Air traffic management performance assessment using flight inefficiency metrics

Transport Policy ∎ (∎∎∎∎) ∎∎∎–∎∎∎

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Air traffic management performance assessment using flight inefficiency metrics Tom G. Reynolds 1 Institute for Aviation and the Environment, University of Cambridge 1‐5 Scroope Terrace, Cambridge, CB2 1PX, United Kingdom

art ic l e i nf o

Keywords: Air traffic management Aircraft operations Flight inefficiency metrics Environmental impacts Policy

a b s t r a c t Air traffic management has a fundamentally important role in reducing the environmental impacts of air transportation by reducing the inefficiencies in the paths flown by aircraft. The potential causes of flight inefficiency are discussed in this paper, followed by the development of flight inefficiency metrics to quantify the performance of the system. Metrics based on track extensions and fuel inefficiency are used with operational data to illustrate how quantifying inefficiencies in different flight phases can be used to indicate where the largest potential scope exists for improving the system and hence can be used to guide policy-makers as they evolve the air traffic management system. For example, the analyses presented here highlight the relative importance of allowing aircraft to fly closer to their optimal fourdimensional trajectories and reducing inefficiencies in high fuel burn phases of flight. It also discusses what operational and technical enablers might be appropriate to help achieve fuel burn and environmental impact reduction. & 2014 Elsevier Ltd. All rights reserved.

1. Introduction In an ideal air transportation system, all aircraft would fly their preferred four-dimensional trajectories between airports. For example, this could comprise the most direct route accounting for winds, at the most efficient altitude and speed profile leading to lowest fuel burn or time. However, real world constraints (such as the need to keep aircraft safely separated) often lead to aircraft flying less efficient trajectories and hence at greater fuel burn and environmental impact than the ideal. The practicalities of tactical Air Traffic Control (ATC) and strategic Air Traffic Management (ATM), which in this paper will be referred to jointly as “ATM”, influence the trajectories that aircraft fly, and hence improvements to the ATM system offer the potential for better environmental performance. The increasing attention being focused on environmental impacts of ATM is highlighted by the Asia and South Pacific Initiative to Reduce Emissions (ASPIRE) (ASPIRE, 2012) and the Atlantic Interoperability Initiative to Reduce Emissions (AIRE) (SESAR Joint Undertaking, 2010). Both programs are designed to quantify the extra fuel burn imposed by current ATM techniques in the Pacific and Atlantic oceanic airspaces, while also improving

E-mail address: [email protected] Current address: MIT Lincoln Laboratory, Air Traffic Control Systems Group, 244 Wood Street, Lexington, MA 02420, USA. 1

interoperability between international ATM providers. In ASPIRE trial flights, ATM constraints were removed as much as possible by giving them explicit priority over other aircraft and they saw fuel burn reductions of 5–6%, while even larger benefits are expected once new technologies associated with next generation ATM systems are introduced. For example, the Intergovernmental Panel on Climate Change suggests that improvements in ATM could help to improve overall fuel efficiency by 6–12% per flight (IPCC, 1999). Some air navigation service providers are incorporating fuel burn and carbon dioxide reduction as part of their environmental performance targets. For example, in the UK, NATS aims at reducing ATM CO2 emissions by an average of 10% per flight by 2020 against a 2006 baseline (NATS, 2013). The major ATM modernization initiatives in Europe (Single European Sky ATM Research (SESAR) (EUROCONTROL/EU, 2012)) and the US (Next Generation Air Transportation System (NextGen) (FAA, (2013)) have broader environmental impact reduction objectives encompassing noise, air quality and climate change rather than specific targets for ATM, but both identify ATM improvement as a crucial element in meeting their overall goals. In order to better understand the environmental impacts of ATM now and in the future, one approach being adopted by ATM providers is to quantify their performance using measures of flight inefficiency (EUROCONTROL/FAA, 2013); (EUROCONTROL, 2013). Section 2 of this paper discusses causes of flight inefficiency, followed by a description of flight inefficiency metrics developed in this work that quantify how far from their optimal trajectory aircraft are flying

http://dx.doi.org/10.1016/j.tranpol.2014.02.019 0967-070X & 2014 Elsevier Ltd. All rights reserved.

Please cite this article as: Reynolds, T.G., Air traffic management performance assessment using flight inefficiency metrics. Transport Policy (2014), http://dx.doi.org/10.1016/j.tranpol.2014.02.019i

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in different flight phases in Section 3. Two different forms of inefficiency metric based on lateral ground track extension and excess fuel usage are developed and used with sample flight data to illustrate their utility in Section 4. By identifying the levels and sources of inefficiency observable in the current system using these two metrics, it is possible to determine how much scope exists for improvement through future ATM system evolution and what elements of ATM system design should be prioritized in future policy-making to minimize environmental impacts. This is discussed in Section 5, followed by conclusions in Section 6. 2. Causes of flight inefficiency For the purposes of this study, flight inefficiency is defined as anything that causes an aircraft to fly a path different to its fueloptimal four-dimensional trajectory (i.e., latitude/longitude ground track, vertical profile and speed profile). This definition is considered appropriate given the relationships between minimizing fuel burn, aircraft direct operating costs and the most significant greenhouse gas

Adverse weather

emissions. Note that the lowest noise or air quality impact trajectory may be different from the fuel optimal one, leading to a need for environmental trade-offs to often be considered when there are competing environmental impact mitigation objectives. Flight inefficiency has different potential causes in different flight phases (simplified as departure terminal area, en route and arrival terminal area) as illustrated in Fig. 1. The causes are based on a comprehensive identification and systematic evaluation of potential operational changes to mitigate environmental impacts due to flight inefficiency (Marais et al., 2013), supplemented with other areas of on-going research identified in the discussions below. Many of the identified inefficiencies were the same ones targeted for reduction during the AIRE and ASPIRE trial flights previously described.

