August 2015 

NASA/CR–2015-218786 

Annualized TASAR Benefit Estimate for 

Virgin America Operations 

Jeffrey Henderson 

Engility Corporation, Billerica, Massachusetts 
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August 2015 

NASA/CR–2015-218786 

Annualized TASAR Benefit Estimate for 

Virgin America Operations 

Jeffrey Henderson 

Engility Corporation, Billerica, Massachusetts 
Available from: 

NASA STI Program / Mail Stop 148 

NASA Langley Research Center 

Hampton, VA 23681-2199 

Fax: 757-864-6500 

The use of trademarks or names of manufacturers in this report is for accu rate reporting and does not constitute an 

official endorsement, either expressed or implied, of such products or manufacturers by the National Aeronautics and 

Space Administration. 
Preface 

This document describes a simulation study to estimate annualized Traffic Aware 

Strategic Aircrew Requests (TASAR) benefits for Virgin America operations. This 

document represents deliverable 41B for TASAR Analysis and Development. 

This document was prepared by Engility Corporation, 900 Technology Park Dr., 

Billerica, MA under Contract No. NNL12AA06C with NASA Langley Research Center, 

Hampton, VA. 
2

Abstract 

The Traffic Aware Strategic Aircrew Request (TASAR) concept offers onboard 

automation for the purpose of advising the pilot of traffic compatible trajectory changes 

that would be beneficial to the flight. A fast-time simulation study was conducted to 

assess the benefits of TASAR to Virgin America. The simulation compares historical 

trajectories without TASAR to trajectories developed with TASAR and evaluated by 

controllers against their objectives. It was estimated that about 25,000 gallons of fuel and 

about 2,500 minutes could be saved annually per aircraft. These savings were applied 

fleet-wide to produce an estimated annual cost savings to Virgin America in excess of $5 

million due to fuel, maintenance, and depreciation cost savings. Switching to a more 

wind-optimal trajectory was found to be the use case that generated the highest benefits 

out of the three TASAR use cases analyzed. Virgin America TASAR requests peaked at 

two to four requests per hour per sector in high-altitude Oakland and Salt Lake City 

center sectors east of San Francisco. 
3

Table of Contents

 

Preface ................................................................................................................................. 1

Abstract ............................................................................................................................... 2

Table of Contents ................................................................................................................ 3

1. Introduction ..................................................................................................................... 5

2. TASAR Use Cases Analyzed ......................................................................................... 6

3. Simulation Platform and Method to Quantify Benefits .................................................. 6

3.1 TASAR Alternative Trajectory Generation (Optimization Model) ....................... 7

3.2 TASAR Request Model ......................................................................................... 8

3.3 Controller Evaluation of TASAR Requests ........................................................... 9

4. Annualized TASAR Benefit Results for Virgin America............................................... 9

4.1 Airport Pair Selected for Analysis ......................................................................... 9

4.2 Simulation Fuel and Time Savings Estimates ..................................................... 10

4.3 Estimating Annualized Cost Savings ................................................................... 12

5. ATC Impacts ................................................................................................................. 13

6. Future Work .................................................................................................................. 16

7. References ..................................................................................................................... 16

Appendix A: Simulation Fuel and Time Savings ......................................................... 17

Appendix B: TASAR Request Trajectory Change Types ............................................ 21

Appendix C: Summary Presentation............................................................................. 22

 
4

List of Tables & Figures 

Table 1. Annual operations per aircraft by airport pair and aircraft type. Airport pairs 

analyzed are shaded. ......................................................................................................... 10

Table 2. Annual fuel and time benefits by use case for A320. ......................................... 11

Table 3. Annual fuel and time benefits by use case for A319. ......................................... 12

Table 6. Summary of fuel cost savings calculation. ......................................................... 12

Table 7. Summary of maintenance and depreciation savings calculation. ....................... 13

Table 8. TASAR requests per day by sector where TASAR request occurs. ................... 14

Table 9. TASAR requests per hour by sector where TASAR request occurs. ................. 15

Table 10. A320 simulation results. ................................................................................... 17

Table 11. A319 simulation results. ................................................................................... 19

Table 13. Percentage of lateral, vertical, and combination lateral and vertical by aircraft 

type. ................................................................................................................................... 21
5

1. Introduction 

The Traffic Aware Strategic Aircrew Request (TASAR) concept offers onboard 

automation for the purpose of advising the pilot of traffic compatible trajectory changes 

that would be beneficial to the flight. The TASAR onboard automation leverages 

surveillance information to increase the likelihood of air traffic control (ATC) approval 

of pilot-initiated trajectory change requests, thereby increasing the portion of the flight 

flown on or near a desired business trajectory. All automation and pilot procedures are 

fully dedicated to a single aircraft which allows tailoring of optimization criteria to the 

objectives of each flight and provides for timely responses to changing situations. 

A preliminary fast-time simulation benefits assessment1

 estimated the benefits of three 

TASAR use cases: (1) lateral change after a reroute traffic management initiative (TMI) 

ends, (2) lateral change in the presence of convective weather, and (3) switch to a more 

wind-optimal trajectory (altitude, lateral, or combination). The agent-based simulation 

contained aircrew/TASAR agents that generate requests that improve on the efficiency of 

historical trajectories and controller agents that evaluate these TASAR requests against 

their objectives. The benefits of TASAR were assessed for generic network, low cost, 

regional, and business jet airspace users. Network carriers saved, on average, 543 lbs of 

fuel (about 80 gallons) per flight and about 3.6 minutes per flight. The rate of Automatic 

Dependent Surveillance-Broadcast (ADS-B) Out equipage among traffic aircraft did not 

significantly impact benefits but lower levels of ADS-B Out adoption caused controllers 

to receive more TASAR requests that may cause conflicts and therefore would not be 

immediately approveable. 

This report builds on the preliminary benefits assessment by tailoring results to a specific 

airspace user, Virgin America. It extends the previous study by developing estimates of 

annual fuel and time cost savings due to TASAR tailored specifically for Virgin America. 

Historical Virgin America trajectories are used as a baseline for comparison to simulated 

trajectories that consider potential TASAR requests. Also, peak requests by sector are 

studied in an attempt to further understand the impact of TASAR on ATC. 

The document is divided into the following sections: 

 Section 1 introduces the annualized benefits assessment 

 Section 2 describes three use cases that were quantified 

 Section 3 describes the simulation platform and method to quantify benefits 

 Section 4 estimates annualized benefits results for Virgin America 

 Section 5 estimates impact of TASAR requests on ATC 

 Section 6 describes potential future refinements of the benefits assessment 
6

2. TASAR Use Cases Analyzed 

Benefits of three types of aircrew requests were quantified. Other types of aircrew 

requests that were not modeled have opportunities for benefits and therefore this analysis 

represents only part of the expected full benefit of TASAR. The benefits of the following 

three types of aircrew requests were quantified in this paper: 

1) An aircraft is part of a reroute initiative to avoid convective weather or mitigate 

congestion. Aircraft in these initiatives are sometimes not shifted back to userpreferred routes after the initiative has ended. The aircrew requests a lateral 

trajectory change to a more efficient route. 

2) An aircraft is impacted by convective weather, and there is sufficient lead time to 

the convective weather to allow a strategic route change rather than a tactical 

heading change. The aircrew requests a lateral trajectory change consisting of one 

or two named waypoints along the trajectory before reconnecting to the route. 

3) The aircrew requests a trajectory change (lateral, altitude, or combination lateral 

and altitude) to switch to a more wind-optimal trajectory. This request for a more 

wind-optimal trajectory is intended to occur when the aircraft is not impacted by a 

reroute initiative or convective weather. 

