# Performance Snapshots Reference Guide

## Access

A global Air Traffic Management 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.

### 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.

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

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.

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

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.

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.

## Environment

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.

### Destination 2025 Targets (2018)

NAS-Wide Energy Efficiency:
3.56kg/km
Noise Exposure:
300,000 people

### Noise Exposure

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
$∑ i = 1 n POP65 i - ∑ j = 1 9 POPREL j$

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 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. POPRELj 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.

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.

### CO2 Emissions

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 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. 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.

This KPI is calculated for calendar year (CY)

### NAS-Wide Energy Efficiency

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
$Fuel Burn (Tg) Distance (Billions of Kilometers)$
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 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. 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.

This KPI is calculated for calendar year (CY)

## Efficiency

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.

### 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 Aviation System Performance Metrics (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 fiscal year 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 (DOT).

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.

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

Reportable hours vary by airport and are based on local time. Additional reportable hours information is included in the Airports 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 fiscal year 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 (DOT).

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.

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

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

### Average Gate to Gate 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 the Actual Gate-In Time at the selected airport and the Actual Gate-Out time at the origin (any Aviation System Performance Metrics (ASPM) airport). Flights may depart outside reportable hours, but must arrive during them. Reportable hours vary by airport.

Formula
$∑ F BT act n F for each G$

where

$BT act = ( t in act - t out act )$

and where G are the groups defined within Scope, BlockTime( $BT act$ ) is defined as $t in act$ (the actual Gate-In time at the selected (arrival) airport) minus $t out act$ (the actual Gate-Out time at the departure airport); F are all flights over the year within each group and nF is the number of F.


Computations

Average Gate-to-Gate Time over all flights in the fiscal year 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 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.

Completeness

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

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.

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

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

### 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 Aviation System Performance Metrics (ASPM) 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 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 fiscal year 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 data reported to the Department of Transportation (DOT).

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.

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 (BTS) webpage.

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

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

Reported as Nautical Miles per Flight for Core Airports

Desired Trend: Decrease

Source: MITRE/Performance Based Navigation Dashboard and Analysis System

The distance flown in level flight as flights descend from cruise altitudes to the arrival airport, averaged for the fiscal year.

Formula
$= ∑ F D n F$

Where D D is the distance flown at level flight 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 sum of the total distance flown at level flight for all flights within the Scope, divided by the count of all flights within the Scope.

Scope

Flights are restricted to jet arrivals at the designated airport from any origin airport.

The distance in level flight is measured starting when each flight reaches Top-of-Descent (TOD) or 200 nautical miles from the airport, whichever is closer.

An aircraft must be in level flight for at least 50 seconds and the change in altitude has to be less than 200 feet for a segment to be considered level.

Statistical Issues

When calculating this metric, flight trajectories are segmented into linear segments, and are classified as either ascending, descending or level. Vertical gradients are used to classify these segments. The threshold on the vertical climb gradient for descent and level segments is between 30 and 50 feet per nautical mile.

This metric is calculated using all hours.

### Average Number of Level-offs Per Flight

Reported as Count per Flight for Core Airports

Desired Trend: Decrease

Source: MITRE/Performance Based Navigation Dashboard and Analysis System

The count of instances 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 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 from any origin airport.

The level flight counts are measured starting when each flight reaches Top-of-Descent (TOD) or 200 nautical miles from the airport, whichever is closer.

An aircraft must be in level flight for at least 50 seconds and the change in altitude has to be less than 200 feet for a segment to be considered level.

Statistical Issues

When calculating this metric, flight trajectories are segmented into linear segments, and are classified as either ascending, descending or level. Vertical gradients are used to classify these segments. The threshold on the vertical climb gradient for descent and level segments is between 30 and 50 feet per nautical mile.

This metric is calculated using all hours.

## City Pairs

When a traveler starts to plan a trip or when an airline operator starts to plan air service, they will look at pairs of cities or pairs of metropolitan areas. For airline operators, city-pair performance is the most direct way to connect two markets.

### Average Gate to Gate Time

Reported as Minutes per Flight for Selected City Pairs during reportable 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 reportable hours at the destination airport, the yearly average of the difference between the Actual Gate-In Time at the destination airport and the Actual Gate-Out time at the origin airport. Flights may depart outside reportable hours, but must arrive during them. The reportable hours vary by airport.

Formula
$∑ F BT act n F for each G$

where

$BT act = ( t in act - t out act )$

and where G are the groups defined within Scope, Block Time ( $BT act$) is defined as $t in act$ (the actual Gate-In time at the arrival airport) minus $t out act$ (the actual Gate-Out time at the departure airport); F are all flights over the year within each group and nF is the number of F.


Computations

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

Scope

This KPI only measures ASQP reporting carriers operating domestic service between the selected airport pair 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 outside the 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 (DOT).

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.

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

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

## Airports

Airports included on the NextGen Performance Snapshots (NPS) website are often identified by several characteristics, including airport code, name and city. Most of the airports are also listed as part of a Metroplex, a large geographic area covering many airports, serving major metropolitan areas and a diversity of aviation stakeholders. 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 2012 Airports Council International, North American Airport Traffic Summary preliminary data.

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 (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.

### Airport Information Table

City Airport Name Airport Code Metroplex Reportable Hours
Atlanta Hartsfield-Jackson Atlanta International ATL Atlanta 07:00 - 22:59
Baltimore Baltimore/Washington International Thurgood Marshall BWI DC 06:00 - 22:59
Boston General Edward Lawrence Logan International BOS Boston 06:00 - 21:59
Charlotte Charlotte-Douglas International CLT Charlotte 07:00 - 22:59
Chicago Chicago O'Hare International ORD Chicago 06:00 - 21:59
Chicago Chicago Midway International MDW Chicago 07:00 - 20:59
Dallas-Fort Worth Dallas/Fort Worth International DFW North Texas 07:00 - 21:59
Denver Denver International DEN Denver 07:00 - 21:59
Fort Lauderdale Fort Lauderdale-Hollywood International FLL South Florida 07:00 - 22:59
Houston George Bush Intercontinental — Houston IAH Houston 07:00 - 21:59
Las Vegas McCarran International LAS Las Vegas 07:00 - 21:59
Los Angeles Los Angeles International LAX Southern California 06:00 - 22:59
Memphis Memphis International MEM Memphis 00:00 - 23:59
Miami Miami International MIA South Florida 07:00 - 22:59
Minneapolis Minneapolis-St Paul International/Wold-Chamberlain MSP Minneapolis 07:00 - 22:59
New York New York LaGuardia Airport LGA New York/Philadelphia 07:00 - 21:59
New York John F. Kennedy International JFK New York/Philadelphia 06:00 - 22:59
Newark Newark Liberty International EWR New York/Philadelphia 07:00 - 22:59
Orlando Orlando International MCO Orlando 07:00 - 21:59