2.1. Departure terminal airspace Inefficiencies can first affect a flight at the flight planning stage in terms of the choice of aircraft equipment and fuel load (e.g., too

Expensive airspace

Congested airspace

Arrival fix

Restricted airspace

Departure fix

En Route Airspace

Holding

Arrival procedures

Departure procedures

Landing Taxi-in/Gate

Flight planning

Take-off

Arrival Terminal Area

Gate Taxi-out

Departure Terminal Area Fig. 1. Potential causes of flight inefficiency.

50 nm terminal area range ring

Heathrow

50 nm terminal area

Holding stacks

Vectoring for final approach

DFW

Departures

Arrivals

Departure fix

Arrival fix

Fig. 2. Terminal area ground track extension around Dallas Fort Worth and London Heathrow airports.

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big an aircraft or excessive fuel reserves) relative to the mission to be flown. Given these choices, the first operational inefficiencies can manifest at the departure gate, for example by using on-board power and cooling systems (driven by the Auxiliary Power Unit or engines) instead of gate-supplied power or cooling. Inefficient gate push-back operations can lead to significant excess congestion, fuel burn and emissions during taxi-out to the runway if too many aircraft are pushed back in a short time period compared to what the departure runways can handle (Simaiakis et al., 2011). In addition, a long taxi route, one with many stops and starts and/ or excessive engine power can all increase taxi-out fuel burn above the theoretical minimum amount. The take-off procedures may also be inefficient, for example using higher engine power than needed, or taking off on a runway not well aligned with the downstream flight direction compared to another available runway. After take off, inefficiencies can be introduced by the departure procedures that might require aircraft to fly predefined trajectories for noise abatement and/or traffic separation purposes which force an aircraft away from its ideal altitude and speed profile during the initial climb-out from the airport. Aircraft may also have to leave the origin airport terminal area over specific departure fixes which link with appropriate downstream air routes but which may require a longer flight path within the terminal area compared to a more direct route, as well as imposing constraints on altitude and speed over these fixes. Example flight

Standard routes SEA BOS

ORD

DEN

SFO

LAX

DFW

Restricted areas

ATL MCO

Adverse weather Hurricane Katrina

29 August 2005 n=2946

Fig. 3. En route ground track extension in the US.

3

tracks into and out of Dallas Fort Worth (DFW) airport are shown on the left side of Fig. 2 in which the ground track extension beyond the direct path from the fixes to the runways is evident in the radar track data. The airport (with its north/south-oriented runways) is in the middle of the 50 nautical mile (nm) circle.

2.2. En route airspace In en route and oceanic airspace, aircraft often fly standard airway routes with a constrained number of flight levels and cruising speeds available. These constraints are often imposed to manage the complexity of the air traffic control process for the human controllers (Histon and Hansman, 2008) and in low traffic conditions the standard lateral routing requirement may be relaxed. The standard routing network is also designed to accommodate the large number of restricted airspace regions in the world. In addition to these airspace constraints introduced by the basic airspace structure, there are also dynamic constraints due to the need to avoid regions of adverse weather or congested airspace in order to maintain flight safety, passenger comfort and/or schedule predictability. Ground tracks for all the flights originating from 10 major US airports are presented in Fig. 3. Several of the en route inefficiency sources are evident: standard routes cause the concentration of flights into a number of transcontinental flows; restricted airspace causes the avoidance of the hashed regions; and adverse weather causes the avoidance of the circular region in the south-east of the US (this flight data corresponds to the day of main impact of Hurricane Katrina in 2005 and its approximate location is shown). Expensive airspace can also be a source of inefficiency. There can be significant differences between the air navigation charges from one country to another (IATA, 2008) and there has been some evidence of airlines exploiting this difference and flying longer routes (with consequent higher fuel burn and emissions) when lower air navigation charges and congestion levels offset the higher fuel costs (Reynolds et al., 2009). Europe is the highest traffic region of the world where large differences in charging occur over a relatively small area and Fig. 4 shows a comparison of charges between Europe and North America. Significant differences between charging rates in neighbouring airspace regions can be observed in the European region and this can make it possible for longer routings to be cheaper in terms of air navigation and fuel costs for the airlines in some city pairs. The much 250

60° N

60° N 200

45° N

45° N 100

30° N

50

30° N

120° W

105° W

90° W

75° W

15° W

0°

15° E

30° E

Air Navigation Charge B757/100nm (2008€)

150

0

Fig. 4. Air navigation charging differences by region.