The following logic is used to classify flights into one of the three request types. If an 

aircraft is part of a reroute initiative that began before the aircraft departed, and the 

reroute initiative is cancelled or ended before the aircraft reached the arrival fix, then the 

aircraft is classified as aircrew request type (1) above (even if convective weather is 

present, since there may be overlap between the three request types). The data source for 

reroute initiatives is the National Traffic Management Log (NTML), available on the 

Federal Aviation Administration (FAA) Command Center website (www.fly.faa.gov). If 

at least one of the alternative routes of the aircraft is projected to enter convective 

weather, and the aircraft is not part of a reroute initiative that ends or is cancelled, then 

the aircraft is classified as request type (2). The data source for convective weather is 

Next-Generation Radar (NEXRAD) radar mosaic base reflectivity (www.ncdc.noaa.gov). 

Certain conditions allow aircraft to request a higher altitude to fly over convective 

weather, but this is not included as part of (2) and so convective weather tops data is not 

considered. All other aircraft are classified as request type (3). However, there is overlap 

between the aircrew request types since the aircrew seeks a wind-optimal solution in all 

cases, but aircrew request type (3) does not have a reroute initiative or severe convective 

weather impacting the aircraft. 

3. Simulation Platform and Method to Quantify Benefits 

An existing fast-time simulation platform that connects to the Future Air Traffic 

Management Concept Evaluation Tool (FACET) through an Application Programming 

Interface (API) was used to model trajectories and airspace structure such as routes and 

sectors. In the integrated platform, two instances of FACET were used. One instance of 

FACET, the simulator FACET, was used to model the current state (simulation clock 
7

time) of aircraft trajectories. The other instance of FACET, the predictor FACET, was 

used to model future states of aircraft trajectories to test TASAR aircrew requests for 

conflicts with surrounding aircraft, conflicts with airspace hazards, and to calculate the 

impacts of TASAR aircrew trajectory change requests on user time and fuel objectives. 

Both the simulator and predictor instances of FACET were updated at one minute 

increments. 

Input files to the simulation platform contain flight plans as well as corresponding 

historically flown four-dimensional (4D) trajectories. Aircraft were modeled to follow 

their flown trajectory until an aircrew request is granted. Traffic information was 

obtained from historical Aircraft Situation Display to Industry (ASDI) data. 

FACET was configured to predict future aircraft positions differently for historically 

flown 4D trajectories as compared to alternate trajectories generated by TASAR. Aircraft 

following their historically flown 4D trajectory did not use aircraft performance or 

atmospheric models and instead, arrived at the 4D waypoints as specified in the input file. 

For synthesizing alternate trajectories generated by TASAR, FACET converted the flight 

plan to a series of latitude and longitude waypoints that were simulated based on aircraft 

performance models. Wind modeling was based on historical Rapid Update Cycle (RUC) 

winds data that was read from outside of FACET and was used to update the aircraft 

groundspeed. 

3.1 TASAR Alternative Trajectory Generation (Optimization Model) 

In the simulation, TASAR evaluated alternative trajectories at five-minute intervals from 

top-of-climb to 200 nmi from the destination airport. Trajectories were evaluated against 

a 50% fuel / 50% time objective and TASAR advisories were rejected if they increased 

fuel burned or flight time (i.e., tradeoffs between fuel burn and flight time were not 

considered). 

The use of voice for aircrew requests limited the alternative lateral trajectories to 

changing one or two named waypoints before reconnecting to the original trajectory. A 

bounding box was created for each origin-destination airport pair. All navigation aids 

inside the bounding box were used to generate alternative trajectories. The bounding box 

was based on the geographical extent of the flown trajectories between each origindestination airport pair. 

Three alternate altitudes were considered at 2,000 feet above, 2,000 feet below, and 4,000 

feet below the assigned altitude. Climbing was only permitted if the aircraft was at flight 

level (FL 350) or below to be conservative since aircraft weight was not modeled in the 

simulation. Alternative trajectories consisted of lateral changes only, altitude changes 

only, and combination altitude and lateral changes. The aircraft in the simulation were 

modeled to follow their historical 4D trajectories once the aircraft were within 200 nmi of 

the destination airport. 
8

3.2 TASAR Request Model 

TASAR logic in the simulation implements filters to prevent the aircrew making requests 

that would be considered unacceptable to the controller. Requests were not made if any 

of the following conditions are true: 

 Aircraft-aircraft conflict was predicted. The alternative trajectories generated by 

TASAR were probed to an eight-minute horizon to determine if there was a conflict 

with the surrounding traffic using a conservative ten nmi lateral and 1,000 ft vertical 

minimum separation shell. It was assumed that 100% of traffic was equipped with 

ADS-B Out since the earlier TASAR benefits study indicated that ADS-B Out 

equipage impacts ATC acceptability and workload but not user benefits since pilots 

could make a user request soon after a denied request. It was assumed that the conflict 

probe did not have access to flight plans and instead relied on state projections using 

current heading, vertical rate, and speed. Post-processing of simulation results to 

assess the impact of ADS-B Out equipage is discussed in Section 5. 

 Aircraft-airspace hazard conflict was predicted. Alternative trajectories were also 

probed for conflicts with airspace hazards including special activity airspace (SAA) 

and severe convective weather. Airspace hazards, either weather or SAA, were 

defined as polygons with a floor, ceiling, and schedule for activation and 

deactivation. Polygons were dynamic in the sense that they are active for a defined 

period of time and then replaced by other polygons at different locations to mimic the 

motion of convective weather. If the aircraft was predicted (using the FACET 

predictor instance) to be inside an airspace hazard polygon, then the TASAR 

automation was modeled to be aware of the airspace hazard conflict. 

 Aircraft had already made a request to current sector controller. Multiple requests in a 

sector are unreasonable and the aircrew waits until the next sector to make another 

request if the initial request is denied. 

 Aircraft was estimated to be in handoff status once the aircraft was within 

approximately 20 nmi of the sector boundary. Any request received while the aircraft 

is in handoff status is likely to be met with the response to make the request to the 

next sector controller. 

 Aircraft was on initial climb from origin airport and had not yet reached cruising 

altitude. Controllers are concerned about potential interference of the departure 

stream with the arrival stream, so requests are generally denied until the aircraft 

reaches cruising altitude. 

 Aircraft is within 200 nmi of a large hub destination airport. Controllers indicated that 

aircraft must generally be on their assigned arrival route within 200 nmi of a large 

hub destination airport. 
9

3.3 Controller Evaluation of TASAR Requests 

The controller was modeled to reject an aircrew request if any of the following conditions 

exist. 

 The aircrew request was projected to cause an aircraft-aircraft conflict. The controller 

had more information about the surrounding traffic than the TASAR-equipped 

aircraft including (1) the flight plans for all aircraft and (2) the ADS-B-equipped 

aircraft beyond the sixty nmi assumed ADS-B range. 

 The aircrew request occurs in a sector that was experiencing traffic exceeding its 

monitor alert parameter value (i.e., a red sector). This was an attempt to model the 

phenomenon that, as traffic demand increases in their sector, controllers develop 

plans to cope with the rising traffic and, unless the request is consistent with the 

controller plan, the aircrew request is likely to be denied. Under higher traffic levels 

the aircrew request is less likely to be consistent with the controller plan 

 The aircrew request was projected to enter an adjacent red sector. Controllers are 

generally not aware of red sectors elsewhere and will not consider traffic demand in 

other sectors when evaluating aircrew requests. However, the area manager may 

instruct the controller not to send traffic through an adjacent sector if the adjacent 

sector is currently experiencing high traffic. 

The TASAR filters described previously, such as not making multiple requests to the 

same sector controller, were not applied again on the controller side since these types of 

requests would not reach the controller in the simulation. 

