Travel Impact Model 1.8.0
(Implementation of the Travalyst Shared Framework by Google)
Table of contents
- Model overview
- Example emission estimation
- Legal base for model data sharing
- API access
- Data quality
In this document we describe the modeling assumptions and input specifications behind the Travel Impact Model (TIM), a state of the art emission estimation model that Google’s Travel Sustainability team has compiled from several external data sources. The TIM aims at predicting carbon emissions for future flights to help travelers plan their travel.
For each flight, the TIM considers several factors, such as the Great Circle distance between the origin and destination airports and the aircraft type being used for the route. Actual carbon emissions at flight time may vary depending on factors not known at modeling time, such as speed and altitude of the aircraft, the actual flight route, and weather conditions at the time of flight.
Flight level emission estimates
Flight level CO2 estimates
The Travel Impact Model estimates fuel burn based on the Tier 3 methodology for emission estimates from the Annex 1.A.3.a Aviation 2019 published by the European Environment Agency (EEA).
There are several resources about the EEA model available:
Additionally, the Travel Impact Model updates the fuel burn to emissions conversion factor to align with the ISO 14083 Fuel Heat Combustion factor and CORSIA Life Cycle Assessment, and breaks down emissions estimates into Well-to-Tank (WTT) and Tank-to-Wake (TTW) emissions.
Tank-to-Wake emissions account for emissions produced by burning jet fuel during flying, take-off and landing. Well-to-Tank emissions account for emissions generated during the production, processing, handling and delivery of jet fuel. When summed, Well-to-Wake (WTW) emissions account for the full life cycle of flying.
The EEA model takes the efficiency of the aircraft into account. As shown in Figure 1, a typical flight is modeled in two stages: take off and landing (LTO, yellow) and cruise, climb, and descend (CCD, blue).
For each stage, there are aircraft-specific and distance-specific fuel burn estimates. Table 1 shows an example fuel burn forecast for a Boeing 787-9 (B789) aircraft:
By using these numbers together with linear interpolation or extrapolation, it is possible to deduce the emission estimate for flights of any length on supported aircraft:
- Interpolation is used for flights that are in between two distance data points. As a theoretical example, a 5250 nautical miles flight on a Boeing 787-9 will burn approximately 54902.5 kg of fuel during the CCD phase (where 54902.5 equals 52375 + (57430 - 52375)/2, with figures for 5000nm and 5500nm taken from Table 1).
- Extrapolation is used for flights that are either shorter than the smallest supported distance, or longer than the longest supported distance for that aircraft type.
- The Lower Heating Value from ISO 14083 (43.1 MJ/kg averaged over US and EU numbers from source Table I1 and Table I2) and CORSIA Carbon Intensity value (74 gCO2e/MJ from source Table 5) are used to calculate the jet fuel combustion to CO2 conversion factor of 3.1894. The CORSIA Life Cycle Assessment methodology is used to calculate a WTT CO2e emissions factor of 0.6465 (TTW 15g CO2e/MJ added to the TTW 74 gCO2e/MJ Carbon Intensity to total up to the WTW lifecycle Carbon Intensity of 89 gCO2e/MJ from source page 22 and Table 7). The factors used are as follows:
CO2e is short for CO2 equivalent and includes Kyoto Gases (GHG) as described here. Warming effects produced by short-lived climate pollutants (such as contrail-induced cirrus clouds) are not yet included in CO2e as calculated by the Travel Impact Model.
There is information for most commonly-used aircraft types in the EEA data, but some are missing. For missing aircraft types, one of the following alternatives is applied in ranked order:
- Supported using the Piano-X data set: If an aircraft type is supported in the Piano-X data set and a comparable type is supported both in the Piano-X and the EEA data set, a correction factor is derived by comparing the Piano-X output for both types across a range of missions. The correction factor will be applied to the LTO and CCD numbers of the comparable type in the EEA database.
- Supported by fallback to non-optimized aircraft type: If there are estimates in the EEA data set for an aircraft that is identical except for the lack of optimizations such as winglets or sharklets, the non-optimized counterpart is used for the estimate.
- Supported by fallback to previous generation aircraft type: If there are estimates in the EEA data set for a previous generation aircraft type in the same family, from the same manufacturer, the previous generation aircraft is used for the estimate.
