The Performance Snapshots Reference Guide provides information about the following:

## Access: Key Performance Indicators (KPI)

As described by ICAO: A global Air Traffic Management (ATM) system should provide an operating environment that ensures all airspace users have right of access to the resources needed to meet their specific operational requirements and that the shared use of airspace by different users can be achieved safely.

### LPV & LP Access at GA Airports without ILS

Reported as Count of Airports for NAS

Desired Trend: Increase

Source: FAA Office of Airport Planning and Programming.

Localizer Performance with Vertical guidance (LPV) & Localizer Performance (LP) data gathered from the FAA Global Navigation Satellite Systems Group.

Airport information gathered from the 2015-2019 National Plan of Integrated Airport Systems (NPIAS) Report and Airport Master Record Form 5010 data.

The count of national, regional, local and basic GA airports (as defined in the 2015-2019 National Plan of Integrated Airport Systems Report) without an Instrument Landing System (ILS) that have an LPV or LP procedure in the indicated year.

Computations

Sum of the count of airports within the defined scope having an LPV or LP procedure for a given fiscal year (FY).

Scope

LPV and LP procedures were counted for airports that meet the following conditions:

• Not be a primary airport as defined in the 2015-2019 NPIAS Report,
• Be listed as either a national, regional, local or basic GA airport in the 2015-2019 NPIAS, and
• Not have any ILS procedures.
Statistical Issues

This data is calculated based on the number of procedures published by the end of the FY; the value may vary within the year due to different procedure publication dates.

Airports are counted per the earliest LP/LPV initial publishing date in their current list of procedures and may not account for procedure updates or changes. Previously published procedures that are no longer available will not be reflected in the data. This may cause historical values to change slightly when updated procedure lists are used to update the metric.

The list and categorization of non-primary airports is subject to change.

Completeness

Procedure data used to calculate the metric was last updated on March 11, 2015 and includes procedures published through the final charting date of FY 2014 (September 18, 2014).

The NPIAS Report was submitted to Congress in September 2014.

Outcome: LPV approaches provide reliable, precise access to airports during low visibility/ceiling weather conditions, particularly for GA aircraft operators.

LPV is similar to LNAV/VNAV except it is much more precise (40 m lateral limit), enables descent as low as 200 feet above the runway, and can only be flown with a Wide Area Augmentation System receiver. LPV approaches are operationally equivalent to the legacy ILS but are more economical because no navigation infrastructure (glideslope and localizer) has to be installed at the runway.

### Percent of Qualified GA Airports with LPV or LP Access

Reported as Cumulative Percent for NAS only

Desired Trend: Increase

Source: FAA Office of Airport Planning and Programming.

LPV and LP data gathered from the FAA Global Navigation Satellite Systems Group.

Airport information gathered from the 2015-2019 National Plan of Integrated Airport Systems (NPIAS) Report and Airport Master Record Form 5010 data.

The cumulative percent of qualified national, regional, local and basic GA airports (as defined in the 2015-2019 NPIAS Report) with an LPV or LP procedure.

Computations

The cumulative percent of qualified airports that have an LPV or LP procedure in a specific year.

Scope

This metric only includes LPV and LP qualified national, regional, local and basic GA airports (as defined in the 2015-2019 NPIAS Report).

The yearly numbers of LPV and LP Procedures counted include those available at airports both with and without ILS procedures.

Statistical Issues

This data is calculated based on the number of procedures published by the end of the FY; the value within a year may vary due to the different charting dates.

Airports are counted per the earliest LP/LPV initial publishing date in their current list of procedures and may not account for procedure updates or changes. Previously published procedures that are no longer available will not be reflected in the data. This may cause historical values to change slightly when updated procedure lists are used to update the metric.

The list and categorization of airports is subject to change.

Completeness

Procedure data used to calculate the metric was last updated on March 11, 2015 and includes procedures published through the final charting date of FY 2014 (September 18, 2014).

The NPIAS Report was submitted to Congress in September 2014.

To be qualified for an LP or LPV procedure an airport must have a paved runway of 3,200 feet or greater (terrain or obstacles around the airport may also affect the ability to develop a procedure for an airport).

LPV is similar to LNAV/VNAV except it is much more precise (40 m lateral limit), enables descent as low as 200 feet above the runway, and can only be flown with a WAAS receiver. LPV approaches are operationally equivalent to the legacy ILS but are more economical because no navigation infrastructure (glideslope and localizer) has to be installed at the runway.

## Capacity: Key Performance Indicators (KPI)

Utilize available airport capacity to meet the National Airspace System users' demand at all times and in all approach conditions.

### Average Daily Capacity

Reported as Number of Operations at Core Airports during reportable hours

Desired Trend: Increase

Source: MITRE/Aviation System Performance Metrics (ASPM) data.

During reportable hours, the average daily sum of the Airport Departure Rate (ADR) and Airport Arrival Rate (AAR) reported by FY. The reportable hours vary by airport.

Computations

The yearly sum of the hourly AAR and the hourly ADR during reportable hours divided by the number of days in the year.

Scope

Called rates include all arrival and departure traffic that an airport can support.

Statistical Issues

Due to the leap year, the number of days for FY 2012 is 366, all other years it is 365.

Due to the units of this metric (capacity), the results were rounded to the nearest whole number.

Completeness

The type of data from which this metric is calculated is intended to capture the full set of ASPM records.

Reliability

The data for FY 2014 is not yet final as amendments may be made to the ASPM source data until six weeks after the end of FY 2015.

In addition to calculating the FY2014 data values, the entire dataset (FY 2009 - 2013) was re-calculated prior to the April 2015 NPS release to ensure that any data amendments would be reflected in the final metric.

Reportable hours vary by airport and are based on local time. Additional reportable hours information is included in the Airports section below.

### Average Hourly Capacity During Instrument Meteorological Conditions (IMC)

Reported as Number of Operations at Core Airports during reportable hours and IMC weather conditions (as defined by ASPM).

Desired Trend: Increase

Source: MITRE/Aviation System Performance Metrics (ASPM) data.

The average hourly capacity reported during IMC weather conditions (as defined by ASPM). Capacity is defined as the sum of Airport Departure Rate (ADR) and Airport Arrival Rate (AAR). It is calculated based on the reportable hours at the destination airport. The reportable hours vary by airport.

Computations

The yearly sum of the hourly AAR and the hourly ADR during IMC during reportable hours divided by the number of days in the year.

Scope

Called rates include all arrival and departure traffic that an airport can support.

Statistical Issues

Due to the leap year, the number of days for FY 2012 is 366, all other years it is 365.

Due to the units of this metric (capacity of operations), the results were rounded to the nearest whole number. The identification of IMC is based on the most recent celling and visibility criteria for each airport.

Completeness

The type of data from which this metric is calculated is intended to capture the full set of ASPM records.

Reliability

The data for FY 2014 is not yet final as amendments may be made to the ASPM source data until six weeks after the end of FY 2015.

In addition to calculating the FY2014 data values, the entire dataset (FY 2009 - 2013) was re-calculated prior to the April 2014 NPS release to ensure that any data amendments would be reflected in the final metric.

