Reported as Cumulative Percent for NAS only

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 General Aviation Airports: A National Asset study Webpage and the Airport Master Record Form 5010 data.

The cumulative percent of qualified National, Regional, Local and Basic GA airports (as defined in the 2012 FAA GA Airports Study) with an LPV or LP procedure.

The cumulative percent of qualified National, Regional, Local and Basic GA airports (as defined in the 2012 FAA GA Airports Study) with an LPV or LP procedure.

Computations

The cumulative percent of qualified airports within scope that have an LPV or LP procedure.

Scope

This metric only includes LPV and LP qualified National, Regional, Local and Basic General Aviation (GA) airports (as defined in the 2012 FAA General Aviation Airports Study).

The yearly numbers of LPV and LP Procedures counted include those available at airports both with and without Instrument Landing System (ILS) Procedures.

Statistical Issues

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

Procedures are counted per the original publishing date and do not account for procedure updates or changes.

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

Completeness

LPV Data used to calculate metric was last updated on August 23 2012

The General Aviation Airports: A National Asset study was published in May 2012

Additional Notes

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

LPV is similar to LNAV/VNAV except it is much more precise (40m lateral limit), enables descent to 200-250 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. All airports with a qualified runway should have an LP or LPV approach by 2018.

Reported as Count of Airports for NAS only

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 General Aviation Airports: A National Asset study.

The count of National, Regional, Local and Basic GA airports (as defined in the 2012 FAA GA Airports Study) without an ILS that have an initial LPV or LP procedure published in the indicated year.

Computations

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

Scope

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

• Not be a primary airport as defined in the 2012 GA Airports Study,
• Be listed as either a National, Regional, Local or Basic GA airport in the 2012 GA Airports Study, and
• Not have any Instrument Landing System (ILS) Procedures

Statistical Issues

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

Procedures are counted per the original publishing date and do not account for procedure updates or changes.

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

Completeness

LPV Data used to calculate the metric was last updated on August 23 2012.

The General Aviation Airports: A National Asset study was published in May 2012.

Additional Notes

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

LPV is similar to LNAV/VNAV except it is much more precise (40m lateral limit), enables descent to 200-250 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.

Reported as Number of People for NAS only

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 65 decibels (dB) Day Night Sound Level (DNL).

Formula

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

Where POP65_i 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 2000 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. POPREL_j is the number of people relocated from the DNL 65 dB contour in the jth FAA region. This data is only available for the years 2000 - 2005. Beginning in 2006, the data is no longer collected by the FAA.

Source: FAA Portfolio of Goals & FAA Office of Environment and Energy

Computations

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 2000 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. PROPRELj is the number of people relocated from the DNL 65 dB contour in the jth FAA region since the year 2000.

Scope

This 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) of 65 decibels (dB). DNL is the 24-hour average sound level, in dB, obtained from the accumulation of all events with the addition of 10 dB penalty to sound levels occurring at night (from 10 pm up to 7 am). The weighting of the nighttime events accounts for the increased interference effect of noise during the night when ambient levels are lower.

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 US airports.

Completeness

No actual count is made of the number of people exposed to 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 ICAO task group and was most recently validated for a sample of airport-specific cases.

Additional Notes

The FAA migrated from the Model for Assessing Global Exposure to the Noise of Transport Aircraft (MAGENTA) to the Aviation Environmental Design Tool (AEDT) with the 2011 annual report. This KPI is calculated for calendar year (CY), and is rounded to three significant figures.

Reported as Kilograms for NAS only

Desired Trend: Decrease

Source: FAA Office of Environment and Energy

Estimated quantity of Carbon Dioxide (CO2) emitted by aircraft engines.

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 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 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 properly 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 the performance target 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 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 Enhanced Traffic Management System (ETMS) are assessed to remove any anomalies, check for completeness, and pre-processed for input to the AEDT model. ETMS 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, ETMS 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. We do expect that in the future, aircraft and engine technology improvements or air traffic management improvements or both may not be enough to offset traffic growth, congestion and delays. In addition, the current metric for measuring and tracking fuel efficiency may not adequately capture performance to the degree that would allow future decisions on technological and operational considerations.