- Supported by fallback to least efficient aircraft in the family: For umbrella codes that refer to a group of aircraft, the least efficient aircraft in the family will be assumed.
- Not supported: For aircraft types for which none of the cases above apply, there are no emissions estimates available.
See Appendix A for a table with detailed information about aircraft type support status.
Used for flight level emissions:
- EEA Report No 13/2019 1.A.3.a Aviation 1 Master emissions calculator 2019 (link)
- Piano-X aircraft database (link)
- CORSIA Eligible Fuels Life Cycle Assessment Methodology (link)
- ISO 14083 (link)
Breakdown from flight level to individual level
In addition to predicting a flight’s emissions, it is possible to estimate the emissions for an individual seat on that flight. To perform this estimate, it’s necessary to perform an individual breakdown based on three relevant factors:
- Number of total seats on the plane in each seating class (first, business, premium economy, economy)
- Number of occupied seats on the plane
- Amount of cargo being carried
The emission estimates are higher for premium economy, business and first seating classes because the seats in these sections take up more space. As a result, those seats account for a larger share of the flight's total emissions. Different space allocations on narrow and wide-body aircraft are considered using separate weighing factors.
Used to determine which aircraft type was used for a given flight:
- Aircraft type from published flight schedules
Used to determine seating configuration and calculate emissions per available seat:
- Aircraft Configuration/Version (ACV) from published flight schedules
- Fleet-level aircraft configuration information from the "Seats (Equipment Configuration) File" provided by OAG
Primary fallback for missing seat configuration
If there are no individual seat configuration numbers for a flight available from the published flight schedules, we query the fleet-level seating data for a unique match by carrier and aircraft. This is only possible in cases where a carrier uses the same seating configuration for all their aircraft of a certain aircraft model.
Outlier detection and basic correctness checking
If there are no individual seat configuration numbers for a flight available from the published flight schedules, nor from the fleet-level data, or if they are incorrectly formatted or implausible, the TIM uses aircraft-specific medians derived from the overall dataset instead. Basic correctness checks based on reference seat configurations for the aircraft are performed, specifically:
- The calculated total seat area for a flight is the total available seating
area. This is calculated based on seating data and seating class factors.
For example, the total seat area for a wide-body aircraft would be:
1.0 * num_economy_class_seats +
1.5 * num_premium_economy_class_seats +
4.0 * num_business_class_seats +
5.0 * num_first_class_seats
- The reference total seat area for an aircraft is roughly the median total seat area.
- During a comparison step: If the calculated total seat area for a given flight is within certain boundaries of the reference for that aircraft, the filed seating data from published flight schedules is used. Otherwise the reference total seat area is used.
Seating class factors
Seating parameters follow IATA RP 1726. An analysis of seat pitch and width in each seating class in typical plane configurations confirmed the accuracy of these factors.
- Narrow-body aircraft
- Economy and Premium Economy 1
- Business and First 1.5
- Wide-body aircraft
- Economy 1
- Premium Economy 1.5
- Business 4
- First 5
Passenger load factors are predicted based on historical passenger statistics. TIM uses a tiered approach to determine passenger load factors. High resolution, specific data (i.e. by route) is preferred where available, and in the absence of more granular data the model falls back to a generic value (i.e. global default) only when no suitable high resolution options are available.
Tier 1: Highly specific passenger load factors
For flights within, to, and from the United States, TIM uses historical data provided by the U.S. Department of Transportation Bureau of Transportation Statistics.
- Where data is available for a given carrier, route, and month of travel, use the average passenger load factor over the last 6 years.
- Where data is available for the given carrier and month of travel, but not the specific route, use the average passenger load factor across all routes over the last 6 years.
- If fewer than three years of data are available for averaging, we do not calculate an average, and fallback to the approach described below instead.
Tier 2: Global default passenger load factor
- For all other flights for which an equivalent public-domain dataset with similar granularity is not currently available, TIM falls back to use a load factor value of 84.5%. This value is derived from historical data for the U.S. from 2019.
- An analysis of load factors sourced from publically available airline investor reports indicates that this value is a good approximation for the passenger load factor globally.
Cargo load factors are not included.