Reportable hours vary by airport and are based on local time. Additional reportable hours information is included in the Airports section below.

## Efficiency: Key Performance Indicators (KPI)

As described by ICAO, efficiency addresses the operational and economic cost-effectiveness of gate-to-gate flight operations from a single-flight perspective. In all phases of flight, airspace users want to depart and arrive at the times they select and fly the trajectory they determine to be optimum.

### Airborne Distance (City Pairs)

Reported as Nautical Miles for Selected City Pairs during reportable hours (based on the local time for the destination airport)

Desired Trend: Decrease

Source: MITRE Threaded Track and Airline Service Quality Performance System (ASQP) data derived from Aviation System Performance Metrics (ASPM) data.

During reportable hours at the destination airport, the average airborne distance of flights between the selected city pair. The reportable hours vary by airport and the results are reported by fiscal year. Additional reportable hour information can be found in the airport information section of the Reference Guide.

Formula
$∑ F D n F$

D is the airborne distance flown for each flight in the Scope, F is the set of all flights in the Scope and nF is the number of F

Computations

The metric is calculated as the sum of airborne distances of the flights within the Scope, divided by the total number of flights within the Scope.

The metric is derived from a fusion of flight position reports throughout the flight envelope into a single synthetic trajectory. These sources currently include National Offload Program (NOP), ARTS, STARS, and ARTCC sensors, Traffic Flow Management System (TFMS) reports, and Airport Surface Detection Equipment, Model X (ASDE-X) surveillance data.

For each flight, the first radar track point greater than 500 ft. above the origin airport is chosen to approximate wheels-up time. Similarly, the last radar track point greater than 500 ft. above the arrival airport is chosen to approximate touchdown time. The along-track distance of the flight trajectory between these two track points, derived from flight position reports, is calculated as the airborne distance. Note that the calculated airborne distance will consistently underestimate the actual airborne distance for a given flight by approximately 1-2 NM, depending on climb and descent gradients, because the chosen first and last track points are always at least 500 ft. above the ground.

Scope

The metric considers ASQP reporting carriers operating domestic service between the selected pair of airports in the direction indicated. Only flights arriving at the destination airport within the reportable hours are included in this measurement. Flights may depart the origin airport outside the reportable hours.

Statistical Issues

The airborne distance computation includes a filter for flights with a first radar track point greater than 4000 ft. above the origin airport or a last radar track point greater than 4000 ft. above the destination airport, to minimize inaccurate airborne distance calculations due to poor radar coverage near the ground. The airborne distance computation also includes a filter for flights with a first radar track point greater than 10NM from the origin airport or a last radar track point greater than 10NM from the destination airport, to eliminate any additional radar coverage issues and deviations to alternative airports. These filters exclude 1.5% of flights in the Scope.

Completeness

The first full year of data for the Newark Liberty International — Chicago Midway International (EWR — MDW) city pair was FY2012.

Reliability

The data for FY 2013 is not yet final as amendments may still be made to the ASPM source data until six weeks after the end of FY 2014.

The metric is first calculated for all IFR operations between the city pairs and then linked to ASQP flights, obtained through the ASPM website. In the original data set the Official Airline Guide Aircraft Identification (OAG_ACID) column must equal "ASQP" in order for the flight to be considered an ASQP flight and used in the calculations.

### Average Airborne Time (City Pairs)

Reported as Minutes for selected city pairs during reportable hours (based on the local time for the destination airport)

Desired Trend: Decrease

Source: Airline Service Quality Performance System (ASQP) data derived from Aviation System Performance Metrics (ASPM) data, for reportable hours only.

During reportable hours at the destination airport, the average Airborne Time for flights between the selected city pair. The reportable hours vary by airport and the results are reported by fiscal year. Additional reportable hour information can be found in the airport information section of the Reference Guide.

Computations

The metric is calculated as the average Airborne Time. Airborne time is the difference between the Actual On Time at the destination airport and the Actual Off Time at the origin airport.

Scope

This metric only measures ASQP reporting carriers operating domestic service between the selected pair of airports in the direction indicated. Only flights arriving at the destination airport within the reportable hours are included in this measurement. Flights may depart the origin airport outside the reportable hours.

Statistical Issues

Calculating the average of all flights helps provide a better picture of the typical airborne time by reducing the effect of atypical data points.

This calculation did not normalize the data for any changes in operator fleet mix.

Completeness

ASQP flights are those with actual data reported to the Department of Transportation.

The first full year of data for the Newark Liberty International — Chicago Midway International (EWRMDW) city pair was FY 2012.

Reliability

The Technical Directive outlining the ASQP reporting carriers is available on the Bureau of Transportation Statistics website.

Reportable hours vary by airport and are based on local time. Additional reportable hours information is included in the Airports section below.

The data for FY 2014 is not yet final as amendments may still be made to the ASPM source data until six weeks after the end of FY 2015.

The metric is derived directly from individual ASQP flight data obtained through the ASPM website. In the original data set the Official Airline Guide Aircraft Identification (OAG_ACID) column must equal “ASQP” in order for the flight to be considered an ASQP flight and used in the calculations.

### Average Gate Arrival Delay

Reported as Minutes per Flight for Core Airports during reportable hours

Desired Trend: Decrease

Source: MITRE/Airline Service Quality Performance System (ASQP) data derived from Aviation System Performance Metrics (ASPM) data.

During reportable hours, the yearly average of the difference between the Actual Gate-In Time and the Scheduled Gate-In Time for flights to the selected airport from any of the ASPM airports. The delay for each FY is calculated based on the 0.5th — 99.5th percentile of the distributions for the year. Flights may depart outside reportable hours, but must arrive during them. The reportable hours vary by airport.

Formula
$∑ F AD n F for each G$

where

$AD = ( t in act - t in sch )$

and where G are the groups defined within scope, Arrival Delay (AD) is equal to the actual time into the gate at the arrival airport ( $t in act$ ) minus the scheduled time in at the arrival airport ( $t in sch$ ); F are all flights over the year within each group and nF is the number of F.


Computations

Average Gate-In Delay against schedule over all flights in the FY for each group defined within the scope.

Scope

Flights are restricted to domestic ASQP flights departing from an ASPM airport and traveling to the selected airport. To be included a flight needs to arrive within the reportable hours, but may depart the origin outside reportable hours.

Statistical Issues

The list of ASQP reporting carriers is subject to change yearly. Additionally, changes in carrier operations at an airport may impact data results over time.

This calculation did not normalize the data for any changes in operator fleet mix.

This calculation may include time an aircraft spends in a non-movement area (defined in the Aeronautical Information Manual as Taxiways and apron (ramp) areas not under the control of air traffic). Reporting carriers (operators) may use slightly different starting and/or ending points when gathering performance data.

After the metric was calculated, the data was truncated to remove outliers. The information provided is based on the 0.5 — 99.5 percentile of the distributions by airport and year.

Completeness

ASQP flights are those with actual data reported to the Department of Transportation.

Reliability

The metrics are derived directly from individual ASQP flight data obtained through the ASPM website. In the original data set, the Official Airline Guide Aircraft Identification (OAG_ACID) column must equal "ASQP" in order for the flight to be considered an ASQP flight and used in the calculations.