Please cite this article as: Reynolds, T.G., Air traffic management performance assessment using flight inefficiency metrics. Transport Policy (2014), http://dx.doi.org/10.1016/j.tranpol.2014.02.019i

available from surveillance (e.g., aircraft weight, winds) assessments (e.g., proportional to carbon dioxide emissions)

 Actual and Optimal fuel burn requires info not currently Optimal block fuel Flown block fuel Fuel

 Captures lateral, vertical and speed elements  Gives excess fuel burn, hence compatible with key environmental performance

from surveillance (e.g., aircraft weight, winds)

 Does not capture lateral and vertical elements  Optimal speed profile requires info not currently available  Captures speed elements  Ground speed readily inferred (radar surveillance) Optimal speed profile

Flown speed profile Speed (also surrogate for Time)

from surveillance (e.g., aircraft weight, winds)

distance for all flights

 Does not capture lateral and speed elements  Optimal vertical profile requires info not currently available  Captures vertical elements  Flown vertical profile readily available (transponder altitude) Optimal vertical profile Flown vertical profile Vertical

Flown ground distance Minimum ground distance (great circle) Lateral

Sample “Optimal” Sample “Actual”

The difference between the “actual” and “optimal” state behavior of a flight can be measured in different dimensions, each with their own set of advantages and disadvantages, as presented in Table 1. The lateral ground track extension inefficiency metric based on the difference between the radar ground track and great circle distance is commonly used by the air navigation service providers due to its ease of interpretation and calculation using readily-available radar data (Kettunen et al., 2005), but suffers from a number of disadvantages. The most important for environmental analysis is that a flight with a low lateral inefficiency (e.g., a trajectory close to a great circle track) may have relatively poor fuel performance due to sub-optimal altitude and speed profiles which are not captured at all by this metric. In addition, the great circle distance is not necessarily the shortest in the presence of winds. Vertical and speed metrics can be defined to complement the lateral metrics. For example, NATS is incorporating lateral and vertical elements into the “3Di” metric used for performance assessment of their airspace, where the vertical inefficiency is defined by the amount of flight time spent in level flight and the deviation from requested cruise levels. More sophisticated vertical and speed metrics are considerably more difficult to calculate than the lateral case because the optimal altitude and speed profiles depend on the characteristics of each flight which are not readily available with current surveillance systems (e.g., weight) and hence their value is limited relative to the complexity of their implementation. Although fuel-based inefficiency metrics (where the actual fuel burn is compared to the optimal fuel burn) suffer

Advantages

ð1Þ

Dimension

Actual  Optimal  100% Optimal

Table 1 Sample inefficiency parameters by flight dimension.

Inef f iciency Metric ð%Þ ¼

 Need accurate wind field information to determine air

A combination of the factors described in the previous section can cause the actual trajectory of any given flight to be inefficient compared to the optimal flight that would have been flown in a completely unconstrained system. The difference between the actual and optimal state behavior of a flight can be measured in absolute terms (e.g., extra track distance flown in any given phase of flight) or form the basis of an inefficiency metric with a general form of:

 Minimum air distance is better “optimal” measure in the presence of wind

3. Flight inefficiency metrics

Minimum air distance

Disadvantages

Aircraft typically enter an arrival terminal area via an arrival fix at a specific altitude (or altitude band) and speed which may require non-optimal descent altitude and/or speed profiles between the top of descent and the arrival fix. Once inside the arrival terminal airspace, if there is airport congestion, aircraft may need to enter holding stacks and/or be vectored for separation purposes: these effects are evident as race-track patterns in the right side of Fig. 2 for London Heathrow airport. The lateral and vertical elements of the arrival procedure will likely be constrained by the need to space, merge and sequence traffic for landing which may force them away from their optimal approach procedures in a similar fashion to that described for the departure case. Finally, the landing, taxi-in and gate management procedures can add inefficiencies, for example by requiring a landing on a runway a long way from the arrival gate necessitating a long taxi route, needing to wait to cross active runways or waiting for the arrival gate to become available.

Flown air distance

2.3. Arrival terminal airspace

 Does not capture vertical and speed elements  Great circle distance is not shortest in presence of wind

more uniform charging structure in North America reduces this tendency.

 Easy to measure and interpret  Flown ground distance readily available (radar surveillance)  Minimum ground distance simple to calculate (great circle equation)

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Please cite this article as: Reynolds, T.G., Air traffic management performance assessment using flight inefficiency metrics. Transport Policy (2014), http://dx.doi.org/10.1016/j.tranpol.2014.02.019i

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Departure fix

DEn_route_actual

En Route

θ

Departure TA

Arrival TA

DHold

DDepart RTA

Arrival fix

φ

DArrival

DDownwind

DTurn DTO

5

DEn_route_GC Sample lateral path Shortest lateral path

DFinal DBase

Fig. 5. Terminal area (TA) and En route ground track extension model.

from implementation complexity as well, they have the distinct advantage of combining the effects in all trajectory dimensions to produce a metric that is directly meaningful for environmental performance assessment, at least in terms of carbon dioxide emissions which are the focus of many ATM environmental performance targets, as well as other species which are directly proportional to fuel burn. In practice, the ease of calculation and interpretation of the lateral inefficiency metric based on flown ground distance relative to great circle makes it the most common metric to be used to assess the performance of the air transportation system (e.g., EUROCONTROL/FAA, 2013; EUROCONTROL, 2013). However, the shortcomings of this metric identified above warrant closer inspection, especially in relation to their consequence for environmental impact analyses which are becoming increasingly important to policy-makers looking for guidance on how best to evolve the system. Therefore, the next section compares inefficiency analyses using a simple lateral metric with a more complex (but potentially more insightful from an environmental performance perspective) fuel-based metric.

