August 2015 

NASA/CR–2015-218787

Annualized TASAR Benefit Estimate for 

Alaska Airlines Operations 

Jeffrey Henderson 

Engility Corporation, Billerica, Massachusetts 
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August 2015 

NASA/CR–2015-218787 

Annualized TASAR Benefit Estimate for 

Alaska Airlines 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 accurate 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 Alaska Airlines operations. This 

document represents deliverable 41A 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 Alaska Airlines. The simulation compares historical 

trajectories without TASAR to trajectories developed with TASAR and evaluated by 

controllers against their objectives. It was estimated that between 8,000 and 12,000 

gallons of fuel and 900 to 1,300 minutes could be saved annually per aircraft. These 

savings were applied fleet-wide to produce an estimated annual cost savings to Alaska 

Airlines 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. Alaska TASAR requests 

peaked at four to eight requests per hour in high-altitude Seattle center sectors south of 

Seattle-Tacoma airport. 
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 Alaska Airlines ............................................... 9

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

4.2 Simulation Fuel and Time Savings Estimates ..................................................... 11

4.3 Estimating Annualized Cost Savings ................................................................... 14

5. ATC Impacts ................................................................................................................. 15

6. Future Work .................................................................................................................. 17

7. References ..................................................................................................................... 17

Appendix A: Simulation Fuel and Time Savings ......................................................... 18

Appendix B: TASAR Request Trajectory Change Types ............................................ 22

Appendix C: Summary Presentation............................................................................. 23

 
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 737-900ER. ................................ 12

Table 3. Annual fuel and time benefits by use case for 737-900. ..................................... 12

Table 4. Annual fuel and time benefits by use case for 737-800. ..................................... 13

Table 5. Annual fuel and time benefits by use case for 737-700. ..................................... 13

Table 6. Summary of fuel cost savings calculation. ......................................................... 14

Table 7. Summary of maintenance and depreciation savings calculation. ....................... 15

Table 8. TASAR requests per day by sector where TASAR request occurs. ................... 16

Table 9. TASAR requests per hour by sector where TASAR request occurs. ................. 17

Table 10. 737-900, 737-900ER simulation results. .......................................................... 18

Table 11. 737-800 simulation results. ............................................................................... 19

Table 12. 737-700 simulation results. ............................................................................... 21

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

type. ................................................................................................................................... 22
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, Alaska Airlines. It extends the previous study by developing estimates of 

annual fuel and time cost savings due to TASAR specifically for Alaska. Historical 

Alaska Airline 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 Alaska Airlines 

 Section 5 estimates impact of Alaska Airline 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 

between the 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 Alaska Airlines 

The benefits analysis focused on Alaska operations in the continental United States. 

TASAR does not currently support oceanic operations and Alaska is not expecting to 

equip 737-400 aircraft operating in Alaska with TASAR. The focus is on 737-900ER, 

737-900, 737-800, and 737-700 Alaska aircraft which are 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 Alaska 

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. Some of the airport pairs were selected since they are longer 

 

i

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

haul with a potential for higher TASAR benefits. The remaining airport pairs in the 

continental United States were not analyzed due to time constraints. 