The data for FY 2014 is not yet final as amendments may still be made to the ASPM source data until six weeks after the end of FY 2015.

Positive delays are considered any time beyond the scheduled arrival time (including delays less than 15 minutes). Due to the inclusion of flights arriving before schedule (negative delays), negative values are possible for this metric.

The Technical Directive outlining the ASQP reporting carriers is available on the Bureau of Transportation Statistics website.

Reportable hours vary by airport and are based on local time. Additional reportable hour information is included in the Airport section below.

### Average Number of Level-offs per Flight

Reported as Counts per Flight for Core Airports

Desired Trend: Decrease

The count of level-offs as flights descend from cruise altitudes to the arrival airport, averaged for the fiscal year.

Formula
$= ∑ F LO n F$

LO is the count of level offs for each arrival in the Scope, F is the set of all flights in Scope and nF is the number of F

Computations

The metric is calculated as the sum of the count of level-offs for each flight within the Scope, divided by the total number of flights within the Scope.

The metric is derived from a fusion of flight position reports throughout the flight envelope into a single synthetic trajectory. These sources currently include National Offload Program (NOP) ARTS, STARS, and ARTCC sensors, Traffic Flow Management System (TFMS) reports, and Airport Surface Detection Equipment, Model X (ASDE-X) surveillance data.

Level-offs are tracked from the Top-of-Descent (TOD) point or 200 nautical miles from the airport, whichever is closer. A trajectory segment is considered as a level-off if the change in altitude of position reports is less than 200 feet and the segment is at least 50 seconds in duration.

Scope

Flights are restricted to jet arrivals at the designated airport from any origin airport. Only IFR operations are considered.

Statistical Issues

For approximately 1% of IFR flights in the Scope, merging of radar track data from multiple sources was not feasible due to data anomalies, and these flights are excluded. The level-off computation also includes a filter for flights with a last radar track point greater than 2.5NM from the destination airport, which excludes 0.5% of flights. The impact of this filter is seen the greatest at HNL, where most arrivals are excluded. Thus, level-off metrics are not reported for HNL.

This metric is calculated using all hours.

### Distance in Level Flight from Top of Descent to Runway Threshold

Reported as Nautical Miles per Flight for Core Airports

Desired Trend: Decrease

The distance flown during level-off segments as flights descend from cruise altitudes to the arrival airport, averaged for the fiscal year.

Formula
$= ∑ F D n F$

D is the distance flown during level-off segments for each arrival in the Scope, F is the set of all flights in the Scope and nF is the number of F

Computations

The metric is calculated as the sum of the total distance flown during level-off segments for all flights within the Scope, divided by the total number of flights within the Scope.

The metric is derived from a fusion of flight position reports throughout the flight envelope into a single synthetic trajectory. These sources currently include National Offload Program (NOP) ARTS, STARS, and ARTCC sensors, Traffic Flow Management System (TFMS) reports, and Airport Surface Detection Equipment, Model X (ASDE-X) surveillance data.

Level-offs are tracked from the Top-of-Descent (TOD) point or 200 nautical miles from the airport, whichever is closer. A trajectory segment is considered as a level-off if the change in altitude of position reports is less than 200 feet and the segment is at least 50 seconds in duration.

Scope

Flights are restricted to jet arrivals at the designated airport from any origin airport. Only IFR operations are considered.

Statistical Issues

For approximately 1% of IFR flights in the Scope, merging of radar track data from multiple sources was not feasible due to data anomalies, and these flights are excluded. The level-off computation also includes a filter for flights with a last radar track point greater than 2.5NM from the destination airport, which excludes 0.5% of flights. The impact of this filter is seen the greatest at HNL, where most arrivals are excluded. Thus, level-off metrics are not reported for HNL.

This metric is calculated using all hours.

### Effective Gate-to-Gate Time (City Pairs)

Reported as Average Minutes per Flight for selected city pairs during reportable hours (based on the local time of the arrival airport)

Desired Trend: Decrease

Source: MITRE/Airline Service Quality Performance System (ASQP) data derived from Aviation System Performance Metrics (ASPM) data.

During reportable hours at the destination airport, the difference between the Actual Gate-In Time at the destination airport and the Scheduled Gate-Out Time at the origin airport. Flights may depart outside reportable hours, but must arrive during them. The reportable hours vary by airport and the results are reported by FY.

Computations

Average Gate-to-Gate Time over all flights in the FY for the selected City Pair within the scope.

Scope

This metric only measures ASQP reporting carriers operating domestic service between the selected pair of airports in the direction indicated. Only flights arriving at the destination airport within the reportable hours are included in this measurement. Flights may depart the origin airport outside reportable hours.

Statistical Issues

The list of ASQP reporting carriers is subject to change yearly. Additionally, changes in carrier operations at, or between the origin and destination may impact data results over time.

This calculation did not normalize the data for any changes in operator fleet mix.

This calculation may include time an aircraft spends in a non-movement area (defined in the Aeronautical Information Manual as taxiways and apron (ramp) areas not under the control of air traffic). Reporting carriers may use slightly different starting and/or ending points when gathering performance data.

Completeness

ASQP flights are those with actual data reported to the Department of Transportation.

The first full year of data for the Newark Liberty International — Chicago Midway International (EWR — MDW) city pair was FY 2012.

Reliability

The metric is derived from individual ASQP flight data obtained through the ASPM website. In the original data set the Official Airline Guide Aircraft Identification (OAG_ACID) column must equal "ASQP" in order for the flight to be considered an ASQP flight and used in the calculations.

The data for FY 2014 is not yet final as amendments may still be made to the ASPM source data until six weeks after the end of FY 2015.

The Technical Directive outlining the ASQP reporting carriers is available on the Bureau of Transportation Statistics website.

Reportable hours vary by airport and are based on local time. Additional reportable hour information is included in the Airport section below.

### Effective Gate-to-Gate Time (Core 30 Airports)

Reported as Minutes per Flight for Core Airports during reportable hours

Desired Trend: Decrease

Source: MITRE/Airline Service Quality Performance System (ASQP) data derived from Aviation System Performance Metrics (ASPM) data.

During reportable hours, the difference between the Actual Gate-In Time at the destination (selected) airport and the Scheduled Gate-Out Time at the origin airport. Flights may depart outside reportable hours, but must arrive during them. The reportable hours vary by airport and the results are reported by FY.

Computations

Gate-to-Gate time over all flights in the FY for each group defined within the scope.

Scope

Flights are restricted to domestic ASQP flights departing from any ASPM airport and traveling to the selected airport by an ASQP reporting carrier. Additionally, to be included, a flight needs to arrive within the reportable hours (but may depart the origin airport outside the reportable hours).

Statistical Issues

The list of ASQP reporting carriers is subject to change yearly. Additionally, changes in carrier operations at an airport may impact data results over time.

This calculation did not normalize the data for any changes in operator fleet mix.

This calculation may include time an aircraft spends in a non-movement area (defined in the Aeronautical Information Manual as Taxiways and apron (ramp) areas not under the control of air traffic). Reporting carriers (operators) may use slightly different starting and/or ending points when gathering performance data.