Load factor data source specifics
- Only data from the last six years is used.
- Data is updated on a monthly basis (TIM version number will not increase).
- Any month of data for which the overall load factor (aggregated over all airlines and routes) differs more than 20% from the average load factor over the last 10 years is removed as an outlier month. March 2020 - February 2021 (inclusive) are removed from the data as a result.
- To account for patterns of seasonality that do not correspond with the exact month of travel (e.g. public holidays), the previous and next month are taken into account for the average load factor of any given month of travel. E.g. For future flights in March, we aggregate over all flights in February, March, and April.
Example emission estimation
Let’s consider the following flight parameters:
- Origin: Zurich ZRH
- Destination: San Francisco SFO
- Aircraft: Boeing 787-9
- Economy seats: 188
- Premium Economy seats: 21
- Business seats: 48
- First seats: 0
To get the total emissions for a flight, let’s follow the process below:
- Calculate great circle distance between ZRH and SFO: 9369 km (= 5058.9 nautical miles)
- Look up the static LTO numbers and the distance-based CCD number from aircraft performance data (see Table 1), and interpolate fuel burn for a 9369 km long flight:
- LTO 1727 kg of fuel burn
- CCD 52970 kg of fuel burn calculated
- 52375 + (5058.9 - 5000) * (57430 - 52375) / (5500 - 5000) = 52970
- Sum LTO and CCD number for total flight-level result:
- 1727 kg + 52970 kg = 54697 kg of fuel burn
- Convert from fuel burn to CO2e emissions for total flight-level result:
- Well-to-Tank (WTT) emissions in kg of CO2e: 54697 * 0.6465 = 35362
- Tank-to-Wake (TTW) emissions in kg of CO2e: 54697 * 3.1894 = 174451
- Well-to-Wake (WTW) emissions in kg of CO2e: (54697 * 0.6465) + (54697 * 3.1894) = 209812
Once the total flight emissions are computed, let’s compute the per passenger break down:
- Determine which seating class factors to use for the given flight. In the ZRH-SFO example, we will use the wide-body factors (Boeing 787-9).
- Calculate the equivalent capacity of the aircraft according to the following
C = first_class_seats * first_class_multiplier + business_class_seats * business_class_multiplier + …
- In this specific example, the estimated area is:
0 * 5 + 48 * 4 + 1.5 * 21 + 188 * 1 = 411.5
- In this specific example, the estimated area is:
- Divide the total CO2e emissions by the equivalent capacity calculated above to get the CO2e emissions per economy passenger.
- Well-to-Tank (WTT) emissions in kg of CO2e: 35362 / 411.5 = 85.934
- Tank-to-Wake (TTW) emissions in kg of CO2e: 174451 / 411.5 = 423.939
- Well-to-Wake (WTW) emissions in kg of CO2e: 85.934 + 423.939 = 509.873
- Emissions per passenger for other cabins can be derived by multiplying by the corresponding cabin factor.
- Well-to-Tank (WTT) emissions in kg of CO2e: 85.934 * 5 = 429.67
- Tank-to-Wake (TTW) emissions in kg of CO2e: 423.939 * 5 = 2119.695
- Well-to-Wake (WTW) emissions in kg of CO2e: 509.873 * 5 = 2549.365
- Well-to-Tank (WTT) emissions in kg of CO2e: 85.934 * 4 = 343.736
- Tank-to-Wake (TTW) emissions in kg of CO2e: 423.939 * 4 = 1695.756
- Well-to-Wake (WTW) emissions in kg of CO2e: 509.873 * 4 = 2039.492
- Premium Economy:
- Well-to-Tank (WTT) emissions in kg of CO2e: 85.934 * 1.5 = 128.901
- Tank-to-Wake (TTW) emissions in kg of CO2e: 423.939 * 1.5 = 635.909
- Well-to-Wake (WTW) emissions in kg of CO2e: 509.873 * 1.5 = 764.81
- Well-to-Tank (WTT) emissions in kg of CO2e: 85.934
- Tank-to-Wake (TTW) emissions in kg of CO2e: 423.939
- Well-to-Wake (WTW) emissions in kg of CO2e: 509.873
- Scale to estimated load factor 0.845 by apportioning emissions to occupied seats:
- Well-to-Tank (WTT) emissions in kg of CO2e: 429.67 / 0.845 = 508.485
- Tank-to-Wake (TTW) emissions in kg of CO2e: 2119.695 / 0.845 = 2508.515
- Well-to-Wake (WTW) emissions in kg of CO2e: 2549.365 / 0.845 = 3017
- Well-to-Tank (WTT) emissions in kg of CO2e: 343.736 / 0.845 = 406.788
- Tank-to-Wake (TTW) emissions in kg of CO2e: 1695.756 / 0.845 = 2006.812
- Well-to-Wake (WTW) emissions in kg of CO2e: 2039.492 / 0.845 = 2413.6