(ADS-B)), allowing fuel-based inefficiency analysis to be conducted and insights to be drawn for the region to which the data applies. Therefore, this comparison can help inform which metric is more useful for identifying inefficiencies and policy-making applications. 4.1. Lateral inefficiency analysis This section summarizes and expands upon the lateral flight inefficiency analysis described in detail in (Reynolds, 2008) where the ground tracks of a large number of flights from the different data sources were analyzed. Distinctions were made between the inefficiencies within 50 nm circles representing the departure and arrival terminal areas, and the en route phase defined as flight outside of these regions. This enabled the identification of the relative importance of each region, as illustrated in Fig. 5. Lateral inefficiencies of the form of ground track extension (GTE) flown beyond the great circle (GC) distance in the departure terminal area (DepTA), en route and arrival terminal area (ArrTA) were calculated by GTEDepTA ¼ ðDTO þ DTurn þ DDepart Þ–RTA

ð2Þ

4. Sample inefficiency analyses

GTEEn_route ¼ DEn_route_actual –DEn_route_GC

ð3Þ

In order to illustrate the application and utility of the flight inefficiency methodology for assessing the performance of ATM, this section presents analyses using lateral ground track extension and fuel-based metrics. A variety of sources of flight data were used, including:

GTEArrTA ¼ ðDArrival þ DHold þ DDownwind þ DBase þ DFinal Þ–RTA

ð4Þ

 Flight Data Recorder (FDR) archives from a selection of commercial aircraft types operating during 2008.

 Enhanced Traffic Management System (ETMS) radar track 

archives from a known four week period in 2005 covering all commercial traffic over the US (with some minimal filtering). MOZAIC2 data taken from revenue flights from five Airbus A340 aircraft serving mostly international routes covering much of the globe from 1995 to 2006.

Latitude and longitude position states were available from all sources with at least 60 second update rates. This permitted a detailed lateral flight inefficiency analysis to be conducted with states that are available in the current radar surveillance environment and to provide insights on the relative performance of the ATM systems in different parts of the world. The FDR data had the advantage of giving access to aircraft states that are not currently surveilled, but which may be available with future surveillance systems (such as Automatic Dependent Surveillance-Broadcast 2 Measurements of OZone and water vapour by in-service AIrbus airCraft, see http://www.iagos.fr/web/rubrique2.html.

The total ground track extension (TGTE) is then given by Total GTE ¼ TGTE ¼ GTEDepTA þ GTEEn_route þ GTEArrTA

ð5Þ

The key findings from this analysis for over 13,000 flights within and between different world regions are summarized in Fig. 6. Flights entirely within the US and European regions exhibit similar average total ground track extension inefficiency metric characteristics: the average route in the US data was observed to be 12% (76 nm) longer than the great circle trajectory, compared to 14% (57 nm) for Europe. A lower value of 8% (41 nm) was observed in the data for African flights: although the ATC system is relatively under-developed in this region, the low traffic levels and the small number of markets in the region mean that the major airways can provide relatively direct routes. North Atlantic flights exhibited approximately 5% ground track extension, although in absolute terms this equated to over 170 nm (much larger than in continental flights) which can be almost entirely attributed to the relatively rigid North Atlantic Track (NAT) airway structure. Note, however, that these tracks change daily to account for the location of the jet stream, so the air track extension (as compared to the ground track extension) may be much less. Similarly, the 7% (316 nm) total ground track extension observed in the Europe to Asia flights can be attributed to the large regions of restricted airspace over parts of Russia and China, leading to relatively few available international airways for flights between these regions. In the extreme case, some routes from

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US Domestic

Europe Domestic

Africa Domestic

50 nm terminal area

50 nm terminal area

29 August 2005 n=2946

n=4420

Average great circle distance: 489 nm Average TGTE: 41 nm (8%) Flight data source: Mozaic (n=525)

Average great circle distance: 415 nm Average TGTE: 57 nm (14%) Flight data source: FDR (n=4420)

Average great circle distance: 635 nm Average TGTE: 76 nm (12%) Flight data source: ETMS (n=2946) North Atlantic

Europe-SE Asia

Average great circle distance: 3430 nm Average TGTE: 176 nm (5%) Flight data source: Mozaic (n=3311)

Average great circle distance: 4705 nm Average TGTE: 316 nm (7%) Flight data source: Mozaic (n=2448) Fig. 6. Lateral analysis results by region.

Africa Domestic

Europe Domestic

US Domestic Departure Arrival proceprocedures dures 13 nm 8 nm (10%) (17%) Standard routes & Holding & restricted vectoring airspace 15 nm 21 nm (20%) (27%)

Arrival procedures 13 nm (22%)

Holding & vectoring 14 nm (25%)

Adverse Congested weather airspace 10 nm 10 nm (13%) (13%)

Departure procedures 8 nm (16%)

Arrival procedures 13 nm (31%)

En route 21 nm (37%)

Departure procedures 8 nm (18%)

En route 19 nm (47%)

Holding & vectoring 2 nm (4%)

Average great circle distance: 635 nm TGTE: 76 nm (12% of GC distance)

Average great circle distance: 415 nm TGTE: 57 nm (14% of GC distance)

Average great circle distance: 489 nm TGTE: 41 nm (8% of GC distance)

Fig. 7. Lateral analysis results breakdown by region.