Completeness

ASQP flights are those with actual data reported to the Department of Transportation.

Reliability

The metric is derived directly from individual ASQP flight data obtained through the ASPM website. In the original data set, the Official Airline Guide Aircraft Identification (OAG_ACID) column must equal “ASQP” in order for the flight to be considered an ASQP flight and used in the calculations.

The data for FY 2014 is not yet final as amendments may still be made to the ASPM source data until six weeks after the end of FY 2015.

The Technical Directive outlining the ASQP reporting carriers is available on the Bureau of Transportation Statistics website.

Reportable hours vary by airport and are based on local time. Additional reportable hour information is included in the Airport section below.

### Taxi-In Time

Reported as Minutes per Flight for Core Airports during reportable hours

Desired Trend: Decrease

Source: MITRE/Airline Service Quality Performance System (ASQP) data derived from Aviation System Performance Metrics (ASPM) data.

During reportable hours, the yearly average of the difference between Wheels-On Time and Gate-In Time for flights arriving at the selected airport from any of the Aviation System Performance Metrics (ASPM) airports. Flights may depart outside reportable hours, but must arrive during them. The reportable hours vary by airport.

Formula
$∑ F TI n F$

where

$TI = t in act - t on act$

The Taxi-In Time metric is calculated as the average over all flights in the FY defined within the scope. The Taxi-In Time for a flight (TI) is defined as the time the aircraft pulls into the gate ( $t in act$ ) minus the time the aircraft wheels touch the ground ( $t on act$ ). This value is added to all the other flights within scope (F) and divided by the number of F (nF).


Computations

The average of the difference between the actual Gate-In Time and actual Wheels-On Time over all arrivals for each group defined within the scope.

Scope

Flights are restricted to domestic ASQP flights departing from an ASPM airport and traveling to the selected airport by an ASQP reporting carrier. To be included, a flight needs to arrive within the reportable hours, but may depart the origin outside reportable hours.

Statistical Issues

The list of ASQP reporting carriers are subject to change yearly. Additionally, changes in carrier operations at an airport may impact data results over time.

This calculation did not normalize the data for any changes in operator fleet mix.

This calculation may include time an aircraft spends in a non-movement area (defined in the Aeronautical Information Manual as taxiways and apron (ramp) areas not under the control of air traffic). Reporting carriers (operators) may use slightly different starting and/or ending points when gathering performance data.

Completeness

ASQP flights are those with actual data reported to the Department of Transportation.

Reliability

The metrics are derived directly from individual ASQP flight data obtained through the ASPM website. In the original data set, the Official Airline Guide Aircraft Identification (OAG_ACID) column must equal "ASQP" in order for the flight to be considered an ASQP flight and used in the calculations.

The data for FY 2014 is not yet final as amendments may still be made to the ASPM source data until six weeks after the end of FY 2015.

The Technical Directive outlining the ASQP reporting carriers is available on the Bureau of Transportation Statistics website.

Reportable hours vary by airport and are based on local time. Additional reportable hour information is included in the Airport section below.

### Taxi-Out Time

Reported as Minutes per Flight for Core Airports during reportable hours

Desired Trend: Decrease

Source: MITRE/Airline Service Quality Performance System (ASQP) data derived from Aviation System Performance Metrics (ASPM) data.

During reportable hours, the yearly average of the difference between Gate-Out Time and Wheels-Off Time for flights from the selected airport to any of the ASPM airports. Flights must depart during reportable hours, but may arrive outside them. The reportable hours vary by airport.

Formula
$∑ F TO n F$

where

$TO = t off act - t out act$

The Taxi-Out Time metric is calculated as the average over all flights in the FY defined within the scope. The Taxi-Out Time for a flight (TO) is defined as the time the aircraft takes off ( $t off act$ ) minus the time the aircraft pushes back from the gate ( $t out act$ ). This value is added to all the other flights within scope (F) and divided by the number of F (nF).


Computations

The average of the difference between the Actual Gate-Out Time and Actual Wheels-Off Time over all departures for each group defined within the scope.

Scope

Flights are restricted to domestic ASQP flights departing from the selected airport and traveling to an ASPM airport. To be included, a flight needs to depart within the reportable hours, but may arrive at the destination outside the reportable hours.

Statistical Issues

The list of ASQP reporting carriers is subject to change yearly. Additionally, changes in carrier operations at an airport may impact data results over time.

This calculation did not normalize the data for any changes in operator fleet mix.

This calculation may include time an aircraft spends in a non-movement area (defined in the Aeronautical Information Manual as Taxiways and apron (ramp) areas not under the control of air traffic). Reporting carriers (operators) may use slightly different starting and/or ending points when gathering performance data.

Completeness

ASQP flights are those with data reported to the Department of Transportation.

Reliability

The metrics are derived directly from individual ASQP flight data obtained through the ASPM website. In the original data set, the Official Airline Guide Aircraft Identification (OAG_ACID) column must equal "ASQP" in order for the flight to be considered an ASQP flight and used in the calculations.

The data for FY 2014 is not yet final as amendments may still be made to the ASPM source data until six weeks after the end of FY 2015.

The Technical Directive outlining the ASQP reporting carriers is available on the Bureau of Transportation Statistics website.

Reportable hours vary by airport and are based on local time. Additional reportable hour information is included in the Airport section below.

## Environment: Key Performance Indicators (KPI)

As described by ICAO: the Air Traffic Management (ATM) should contribute to the protection of the environment by considering noise, gaseous emissions and other environmental issues in the implementation and operation of the global ATM system.

### CO2 Emissions

Reported as Kilograms for NAS only

Desired Trend: Decrease

Source: FAA Office of Environment and Energy

Estimated quantity of carbon dioxide emissions (CO2) emitted by commercial aircraft within the NAS.

Formula

Fuel Burn (kg) × 3.155 (CO2 kg per kg of fuel burn) = CO2 in kilograms

Computations

As part of measuring and tracking NAS fuel efficiency from commercial aircraft operations, the FAA quantifies annual aircraft fuel burn using FAA’s Aviation Environmental Design Tool (AEDT). AEDT is a FAA-developed computer model that estimates aircraft fuel burn and emissions for variable year emissions inventories and for operational, policy, and technology-related scenarios.

Statistical Issues

Potential seasonal variability and variability from year-to-year can be expected when analyzing air traffic data and commercial operations.

The extent to which enhancements are incorporated to improve AEDT model accuracy, for example via more robust aerodynamic performance modeling algorithms and databases of aircraft/engine fuel burn information, will impact the overall results. This could create statistical variability from year-to-year if not taken into account. In cases where such enhancements have the potential to create a significant shift in baseline, annual inventories may need to be re-processed and/or adjusted to ensure consistency and accuracy of results.

The extent to which aircraft fleet improvements cannot be sufficiently modeled because of a lack of manufacturer proprietary data may also influence results. In this case, attempts will be made to characterize such aircraft with the best publicly available information, recognizing that newer aircraft types in the fleet will likely exist in significantly lesser numbers, thus minimizing the influence upon results.

The results for calendar years 2005, 2010 and 2011 have been re-calculated using the latest version of the AEDT. The NPS was updated in April 2015 to reflect these values.