Additional Notes

This KPI is calculated for calendar year (CY)

Reported as Kilograms per Kilometer for NAS only

Desired Trend: Increase

Source: FAA Office of Environment and Energy

Estimated fuel Burn in kilograms per kilometer

Formula

$$\frac{\text{Fuel Burn (Tg)}}{\text{Distance (Billions of Kilometers)}}$$

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 against this target 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 properly 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 the performance target 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 Enhanced Traffic Management System (ETMS) are assessed to remove any anomalies, check for completeness, and pre-processed for input to the AEDT model. ETMS 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, ETMS 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. We do expect that in the future, aircraft and engine technology improvements or air traffic management improvements or both may not be enough to offset traffic growth, congestion and delays. In addition, the current metric for measuring and tracking fuel efficiency may not adequately capture performance to the degree that would allow future decisions on technological and operational considerations.

Additional Notes

This KPI is calculated for calendar year (CY)

Reported as Minutes per Flight for Core Airports during Core Hours

Desired Trend: Decrease

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

During Core Hours (7:00 – 21:59 local), the yearly average of the difference between Gate-Out time and Wheels-Off time for flights between the selected airport and any of the Aviation System Performance Metrics airports. Flights must depart during Core Hours, but may arrive outside them.

Formula

Taxi-Out Time is calculated as the average over all flights in the fiscal year defined within the scope.

$$\frac{{\sum }_F\mathrm{TO}}{n_F}$$

where F is the set of all flights defined within Scope, TO is the Taxi-Out time for the flight, and n_F is the number of F

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 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 Core Hours (however it can still arrive at the destination outside the Core Hours).

Statistical Issues

Calculations are based on individual ASQP flight data. The list of ASQP reporting carriers is subject to change.

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 Aviation Performance Metrics (APM) 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.

Additional Notes

The Technical Directive outlining the reporting carriers is available on the Bureau of Transportation Statistics (BTS) webpage.

Core Hours are defined as 7:00 – 21:59 local time.

Reported as Minutes per Flight for Core Airports during Core Hours

Desired Trend: Decrease

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

During Core Hours (7:00 – 21:59 local), the yearly average of the difference between Wheels-On time and Gate-In time for flights between the selected airport and any of the Aviation System Performance Metrics airports. Flights may depart outside Core Hours, but must arrive during them.

Formula

Taxi-In Time is calculated as the average over all flights in the fiscal year defined within the scope.

$$\frac{{\sum }_F\mathrm{TI}}{n_F}$$

where F is the set of all flights defined within Scope, TI is the Taxi-In time for the flight, and n_F is the number of F

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 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 Core Hours (but may depart the origin outside Core Hours).

Statistical Issues

Calculations are based on individual ASQP flight data. The list of ASQP reporting carriers are subject to change.

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 Aviation Performance Metrics (APM) 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.

Additional Notes

The Technical Directive outlining the reporting carriers is available on the Bureau of Transportation Statistics (BTS) webpage.

Core Hours are defined as 7:00 – 21:59 local time.

Reported as Minutes per Flight for Core Airports during Core Hours

Desired Trend: Decrease

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

During Core Hours (7:00 – 21:59 local), the yearly average of the difference between the Actual Gate-In time at the selected airport and the Actual Gate-Out time at the origin (any Aviation System Performance Metrics airport). Flights may depart outside Core Hours, but must arrive during them.

Formula

$$\frac{{\sum }_F{\mathrm{BT}}^{\mathrm{act}}}{n_F}\text{for each G}$$

where

$${\mathrm{BT}}^{\mathrm{act}}=(t_{\mathrm{in}}^{\mathrm{act}}-t_{\mathrm{out}}^{\mathrm{act}})$$

and where G are the groups defined within Scope, F are all flights over the year within each group and n_F is the number of F

Computations

Average Gate to Gate Time over all flights in the fiscal year for each group defined within 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 Core Hours (but may depart the origin outside Core Hours).

Statistical Issues

The list of ASQP reporting carriers is subject to change.

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

Calculations are based on individual ASQP flight 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 Aviation Performance Metrics (APM) 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.

Additional Notes

The Technical Directive outlining the Reporting Carriers is available on the Bureau of Transportation Statistics (BTS) webpage.