Europe to parts of SE Asia were observed to regularly exhibit total ground track extensions of over 20% (1100 nm), but these are believed to be due to geo-political issues (e.g., over-flight restrictions imposed on some airlines). The total ground track extension is broken down into the different flight phases for the European, US and African domestic

regions in Fig. 7, assuming circular departure and arrival terminal areas with radius of 50 nm. The results show the regional ground track extension by flight phase and highlight the importance of considering the inefficiencies in the en route and terminal areas. The pie charts also provide a breakdown by flight inefficiency factors identified in Fig. 1 when

Please cite this article as: Reynolds, T.G., Air traffic management performance assessment using flight inefficiency metrics. Transport Policy (2014), http://dx.doi.org/10.1016/j.tranpol.2014.02.019i

Simplified Terminal Area Model

Key:

Arrivals

7

80

75

30

Average Origin Extra Distance: 7.6 nm Average Dest Extra Distance: 12.7 nm

25

70

20

65

15

60

10

55

5

50 -180

Extra Distance Flown (nm)

Distance Flown in Terminal Area (nm)

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0 -90

0

90

180

Entry/Exit Angle Relative to Runway, θ or φ (degs)

Departures

Fig. 8. Simplified terminal area extra distance model.

possible through further analysis of the flight data. It is apparent that the departure procedures account for approximately 8 nm of ground track extension (10–18% of the TGTE depending on the region). A simple model of the theoretical shortest paths flown in a generic terminal area can be developed as shown in Fig. 8 (compare the modeled tracks to those for DFW in Fig. 2). Assuming random terminal area entry/exit angles (i.e., uniform random distribution of θ and ϕ in Fig. 5) the average extra distance flown in a departure terminal area is calculated to be 7.6 nm, while in the arrival terminal area it is calculated to be 12.7 nm, i.e., an additional 5 nm on average is expected in the arrival terminal area, primarily due to the longer final approach path (DFinal) compared to the straight-ahead take-off distance (DTO). Hence, the average departure terminal area track extension in all three regions can be virtually entirely attributed to the standard departure process of needing to exit the terminal area over a departure fix which does not align with the runway orientation angle. By contrast, the standard arrival procedures only account for about half the ground track extension in the arrival terminal area in the US and Europe data. The balance can be attributed to the need to hold and vector traffic to account for the high traffic levels and make maximum use of limited runway resources in these regions, as illustrated on the right side of Fig. 2. Interestingly, virtually no holding and vectoring was seen in the African data. Although typical runway capacities are very low at African airports (capacities of 6 movements/hour are quite common, even at international airports which may still use non-radar separation requirements), demand is often even lower such that the observation of minimal holding and vectoring in the data is not a surprise. The ETMS data used in the US analysis also allowed an exploration of the causes of en route ground track extension because it contained data on virtually all commercial flights on specific dates with known presence of adverse weather conditions. By comparing average en route ground track extension during days of relatively high and low traffic conditions, as well as days of high and low adverse weather conditions (e.g., convective activity), it was possible to determine the general impact of these inefficiency sources. It was found in (Reynolds, 2008) that an extra 10% of system traffic was associated with approximately 10–30 nm extra en route track distance on average. A similar effect was observed when a major adverse weather event, such as the impact of Hurricane Katrina, was analyzed. It is difficult to generalize these results because the actual impacts are strongly affected by a

number of situation-specific variables, such as the location of the congestion or adverse weather events relative to the demand and this is a major need for additional research. But the observed impacts give pointers to the relative importance of these causes of inefficiency, with the balance being attributed to standard routes and restricted airspace. There are still a few potential causes of flight inefficiency highlighted in Fig. 1 that have not yet been considered: standard altitudes/speeds and expensive airspace. As previously described, a shortcoming of the lateral analysis is that altitude and speed effects are not captured, and hence the effects of standard altitudes/speeds cannot be assessed with this metric. But further analysis of the flight data did make it possible to undertake an assessment of the relative importance of expensive airspace on track extension. As mentioned previously, this is most likely to be an issue in European airspace, where differences in ATM en route charges between neighboring airspace regions can be significant (see Fig. 4), but analysis showed that even in this region it was a very minor contributor to track extension except on a few low density routes, as described in detail in (Reynolds et al., 2009). So, overall, this can be ignored as an inefficiency source in all but a few specific cases as long as airspace charging differentials do not increase near high density routes. This section has highlighted that there are significant insights that can be gained from the ground track extension lateral inefficiency metric which can be easily calculated for any world region with radar surveillance data. However, it does not provide any way of determining the relative importance of altitude and speed constraints in terms of environmental impacts. The additional insights that can be gained in this regard from using a fuelbased inefficiency metric, along with the added complications this brings, are discussed in the next section. 4.2. Fuel inefficiency analysis As previously discussed, fuel-based inefficiency analysis is more complicated because it requires availability of aircraft states that are not routinely surveilled, as well as more detailed modeling of aircraft performance in order to determine the optimum fuel burn (Reynolds, 2009). However, FDR data and an advanced aircraft performance model were available for this analysis which allowed some of these challenges to be overcome and provides an opportunity to explore what additional insights can be gained

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through a fuel-based inefficiency analysis. The FDR data available for this study was from A320-200 narrow-body aircraft from a single airline serving shorthaul European destinations, and A340500 wide-body aircraft serving a variety of longhaul routes between the US, Europe, Middle East and Australia. Therefore, these aircraft types and geographic regions are the focus of this analysis. The first challenge in fuel-based analysis is to obtain optimal fuel burn estimates for a given route. In this work, the Piano-X aircraft performance model (Lissys, 2009) was used because it is the most sophisticated publicly-available tool. However, it is still a

Aircraft/ Region

challenge to determine the optimum fuel burn (which is the baseline for the fuel-based inefficiency analysis) on any given route. This is because, in addition to aircraft type, weight and route length (all of which are known for this analysis), optimum fuel also depends on a number of other factors which were not available in the data, such as winds and the operator's “cost index” (Airbus, 1998). The effects of winds, although potentially significant for any particular flight, were assumed to cancel out over the course of a sufficient number of return flights (as used in this analysis) due to dominant wind directions such as the jet stream being on average favorable for one leg but adverse on average for the return. The