- Premium Economy:
- Well-to-Tank (WTT) emissions in kg of CO2e: 128.901 / 0.845 = 152.546
- Tank-to-Wake (TTW) emissions in kg of CO2e: 635.909 / 0.845 = 752.555
- Well-to-Wake (WTW) emissions in kg of CO2e: 764.81 / 0.845 = 905.101
- Well-to-Tank (WTT) emissions in kg of CO2e: 85.934 / 0.845 = 101.697
- Tank-to-Wake (TTW) emissions in kg of CO2e: 423.939 / 0.845 = 501.703
- Well-to-Wake (WTW) emissions in kg of CO2e: 509.873 / 0.845 = 603.4
Note that the model generates emission estimates for all cabin classes, including cabin classes where the seat count is zero, as cabin classifications are not always consistent across data providers. Therefore, providing estimates for all cabin classes simplifies integration of TIM data with other datasets.
Legal base for model data sharing
The carbon emission estimate data are available via API under the Creative Commons Attribution-ShareAlike CC BY-SA 4.0 open source license (legal code).
Developer documentation is available on the Google Developers site for the Travel Impact Model API.
The model will be developed further over time, e.g. with improved load factors methodology or more fine grained seat area ratios calculation. New versions will be published.
A full model version will have four components: MAJOR.MINOR.PATCH.DATE, e.g. 184.108.40.20630101. The four tiers of change tracking are handled differently:
- Major versions: Changes to the model that would break existing client implementations if not addressed (e.g. changes in data types or schema) or major methodology changes (e.g. adding new data sources to the model that lead to major output changes). We expect these to be infrequent but they need to be managed with special care.
- Minor versions: Changes to the model that, while being consistent across schema versions, change the model parameters or implementation.
- Patch versions: Implementation changes meant to address bugs or inaccuracies in the model implementation.
- Dated versions: Model datasets are recreated with refreshed input data but no change to the algorithms regularly.
Adding Well-to-Tank (WTT) and Tank-to-Wake (TTW) emissions break-downs to all flight emissions. Updating the jet fuel combustion to CO2 conversion factor from the minimum value of 3.1672 to the value of 3.1894 (using Lower Heating Value from ISO 14083 and CORSIA Carbon Intensity value), and using the CORSIA Life Cycle Assessment methodology to implement a WTT CO2e emissions factor 0.6465. Reference: ISO, CORSIA.
Updating the jet fuel combustion to CO2 conversion factor from 3.15 based on the EEA methodology to 3.1672 to align with the CORSIA methodology’s recommended factor.
Adding carrier and route specific passenger load factors for flights from, to, and within the U.S., taking seasonality patterns into account. We are using data from the U.S. Department of Transportation Bureau of Transportation Statistics. For more details, see the section on load factors.
Adding a fleet-level source for seating configuration data. For airlines that don't file seating configuration information in flight schedules but use the same seating configuration for all their aircraft of a certain model, a fall back to the "Seats (Equipment Configuration) File" provided by OAG is performed.
Following recent discussions with academic and industry partners, we are adjusting the TIM to focus on CO2 emissions. While we strongly believe in including non-CO2 effects in the model long-term, the details of how and when to include these factors requires more input from our stakeholders as part of a governance model that’s in development. With this change, we are provisionally removing contrails effects from our CO2e estimates but will keep the labeling as “CO2e” in the model to ensure future compatibility.
We believe CO2e factors are critical to include in the model, given the emphasis on them in the IPCC’s AR6 report. We want to make sure that when we do incorporate them into the model, we have a strong plan to account for time of day and regional variations in contrails’ warming impact. We are committed to providing consumers the most accurate information as they make informed choices about their travel options.
