors" of
information, the results of which
are used to generate more specific queries returning a reduced amount of data.
[0036] A "unique factor" is a set of values for a specific combination of
variables specified in
the user input (Step 304). For example, if a user specified three variables
including "ArrDep"
(Arrival, Departure), "Runway", and "DEN" (aka Day, Evening or Night), there
would
necessarily be a minimum number of unique factors. Every flight must be an
arrival or departure
(2 values), must use a runway, and must depart or arrive in the day, evening
or night (3 values).
If there are two runways (2 values) at the specified airport (01 and 19), in
this scenario, there
would be 12 unique factors. All 12 combinations are shown in Table 2 below.
Table 2
Arrival, Runway 01, Day
Arrival, Runway 01, Evening
Arrival, Runway 01, Night
Arrival, Runway 19, Day
Arrival, Runway 19, Evening
Arrival, Runway 19, Night
Departure, Runway 01, Day
Departure, Runway 01, Evening
Departure, Runway 01, Night
Departure, Runway 19, Day
Departure, Runway 19, Evening
Departure, Runway 19, Night
[0037] There may be far more than 12 unique factors, and a number of unique
factors may
depend on how many variables are chosen by the user in the user interface. In
fact, INM Type (or
INM TYPE) must be added as a factor so that all the different aircraft types
passing through an
airport can be considered. When INM TYPE is added to the above example, then
the number of
unique factors would be equal to 12 times the number of unique aircraft models
that flew at this
airport during the specified time period.
[0038] All the unique factors and the row counts (i.e., how many flight
operations there were in
the time period) for that unique factor set can be determined by performing an
SQL Query on the
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database 104. A sample query might look like the following, for the above
example factors +
INM Type for the year 2017:
SELECT RUNWAY, DEN, INMTYPE ID, COUNT(*) as CNT FROM FLIGHT DATA
WHERE datetime da BETWEEN '2017-01-01 00:00:00' AND '2017-12-31 23:59:59'
AND RUNWAY IN (' 01, ' 19' )
GROUP BY RUNWAY,DEN,INMTYPE ID
ORDER BY RUNWAY,DEN,INMTYPE ID
[0039] The result of this query will provide a count of flight operations
throughout the year for
each unique set of factors that have been selected. This will serve as the
basis for how to query a
much smaller amount of data but keep similar proportions to what occurred
throughout the year.
Note that in this query the value of two new variables that will be used below
in further steps can
be determined. Those new variables include the following:
InmTypeTotDep = Total # of departures for this INM Type
InmTypeTotArr = Total # of arrivals for this INM Type
TotalNumTracks = total number of flight tracks for the year for this airport
[0040] "FLIGHT DATA" is broadly defined as a table of all flight tracks stored
on the database
104, such data having different columns for different characteristics of the
data (e.g., aircraft
type, arrival/departure, Day-Evening-Night (DEN)).
[0041] A next step 310 includes determining the ideal number of flight tracks
("IdealNumTracks") to use with the AEDT. In other words, this step is to
identify how many
tracks are to be queried under ideal circumstances to arrive at substantially
the same output using
the AEDT as if all flight tracks had been put into the AEDT. This is
accomplished based on input
given in Step 304, namely "AvgDaily0ps", i.e., the number of operations
(departures + arrivals)
the particular airport has in a typical day, and "Num0fDays", i.e., the ideal
number of days for
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the calculation to be minimized to. This will provide an ideal number of
flight tracks to be
created. This calculation is made using Equation 1 below:
Equation 1: IdealNumTracks = AvgDaily0ps * Num0fDays
[0042] For example, if it is known that an airport has 2,000 operations per
day, and a goal is to
use 5 days' worth of data, then about 10,000 flight tracks should be sent to
the AEDT. If the
specified airport has 100,000 operations per year, then about 10% of the
flight tracks from the
database 104 need to be queried. Note that in this example the use of
"approximately" 10,000
tracks¨it can be plus or minus some flight tracks to account for certain
factors and make sure
the data is representational of all 100,000 flight tracks.
[0043] Step 312 includes determining the number of flight tracks to query and
the "weight" to be
allocated to each track belonging to each particular unique factor. Step 312
takes all the
information obtained or calculated in prior steps to determine two variables
per unique factor as
follows:
ExactNumTracks: the number of flight tracks to query for this unique factor
WeightFinal: the weight value to be sent to AEDT for each track belonging to
this unique factor
[0044] For example, a unique factor might be as follows: Runway 01, Evening,
Arrival, INM
Type 32
[0045] In this example, through the course of the year, there were 500 flights
matching that
unique factor. Based on previous calculations, the goal is to query a 10%
random sample of the
data, so ExactNumTracks might be 50, and the WeightFinal would be 0.1, meaning
these 50
tracks represents 0.1 times the 500 tracks available for the year for this
unique factor. In reality,
however the process is not so simple because of the complex nature of getting
the most exact
representation and weighting, based not only on the data but on a user's
desire to give extra
weight to certain variables. This complexity is represented in the steps shown
in FIG. 4 which
are discussed in more detail below regarding Step 312.