A320-200 European Domestic

A340-500 International Longhaul

Ground Tracks

0

0

300 600 nm

2000

4000 nm

2008, n=1124

2008, n=1794

Average great circle distance: 403 nm Average optimal LRC fuel burn: 2,840 kg

Departure Arrival procedures procedures 9 nm 13 nm (18%) (25%)

Lateral Analysis

Holding & vectoring 13 nm (25%)

Fuel Analysis

Arrival proc 50 kg (8%) Holding, vectoring, level segments 129kg (20%)

Holding & vectoring 6 nm (4%)

Arrival proc 13 nm (9%)

En route 16 nm (31%)

Average TGTE: 51 nm (13% of great circle distance) Taxi-in 5 kg (2%)

Average great circle distance: 4,383 nm Average optimal LRC fuel burn: 76,914 kg

Taxi-out 20 kg (3%)

Departure procedures 186 kg (29%)

Sub-optimal En-route altitude & speed track extension profiles 72 kg (11%) 178 kg (28%)

Average excess fuel burn: 640 kg (23% of great circle LRC fuel burn)

Dep proc 12 nm (8%)

En route 115 nm (79%)

Average TGTE: 146 nm (3.3% of great circle distance) Taxi-in Arrival Holding, 100 kg proc vectoring, 96 kg (3%) level (3%) Taxisegments out 171kg 100 kg (6%) (3%) Sub-optimal Departure altitude & speed procedures 415 kg 790 kg (14%) (26%)

En-route track extension 1375 kg (45%)

Average excess fuel burn: 3047 kg (4.0% of great circle LRC fuel burn)

Fig. 9. Lateral and fuel analysis A320 and A340 results comparison.

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cost index is the ratio of time-related costs per minute of flight relative to the fuel-related costs per kg of fuel burnt. The priority of one over the other varies from one operator and flight to another and can be entered into a modern aircraft's Flight Management System (FMS). The choice of cost index can significantly affect the optimum fuel burn: with a very high cost index, reducing time costs are prioritized and hence a minimum time (maximum speed) profile is flown, and this has a fuel burn penalty. By contrast, a low cost index prioritizes minimizing fuel (maximizing range), which has a time penalty. In between these extremes, the fuel and time responses are not linear and many operators opt to fly a “Long Range Cruise” (LRC) cost index which gives a speed at which 99% of the maximum range is achieved but does not have a major time penalty. Therefore, the LRC cost index was used as the basis for this analysis. The fuel inefficiency analysis is compared with the equivalent lateral analysis results for the two aircraft types in the European domestic and international longhaul markets in Fig. 9. The lateral inefficiency results for the A320 (13% total ground track extension) are very similar to those from the more extensive full European set covering many more aircraft types (14% total ground track extension). But the average fuel inefficiency is significantly greater than the average lateral inefficiency, with 23% more total fuel being burnt on average compared to the optimal LRC fuel burn. In addition to the overall inefficiency levels differences, there are also differences in the relative contribution of each flight phase between the lateral and fuel inefficiency results, with departure terminal area and en route phases having relatively greater contributions, while the arrival terminal area plays a relatively smaller contribution. The departure terminal area contributes 32% of the overall fuel inefficiency compared to only 18% of the lateral inefficiency. This is due to a combination of two factors. Firstly, because the fuel burn rate is high in the initial climb phase, even the relatively small amount of track extension in the origin terminal area leads to disproportionately greater fuel burn. Secondly, the fuel-based metrics include taxi-out fuel, so ground inefficiency is being captured whereas this was not included in the lateral results due to absence of ground surveillance in the ETMS data used in the analysis. In the en route phase, the extra fuel burn can be attributed to two primary factors: using the typical fuel burn per nautical mile in cruise from the aircraft performance model, about a quarter of the observed extra fuel burn is due to en route track extension. The remainder can be attributed to inefficiencies in the other dimensions, i.e., suboptimal cruise altitude and speed which are not captured in the lateral inefficiency analysis. The sub-optimal altitude and speed profiles are seen to increase the great circle LRC fuel burn by about 6%, which is similar to values from more extensive assessments of altitude and vertical inefficiencies in cruise for narrow-body aircraft (Lovegren, 2011). In the arrival terminal area, the fuel burn rate is relatively low in a typical descent phase when the engines are near flight idle and this translates to a relatively small excess fuel burn contribution from track extension in the arrival phase. However, engine thrust increases are required to execute the holding patterns identified in the lateral analysis or to accommodate level flight segments in non-Continuous Descent Approach procedures, and these are the cause of the remainder of extra fuel burn in this phase. Taxi-in inefficiency levels are also included in the fuel inefficiency analysis, but this is seen to not have a major effect on the overall fuel inefficiency in the flights analysed. The lateral inefficiency results for the A340 flights show that the total ground track extension for these ultra-longhaul routes is relatively small in terms of percentage (3.3% total ground track extension) and that the en route portion of the flight makes up the large majority (79%) of the track extension that is observed. The departure and arrival procedure contributions are similar to the

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other results presented, but the amount of holding and vectoring is somewhat less than the other flights flying in similar regions, possibly indicating some preferential treatment of these widebody longhaul flights at the destination airport compared to other aircraft. The A340 fuel inefficiency analysis reveals only slightly higher inefficiency than the lateral analysis, suggesting overall they are able to fly much closer to their fuel optimal trajectories than the A320s. As in the A320 results, the relative contributions of the departure terminal area inefficiencies increase and arrival terminal area decrease due to the relative fuel flows in these regions. In the en route phase, the track extension inefficiency dominates over the altitude and speed impacts (opposite to the case seen in the A320 results), suggesting more optimal altitudes and speeds are being used for these longhaul flights relative to the shorthaul flights.