Completeness

Data used to measure aircraft performance are assessed for quality control purposes. Input data for the AEDT model are validated before proceeding with model runs. Radar data from the Traffic Flow Management System (TFMS) are assessed to remove any anomalies, check for completeness, and pre-processed for input to the AEDT model. TFMS data are verified against the Official Airline Guide (OAG) information in order to avoid any duplication of flights in the annual inventory.

In some cases, TFMS data lack appropriate fields to conduct quality control and in these cases the data are removed. Data from the AEDT model are verified by comparing output from previous years and analyzing trends to ensure that they are consistent with expectations. In other cases monthly inventories may be analyzed to validate the results. Model output is subsequently post-processed to perform the calculations. Formulae and calculations are checked in order to ensure accuracy.

Reliability

The measuring procedure used is highly reliable. That is to say that the processing of data through the AEDT model including the performance of algorithms is not subject to random factors that could influence the results. However, this is potentially influenced by factors outside the control of FAA.

We do not expect increases in fuel burn or decreases in distance traveled or both to degrade the fleet fuel efficiency significantly.

This metric is shown by calendar year (CY).

### NAS-Wide Energy Efficiency

Reported as Kilograms per Tonne-Kilometer for NAS only

Desired Trend: Decrease

Source: FAA Office of Environment and Energy

Estimated fuel burn in kilograms per revenue tonne kilometer

Formula
$Fuel Burn (Revenue Payload x Distance Flown)$
Computations

Measuring and tracking fuel efficiency from commercial aircraft operations allows FAA to monitor improvements in aircraft/engine technology and operational procedures, as well as enhancements in the airspace transportation system. The FAA measures performance using the Aviation Environmental Design Tool (AEDT). AEDT is a FAA-developed computer model that estimates aircraft fuel burn and emissions for variable year emissions inventories and for operational, policy, and technology-related scenarios.

Scope

This metric focuses on all U.S. commercial operations.

Statistical Issues

Potential seasonal variability and variability from year-to-year can be expected when analyzing air traffic data and commercial operations.

The extent to which enhancements are incorporated to improve model accuracy, for example via more robust aerodynamic performance modeling algorithms and database of aircraft/engine fuel burn information, will impact the overall results. This could create some statistical variability from year-to-year if not taken into account. In cases where such enhancements have the potential to create a significant shift in baseline, annual inventories may need to be re-processed and/or adjusted to ensure consistency and accuracy of results.

The extent to which aircraft fleet improvements cannot be sufficiently modeled due to a lack of manufacturer proprietary data may also influence the performance results. In this case, attempts will be made to characterize such aircraft with the best publicly available information, recognizing that newer aircraft types in the fleet will likely exist in significantly lesser numbers, thus minimizing the influence upon the results.

Completeness

Data used to measure performance are assessed for quality control purposes. Input data for the AEDT model are validated before proceeding with model runs. Radar data from the Traffic Flow Management System (TFMS) are assessed to remove any anomalies, check for completeness, and pre-processed for input to the AEDT model. TFMS data are verified against the Official Airline Guide (OAG) information in order to avoid any duplication of flights in the annual inventory.

In some cases, TFMS data lack appropriate fields to conduct quality control and in these cases the data are removed. Data from the AEDT model are verified by comparing output from previous years and analyzing trends to ensure that they are consistent with expectations. In other cases monthly inventories may be analyzed to validate the results. Model output is subsequently post-processed through spreadsheets to perform the calculations for the performance target. Formulae and calculations are checked in order to ensure accuracy.

Reliability

The measuring procedure used is highly reliable. That is to say that the processing of data through the AEDT model including the performance of algorithms is not subject to random factors that could influence the results. However this is potentially influenced by factors outside the control of FAA.

We do not expect increases in fuel burn or decreases in distance traveled or both to degrade the fleet fuel efficiency significantly.

This KPI is shown by calendar year (CY).

### Noise Exposure

Reported as Number of People

Desired Trend: Decrease

Source: FAA Office of Environment and Energy

Number of persons exposed to significant aircraft noise (regardless of whether their houses or apartments have been sound-insulated). Significant aircraft noise levels are currently defined as values greater than or equal to Day-Night Average Sound Level (DNL) 65 decibels (dB).

Formula
$\sum _{i=1}^{n}{{\mathrm{POP65}}_{i}}^{}-\sum _{j=1}^{9}{{\mathrm{POPREL}}_{j}}^{}$

Where POP65i is the number of people residing in the DNL 65 dB contour at the ith "Noise Inventory" airport as of the current year projected from the 2010 Census, and n is the number of "Noise Inventory" airports. A Noise Inventory airport is defined as any airport that reported having at least 365 jet departures for the year being used in the analysis. POPRELjis the number of people relocated from the DNL 65 dB contour in the jth FAA region.

Computations

Beginning in FY 2012, the estimates of the number of people exposed to significant noise are calculated using the Aviation Environmental Design Tool (AEDT). Prior to the use of AEDT, estimates were calculated using the Model for Assessing Global Exposure to the Noise of Transport Aircraft (MAGENTA). The computational core of AEDT is FAA’s Integrated Noise Model (INM) with methodological improvements. INM is the most widely used computer program for the calculation of aircraft noise around airports. In FY 2015, INM will be replaced by AEDT as the regulatory tool to calculate airport noise around airports. Major assumptions on local traffic utilization come from obtaining INM datasets that were developed for an airport and ETMS.

The AEDT model calculates individual DNL contours for the top 101 U.S. airports using detailed flight tracks, runway use and track utilization. The contours are superimposed on year 2010 Census population densities projected to the current year being computed to calculate the number of people within the DNL 65 dB contour at each airport. For smaller airports, AEDT uses less detailed information consisting of flight tracks that extend straight-in and straight-out from the runway ends. The contours areas are then used to calculate people exposed using 2010 Census population densities projected to the current year being computed. The projection is used to account for population growth between 2010 and the computed year. The individual airport exposure data are then summed to the national level. Finally, the number of people relocated through the Airport Improvement Program is subtracted from the total number of people exposed.

Scope

The metric tracks the residential population exposed to significant aircraft noise around U.S. airports. Significant aircraft noise is defined as aircraft noise above a Day-Night Average Sound Level (DNL) 65 decibels. In 1981, FAA issued 14 CFR Part 150, Airport Noise Compatibility Planning, and as part of that regulation, formally adopted DNL. DNL, symbolized as Ldn, is the 24-hour average sound level, in dB, obtained from the accumulation of all events with the addition of 10 decibels to sound levels in the night from 10 PM to 7 AM. The weighting of the nighttime events accounts for the increased interfering effects of noise during the night when ambient levels are lower and people are trying to sleep.

In the promulgation of 14 CFR Part 150, FAA also published a table of land uses that are compatible or incompatible with various levels of airport noise exposure in DNL. This table established that levels below DNL 65 dB are considered compatible for all indicated land uses and related structures without restriction.

Statistical Issues

This metric is derived from model estimates that are subject to errors in model specification. Trends of U.S. noise exposure may change due to annual improvements to the noise exposure model. A major change to the model may result in a large change in the estimate of the number of people exposed to significant noise levels around U.S. airports.