Core Hours are defined as 7:00 – 21:59 local time.

Reported as Minutes per Flight for Core Airports during Core Hours

Desired Trend: Decrease

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

During Core Hours (7:00 – 21:59 local), the yearly average of the difference between the Actual Gate-In Time and the Scheduled Gate-In time for flights between the selected airport and any of the Aviation System Performance Metrics airports. The delay for each fiscal year is calculated based on the 0.5 - 99.5 percentile of the distributions for the year. Flights may depart outside Core Hours, but must arrive during them.

Formula

$$\frac{{\sum }_F\mathrm{ID}}{n_F}\text{for each G}$$

where

$$\mathrm{ID}=(t_{\mathrm{in}}^{\mathrm{act}}-t_{\mathrm{in}}^{\mathrm{sch}})$$

and where G are the groups defined within Scope, F are all flights over the year within each group and n_F is the number of F

Computations

Average Gate In Delay against schedule over all flights in the fiscal year for each group defined within 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 Core Hours (but may depart the origin outside Core Hours).

Statistical Issues

The list of ASQP reporting carriers is subject to change.

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

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 Aviation Performance Metrics (APM) 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.

Additional Notes

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 reporting carriers is available on the Bureau of Transportation Statistics (BTS) webpage.

Core Hours are defined as 7:00 – 21:59 local time.

Reported as Nautical Miles per Flight for Core Airports

Desired Trend: Decrease

Source: MITRE/Performance Based Navigation Dashboard and Analysis System

The distance flown while maintaining a level altitude from when an aircraft begins its descent until it reaches the runway threshold, averaged for the fiscal year.

Formula

$$=\frac{{\sum }_{F}D}{n_F}$$

Where D is the distance flown at level flight, F is the set of all flights in Scope, and n_F is the number of F

Computations

The sum of the total distance flown at level flight for all flights within Scope divided by the count of all flights within Scope.

Scope

Flights are restricted to jet arrivals at the designated airport.

Additional Notes

This metric is calculated using all hours.

Reported as Count per Flight for Core Airports

Desired Trend: Decrease

Source: MITRE/Performance Based Navigation Dashboard and Analysis System

The count of instances when an arriving aircraft will maintain a single altitude during their descent to an airport averaged for the fiscal year.

Formula

$$=\frac{{\sum }_F\mathrm{LO}}{n_F}$$

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

Computations

The sum of the count of level offs for each flight within Scope; divided by the total number of flights within the Scope.

Scope

Flights are restricted to jet arrivals at the designated airport.

Additional Notes

This metric is calculated using all hours.

Reported as Minutes per Flight for Selected City Pairs during Core Hours (based on local time for the arrival airport).

Desired Trend: Decrease

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

During Core Hours (7:00-21:59 local), the yearly average of the difference between the Actual Gate-In time at the selected airport and the Actual Gate-Out time at the origin airport. Flights may depart outside Core Hours, but must arrive during them.

Formula

$$\frac{{\sum }_F{\mathrm{BT}}^{\mathrm{act}}}{n_F}\text{for each G}$$

where

$${\mathrm{BT}}^{\mathrm{act}}=(t_{\mathrm{in}}^{\mathrm{act}}-t_{\mathrm{out}}^{\mathrm{act}})$$

and where G are the groups defined within Scope, F are all flights over the year within each group and n_F is the number of F

Computations

Average Gate to Gate Time over all flights within Scope between the selected City Pair in the fiscal year.

Scope

This KPI only measures ASQP reporting carriers operating domestic service between the selected airport pair. Additionally, only flights arriving at the destination airport within the Core Hours are included in this measurement (flights departing the origin outside the Core Hours but arriving at the destination within the Core Hours are included).

Statistical Issues

The list of ASQP reporting carriers is subject to change.

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.

No major carriers reported data between EWR and MDW until the third quarter of FY 2011; therefore data is only reported for FY 2012.

Reliability

The metric is derived directly from individual ASQP flight data obtained through the Aviation Performance Metrics (APM) 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.

Additional Notes

The Technical Directive outlining the ASQP reporting carriers is available on the Bureau of Transportation Statistics (BTS) webpage.

Core Hours are defined as 7:00 – 21:59 local time.