5. Implications for prioritizing Air Traffic Management improvement options The analysis presented in the previous section was limited in geographic, temporal and aircraft type scopes and a more extensive assessment would be needed before drawing major conclusions on ATM system-wide performance. However, a large number of flights of different aircraft types from different world regions have been analyzed and hence the findings do give pointers towards appropriate priorities for future ATM in terms of what operational and technological capabilities could address some of the more important causes of inefficiency observed. One of the biggest causes of inefficiency in the A320 and A340 fuel analysis (and a major one in the lateral analysis) was observed to be standard routes (leading to track extension), altitudes and speeds. These inefficiencies could be improved through operating paradigms that allow more widespread use of optimal trajectories away from the rigid airway structure (made up of standardized routes, altitudes and speeds), as proposed in many “free flight” or user-preferred trajectory concepts which are included in SESAR and NextGen initiatives. Care is needed to assess how this removal of airspace structure affects the air traffic control process in order to maintain safety at high levels. The current need for airspace structure is also tied to the Communication, Navigation and Surveillance (CNS) limitations in en route and oceanic airspace in many parts of the world. There are moves in the US and Europe to transition away from the legacy system design of VHF radio communication, ground-based navigation and radar surveillance to more sophisticated infrastructures involving datalink communication, satellite-based navigation and aircraft-based automatic dependent surveillance. These technologies should enable inefficiencies in these regions to be reduced to handle the forecast traffic growth, for example by reducing separation minima by implementing aircraft self-separation and automated conflict detection and resolution. Traffic is growing most rapidly in some parts of the world where the current infrastructure is unlikely to be able to accommodate it (e.g., India and China). However, it is likely that technological advances and global ATM harmonization efforts will enable step-changes in CNS capability in these regions instead of the slower incremental evolution observed in the more developed regions of the world where growth has been more gradual. The standard route structure is also used to accommodate regions of restricted airspace, which in some regions can lead to significant extra distance being flown. Increasing the number of available airways in these regions with the ultimate goal of wholesale removal of these large restricted areas would therefore be highly beneficial, but this may be a political rather than technical challenge given the other important

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stakeholders using the restricted airspace (e.g., military, general aviation, etc.). In the terminal areas, track extension in the arrival terminal area was a big lateral inefficiency source, but was less significant in terms of fuel burn during standard arrival procedures when engine power is generally lower (until final approach). By contrast, standard departure procedures were seen to be much more important in the fuel analysis compared to the lateral analysis due to the high fuel burn rates during that flight phase. This stresses the importance of efficient departure profiles as an important mitigation strategy enabled, for example, by deconfliction of arrival and departure flows to permit optimal continuous climb departures. In both standard departure and arrival procedures, the need for alignment of the flight path with the limited set of runway orientations available at any airport and the need to maintain a minimum separation distance between aircraft to ensure safety implies there will always be some excess track distance or fuel burn observed in these phases. However, careful relaxation of constraints (such as reduced separation minima and/or final approach stabilization criteria) imposed on standard procedure design without compromising safety could help to minimize these contributors to overall flight inefficiency. The other major lateral and fuel inefficiency source in the arrival terminal area was from holding and vectoring. Limited airport capacity causing arrival delay is the root cause of this issue. Planned increases in airport capacity are unlikely to keep pace with growth in aircraft movements (at least in the more mature parts of the air transport system such as US and Europe), and hence it will become increasingly important for ATM to manage arrival delay in a more environmentally-friendly way. Future concepts that involve four-dimensional trajectory management should greatly reduce the need for holding and vectoring within the destination terminal area. It would enable delays to be

determined far in advance of an aircraft's arrival into the terminal area, allowing a more efficient accommodation of delay. For example, by slowing the cruise speed of an aircraft by a few knots on a long distance flight to manage its arrival into the terminal area at a pre-determined time when it can be accepted without delay is much more efficient than having aircraft enter the terminal area at an unplanned time, then holding them until a runway slot is available (Idris et al., 2004). Elements of fourdimensional trajectory management are already deployed in some parts of the world, but major efficiency gains could be achieved by system-wide application, as is proposed in the European SESAR and US NextGen concepts. The other factors considered in this analysis have been inefficiencies due to congestion and adverse weather. Congested airspacerelated inefficiency should also be helped by the four-dimensional trajectory management previously discussed. However, the relationship between traffic levels (which are likely to continue to increase in the future), airspace capacity and congestion-related inefficiency is highly complex and will need further research. The need to avoid regions of adverse weather is likely to continue into the future to maintain passenger comfort and safety. However, better forecasting and adverse weather detection allows affected regions to be avoided more efficiently (Evans et al., 2006). The discussions above illustrate that there is significant scope for ATM advanced technologies and procedures to improve efficiency of the air transportation system. The main opportunities are summarized in Fig. 10 along with a mapping of associated operational concepts and enabling technologies which are covered in more detail in (Marais et al., 2013). Other enablers such as Collaborative Decision Management (CDM) and Collaborative Environment Management (CEM) are also identified which are aimed at improving air traffic management through increased information exchange among the various stakeholders in the aviation community.