4. Annualized TASAR Benefit Results for Virgin America 

The benefits analysis focused on Virgin America operations in the continental United 

States. Operations performed using Airbus A320 and A319 aircraft were analyzed since 

these are both candidates to be equipped with TASAR. 

4.1 Airport Pair Selected for Analysis 

The Bureau of Transportation Statistics (BTS) T-100 Domestic Segment databasei from 

April 2013 to March 2014 was used to determine the annual frequency of Virgin America 

operations between airport pairs by aircraft type. The departures performed and aircraft 

type fields in the T-100 database were used to determine annual operations by aircraft 

type. These annual operations were then divided by the number of aircraft of each type to 

obtain the operations per aircraft shown in Table 1. The airport pairs that were analyzed 

are shown as shaded cells. The remaining airport pairs in the continental United States 

were not analyzed due to time constraints. 

 

i

 http://www.transtats.bts.gov/Fields.asp?Table_ID=311 
10

Table 1. Annual operations per aircraft by airport pair and aircraft type. Airport pairs analyzed are 

shaded. 

Airport 1 Airport 2

Annual Operations per 

Aircraft 

A319 A320 

LAX SFO 133 104

LAS SFO 181 64

JFK LAX 0 81

SAN SFO 96 55

SEA SFO 65 58

JFK SFO 2 64

LAX SEA 52 48

DFW LAX 129 19

LAX SJC 48 46

DFW SFO 70 30

IAD SFO 53 28

FLL LAX 1 39

ORD SFO 30 35

EWR LAX 23 41

LAS LAX 156 10

BOS LAX 22 33

EWR SFO 38 37

BOS SFO 8 36

IAD LAX 1 34

PDX SFO 97 15

LAX ORD 1 32

LAX PHL 42 19

FLL SFO 20 18

PHL SFO 45 7

JFK LAS 0 17

DCA SFO 19 12

LAX MCO 0 17

LAX PDX 0 11

AUS SFO 3 14

PSP SFO 19 3

MCO SFO 1 4

Total annual operations 

by aircraft type 1,354 1,033

4.2 Simulation Fuel and Time Savings Estimates 

A total of 1,554 historical Virgin America flights in July, August, and September 2012 

were analyzed using the simulation platform to produce the simulation results detailed in 

Appendix A. The expired reroute initiative and convective weather use cases did not 

occur frequently (less than 10% of historical flights). This does not imply that 10% of 

flights were impacted by convective weather since flights may be delayed or cancelled at 

large hub airports until the convective weather passes and therefore TASAR would not 

interact with convective weather data. For the more common A320 aircraft, the expired 
11

reroute initiative had highest average benefit (597 gallons/operation, 7.0 min/operation) 

and the convective weather and wind use cases had similar benefits (about 210 

gallons/operation, about 2.5 min/operation). 

Due to the similar convective weather use case per operation benefit as compared to the 

wind use case, the results are scaled without attempting to estimate the number of annual 

convective weather use cases. For example, 3 out of 76 historical A320 flights between 

New York (JFK) and Los Angeles (LAX) were classified as expired reroute initiative and 

each A320 operates between JFK and LAX an average of 64 times annually so (3/76)*64 

= 2.5 annual cancelled expired reroute initiative use cases between JFK and SFO 

occurred per A320. The fuel and time raw simulation outcomes in Appendix A are 

scaled, and the resulting fuel and time benefits are shown in Tables 2 to 3. Benefits are a 

function of both the benefit per operation and number of operations, so that the New 

York-San Francisco (JFK-SFO) airport pair fuel benefit of about 4,900 gallons per 

aircraft per year is higher than the Los Angeles-San Francisco (LAX-SFO) airport pair 

fuel benefit of about 700 gallons per aircraft per year, even though there are almost twice 

as many flights between LAX-SFO than JFK-SFO. 

Table 2. Annual fuel and time benefits by use case for A320. 

Apt

1 

Apt

2 

Annual Benefit 

Cancelled Initiative Use 

Case (1) 

Annual Benefit Weather 

Use Case (2) 

Annual Benefit Wind Use 

Case (3) 

Num Fuel 

(Gal) 

Time 

(Min) Num Fuel (Gal) 

Time 

(Min) Num Fuel (Gal) 

Time 

(Min) 

JFK SFO 2.5 0.0 5.9 5.1 230.3 21.9 56.4 4929.9 380.6

JFK LAX 2.0 150.9 20.1 8.0 306.7 19.4 71.0 3847.1 329.4

BOS SFO 1.9 641.6 47.4 1.9 113.0 0.0 32.2 2471.4 255.8

BOS LAX 0.6 0.0 0.0 7.1 289.3 36.9 25.2 2443.8 154.0

FLL LAX 1.1 0.0 13.4 2.2 0.0 4.5 35.7 1456.5 171.6

ORD SFO 0.0 0.0 0.0 5.7 47.1 11.8 29.3 1382.0 105.5

IAD SFO 0.0 0.0 0.0 2.7 67.0 4.7 25.3 1086.1 162.0

DFW SFO 0.0 0.0 0.0 4.4 6.5 4.4 25.6 963.2 69.4

LAX PHL 1.1 32.5 0.0 2.2 137.3 5.4 15.7 876.2 46.1

LAS SFO 0.0 0.0 0.0 0.9 0.0 0.0 63.1 776.8 66.7

LAX SFO 0.0 0.0 0.0 0.0 0.0 0.0 104.0 761.3 66.3

LAX SEA 0.0 0.0 0.0 1.2 41.4 3.6 46.8 550.5 32.2

LAX ORD 7.1 430.1 26.7 2.7 0.6 0.0 22.2 529.3 19.6

PHL SFO 0.5 18.6 9.6 1.2 114.2 8.9 5.4 485.5 56.5

SAN SFO 0.0 0.0 0.0 0.0 0.0 0.0 55.0 410.7 34.4

IAD LAX 0.0 0.0 0.0 4.9 0.0 0.0 29.1 405.5 176.5

LAX MCO 0.0 0.0 0.0 3.7 211.3 7.4 13.3 395.6 25.0

FLL SFO 0.0 0.0 0.0 1.2 0.0 0.0 16.8 387.6 90.6

DFW LAX 0.0 0.0 0.0 2.3 70.8 2.3 16.7 277.1 31.2

SEA SFO 0.0 0.0 0.0 0.0 0.0 0.0 58.0 114.0 6.8

LAX PDX 0.0 0.0 0.0 0.0 0.0 0.0 11.0 105.4 6.4

PDX SFO 0.0 0.0 0.0 0.0 0.0 0.0 15.0 45.0 3.7

MCO SFO 0.0 0.0 0.0 0.0 0.0 0.0 4.0 0.0 0.0

Sum 1273.9 123.0 1635.5 131.2 24700.3 2290.2
12

Table 3. Annual fuel and time benefits by use case for A319. 