Note: In April 2015, the NPS was updated to reflect a revision to the calendar year 2012 metric value.

Completeness

No actual count is made of the number of people exposed to significant aircraft noise. Aircraft type and event level are current. However, some of the databases used to establish route and runway utilization were developed from 1990 to 1997. Changes in airport layout including expansions may not be reflected. The FAA continues to update these databases as they become available. The benefits of federally funded mitigation, such as buyout, are accounted for.

Reliability

The Integrated Noise Model (the core of the MAGENTA and AEDT tool) has been validated with actual acoustic measurements at airports. The population exposure methodology has been thoroughly reviewed by an International Civil Aviation Organization (ICAO) task group and was most recently validated for a sample of airport-specific cases.

The FAA migrated from the Model for Assessing Global Exposure to the Noise of Transport Aircraft (MAGENTA) to the AEDT with the FY 2012 report. This metric is shown by calendar year, and is rounded to three significant figures.

## Predictability: Key Performance Indicators (KPI)

As described by ICAO: Predictability refers to the ability of airspace users and ATM service providers to provide consistent and dependable levels of performance.

### Airborne Time Predictability

Reported as Minutes for selected city pairs during reportable hours (based on the local time for the destination airport)

Desired Trend: Decrease

Source: MITRE/Airline Service Quality Performance System (ASQP) data derived from Aviation System Performance Metrics (ASPM) data, for reportable hours only.

During reportable hours at the destination airport, the difference between the 85th and 15th percentiles of Airborne Time for flights between the selected city pair. The reportable hours vary by airport and the results are reported by FY. Additional reportable hour information can be found in the airport information section of the Reference Guide.

Computations

The metric is calculated as the difference between the 85th and 15th percentiles of Airborne Time. Airborne Time is the difference between the Actual On Time at the destination airport and the Actual Off Time at the origin airport.

Scope

This metric only measures ASQP reporting carriers operating domestic service between the selected pair of airports in the direction indicated. Only flights arriving at the destination airport within the reportable hours are included in this measurement. Flights may depart the origin airport outside the reportable hours.

Statistical Issues

Calculating the difference between the 85th and 15thpercentile helps provide a better picture of the actual predictability by removing atypical data points.

A lower value for this metric is desired because it shows less variation in the airborne time for flights between the specified city pair within the indicated year.

Completeness

ASQP flights are those with actual data reported to the Department of Transportation.

The first full year of data for the Newark Liberty International — Chicago Midway International (EWR — MDW) city pair was FY 2012.

Reliability

The Technical Directive outlining the ASQP reporting carriers is available on the Bureau of Transportation Statistics website.

Reportable hours vary by airport and are based on local time. Additional reportable hour information is included in the Airport section below.

The data for FY 2014 is not yet final as amendments may still be made to the ASPM source data until six weeks after the end of FY 2015.

The metric is derived directly from individual ASQP flight data obtained through the ASMP website. In the original data set the Official Airline Guide Aircraft Identification (OAG_ACID) column must equal “ASQP” in order for the flight to be considered an ASQP flight and used in the calculations.

### Effective Gate-to-Gate Time Predictability

Reported as Minutes for selected city pairs during reportable hours.

Desired Trend: Decrease

Source: MITRE/Airline Service Quality Performance System (ASQP) data derived from Aviation System Performance Metrics (ASPM) data.

During reportable hours, the difference between the 85th and 15th percentiles of the Effective Gate-to-Gate Time metric. The reportable hours vary by airport and the results are reported by FY. Additional percentile and reportable hour information can be found in the Reference Guide.

Computations

The value for the 15th percentile of the Effective Gate-to-Gate Time subtracted from the value of the 85th percentile of the Effective Gate-to-Gate Time.

Scope

This metric only measures ASQP reporting carriers operating domestic service between the selected pair of airports in the direction indicated. Only flights arriving at the destination airport within the reportable hours are included in this measurement. Flights may depart the origin airport outside the reportable hours.

Statistical Issues

Calculating the difference between the 85th and 15th percentile helps provide a better picture of the actual predictability by removing atypical data points.

A lower value for this metric is desired because it shows less variation in the Effective Gate-to-Gate time for flights for the specified city pair within the indicated year.

Completeness

ASQP flights are those with actual data reported to the Department of Transportation.

The first full year of data for the Newark Liberty International — Chicago Midway International (EWR — MDW) city pair was FY 2012.

Reliability

The metric is derived directly from individual ASQP flight data obtained through the ASPM website. In the original data set the Official Airline Guide Aircraft Identification (OAG_ACID) column must equal "ASQP" in order for the flight to be considered an ASQP flight and used in the calculations.

The data for FY 2014 is not yet final as amendments may still be made to the ASPM source data until six weeks after the end of FY 2015.

The Technical Directive outlining the ASQP reporting carriers is available on the Bureau of Transportation Statistics website.

Reportable hours vary by airport and are based on local time. Additional reportable hour information is included in the Airport section below.

## Airport and Facility Information

Airports included on the NextGen Performance Snapshots (NPS) website are often identified by several characteristics, including airport code, name and city. The locations detailed in the Airport Performance pages also include additional background information such as, operations and freight volume at the specified airport; this information is gathered from the 2013 Airports Council International, North American Airport Traffic Summary preliminary data. The subsections below provide information on the airports and FAA facilities referenced throughout the NPS.

### Core 30 Airport Information Table

Several metrics on the NPS are measured during reportable hours. These hours, which are measured in local time, vary by airport and are selected to capture at least 90 percent of the total operations (arrivals and departures) at an airport. The entire percentage of total operations covered under these reportable hours may not be reflected in the NPS metrics due to the characteristics of the data sources used. Please see the individual metric definitions for additional data source information. In addition to general airport information, the table below includes the reportable hours for the airports measured on the NPS. Memphis International Airport (MEM) is unique in this list as it is the only airport in which the reportable hours span a full 24-hour day due to its large number of freight operations during night hours.