We continue to invest into research and collaborate with leading scientists, NGOs, and partners to better incorporate contrails and other non-GHG impact into our model, and we look forward to sharing updates at a later date.
Initial public version of the Travel Impact Model.
The model described in this document produces estimates of carbon emissions. Emission estimates aim to be representative of what the typical emissions for a flight matching the model inputs would be. Estimates might differ from actual emissions based on a number of factors.
Actual flight distances: When modeling the distance between a given origin and destination, the Great Circle Distance between the origin and destination airport is used, as opposed to the actual distance flown.
This simplifying assumption enables the model to be used even when precise flight path information is not available, such as when computing emission estimates for future flights.
Aircraft types: The emissions model accounts for the equipment type as published in the flight schedules. The majority of aircraft types in use are covered. See Appendix A for a list of supported aircraft types.
Some aircraft types are supported by falling back to a related model thought to have comparable emissions. See Flight level emission estimates for more details.
If no reasonable approximation is available for a given aircraft, the model will not produce estimates for it.
Cargo load factors: Cargo load is not yet supported in the model.
Engine information: Beyond the aircraft type, there are other aircraft characteristics that can have an effect on the flight emissions (e.g. engine type, engine age, etc.) that are not currently included when computing emission estimates.
Fuel type: The emissions model assumes that all flights operate on 100% conventional fuel. Alternative fuel types (e.g. Sustainable Aviation Fuel) are not supported.
Seat configurations: If there are no seat configurations individual numbers for a flight available from published flight schedules, or if they are incorrectly formatted or implausible, aircraft specific medians derived from the overall dataset are employed.
Contrail-induced cirrus clouds: Warming effects produced by short-lived climate pollutants such as contrail-induced cirrus clouds are not yet included in emissions as calculated by the Travel Impact Model.
The CO2 estimates were validated by comparing against a limited amount of real-world fuel burn data. The finding was that the TIM is underestimating by 7% on average.
The EEA guidebook (chapter 4) cites sources from ICAO that estimate the uncertainty of the LTO factors between 5 and 10%. The CCD factor uncertainty is estimated between 15 and 40%.
We are welcoming feedback and enquiries. Please get in touch using this form.
CCD: The flight phases Climb, Cruise, and Descend occur above a flight altitude of 3,000 feet.
CO2: Carbon dioxide is the most significant long-lived greenhouse gas in Earth's atmosphere. Since the Industrial Revolution anthropogenic emissions – primarily from use of fossil fuels and deforestation – have rapidly increased its concentration in the atmosphere, leading to global warming.
CO2e: CO2e is short for CO2 equivalent, and is a metric measure used to compare the emissions from various greenhouse gases on the basis of their global-warming potential (GWP), by converting amounts of other gases to the equivalent amount of carbon dioxide with the same global warming potential (source).
Contrail-induced cirrus clouds: Cirrus clouds are atmospheric clouds that look like thin strands. There are natural cirrus clouds, and also contrail induced cirrus clouds that under certain conditions occur as the result of a contrail formation from aircraft engine exhaust.
CORSIA: Carbon Offsetting and Reduction Scheme for International Aviation, a carbon offset and reduction scheme to curb the aviation impact on climate change developed by the International Civil Aviation Organization.
Radiative Forcing (RF): Radiative Forcing is the instantaneous difference in radiative energy flux stemming from a climate perturbation, measured at the top of the atmosphere.
Effective Radiative Forcing (ERF): Radiative forcing effects can create rapid responses in the troposphere, which can either enhance or reduce the flux over time, and makes RF a difficult proxy for calculating long-term climate effects. ERF attempts to capture long-term climate forcing, and represents the change in net radiative flux after allowing for short-term responses in atmospheric temperatures, water vapor and clouds.
European Environment Agency (EEA): An agency of the European Union whose task is to provide sound, independent information on the environment.
Google’s Travel Sustainability team: A team at Google focusing on travel sustainability, based in Zurich (Switzerland) and Cambridge (U.S.), with the goal to enable users to make more sustainable travel choices.
Great circle distance: Defined as the shortest distance between two points on the surface of a sphere when measured along the surface of the sphere.