Apt

1 

Apt

2 

Annual Benefit 

Cancelled Initiative Use 

Case (1) 

Annual Benefit Weather 

Use Case (2) 

Annual Benefit Wind Use 

Case (3) 

Num Fuel 

(Gal) 

Time 

(Min) Num Fuel (Gal) 

Time 

(Min) Num Fuel (Gal) 

Time 

(Min) 

DFW LAX 0.0 0.0 0.0 20.4 755.0 30.6 108.6 5120.6 234.2

PHL SFO 3.0 119.8 61.5 7.5 734.1 57.0 34.5 3121.0 363.0

DFW SFO 0.0 0.0 0.0 11.7 494.2 25.7 58.3 2919.5 142.3

LAS SFO 0.0 0.0 0.0 0.0 0.0 0.0 181.0 2249.5 181.0

LAX PHL 2.4 71.9 0.0 4.8 303.5 12.0 34.8 1936.9 102.0

BOS LAX 0.4 0.0 0.0 4.7 192.8 24.6 16.8 1629.2 102.7

LAX SEA 0.0 0.0 0.0 5.0 107.1 2.5 47.0 1351.3 61.9

ORD SFO 0.0 0.0 0.0 4.9 40.3 10.1 25.1 1184.6 90.5

SAN SFO 0.0 0.0 0.0 0.0 0.0 0.0 96.0 1135.1 96.0

LAX SFO 0.0 0.0 0.0 0.0 0.0 0.0 133.0 1133.3 105.0

FLL SFO 0.0 0.0 0.0 1.4 0.0 0.0 18.6 430.7 100.7

PDX SFO 0.0 0.0 0.0 0.0 0.0 0.0 97.0 291.2 23.7

SEA SFO 0.0 0.0 0.0 0.0 0.0 0.0 65.0 127.8 7.6

JFK SFO 0.1 0.0 0.0 0.4 0.0 0.6 1.5 107.8 13.4

FLL LAX 0.0 0.0 0.3 0.1 0.0 0.1 0.9 37.3 4.4

LAX ORD 0.2 3.7 0.3 0.1 0.0 0.0 0.7 13.6 1.3

IAD SFO 0.0 0.0 0.0 0.0 0.0 0.0 53.0 0.0 773.8

BOS SFO 0.0 0.0 0.0 0.3 1.6 1.1 7.7 0.0 54.6

IAD LAX 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0

Sum 195.5 62.2 2628.6 164.3 22789.5 2458.0

4.3 Estimating Annualized Cost Savings 

Virgin America indicated that $3.03 is the current fuel cost being used in similar 

economic analysis to convert fuel savings to cost savings. A fuel cost of $3.03/gallon 

was multiplied by fuel savings for the three use cases (i.e., 195.5 + 2628.6 + 22789.5 = 

25613.1 gallons for A319, rounded down to 25,000 gallons) and the number of aircraft of 

that type to obtain a total annual savings of $4.27 million per year as shown in Table 4. 

Table 4. Summary of fuel cost savings calculation. 

Aircraft 

Type 

Number of 

Aircraft of 

Type 

Annual Ops 

Simulated / 

Estimated 

Annual Ops i

Annual Fuel 

Savings per 

Aircraft 

(gallons) 

Fuel Cost 

Fuel Cost 

Savings for All 

Aircraft of 

Type 

A320 43 852/1033 27,000 $3.03 $3,517,830

A319 10 1047/1354 25,000 $3.03 $757,500

Sum $4,275,330

 

i

 Already used in fuel savings column to the right. Shown to illustrate that different amount of operations 

for each aircraft type cause difference in benefits. 
13

BTS Form 41ii financial data was used to obtain maintenance and depreciation costs in 

order to convert time savings to cost savings. Schedule P-5.2 reports the total 

maintenance, depreciation, and aircraft hours by aircraft type for Virgin America and 

other large carriers. These figures were used to estimate maintenance and depreciation 

costs per minute by aircraft type shown in Table 5. These costs were used to convert time 

savings into annual maintenance ($740,005) and depreciation ($72,090) savings. 

Virgin America incurs other costs, including crew costs, which are based on actual block 

time. Flight crews are paid based on the scheduled block time or actual block time, 

whichever is greater. TASAR was found to reduce actual block time above the scheduled 

block time by an average of about 0.4 minutes per flight out of an average time savings 

per flight of 2.8 minutes to 3.3 minutes for the A320 and A319 respectively. These time 

savings result in an additional crew cost savings that were not quantified but represent a 

potential additional TASAR benefit. The time savings may also result in increased 

customer satisfaction over time, but no attempt was made to quantify that benefit. 

Table 5. Summary of maintenance and depreciation savings calculation. 

Aircraft 

Type 

Number 

of 

Aircraft 

of Type 

Time 

Savings 

per 

Aircraft 

(min) 

Maintenance 

Cost per min

Maintenance 

Cost Savings 

for All 

Aircraft of 

Type 

Depreciation 

Cost per min 

Depreciation

Cost Savings 

for All 

Aircraft of 

Type 

A320 43 2,500 $5.51 $592,325 $0.54 $58,050

A319 10 2,600 $5.68 $147,680 $0.54 $14,040

Sum $740,005 Sum $72,090

The fuel, maintenance, and depreciation costs were added to obtain a total cost savings of 

about $5.09 million annually ($4,275,330 + $740,005 + $72,090 = $5,087,425). 

These benefits were a result of lateral (58% of requests), vertical (5% of requests), and 

combination lateral and vertical TASAR requests (37% of requests). A breakdown of 

these percentages by aircraft type is included in Appendix B. 

5. ATC Impacts 

A total of 6,038 TASAR requests were simulated of which 470 (8%) were rejected due to 

conflicts (305) and other factors (165). Recall that it was assumed in the simulation that 

100% of traffic aircraft was equipped with ADS-B Out. However, this did not result in 

TASAR detecting all conflicts since TASAR does not have as much information as the 

controller. A total of 7,403 requests which, if approved, would save fuel and time were 

not made by TASAR aircraft since they were predicted to be unapproveable to ATC 

including 1,162 due to conflicts. If the surrounding traffic was not equipped with ADS-B 

Out or the TASAR ownship was not equipped with ADS-B In, then this would imply that 

approximately (470 + 1,162) / (6,038 + 1,162) = 23% would reasonably be expected to be 

rejected. The (470 + 1,162) includes the original 470 rejections and the 1,162 requests not 

made since they were predicted by TASAR to contain conflicts and, without both 

 

ii http://www.transtats.bts.gov/Tables.asp?DB_ID=135 
14

surrounding traffic being equipped with ADS-B Out and TASAR ownship being 

equipped without ADS-B In, these conflicts would not be known to TASAR and the 

requests would have been made. Therefore, while the previous benefit study indicated 

that ADS-B Out equipage rate and TASAR ownship ADS-B In equipage does not 

significantly impact benefits, they are important in reducing nuissance requests that 

increase controller workload. Also, while an attempt has been made to model controller 

behavior as closely as possible, there is still uncertainty as to whether a controller will or 

will not grant a request. Even if a request would cause a conflict, the controller may hold 

onto the request and wait for the traffic to pass and be clear of projected conflicts before 

granting the request. 

It was found that Virgin America TASAR requests were spread across the country and 

not concentrated in a single Air Route Traffic Control Center (ARTCC). The two sectors 

that experienced the most TASAR requests at about 3% of total requests each, ZOA33 

and ZOA34, are high altitude (FL 240+) sectors east of San Francisco. ZLC45, which lies 

on ZOA33’s eastern border, is the sector with the next highest number of requests . The 

higher number of requests in ZOA33, ZOA34, and ZLC45 is due to east-west traffic 

going to and from Virgin America’s hub at San Francisco (SFO). 

Due to computational reasons, there was only one TASAR aircraft active in the 

simulation at once, and so the following procedure, which also takes into account that not 

all airport pairs were simulated, was used to estimate daily requests by sector across 

multiple simulation runs. The following statistics were used to derive (1) the expected 

daily TASAR requests per day and (2) TASAR requests not made due to conflicts: 

average daily Virgin America continental US flights (159) derived from Table 1, the 

number of flights simulated (1,554), the number of TASAR requests by sector, and 

TASAR requests not made due to conflicts (i.e., filtered) by sector. For example, ZOA33 

had 203 requests reported in the simulation so it was estimated that (203)(159/1554) = 21 

requests per day occur in ZOA33. The requests not made (filtered) were used to 

approximate the number of requests if the aircraft was not equipped with ADS-B In. 