Core 30 Airport Information
City Airport Name Airport Code Metroplex Reportable Hours
Atlanta Hartsfield-Jackson Atlanta International Airport ATL Atlanta 07:00 - 22:59
Baltimore Baltimore/ Washington International Thurgood Marshall Airport BWI D.C. 06:00 - 22:59
Boston Boston - General Edward Lawrence Logan Airport BOS Boston 06:00 - 21:59
Charlotte Charlotte-Douglas International Airport CLT Charlotte 07:00 - 22:59
Chicago Chicago Midway International Airport MDW Chicago 07:00 - 20:59
Chicago Chicago O'Hare International Airport ORD Chicago 06:00 - 21:59
Dallas-Fort Worth Dallas/ Fort Worth International Airport DFW North Texas 07:00 - 21:59
Denver Denver International Airport DEN Denver 07:00 - 21:59
Detroit Detroit Metropolitan Wayne County Airport DTW Detroit 06:00 - 22:59
Fort Lauderdale Fort Lauderdale-Hollywood International Airport FLL South Florida 07:00 - 22:59
Honolulu Honolulu International Airport HNL 06:00 - 22:59
Houston Houston - George Bush Intercontinental Airport IAH Houston 07:00 - 21:59
Las Vegas Las Vegas - McCarran International Airport LAS Las Vegas Valley 07:00 - 21:59
Los Angeles Los Angeles International Airport LAX Southern California 06:00 - 22:59
Memphis Memphis International Airport MEM Memphis 00:00 - 23:59
Miami Miami International Airport MIA South Florida 07:00 - 22:59
Minneapolis Minneapolis-St. Paul International/ Wold-Chamberlain Airport MSP Minneapolis-St. Paul 07:00 - 22:59
New York New York - John F. Kennedy International Airport JFK New York/ Philadelphia 06:00 - 22:59
New York New York - La Guardia Airport LGA New York/ Philadelphia 07:00 - 21:59
Newark Newark Liberty International Airport EWR New York/ Philadelphia 07:00 - 22:59
Orlando Orlando International Airport MCO Orlando 07:00 - 21:59
Phoenix Phoenix Sky Harbor International Airport PHX Phoenix 07:00 - 21:59
Salt Lake City Salt Lake City International Airport SLC 07:00 - 21:59
San Diego San Diego International Airport SAN Southern California 06:00 - 22:59
San Francisco San Francisco International Airport SFO Northern California 07:00 -22:59
Seattle Seattle-Tacoma International Airport SEA Seattle 07:00 - 21:59
Tampa Tampa International Airport TPA Tampa 07:00 - 22:59
Washington Ronald Reagan Washington National Airport DCA D.C. 06:00 - 21:59
Washington Washington Dulles International Airport IAD D.C. 07:00 - 22:59

As shown on the NPS Portfolio Pages, NextGen's impacts touch many locations and stakeholders throughout the National Airspace System (NPS) including locations outside of the Core 30 Airports. The following table provides information on the non-Core 30 Airports referenced throughout the NPS.

Airport Code Airport Name
AFAFairbanks International
AFFUnited States Air Force Academy Airfield
AFWFort Worth Alliance Airport
ANCTed Stevens Anchorage International
ANEAnoka County-Blaine Airport (Janes Field)
APACentennial Airport
ARRAurora Municipal Airport
AUSAustin-Bergstrom International
BEDLaurence G. Hanscom Field
BFIBoeing Field/King County International
BJCRocky Mountain Metropolitan
BKFBuckley Air Force Base
BKLCleveland Burke Lakefront Airport
BLIBellingham International
BURBob Hope Airport
BVYBeverly Municipal Airport
CAEColumbia Metropolitan Airport
CAGCraig-Moffat County
CGFCuyahoga County Airport
CHSCharleston Air Force Base/International
CLECleveland-Hopkins International Airport
CRQMcClellan-Palomar Airport
CVGCincinnati/Northern Kentucky International
DALDallas Love Field
DETColeman A. Young Municipal Airport
DRODurango-La Plata County
DTODenton Municipal Airport
DWHDavid Wayne Hooks Memorial Airport
FMCFlying Cloud Airport
FNLFort Collins-Loveland
FRGRepublic Airport
FTGFront Range
FTWFort Worth Meacham International Airport
FTYFulton County Airport-Brown Field
FXEFort Lauderdale Executive Airport
GEGSpokane International
GKYArlington Municipal Airport
GSPGreenville Spartanburg International
GTFGreat Falls International
GUCGunnison-Crested Butte Regional
GXYGreeley-Weld County
GYYGary/Chicago International
HAFHalf Moon Bay Airport
HDNYampa Valley Regional
HEFManassas Regional Airport-Harry P. Davis Field
HHRHawthorne Municipal Airport
HNDHenderson Executive Airport
HOUWilliam P. Hobby Airport
HPNWestchester County Airport
HWDHayward Executive Airport
ILGNew Castle Airport
ISMKissimmee Gateway Airport
ISPLong Island MacArthur Airport
IWAPhoenix-Mesa Gateway Airport
JACJackson Hole Airport
JAXJacksonville International
JNUJuneau International Airport
JQFConcord Regional Airport
LCKRickenbacker International
LGBLong Beach Airport
MCIKansas City International
MHTManchester-Boston Regional Airport
MKEGeneral Mitchell International
MMUMorristown Municipal Airport
MSYLouis Armstrong New Orleans International Airport
MTJMontrose Regional Airport
MTNMartin State Airport
MYFMontgomery Field
NKXMiramar MCAS
OAKMetropolitan Oakland International
OKCWill Rogers World Airport
OLVOlive Branch Airport
ONTOntario International Airport
OPFOpa-locka Executive Airport
ORLOrlando Executive Airport
OWDNorwood Memorial Airport
OXROxnard Airport
PAESnohomish County Airport (Paine Field)
PAOPalo Alto Airport of Santa Clara County
PBIPalm Beach International Airport
PDKDeKalb-Peachtree Airport
PDXPortland International (OR)
PIESt. Petersburg-Clearwater International Airport
PSPPalm Springs International Airport
PTKOakland County International Airport
PVDTheodore Francis Green State
PWKChicago Executive Airport
PWMPortland International Jetport (ME)
RDURaleigh-Durham International
RFDChicago/Rockford International
RICRichmond International
RILGarfield County Regional
RNOReno/Tahoe International
RYYCobb County Airport-McCollum Field
SATSan Antonio International
SDFLouisville International-Standiford Field
SFBOrlando Sanford International Airport
SGRSugar Land Regional Airport
SJCNorman Y. Mineta San Jose International Airport
SMFSacramento International Airport
SMOSanta Monica Municipal Airport
SNAJohn Wayne Airport-Orange County
STLLambert-St. Louis International
STPSt. Paul Downtown Holman Field
STSCharles M. Schulz - Sonoma County Airport
SWFStewart International Airport
SYRSyracuse Hancock International
TEBTeterboro
TEXTelluride Regional
TIWTacoma Narrows Airport
TKIMcKinney National Airport
TMBKendall-Tamiami Executive Airport
TYSMcGhee Tyson Airport
UGNWaukegan Regional Airport
VGTNorth Las Vegas Airport
VNYVan Nuys Airport
YIPWillow Run Airport

### FAA Facility Information

The NPS portfolio pages reference several types of FAA facilities where NextGen capabilities have been implemented. As defined in FAA Order 7110.65, an Air Route Traffic Control Center (ARTCC) is a facility established to provide air traffic control service to aircraft operating on Instrument Flight Rules (IFR) flight plans within controlled airspace and principally during the en route phase of flight. There are several different facilities that separate traffic in terminal areas. In some locations an air traffic control tower (ATCT) provides this service, while in other areas a designated Terminal Radar Approach Control (TRACON) facility is responsible. Some towers and TRACONs are co-located.