ICAO: The International Civil Aviation Organization, a specialized agency of the United Nations.
ISO 14083: The international standard that establishes a common methodology for the quantification and reporting of greenhouse gas (GHG) emissions arising from the operation of transport chains of passengers and freight (source), published by the International Organization for Standardization (ISO).
LTO: The flight phases Take Off and Landing occur below a flight altitude of 3000 feet at the beginning and the end of a flight. They include the following phases: taxi-out, taxi-in (idle), take-off, climb-out, approach and landing.
TIM: The Travel Impact Model described in this document.
Short Lived Climate Pollutants (SLCPs): Pollutants that stay in the atmosphere for a short time (e.g. weeks) in comparison to Long Lived Climate Pollutants such as CO2 that stay in the atmosphere for hundreds of years.
Appendix A: Aircraft type support
|Aircraft full name||IATA aircraft code||Mapping (ICAO aircraft code)||Support status|
|Airbus A220-100||221||Supported via correction factor derived from Piano data|
|Airbus A220-300||223||Supported via correction factor derived from Piano data|
|Airbus A300-600 Freighter||ABY||A306||Direct match in EEA|
|Airbus A300-600/600C||AB6||A306||Direct match in EEA|
|Airbus A300B2/B4/C4||AB4||A30B||Direct match in EEA|
|Airbus A310||310||A310||Direct match in EEA|
|Airbus A310-300||313||A310||Direct match in EEA|
|Airbus A318||318||A318||Direct match in EEA|
|Airbus A318/A319/A320/A321||32S||A321||Mapped to least efficient in family|
|Airbus A319||319||A319||Direct match in EEA|
|Airbus A320-100/200||320||A320||Direct match in EEA|
|Airbus A320neo||32N||Supported via correction factor derived from Piano data|
|Airbus A321||321||A321||Direct match in EEA|
|Airbus A321neo||32Q||Supported via correction factor derived from Piano data|
|Airbus A330||330||A332||Mapped to least efficient in family|
|Airbus A330-200||332||A332||Direct match in EEA|
|Airbus A330-300||333||A333||Direct match in EEA|
|Airbus A330-900neo||339||A333||Supported via correction factor derived from Piano data|
|Airbus A340||340||A345||Mapped to least efficient in family|
|Airbus A340-300||343||A343||Direct match in EEA|
|Airbus A340-500||345||A345||Direct match in EEA|
|Airbus A340-600||346||A346||Direct match in EEA|
|Airbus A350||350||A350||Mapped to least efficient in family|
|Airbus A350-900||359||A350||Direct match in EEA|
|Airbus A380||380||A380||Mapped to least efficient in family|
|Airbus A380-800||388||A380||Direct match in EEA|
|Airbus A320 (Sharklets)||32A||Supported via correction factor derived from Piano data|
|Airbus A321 (Sharklets)||32B||Supported via correction factor derived from Piano data|
|Airbus A350-1000||351||A350||Supported via correction factor derived from Piano data|
|Antonov AN-148-100||A81||AN148||Direct match in EEA|
|Antonov AN-24||AN4||AN24||Direct match in EEA|
|Antonov AN-26/30/32||AN6||AN32||Mapped to least efficient in family|
|Antonov AN-32||A32||AN32||Direct match in EEA|
|ATR 42-300/320||AT4||ATR42||Mapped to similar model|
|ATR 42-500||AT5||ATR42||Direct match in EEA|
|ATR 42/ATR 72||ATR||ATR72||Mapped to least efficient in family|
|ATR 72||AT7||ATR72||Direct match in EEA|
|Avro Regional Jet Avroliner||ARJ||Not supported|
|Avro Regional Jet RJ100 Avroliner||AR1||Not supported|
|Avro Regional Jet RJ85 Avroliner||AR8||Not supported|