These filtered requests were added to requests made to approximate the number of 

requests if the TASAR aircraft was not equipped with ADS-B In or traffic aircraft were 

not equipped with ADS-B Out. A summary of this calculation is shown in Table 6 for the 

ten sectors receiving the most TASAR requests. 

Table 6. TASAR requests per day by sector where TASAR request occurs. 

Sector where 

TASAR 

Request 

Occurs 

 TASAR 

Requests 

(1) 

TASAR 

Requests not 

Made due to 

Conflicts (2) 

Requests Made 

+ Requests not 

Made: (1) + (2) 

= (3) 

Requests per 

Day with 

ADS-B In: 

(1) * (159 / 

1554) 

Requests per Day

without ADS-B 

In: (3) * (159 / 

1554) 

ZOA33 203 20 223 21 23

ZOA34 199 8 207 20 21

ZLC45 154 87 241 16 25

ZDV24 105 38 143 11 15

ZMP42 103 18 121 11 12

ZSE14 97 42 139 10 14

ZLA39 90 8 98 9 10
15

Sector where 

TASAR 

Request 

Occurs 

 TASAR 

Requests 

(1) 

TASAR 

Requests not 

Made due to 

Conflicts (2) 

Requests Made 

+ Requests not 

Made: (1) + (2) 

= (3) 

Requests per 

Day with 

ADS-B In: 

(1) * (159 / 

1554) 

Requests per Day

without ADS-B 

In: (3) * (159 / 

1554) 

ZLC34 84 29 113 9 12

ZLA37 83 2 85 8 9

ZOA31 81 54 135 8 14

Requests per hour by sector was approximated by binning the TASAR request times into 

hours and scaling by requests per day (e.g., scale ZOA33 hourly results by 21/203) to 

account for the fact that flights were simulated across multiple days. Table 7 shows 

hourly results for the three sectors with the most requests which indicate that 2 to 4 

requests per sector occur during the peak hours between about 9 AM and 2 PM. If 

necessary, the peak requests of 2 to 4 requests per sector per hour could potentially be 

managed through coordination with dispatchers or another procedure. 

Table 7. TASAR requests per hour by sector where TASAR request occurs. 

Hour of 

Request 

(Pacific time) 

ZOA33 Average 

Requests in Hour

ZOA34 Average 

Requests in Hour

ZLC45 Average 

Requests in Hour 

0 0.2 0.0 0.2

1 0.1 0.2 0.0

2 0.0 0.0 0.1

3 0.0 0.0 0.0

4 0.0 0.0 0.0

5 0.0 0.0 0.0

6 0.0 0.0 0.0

7 0.2 0.2 0.8

8 2.1 1.2 2.7

9 2.1 3.5 0.8

10 1.2 1.2 1.0

11 2.9 1.9 2.9

12 2.3 2.2 2.4

13 1.4 2.1 0.5

14 3.0 2.2 1.7

15 1.6 1.2 1.1

16 0.3 0.9 0.1

17 0.0 0.0 0.0

18 0.1 0.3 0.0

19 0.0 0.4 0.1

20 1.1 0.7 0.5

21 1.5 1.6 0.4

22 0.7 0.5 0.3

23 0.1 0.1 0.1

 
16

6. Future Work 

A TASAR flight trial is planned for 2015 with one of the objectives to develop a 

methodology to verify the accuracy of the TASAR Traffic Aware Planner (TAP) 

software computed outcomes. This method could be applied to the simulation benefits 

results presented in this report to verify that benefits are not systematically being over or 

under reported. Following that flight test, it is expected that TASAR will be placed on a 

revenue flight so the method can be applied and suitable adjustments made to TAP and 

the benefits assessment. 

Observations at ATC facilities are also planned which could be used to refine controller 

models in the simulation to better estimate the conditions under which a TASAR request 

is accepted or rejected. 

7. References 

1

Henderson, J., Wing, D.J., and Idris, H., “Preliminary Benefits Assessment of Traffic 

Aware Strategic Aircrew Requests (TASAR)”, AIAA 12th Aviation Technology, 

Integration, and Operations Conference (ATIO), Indianapolis, IN, September 2012.
17

Appendix A: Simulation Fuel and Time Savings 

This appendix includes fuel and time savings output from the fast-time simulation 

platform for each aircraft type. 

Table 8. A320 simulation results. 

Airport 

1 

Airport 

2 Use Case Flights Simulated

Time Savings 

(min) 

Fuel Savings 

(lbs) 

KBOS KLAX Cx Reroute TMI 1 0.0 0.0

KBOS KLAX Weather 11 ‐5.2 ‐278.0

KBOS KLAX Wind 39 ‐6.1 ‐662.4

KBOS KLAX All 51 ‐5.8 ‐565.3

KBOS KSFO Cx Reroute TMI 1 ‐25.0 ‐2316.3

KBOS KSFO Weather 1 1.0 ‐407.9

KBOS KSFO Wind 17 ‐7.9 ‐524.8

KBOS KSFO All 19 ‐8.4 ‐612.9

KDFW KLAX Cx Reroute TMI 0 0.0 0.0

KDFW KLAX Weather 3 ‐1.0 ‐212.5

KDFW KLAX Wind 22 ‐1.9 ‐113.3

KDFW KLAX All 25 ‐1.8 ‐125.2

KDFW KSFO Cx Reroute TMI 0 0.0 0.0

KDFW KSFO Weather 8 ‐1.0 ‐9.9

KDFW KSFO Wind 46 ‐2.7 ‐257.8

KDFW KSFO All 54 ‐2.5 ‐221.1

KFLL KLAX Cx Reroute TMI 1 ‐12.0 0.0

KFLL KLAX Weather 2 ‐2.0 0.0

KFLL KLAX Wind 32 ‐4.8 ‐279.4

KFLL KLAX All 35 ‐4.9 ‐191.9

KIAD KLAX Cx Reroute TMI 0 0.0 0.0

KIAD KLAX Weather 3 0.0 0.0

KIAD KLAX Wind 18 ‐6.1 ‐95.2

KIAD KLAX All 21 ‐5.1 ‐73.0

KIAD KSFO Cx Reroute TMI 1 ‐36.0 ‐6893.6

KIAD KSFO Weather 4 ‐1.8 ‐172.0

KIAD KSFO Wind 53 ‐7.1 ‐206.4

KIAD KSFO All 58 ‐7.2 ‐319.3

KJFK KLAX Cx Reroute TMI 3 ‐10.0 ‐514.0

KJFK KLAX Weather 12 ‐2.4 ‐261.2

KJFK KLAX Wind 106 ‐4.6 ‐370.8

KJFK KLAX All 121 ‐4.6 ‐363.5

KJFK KSFO Cx Reroute TMI 3 ‐2.3 141.7

KJFK KSFO Weather 6 ‐4.3 ‐311.8

KJFK KSFO Wind 67 ‐6.7 ‐597.7

KJFK KSFO All 76 ‐6.4 ‐545.9

KLAS KSFO Cx Reroute TMI 0 0.0 0.0
18

Airport 

1 

Airport 

2 Use Case Flights Simulated

Time Savings 

(min) 

Fuel Savings 

(lbs) 