ATCTs, TRACONs and ARTCCs referenced on the NPS
Code Facility
A80Atlanta TRACON
A90Boston TRACON
C90Chicago TRACON
CLECleveland Tower
CLTCharlotte Tower
CVGCincinnati Tower
D01Denver TRACON
D10Dallas-Ft. Worth TRACON
D21Detroit TRACON
I90Houston TRACON
L30Las Vegas TRACON
M03Memphis TRACON
M98Minneapolis TRACON
MIAMiami Tower
N90New York TRACON
NCTNorthern California TRACON
P50Phoenix TRACON
P80Portland TRACON
PCTPotomac TRACON
S46Seattle TRACON
S56Salt Lake City TRACON
SCTSouthern California TRACON
T75St. Louis TRACON
ZABAlbuquerque Center
ZAUChicago Center
ZBWBoston Center
ZDCWashington Center
ZDVDenver Center
ZFWFort Worth Center
ZHUHouston Center
ZIDIndianapolis Center
ZJXJacksonville Center
ZKCKansas City Center
ZLALos Angeles Center
ZLCSalt Lake City Center
ZMAMiami Center
ZMEMemphis Center
ZMPMinneapolis Center
ZNYNew York Center
ZOAOakland Center
ZOBCleveland Center
ZSESeattle Center
ZTLAtlanta Center

## Metroplex Information

The NPS website provides an overview of several National Airspace System (NAS) metroplexes, showing their importance to the area in which they are located and to the NAS as a whole.

### Metroplex Definition

Each metroplex is a unique system of airports, aircraft, weather patterns and geography. The following is a consolidated list of the airports in each of the metroplexes on the NPS. Due to data availability, all the airports included for a metroplex may not be included in the data describing it. For the full name of an airport, please see the individual metroplex page.

Metroplexes
Metroplex Airports
Atlanta ATL; FTY; PDK; RYY.
Boston BDL; BED; BOS; BVY; MHT; OWD; PVD.
Charlotte CAE; CLT; GSO; GSP; JQF; RDU.
Chicago ARR ; DPA; GYY; MDW; MKE; ORD; PWK; RFD; UGN.
Cleveland BKL; CGF; CLE.
D.C. BWI; DCA; HEF; IAD; MTN.
Denver APA; BJC; DEN.
Detroit DET; DTW; PTK; YIP.
Houston DWH; HOU; IAH; SGR.
Las Vegas Valley HND; LAS; VGT.
Memphis MEM; OLV.
Minneapolis-St. Paul ANE; FCM; MSP; STP.
New York/Philadelphia EWR; FRG; HPN; ILG; ISP; JFK; LGA; MMU; PHL; PNE; SWF; TEB.
North Texas ADS; AFW; DAL; DFW; DTO; FTW; GKY; TKI.
Northern California HWD; OAK; PAO; SFO; SJC; SMF.
Orlando ISM; MCO; ORL; SFB.
Phoenix IWA; PHX.
Seattle BFI; PAE; SEA; TIW.
South Florida FLL; FXE; MIA; OPF; TMB.
Southern California BUR; HHR; LAX; LGB; ONT; OXR; PSP; SAN; SMO; SNA; VNY.
Tampa PIE; SRQ; TPA.

### Metroplex Traffic

The Metroplex Traffic section provides an overview of the aircraft types operating within the selected metroplex. This traffic mix is segmented as Commercial Air Carrier, General Aviation (GA) and Military operations. Commercial Air Carrier operations are further defined as the sum of Air Carrier and Air Taxi operations. The information is calculated from FAA Operational Network (OPSNET) data and is reported for FY 2009 - 2013.

The data set used to calculate this metric includes all the airports in each metroplex, although some airports may not support a given traffic segment. The data include both itinerant and local operations for the airports being measured. Per the OPSNET user guide:

• Local: Operations that remain in the local traffic pattern, execute simulated instrument approaches or low passes at the airport, and operations to or from the same airport within a designated practice area within a 20-miles radius of the tower.
• Itinerant: Operations that land at an airport arriving from outside the airport area, or depart from an airport and leave the airport area.

### Average Daily Scheduled Flights

The number of average daily scheduled flights is calculated for FY 2009 through FY 2013 and is based on the sum of the departures scheduled to and from all the metroplex airports that offer scheduled service and for which data are available. To obtain the average daily value, the yearly sum is divided by the number of days in the year (366 for FY 2012 and 365 for the rest of the years). The source for this data is the U.S. Department of Transportation, Bureau of Transportation Statistics (BTS) Air Carrier Statistics Database.

Since some airports do not have scheduled service, this information may not include data for all the airports within the metroplex. The following table identifies the airports included in this information for each metroplex. The airports listed may not have scheduled service for each year provided in the data set. To see the full names for these airports, please consult the appropriate metroplex page.

Metroplexes
Metroplex Airports
Atlanta ATL.
Boston BDL; BED; BOS; MHT; PVD.
Charlotte CAE; CLT; GSO; GSP; RDU.
Chicago MDW; MKE; ORD; RFD.
Cleveland CLE.
Denver DEN.
Detroit DTW.
Houston HOU; IAH.
Las Vegas Valley LAS; VGT.
Memphis MEM.
Minneapolis-St. Paul MSP.
New York/Philadelphia EWR; FRG; HPN; ISP; JFK; LGA; PHL; SWF.
North Texas AFW; DAL; DFW.
Northern California OAK; SFO; SJC; SMF.
Orlando MCO; SFB.
Phoenix PHX.
Seattle BFI; SEA.
South Florida FLL; MIA; PBI.
Southern California BUR; LAX; LGB; ONT; OXR; PSP; SAN; SNA.
Tampa PIE; SRQ; TPA.

### Projected Annual Benefits

Several of the metroplex pages contain projected annual benefits of NextGen airspace and procedure improvements. The source of this data is the FAA Office of Airspace Services.

The projected benefits shown on the NPS are annual values expected to accrue upon completion of the near-term NextGen procedural improvements implemented by the Metroplex program. They are based on the FAA's preliminary assessment of proposed airspace improvements compared to operations before any improvements were made and may use a different set of airports than those listed above. The value of the projected fuel savings is based on prevailing fuel prices at the time the analysis was conducted and could therefore differ between metroplexes. Continued study may warrant that the operational vision used to calculate these projected benefits be modified, thus changing the value of the projected benefits.

## NextGen Priorities — Joint Implementation Plan Milestones

The NextGen Priorities Joint Implementation Plan (PDF) is the result of an FAA-aviation community collaborative effort in response to a request from the House of Representatives Committee on Transportation and Infrastructure, Subcommittee on Aviation. Through the NextGen Advisory Committee (NAC), a federal advisory committee, the FAA and the aviation industry has agreed to high-level commitments that will provide significant near-term benefits to National Airspace System users in four focus areas: Multiple Runway Operations, Performance Based Navigation, Surface Operations and Data Communications. Commitments will be completed over three years and include operational implementations of capabilities at specific locations; pre-implementation activities, such as safety analyses or engineering studies; and commitments by industry to complete activities required for successful implementation.

The milestones in the plan are a subset of the overall series of programs and activities the FAA is executing for NextGen, which are broader in scope and timeline, creating a more extensive transformation of National Airspace System (NAS) operations.

## Acronym Information

The NPS utilizes many acronyms to refer to NextGen and legacy technologies, programs, locations and data sources. Although most acronyms are accompanied by a brief description of their meaning, the following table provides links to sources that may be used to find additional detail and meaning behind these acronyms. Please note that acronyms occasionally have different meanings depending on their context, so a definition in these sources, while accurate, may not reflect the intended definition.