[0046] Step 314 includes querying a random sample of data on the database 104
for each unique
factor. For each unique factor, calculations have been made to determine the
ExactNumTracks to
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Attorney Docket No. 017271-0011
query and the WeightFinal value to use for the AEDT. At this point, a random
sample of data
must be queried that meets each unique factor criteria, such that the random
sample returns
ExactNumTracks rows. To do this most efficiently, the first query includes the
following:
SELECT OPER ID from FLIGHT DATA where... (data meets a specific unique factor
and
date range)
[0047] OPER ID is a unique number for every flight track in a data table of
flight track data
generated by the query listed above. This list of OPER IDs is stored in memory
204 in the
code's variables and then, using the software program 102 in conjunction with
the computer 200,
OPER IDs are randomly selected from the list until the computer 200 has
ExactNumTracks of
the OPER IDs stored in memory 204 in the code's variables. For example, if the
particular
unique factor being queried has 500 tracks during the applicable year, the
SELECT statement
above will return 500 OPER ID values. But if ExactNumTracks is 50, then the
software
program 102 on the computer 200 randomly selects 50 of those OPER IDs. Once
the set of 50
random OPER IDs are stored, a query is conducted on the database 104 for ALL
columns in the
FLIGHT DATA table for just those OPER IDs, thus providing all the flight track
data required
for that specific unique factor. Each row of data returned is assigned a
"weight", found in a
column called WEIGHT which is the WeightFinal value for this unique factor.
The selected
flight track data and WeightFinal values are stored in memory 204 in the
code's variables. Step
314 is preferably repeated for every unique factor.
[0048] A final step 316 involves creating an AEDT input file¨the file that is
substantially
smaller than the file of all flight tracks that would otherwise have been used
but for the software
program 102. At this point, there are rows of flight track data and associated
WEIGHT values
that have been stored. The flight track data is translated into the format
required by AEDT which
is generally an XML file format with specific tags, such format known to
persons having
ordinary skill in the art. The formatted file includes the random sample of
flight track data, with
each flight track containing a unique WEIGHT value to represent how many times
each
particular track should be counted by the AEDT. WEIGHT values need not be
whole numbers.
The system 100 can calculate a WEIGHT value of, for example, 5.573, meaning
that that
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Attorney Docket No. 017271-0011
particular flight track should be considered to have occurred 5.573 times
during the selected time
period (typically a year).
[0049] With reference back to Step 312 and FIG. 4, a first sub step 400 of
step 312 is calculating
an initial value for percentage of tracks to query ("PercInitial") defined
below in Equation 2.
This is done by dividing the ideal number of tracks ("IdealNumTracks")
calculated in step 310
using Equation 1.
Equation 2: PercInitial = IdealNumTracks / TotalNumTracks
[0050] Next, substep 402 includes calculating the total number of tracks of
arrivals and
departures ("TotalTracksAD") defined below in Equation 3.
Equation 3: TotalTracksAD = TotalNumTracks / 2
[0051] Substep 404 includes calculating an adjusted value for the percentage
of arrivals and
departures ("PercAdjDepArr") calculated using Equation 4 below. "depWeight"
and "arrWeight"
were given in Step 306.
Equation 4: PercAdjDepArr = PercInitial * dep Weight * arrWeight
[0052] Substep 406 includes creating UserWeights for each ExtraWeight input
value and
assigning values to the UserWeights. The ExtraWeight input was created/input
in Step 304. The
value of each UserWeight should be equal to "1" if there is no match with that
variable or the
given weight value if there is a match. For example, if a user added an
ExtraWeight of "2" when
DEN = "E", then if the applicable unique factor has a DEN value of "E" the
UserWeight would
be set to "2". If not, the UserWeight would be set to "1". There should be one
UserWeight
created for each ExtraWeight that the user entered in Step 304. For example,
if there is a
ExtraWeightl and an ExtraWeight2, there should also be a UserWeightl and
UserWeight2.
[0053] Substep 408 includes calculating the Total User Weight
("TotalUserWeight") for the
unique factor wherein Total User Weight equals all of the UserWeight values
multiplied
together. In an example in which there is a UserWeight1 of 1.3 and a
UserWeight2 of 2, the
Total User Weight would be 2.6.
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Attorney Docket No. 017271-0011
[0054] Substep 410 includes calculating the percentage of adjusted weights
("PercAdjWeights")
based on the adjusted percentage of arrivals and departures and the Total User
Weight using
Equation 5 below.
Equation 5: PercAdj Weights = PercAdjDepArr * (1 / TotalUserWeight)
[0055] Substep 412 includes calculating an adjusted number of tracks
("NumTracksAdj") which
equals the total number of tracks ("TotalNumTracks") multiplied by percentage
of adjusted
weights ("PercAdjWeights").