KLAS KSFO Weather 1 0.0 0.0

KLAS KSFO Wind 70 ‐1.1 ‐84.2

KLAS KSFO All 71 ‐1.0 ‐83.0

KLAX KMCO Cx Reroute TMI 0 0.0 0.0

KLAX KMCO Weather 7 ‐2.0 ‐388.7

KLAX KMCO Wind 25 ‐1.9 ‐203.7

KLAX KMCO All 32 ‐1.9 ‐244.2

KLAX KORD Cx Reroute TMI 8 ‐3.8 ‐413.7

KLAX KORD Weather 3 0.7 ‐1.6

KLAX KORD Wind 25 ‐0.9 ‐162.9

KLAX KORD All 36 ‐1.4 ‐205.2

KLAX KPDX Cx Reroute TMI 0 0.0 0.0

KLAX KPDX Weather 0 0.0 0.0

KLAX KPDX Wind 60 ‐0.6 ‐65.5

KLAX KPDX All 60 ‐0.6 ‐65.5

KLAX KPHL Cx Reroute TMI 2 0.0 ‐205.0

KLAX KPHL Weather 4 ‐2.5 ‐432.5

KLAX KPHL Wind 29 ‐2.9 ‐380.7

KLAX KPHL All 35 ‐2.7 ‐376.6

KLAX KSEA Cx Reroute TMI 0 0.0 0.0

KLAX KSEA Weather 2 ‐3.0 ‐232.8

KLAX KSEA Wind 77 ‐0.7 ‐80.5

KLAX KSEA All 79 ‐0.7 ‐84.3

KLAX KSFO Cx Reroute TMI 0 0.0 0.0

KLAX KSFO Weather 0 0.0 0.0

KLAX KSFO Wind 113 ‐0.6 ‐50.1

KLAX KSFO All 113 ‐0.6 ‐50.1

KMCO KSFO Cx Reroute TMI 0 0.0 0.0

KMCO KSFO Weather 0 0.0 0.0

KMCO KSFO Wind 1 0.0 0.0

KMCO KSFO All 1 0.0 0.0

KORD KLAX Cx Reroute TMI 8 ‐3.8 ‐413.7

KORD KLAX Weather 3 0.7 ‐1.6

KORD KLAX Wind 25 ‐0.9 ‐162.9

KORD KLAX All 36 ‐1.4 ‐205.2

KORD KSEA Cx Reroute TMI 0 0.0 0.0

KORD KSEA Weather 0 0.0 0.0

KORD KSEA Wind 0 0.0 0.0

KORD KSEA All 0 0.0 0.0

KORD KSFO Cx Reroute TMI 0 0.0 0.0

KORD KSFO Weather 13 ‐2.1 ‐56.6

KORD KSFO Wind 67 ‐3.9 ‐374.1

KORD KSFO All 80 ‐3.6 ‐322.5

KPDX KPHX Cx Reroute TMI 0 0.0 0.0
19

Airport 

1 

Airport 

2 Use Case Flights Simulated

Time Savings 

(min) 

Fuel Savings 

(lbs) 

KPDX KPHX Weather 0 0.0 0.0

KPDX KPHX Wind 0 0.0 0.0

KPDX KPHX All 0 0.0 0.0

KPDX KSFO Cx Reroute TMI 0 0.0 0.0

KPDX KSFO Weather 0 0.0 0.0

KPDX KSFO Wind 41 ‐0.2 ‐20.5

KPDX KSFO All 41 ‐0.2 ‐20.5

KPHL KSFO Cx Reroute TMI 2 ‐20.5 ‐273.2

KPHL KSFO Weather 5 ‐7.6 ‐669.5

KPHL KSFO Wind 23 ‐10.5 ‐618.8

KPHL KSFO All 30 ‐10.7 ‐604.2

KSAN KSFO Cx Reroute TMI 0 0.0 0.0

KSAN KSFO Weather 0 0.0 0.0

KSAN KSFO Wind 99 ‐0.6 ‐51.1

KSAN KSFO All 99 ‐0.6 ‐51.1

KSEA KSFO Cx Reroute TMI 0 0.0 0.0

KSEA KSFO Weather 0 0.0 0.0

KSEA KSFO Wind 94 ‐0.1 ‐13.4

KSEA KSFO All 94 ‐0.1 ‐13.4

All All Cx Reroute TMI 30 ‐7.0 ‐596.7

All All Weather 88 ‐2.5 ‐214.6

All All Wind 1149 ‐2.7 ‐211.0

All All All 1267 ‐2.8 ‐218.5

Table 9. A319 simulation results. 

Airport 

1 

Airport 

2 Use Case Flights Simulated

Time Savings 

(min) 

Fuel Savings 

(lbs) 

KBOS KSFO Cx Reroute TMI 0 0.0 0.0

KBOS KSFO Weather 1 ‐4.0 ‐40.0

KBOS KSFO Wind 28 ‐7.1 0.0

KBOS KSFO All 29 ‐7.0 0.0

KDFW KLAX Cx Reroute TMI 0 0.0 0.0

KDFW KLAX Weather 6 ‐1.5 ‐253.5

KDFW KLAX Wind 32 ‐2.2 ‐322.4

KDFW KLAX All 38 ‐2.1 ‐311.5

KDFW KSFO Cx Reroute TMI 0 0.0 0.0

KDFW KSFO Weather 5 ‐2.2 ‐289.8

KDFW KSFO Wind 25 ‐2.4 ‐342.3

KDFW KSFO All 30 ‐2.4 ‐333.6

KFLL KSFO Cx Reroute TMI 0 0.0 0.0

KFLL KSFO Weather 2 ‐1.5 0.0

KFLL KSFO Wind 27 ‐5.4 ‐158.2
20

Airport 

1 

Airport 

2 Use Case Flights Simulated

Time Savings 

(min) 

Fuel Savings 

(lbs) 

KFLL KSFO All 29 ‐5.1 ‐146.3

KIAD KLAX Cx Reroute TMI 0 0.0 0.0

KIAD KLAX Weather 0 0.0 0.0

KIAD KLAX Wind 1 ‐21.0 0.0

KIAD KLAX All 1 ‐21.0 0.0

KIAD KSFO Cx Reroute TMI 1 ‐2.0 ‐19.6

KIAD KSFO Weather 0 0.0 0.0

KIAD KSFO Wind 10 ‐13.5 ‐15.1

KIAD KSFO All 11 ‐12.5 ‐15.5

KJFK KSFO Cx Reroute TMI 1 0.0 0.0

KJFK KSFO Weather 3 ‐1.7 0.0

KJFK KSFO Wind 12 ‐8.9 ‐491.5

KJFK KSFO All 16 ‐7.0 ‐325.0

KLAS KSFO Cx Reroute TMI 0 0.0 0.0

KLAS KSFO Weather 0 0.0 0.0

KLAS KSFO Wind 30 ‐1.0 ‐85.0

KLAS KSFO All 30 ‐1.0 ‐85.0

KLAX KORD Cx Reroute TMI 6 ‐1.7 ‐131.5

KLAX KORD Weather 3 ‐1.7 0.0

KLAX KORD Wind 22 ‐1.8 ‐130.8

KLAX KORD All 31 ‐1.8 ‐114.5

KLAX KSEA Cx Reroute TMI 0 0.0 0.0

KLAX KSEA Weather 2 ‐0.5 ‐147.9

KLAX KSEA Wind 19 ‐1.3 ‐196.5

KLAX KSEA All 21 ‐1.2 ‐191.8

KLAX KSFO Cx Reroute TMI 0 0.0 0.0

KLAX KSFO Weather 0 0.0 0.0

KLAX KSFO Wind 19 ‐0.8 ‐58.3

KLAX KSFO All 19 ‐0.8 ‐58.3

KORD KLAX Cx Reroute TMI 6 ‐1.7 ‐131.5

KORD KLAX Weather 3 ‐1.7 0.0

KORD KLAX Wind 22 ‐1.8 ‐130.8

KORD KLAX All 31 ‐1.8 ‐114.5

KSAN KSFO Cx Reroute TMI 0 0.0 0.0

KSAN KSFO Weather 0 0.0 0.0

KSAN KSFO Wind 1 ‐1.0 ‐80.9

KSAN KSFO All 1 ‐1.0 ‐80.9

All All Cx Reroute TMI 14 ‐1.6 ‐114.1

All All Weather 25 ‐1.7 ‐132.2

All All Wind 248 ‐3.6 ‐171.1

All All All 287 ‐3.3 ‐161.4
21

Appendix B: TASAR Request Trajectory Change Types 

Table 10 summarizes the percentage of requests that are lateral, vertical, or combination 

lateral and vertical by aircraft type. The count of requests by aircraft type in the 

simulation are shown in the top half of the table and then shown as percentages in the 

lower half of the table. 