Origin Terminal Area

En Route

Destination Terminal Area

Cruise Departure/Climb

Descent/Approach DESTINATION AIRPORT

ORIGIN AIRPORT

Take-Off Pushback

TaxiOut

Taxi-In

• Optimised lateral, vertical, speed profiles • Strategic de-confliction

FUTURE OPERATIONAL CONCEPTS FOR IMPROVED LATERAL/FUELBASED ENVIRONMENTAL PERFORMANCE

• Optimised push-back time and sequence

• Single-engine optimal taxi routing with no holding

• Engine power optimisation

ENABLING TECHNOLOGIES

• Push-back optimisation algorithms (G) • Datalink (G-A)

• Taxi optimisation algorithms (G) • Datalink (G-A)

• Take-off power management (A)

OTHER ENABLERS

Key:

Landing

• Modified standard operating procedures • CDM/CEM

• e.g. Continuous Climb Departures

• e.g. windoptimised ground track at optimal cruise altitude & speed

• e.g. Continuous Descent Approach, Delayed Deceleration Approach

• 4D trajectory management algorithms (G,A) • Advanced Communication: Datalink (G-A) • Advanced Navigation: RNP/RNAV (A) • Advanced Surveillance: ADS-B (G,A) • Airspace re-design • Modified standard operating procedures • CDM/CEM

• Displaced thresholds • Steeper glideslope angles • Runway allocation for optimal taxi routing

• Singleengine optimal taxi routing with no holding

• Runway allocation algorithms (G) • Datalink (G-A) • RNP/RNAV (A)

• Taxi optimisation algorithms (G) • Datalink (GA)

• Modified standard operating procedures • CDM/CEM

(G) = Ground, (A) = Aircraft, (G-A) = Ground to Aircraft, ADS-B = Automatic Dependent Surveillance-Broadcast, CDM = Collaborative Decision Making, CEM = Collaborative Environment Management, RNP = Required Navigation Performance, RNAV = Area Navigation

Fig. 10. Future ATM concepts summary.

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Fig. 10 also includes initiatives designed to reduce local environmental impacts of air quality and noise because future ATM systems will need to address these as well (see also BrunelleYeung et al., this volume; Wolfe et al., this volume; He et al., this volume). Air quality and noise would be helped through initiatives during the take-off and landing stages of flight that allow aircraft to fly closer to their optimal vertical and speed profiles, e.g., Continuous Climb Departures (CCDs), Continuous Descent Approaches (CDAs) and Delayed Deceleration Approaches (DDAs). The objective of a CDA, for example, is to minimize periods of level flight during the descent and approach phases, thereby keeping aircraft higher and at lower thrust for longer, reducing fuel burn, emissions and noise impacts. Enabling aircraft to do this during the entire descent and approach phases can reduce fuel burn and associated emissions by as much as 50% per flight compared to a standard descent and approach, while peak noise is also reduced by 3–6 dBA per flight in some regions (Reynolds et al., 2007). DDAs minimize fuel burn and emissions during approach and landing by maintaining airspeed above the initial flap speed for as long as possible during approach, lowering drag and engine thrust requirements (Rodriguez et al., 2013). The major challenge in all of these future ATM concepts will be improving environmental performance in the face of growing demand. Congestion was identified as an important contributor to flight inefficiency in the current system, and its importance is likely to increase in the future without major capacity enhancements. Capacity is needed on the ground and in the air, through added infrastructure (e.g., runways and airspace), technological investment and procedural changes that allow more efficient use of the capacity that is available. Even then, the aggregate emissions from aviation are likely to increase in the coming decades because traffic growth will exceed the possible efficiency gains (even given aircraft technological improvements). Hence, policy measures such as Emissions Trading Schemes which incorporate aviation in an effective manner will have a major part to play in managing aviation's environmental impacts (Dray et al., 2010).

6. Conclusions This paper has identified the importance of quantifying the performance of the ATM system to understand its current and potential future role in environmental impact mitigation of air transportation. ATM inefficiencies are used for this purpose to identify system constraints which cause aircraft to fly away from their optimal four dimensional trajectories. Flight inefficiency metrics have been discussed against a number of flight dimensions. Lateral ground track extension-based metrics are easiest to implement but may significantly underestimate inefficiency from a fuel and emissions perspective, while fuel-based metrics are much more meaningful with respect to environmental impacts but are more complex to calculate. These two types of metrics have been used to analyze flight data and the results help determine the relative inefficiencies in different flight phases and regions, which can then be used to help determine appropriate mitigation strategies. For example, mitigations that allow aircraft to fly closer to their optimal four-dimensional trajectories, especially in high-fuel burn phases of flight, are particularly important. Elements of NextGen and SESAR which enable more efficient four dimensional paths in climb and cruise phases of flight may have the biggest benefit potential as a result (for example continuous climb and optimized four-dimensional trajectory based operations (4D-TBO)). In the nearer term, technical and procedural barriers to implementation may be lower for mitigations in other flight phases at certain locations (for example improved approach operations at some airports) and these should be pursued in parallel to enable incremental efficiency improvements. The key enabling technologies required to implement

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the operational mitigations have also been identified. The results from this work can inform what elements of the ATM system should be prioritised in future policy-making to reduce environmental impacts.

Acknowledgements This work was funded through the Aviation Integrated Modelling (see www.AIMproject.aero) grant from the UK Engineering and Physical Sciences Research Council (EPSRC) and the Natural Environment Research Council (NERC), as well as the Omega Consortium. Support for flight data acquisition came from the Sir Arthur Marshall Institute for Aeronautics (SAMIA). MOZAIC flight data was available thanks to European Commission funding, IAGOS and ETHER Research Institutes, as well as participating airlines. All of this support is gratefully acknowledged. The author would also like to thank colleagues in the AIM group and the Institute for Aviation and Environment at the University of Cambridge, as well as the Air Traffic Control Systems Group at MIT Lincoln Laboratory.

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