[0056] At this point, in substep 414, the adjusted minimum number of tracks
("NumTracksAdjMin") is set equal to the adjusted number of tracks
("NumTracksAdj")
calculated in substep 412. At substep 416, a determination is made as to
whether the adjusted
minimum number of tracks ("NumTracksAdjMin") is less than the minimum number
of tracks
per factor ("MinTracksPerFactor"). If so, in substep 418, the adjusted minimum
number of tracks
("NumTracksAdjMin") is set equal to the minimum number of tracks per factor
("MinTracksPerF actor").
[0057] Next, at substep 420, the adjusted percentage of minimum tracks
("PercAdjMin") is
calculated according to Equation 6. The purpose of this equation is to take
the current
"percentage of tracks to use" value for this unique factor and adjust it based
on a possible change
in number of tracks due to not reaching a minimum. PercAdjMin is fed into the
calculation for
PercAdjFinal in a later substep described below shown in Equation 7.
Equation 6: PercAdjMin = PercAdj Weights * (NumTracksAdj / NumTracksAdjMin)
[0058] At substep 422 the adjusted maximum number of tracks
("NumTracksAdjMax") is set
equal to the adjusted minimum number of tracks ("NumTracksAdjMin"). In substep
424, a
determination is made as to whether the adjusted maximum number of tracks
("NumTracksAdjMax") is greater than maximum number of tracks per factor. If
so, in substep
426, the adjusted maximum number of tracks ("NumTracksAdjMax") is set equal to
the
maximum number of tracks per factor ("MaxTracksPerFactor").
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Attorney Docket No. 017271-0011
[0059] At this point, in substep 428, a final adjusted percentage of tracks
("PercAdjFinal") is
calculated using Equation 7 below. The purpose of this equation is to take the
current
"percentage of tracks to use" value for this unique factor and adjust it based
on a possible change
in the number of tracks due to having too many tracks (i.e., greater than the
maximum per
factor). PercAdjFinal is later fed into the calculation at the next step for
FloatNumTracks.
Equation 7: PercAdjFinal = PercAdjMin * (NumTracksAdjMin / NumTracksAdjMax)
[0060] In substep 430, "FloatNumTracks" is calculated by multiplying the total
tracks of arrivals
and departures ("TotalTracksAD") by the adjusted final percentage of tracks
("PercAdjFinal").
FloatNumTracks is the number of tracks to be queried for this unique factor,
taking into account
all the prior reductions and minimum/maximum restrictions. For example, there
may be 500
tracks for this unique factor (e.g., for this runway/INM Type/DEN
combination), but
FloatNumTracks might be approximately 1/10th of that, or, for example, 50.6,
after all
calculations are completed.
[0061] In substep 432, the exact number of tracks ("ExactNumTracks") is
calculated by
rounding up the FloatNumTracks to the next whole number. This is done because
FloatNumTracks may not be a whole number, based on all the calculations. But
there are always
a whole number of tracks in the database. So, for example, if FloatNumTracks
is 50.6,
ExactNumTracks would be 51.
[0062] Next, in substep 434, weight value for the tracks ("WeightForTracks")
is calculated
according to Equation 8 below. Since the exact number of tracks is a rounded
value of the
calculated ideal number of tracks, we have to calculate what weighting
adjustment is necessary
to account for the percentage difference between the two.
Equation 8: WeightForTracks = FloatNumTracks / ExactNumTracks
[0063] Finally, in substep 436, the final weight value for the tracks
("WeightFinal") is calculated
as the total user weight ("TotalUserWeight") from substep 408 multiplied by
the weight value
for the tracks ("WeightForTracks"). Thus, at this point, there is an exact
number of tracks
("ExactNumTracks") that has been calculated as well as a final weight value
("WeightFinal") to
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Attorney Docket No. 017271-0011
be attributed to the tracks. These two values are then used in subsequent step
314 as discussed
above.
[0064] The previously described embodiments of the present disclosure have
many advantages,
including substantially reducing the amount of time it takes the AEDT to
calculate noise
contours for an airport using flight track data from that airport, such flight
track data taken over
an extended period of time (most often, a year). The method generates an AEDT
input file with
significantly less flight tracks, but some or all of those flight tracks are
weighted to varying
degrees to ensure that, when using the generated input file, output from the
AEDT would be
substantially the same as if all flight tracks had been input to the AEDT. The
system and method
described herein also provides a way for a user to add extra emphases to one
or more variables of
flight track data in the generated input file for specific flight tracks, thus
tailoring the generated
input data to variables that are the most important to the particular user.
[0065] The foregoing description of preferred embodiments of the present
disclosure has been
presented for purposes of illustration and description. The described
preferred embodiments are
not intended to be exhaustive or to limit the scope of the disclosure to the
precise form(s)
disclosed. Obvious modifications or variations are possible in light of the
above teachings. The
embodiments are chosen and described in an effort to provide the best
illustrations of the
principles of the disclosure and its practical application, and to thereby
enable one of ordinary
skill in the art to utilize the concepts revealed in the disclosure in various
embodiments and with
various modifications as are suited to the particular use contemplated. All
such modifications and
variations are within the scope of the disclosure.
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