Table 10. Percentage of lateral, vertical, and combination lateral and vertical by aircraft type. 

Trajectory Change 

Type A320 A319 All 

Lateral 2,679 359 3,038

Vertical Lower 135 28 163

Vertical Higher 92 14 106

Lateral and Lower 894 233 1,127

Lateral and Higher 628 197 825

Sum 4,428 831 5,259

Lateral (%) 60.5% 43.2% 57.8%

Vertical Lower (%) 3.0% 3.4% 3.1%

Vertical Higher (%) 2.1% 1.7% 2.0%

Lateral and Lower (%) 20.2% 28.0% 21.4%

Lateral and Higher (%) 14.2% 23.7% 15.7%

Sum 100.0% 100.0% 100.0%
22

Appendix C: Summary Presentation 

The following presentation summarizes the Virgin America annualized benefit 

estimation. Data in tables and figures are the same as that found in other areas of the 

report. Data values were rounded for presentation purposes. 

1

Virgin America Traffic Aware Strategic Aircrew 

Requests (TASAR) Benefits Assessment

Jeff Henderson

Engility Corporation

David Wing

NASA Langley

San Francisco, CA

October 2014
23

2

Overview of benefits assessment

•TASAR use-cases analyzed

•Method used to estimate fuel and time benefits

•Results

– Benefits for Virgin America routes

– ATC impacts

•Future work

– Flight test to validate methodology

3

Quantified benefits of three use cases

1. Lateral change after reroute 

initiative has ended

2. Lateral change avoiding 

convective weather

3. Change to more wind-optimal 

trajectory (lateral, altitude, or 

combination)

• Other use cases (not modeled) expected 

to provide additional benefit

http://www.fly.faa.gov/PLAYBOOK/pbindex.html http://aviationweather.gov/adds/winds/
24

4

Quantified benefits of three use cases

1. Aircraft part of re-route initiative 

that has ended classified as (1). 

2. Aircraft with alternative route 

through convective weather 

classified as (2) if not part of (1).

3. Remaining aircraft not in (1) or 

(2) classified as (3). TASAR uses 

RAP winds to convert 

groundspeed to/from true 

airspeed.

http://www.fly.faa.gov/PLAYBOOK/pbindex.html http://aviationweather.gov/adds/winds/

5

Method to estimate fuel and time benefits

•Baseline

– Aircraft follow historical 4D 

trajectories derived from ASA 

radar tracks

•With TASAR

– Aircraft follow historical 4D 

trajectories until TASAR request 

granted

– Aircrew model uses TASAR to 

consider fuel, time, and ATC 

acceptability

– Controller model evaluates 

request using more traffic info

Playback historical 4D trajectories

Generate alternative trajectories

Evaluate alternative trajectories Make request?

Evaluate request

Approve request?

Simulate approved requests YES NO

Aircrew / TASAR 

automation

Controller
25

6

Aircrew generates request according to their 

objectives

• Objective used in simulation: 50% fuel, 50% time

– Constraint: fuel and time savings both ≥ 0 (i.e., exclude solutions that decrease fuel burned 

but increase flight time and vice versa)

• Voice communication limits requests to two named waypoints

• Considered lateral, altitude, and combination lateral and altitude trajectory 

changes

DFW

Aircraft current 

location (following 

historical trajectory)

Aircraft follow historical 

trajectories within 200 

nmi of airport

LAX

Alternate lateral trajectories 

based on current aircraft location

Example alternate waypoints

7

Alternative waypoints limited for computational 

reasons

•Bounding box used to limit alternative trajectories

•Box limits based on historical tracks between airport pair

* Note: Additional alternative waypoints not shown in figure.

All named waypoints inside bounding box used to generate 

alternative trajectories*
26

8

Aircrew decides whether to make request 

based on estimate of controller acceptability

•Aircrew request withheld if:

– Aircraft-aircraft conflict (depends 

on ADS-B equipment)

– Aircraft-airspace hazard conflict

– Already made request to current 

controller

– Request has no impact on current 

sector

– Aircraft in handoff status – 20 nmi 

from sector boundary

– Aircraft on initial climb – potential 

interference with arrival traffic

– Aircraft within 200 nmi of large 

hub destination airport

Playback historical 4D trajectories

Generate alternative trajectories

Evaluate alternative trajectories Make request?

Evaluate request

Approve request?

Simulate approved requests YES NO

Aircrew / TASAR 

automation

Controller

•Benefit results assume 100% ADS-B OUT equipage

– Earlier study indicated that ADS-B OUT equipage impacts ATC acceptability but not 

TASAR benefits

– ATC impacts included later in presentation 9

Controller evaluates requests against ATC 

objectives using ATC knowledge

•Additional ATC knowledge

– Aircraft intent (flight plans)

– ADS-B Aircraft outside 60 nmi 

assumed ADS-B range

– Demand exceeding monitor alert 

parameter (MAP) – red sectors

Playback historical 4D trajectories

Generate alternative trajectories

Evaluate alternative trajectories Make request?

Evaluate request

Approve request?

Simulate approved requests YES NO

Aircrew / TASAR 

automation

Controller
27

10

Fast-time simulation used for both baseline and 

TASAR scenarios

•Platform leverages Future ATM Concept Evaluation Tool (FACET)

– Aircraft performance model

– Airspace

Aircraft 

(yellow triangle)

Navaid

(blue/green circle)

Fix/intersection

(magenta cross)

Center 

boundary 

(grey line)

Sector 

boundary 

(grey line)

Jet route

(brown line)

SUAs

(orange/yellow line)

Playback historical 4D trajectories

Generate alternative trajectories

Evaluate alternative trajectories Make request?

Evaluate request

Approve request?

Simulate approved requests YES NO

Aircrew / TASAR 

automation

Controller

11

Results overview

•Fuel and time benefits for airport pairs by aircraft type 

shown in following order

– A320

– A319

•Summary of annual fuel and time benefits:

– by aircraft type, and

– across all aircraft
28

12

A320 benefits by airport pair

• Line thickness represents relative fuel benefit per aircraft per year between airports

• TASAR is expected to have highest benefit for A320 operations between New York 

(JFK) and San Francisco (SFO)

– Benefit = (fuel benefit per operation) * (estimated annual operations between airport pair per aircraft)

13

Airport pairs with highest expected annual fuel 

benefits for A320 when using TASAR

Airport 1 Airport 2 Per Operation 

Benefit

Annual Benefit

Fuel 

(gal)

Time

(min)

Ops per 

A320

Fuel 

(gal)

Time 

(min)

New York (JFK) San Francisco (SFO) 87.4 6.7 64 4,930 381

New York (JFK) Los Angeles (LAX) 54.2 4.6 81 3,847 329

Boston (BOS) San Francisco (SFO) 76.7 7.9 36 2,471 256

Boston (BOS) Los Angeles (LAX) 96.8 6.1 33 2,444 154

Fort Lauderdale (FLL) Los Angeles (LAX) 40.8 4.8 39 1,456 172

Chicago (ORD) San Francisco (SFO) 47.1 3.6 35 1,382 106

Washington (IAD) San Francisco 42.9 6.4 28 1,086 162

Total Annual Benefit (Wind Use Case) 851* 24,700 2,290

Total Annual Benefit (Wind, Convective Wx, and expired TMI) 851* 27,610 2,544

• Data source for annual operations: BTS T100 database derived from Form 41 air carrier 

reported operations (divide annual ops by number of aircraft to obtain ops per aircraft)

• Table shows results for wind use case which occurs most frequently

• Convective weather and expired TMI use cases included for completeness

* Note: Not all airport pairs shown.