|Beechcraft 1900||BE1||Not supported|
|Beechcraft 1900/1900C||BES||Not supported|
|Beechcraft 1900D||BEH||Not supported|
|Beechcraft C99 Airliner||BE9||Not supported|
|Beechcraft Light Aircraft twin engine||BET||Not supported|
|Boeing 717-200||717||B717||Direct match in EEA|
|Boeing 737||737||B734||Mapped to least efficient in family|
|Boeing 737 Freighter||73F||B734||Mapped to least efficient in family|
|Boeing 737-200||732||B732||Direct match in EEA|
|Boeing 737-200||73M||B732||Direct match in EEA|
|Boeing 737-200/200 Advanced||73S||B732||Direct match in EEA|
|Boeing 737-300||733||B733||Direct match in EEA|
|Boeing 737-300||73N||B733||Direct match in EEA|
|Boeing 737-300 (winglets)||73C||B733||Mapped to non-optimized aircraft|
|Boeing 737-400||734||B734||Direct match in EEA|
|Boeing 737-400||73Q||B734||Direct match in EEA|
|Boeing 737-500||735||B735||Direct match in EEA|
|Boeing 737-500 (winglets)||73E||B735||Mapped to non-optimized aircraft|
|Boeing 737-600||736||B736||Direct match in EEA|
|Boeing 737-700||73G||B737||Direct match in EEA|
|Boeing 737-700 (winglets)||73W||Supported via correction factor derived from Piano data|
|Boeing 737-800||738||B738||Direct match in EEA|
|Boeing 737-800 (Scimitar Winglets)||7S8||Supported via correction factor derived from Piano data|
|Boeing 737-800 (winglets)||73H||Supported via correction factor derived from Piano data|
|Boeing 737-900||739||B739||Direct match in EEA|
|Boeing 737-900 (winglets)||73J||B739||Mapped to non-optimized aircraft|
|Boeing 737MAX 8||7M8||Supported via correction factor derived from Piano data|
|Boeing 737MAX 9||7M9||Supported via correction factor derived from Piano data|
|Boeing 747||747||B744||Mapped to least efficient in family|
|Boeing 747 Freighter||74F||B744||Mapped to least efficient in family|
|Boeing 747-400||744||B744||Direct match in EEA|
|Boeing 747-400 Mixed||74E||B744||Direct match in EEA|
|Boeing 747-400F Freighter||74Y||B744||Direct match in EEA|
|Boeing 747-8F (Freighter)||74N||B744||Mapped onto older model|
|Boeing 747-8I||74H||B744||Mapped onto older model|
|Boeing 757||757||B753||Mapped to least efficient in family|
|Boeing 757-200||752||B752||Direct match in EEA|
|Boeing 757-200 (winglets)||75W||Supported via correction factor derived from Piano data|
|Boeing 757-300||753||B753||Direct match in EEA|
|Boeing 757-300 (winglets)||75T||B753||Mapped to non-optimized aircraft|
|Boeing 767||767||B764||Mapped to least efficient in family|
|Boeing 767-200||762||B762||Direct match in EEA|
|Boeing 767-300||763||B763||Direct match in EEA|
|Boeing 767-300 (winglets)||76W||Supported via correction factor derived from Piano data|
|Boeing 767-400||764||B764||Direct match in EEA|
|Boeing 777||777||B773||Mapped to least efficient in family|
|Boeing 777 Freighter||77F||B773||Mapped to least efficient in family|
|Boeing 777-200/200ER||772||B772||Direct match in EEA|
|Boeing 777-200F Freighter||77X||B772||Direct match in EEA|
|Boeing 777-200LR||77L||B772||Mapped to similar model|
|Boeing 777-300||773||B773||Direct match in EEA|
|Boeing 777-300ER||77W||B77W||Direct match in EEA|
|Boeing 787||787||B789||Mapped to least efficient in family|
|Boeing 787-10||781||Supported via correction factor derived from Piano data|
|Boeing 787-8||788||B788||Direct match in EEA|
|Boeing 787-9||789||B789||Direct match in EEA|
|Bombardier CS100||CS1||Not supported|
|Bombardier CS300||CS3||Not supported|