TASAR fuel 

savings is at 

least 27,000 

gal per A320 

per year
29

14

Trajectories between JFK and SFO (highest 

annual A320 TASAR fuel benefit)

Historically Flown VRD Lateral Trajectories

VRD Lateral Trajectories with TASAR Requests SFO JFK

JFK SFO

TASAR requests not 

simple directs. Larger 

spread result of TASAR 

taking advantage of 

changing atmospheric 

conditions and ATC 

restrictions.

Expired TMI

15

Altitudes between JFK and SFO (highest annual 

A320 TASAR fuel benefit)

•On average TASAR requests result in aircraft cruising at 

lower altitudes between JFK and SFO

•Pure altitude changes are least frequent TASAR solution

– TASAR requests generally lateral or combination lateral and vertical

0%

10%

20%

30%

40%

50%

60%

70%

FL300 FL320 FL340 FL360 FL380 FL400

Percentage of Time at 

Cruising Altitude

Cruising Altitude Range between JFK and SFO

Historical

TASAR

0%

20%

40%

60%

80%

Lateral Lower Higher Lateral 

and 

Higher

Lateral 

and 

Lower

Percentage of A320 

Requests

TASAR Request Trajectory Change Type
30

16

A319 benefits by airport pair

• Line thickness represents relative fuel benefit per aircraft per year between airports

• TASAR is expected to have highest benefit for A319 operations between Dallas-Fort 

Worth (DFW) and Los Angeles (LAX)

– Benefit = (fuel benefit per operation) * (estimated annual operations between airport pair per aircraft)

17

Airport pairs with highest expected annual fuel 

benefits for A319 when using TASAR

Airport 1 Airport 2 Per Operation 

Benefit

Annual Benefit

Fuel 

(gal)

Time

(min)

Ops per 

A319

Fuel 

(gal)

Time 

(min)

Dallas (DFW) Los Angeles (LAX) 47.1 2.2 129 5,121 234

Dallas (DFW) San Francisco (SFO) 50.0 2.4 70 2,920 142

Las Vegas (LAS) San Francisco (SFO) 12.4 1.0 181 2,250 181

Los Angeles (LAX) Philadelphia (PHL) 55.7 2.9 42 1,937 102

Boston (BOS) Los Angeles (LAX) 96.8 6.1 22 1,629 103

Los Angeles (LAX) Seattle (SEA) 28.7 1.3 52 1,351 62

Chicago (ORD) San Francisco (SFO) 47.1 3.6 30 1,185 90

Total Annual Benefit (Wind Use Case) 1048* 22,789 2,458

Total Annual Benefit (Wind, Convective Wx, and expired TMI) 1048* 25,614 2,684

* Note: Not all airport pairs shown.

TASAR fuel 

savings is at 

least 25,000 

gal per A319 

per year
31

18

Fuel and Time Savings Summary

Aircraft Type Annual TASAR Fuel 

Benefit

Annual TASAR 

Time Benefit

Airport Pair with 

Highest TASAR 

Benefit

A320 27,000 gallons/aircraft 2,500 min/aircraft JFK – SFO

A319 25,000 gallons/aircraft 2,600 min/aircraft DFW – LAX

All aircraft 1,411,000 gallons* 133,500 min* DFW – SFO

* Assumes 43 A320s and 10 A319s.

19

Estimated Cost Savings Summary

Aircraft Type Fuel Cost/Gallon Maintenance 

Cost/min

(2013)*

Depreciation 

Cost/Min

(2013)*

A320 $3.03 $5.51 $0.54

A319 $3.03 $5.68 $0.54

Total Cost 

Savings by 

Category

$4,275,000/year** $740,000/year** $72,000/year**

Total Cost 

Savings for all 

Categories

$5,087,000/year**

* Obtained from BTS Form 41 data

** Applies fuel and time savings from previous slide.
32

20

ATC Impacts

•Average of 3.9 requests per flight

– Without ADS-B IN – 23% of requests rejected due to conflicts or 

other reasons

– With ADS-B IN – 8% of requests rejected due to conflicts or other 

reasons

– Request rejections do not significantly impact benefits – aircrew 

waits until next sector then makes same or similar request

21

ATC Impacts

•Virgin America requests spread across country

•Top three Virgin America TASAR request sectors

– High altitude ZOA/ZLC sectors

– Experienced 10% of total TASAR requests

SFO

OAK

SLC ZOA34

With ADS-B: 21 

TASAR requests/day

Without ADS-B: 23 

TASAR requests/day

ZOA33

With ADS-B: 20 

TASAR requests/day

Without ADS-B: 21 

TASAR requests/day

ZLC45

With ADS-B: 16 

TASAR requests/day

Without ADS-B: 25 

TASAR requests/day
33

22

ATC Impacts

• Peak Virgin America TASAR requests are about 2 to 4 requests 

per hour per sector

• Could be managed through dispatcher coordination or other 

procedure

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3

TASAR Requests in during Hour

Local Hour (Pacific Time)

ZOA33

ZOA34

ZLC45

Peak requests between 

about 9 AM and 2 PM 

Pacific.

23

Future Work

•Second TASAR flight trial planned for 2015

– Generating method to validate TAP computed outcomes is an 

objective of the flight trial

– Controller observations to better understand TASAR request 

acceptability

•Expected TASAR to be placed on a Virgin America revenue 

flight after flight trial

•Validation method, controller observations, and Virgin 

America revenue flight data could be used to refine 

benefits estimated by simulation
REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

2. REPORT TYPE 

Contractor Report

 4. TITLE AND SUBTITLE

Annualized TASAR Benefit Estimate for Virgin America Operations 

5a. CONTRACT NUMBER

NNL12AA06C

 6. AUTHOR(S)

Henderson, Jeffrey

 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

 NASA Langley Research Center

 Hampton, Virginia 23681-2199 

 

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National Aeronautics and Space Administration

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NASA

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Langley Technical Monitor: David J. Wing

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Availability: NASA STI Program (757) 864-9658

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14. ABSTRACT

The Traffic Aware Strategic Aircrew Request (TASAR) concept offers onboard automation for the purpose of advising the 

pilot of traffic compatible trajectory changes that would be beneficial to the flight. A fast-time simulation study was conducted 

to assess the benefits of TASAR to Virgin America. The simulation compares historical trajectories without TASAR to 

trajectories developed with TASAR and evaluated by controllers against their objectives. It was estimated that about 25,000 

gallons of fuel and about 2,500 minutes could be saved annually per aircraft. These savings were applied fleet-wide to produce 

an estimated annual cost savings to Virgin America in excess of $5 million due to fuel, maintenance, and depreciation cost 

savings. Switching to a more wind-optimal trajectory was found to be the use case that generated the highest benefits out of 

the three TASAR use cases analyzed. Virgin America TASAR requests peaked at two to four requests per hour per sector in 

high-altitude Oakland and Salt Lake City center sectors east of San Francisco. 

15. SUBJECT TERMS

Benefits; Electronic flight bag; Route optimization; Traffic aware strategic aircrew request; User requests; Virgin America

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