|British Aerospace 146||146||BAE146||Direct match in EEA|
|British Aerospace Jetstream 31/32/41||JST||Not supported|
|British Aerospace Jetstream 32||J32||Not supported|
|British Aerospace Jetstream 41||J41||Not supported|
|Canadair Regional Jet||CRJ||CS900RJ||Mapped to least efficient in family|
|Canadair Regional Jet 100||CR1||Not supported|
|Canadair Regional Jet 1000||CRK||Not supported|
|Canadair Regional Jet 200||CR2||Not supported|
|Canadair Regional Jet 550||CR5||Supported via correction factor derived from Piano data|
|Canadair Regional Jet 700||CR7||CS700RJ||Direct match in EEA|
|Canadair Regional Jet 900||CR9||CS900RJ||Direct match in EEA|
|Cessna (Light Aircraft - single engine)||CNC||C208||Direct match in EEA|
|Cessna (Light Aircraft)||CNA||C208||Direct match in EEA|
|Cessna 208B Freighter||CNF||C208||Direct match in EEA|
|Cessna Citation||CNJ||C500||Direct match in EEA|
|Comac ARJ21-700||C27||Not supported|
|Convair 440/580/600/640 Freighter||CVF||Not supported|
|De Havilland-Bombardier DHC-4 Caribou||DHC||Not supported|
|De Havilland-Bombardier DHC-6 Twin Otter||DHT||DHC6||Direct match in EEA|
|De Havilland-Bombardier DHC-8 Dash 8||DH8||DHC8||Direct match in EEA|
|De Havilland-Bombardier DHC-8 Dash 8 Series 200||DH2||DHC8||Mapped to non-optimized aircraft|
|De Havilland-Bombardier DHC-8 Dash 8 Series 300||DH3||DHC8||Mapped to non-optimized aircraft|
|De Havilland-Bombardier DHC-8 Dash 8 Series 400||DH4||DHC8||Mapped to non-optimized aircraft|
|Embraer 170 Regional Jet||E70||E170||Direct match in EEA|
|Embraer 175 (Enhanced Winglets)||E7W||Supported via correction factor derived from Piano data|
|Embraer 175 Regional Jet||E75||E175||Direct match in EEA|
|Embraer 190 E2||290||E190||Mapped onto older model|
|Embraer 190 Regional Jet||E90||E190||Direct match in EEA|
|Embraer 195 E2||295||E195||Mapped onto older model|
|Embraer 195 Regional Jet||E95||E195||Direct match in EEA|
|Embraer EMB-110 Bandeirante||EMB||E110||Direct match in EEA|
|Embraer EMB-120 Brasilia||EM2||E120||Direct match in EEA|
|Embraer ERJ-135 Regional Jet||ER3||E135||Direct match in EEA|
|Embraer ERJ-135/140/145 Regional Jet||ERJ||Mapped to least efficient in family|
|Embraer ERJ-140 Regional Jet||ERD||E145||Direct match in EEA|
|Embraer ERJ-145 Regional Jet||ER4||E145||Direct match in EEA|
|Embraer RJ-170/175/190/195 Regional Jet||EMJ||Mapped to least efficient in family|
|Fairchild (Swearingen) Metro/Merlin||SWM||Not supported|
|Fairchild Dornier 328JET||FRJ||Not supported|
|Fokker 100||100||F100||Direct match in EEA|
|Fokker 50||F50||F50||Direct match in EEA|
|Fokker 70||F70||F70||Direct match in EEA|
|Ilyushin IL-76||IL7||IL76||Direct match in EEA|
|Ilyushin IL-96-300||IL9||IL96||Direct match in EEA|
|LET L410 Turbolet||L4T||L410||Direct match in EEA|
|McDonnell Douglas MD-11 Freighter||M1F||MD11||Direct match in EEA|
|McDonnell Douglas MD-80||M80||Not supported|
|McDonnell Douglas MD-83||M83||Not supported|
|McDonnell Douglas MD-87||M87||Not supported|
|McDonnell Douglas MD-88||M88||Not supported|
|McDonnell Douglas MD-90||M90||MD90||Direct match in EEA|
|Pilatus Brit-Norm BN-2A/B ISL/BN-2T||BNI||Not supported|
|SAAB 2000||S20||Not supported|
|Saab 340B||SFB||Not supported|
|SAAB SF 340||SF3||Not supported|
|Sukhoi Superjet 100-95||SU9||Not supported|
|Tupolev TU-154||TU5||Not supported|
|Xian Yunshuji MA-60||MA6||Not supported|
|Yakovlev YAK-40||YK4||Not supported|
|Yakovlev YAK-42||YK2||Not supported|