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 

737-900ER 737-900 737-800 737-700 

ANC SEA 118 58 49 64

LAX SEA 61 133 38 66

LAS SEA 78 83 32 76

SAN SEA 15 23 47 45

SEA SFO 32 18 26 63

SEA SNA 0 0 18 235

ANC FAI 0 0 4 37

PHX SEA 30 33 10 68

SEA SJC 11 37 36 19

LAX PDX 26 36 15 21

SEA SMF 4 3 39 11

OAK SEA 5 3 29 30

DEN SEA 2 19 12 134

ANC JNU 0 2 0 0

LAS PDX 7 18 15 78

JNU SEA 4 9 6 0

GEG SEA 0 0 0 44

PDX SAN 7 11 21 7

BUR SEA 0 0 10 95

FAI SEA 19 11 6 44

PDX SNA 0 0 3 124

HNL SEA 0 0 3 0

ANC PDX 11 10 16 15

PDX SFO 8 5 8 39

ONT SEA 4 16 4 23

ORD SEA 19 77 9 0

DFW SEA 32 39 6 15

PDX SJC 2 12 13 29

MSP SEA 11 11 8 34

DCA SEA 0 0 22 0

EWR SEA 40 1 8 0

PSP SEA 6 3 3 17

BOS SEA 30 4 13 0

OGG SEA 0 0 1 0

PSP SFO 0 0 0 14

SEA SLC 1 15 17 5

PDX PHX 3 14 5 19

ATL SEA 12 3 9 11

MCO SEA 13 1 10 0

KOA SEA 0 0 1 0

BLI LAS 1 2 12 0
11

Airport 1 Airport 2 Annual Operations per Aircraft 

737-900ER 737-900 737-800 737-700 

BRW SCC 0 0 1 0

SAT SEA 4 8 9 0

AUS SEA 8 36 2 0

SEA TUS 12 8 6 0

IAH SEA 31 1 1 0

MCI SEA 9 9 7 0

ORD PDX 5 12 2 25

PHL SEA 16 3 5 0

SEA STL 4 2 9 0

FLL SEA 0 0 11 0

DCA PDX 0 0 11 0

DCA LAX 0 0 11 0

BOS PDX 0 0 10 0

ANC LAX 8 1 6 13

BOS SAN 0 0 11 0

PDX PSP 2219

MCO SAN 2 0 7 0

ANC ORD 0 0 9 0

BRW FAI 0 0 1 0

FAI SCC 0 0 1 0

ATL PDX 5 1 5 0

DFW PDX 4 8 3 0

Total annual operations 

by aircraft type 720 802 712 1528

4.2 Simulation Fuel and Time Savings Estimates 

Historical Alaska flights in July, August, and September 2012 were analyzed in 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 5% of historical flights). This does not imply that 5% 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. The expired reroute initiative had highest average benefit (103 

gallons/operation, 7.8 min/operation) and the convective weather use cases had the 

lowest benefit (12 gallons/operation, 1.3 min/operation) with the wind use case falling in 

between (27 gallons/operation, 2.3 min/operation). 

Due to the lower convective weather use case per operation benefit, the results are scaled 

without attempting to estimate the number of annual convective weather use cases. For 

example, 2 out of 29 historical 737-800 flights between Washington National (DCA) and 

Seattle (SEA) were classified as expired reroute initiative and each 737-800 operates 

between DCA and SEA an average of 22 times annually so (2/29)*22 = 1.5 annual 

cancelled expired reroute initiative use cases between DCA and SEA per 737-800. This 

will be a conservative underestimation since convective weather is more common in the 

months analyzed than other times of the year. The result of scaling the fuel and time 
12

results in Appendix A are show in Tables 2 to 5. Benefits are a function of both the 

benefit per operation and number of operations so that the Newark-Seattle (EWR-SEA) 

airport pair fuel benefit of about 2,200 gallons per aircraft per year is higher than the Los 

Angeles-Seattle (LAX-SEA) airport pair fuel benefit of about 900 gallons per aircraft per 

year even though there are about 50% more flights between LAX-SEA than EWR-SEA. 

Table 2. Annual fuel and time benefits by use case for 737-900ER. 

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) 

EWR SEA 0.0 0.0 0.0 2.5 0.0 0.0 37.5 2267.9 325.8

BOS SEA 0.0 0.0 0.0 0.0 0.0 0.0 30.0 1763.8 271.9

MCO SEA 0.0 0.0 0.0 0.0 0.0 0.0 13.0 1550.3 143.0

LAS SEA 0.0 0.0 0.0 0.0 0.0 0.0 78.0 1291.9 106.5

DFW SEA 0.0 0.0 0.0 6.2 191.6 6.2 24.8 1119.7 148.8

LAX SEA 0.0 0.0 0.0 0.0 0.0 0.0 61.0 902.6 87.6

ORD SEA 2.1 561.6 52.8 2.1 72.7 8.4 14.8 616.6 84.7

MSP SEA 0.3 6.8 0.9 0.6 7.1 0.6 10.1 535.0 35.2

PHX SEA 0.0 0.0 0.0 0.0 0.0 0.0 30.0 433.6 40.0

SEA SFO 0.0 0.0 0.0 0.0 0.0 0.0 32.0 293.9 28.8

SAN SEA 0.0 0.0 0.0 0.0 0.0 0.0 15.0 179.1 15.0

SEA STL 0.0 0.0 0.0 0.0 0.0 0.0 4.0 110.3 9.3

SEA TUS 0.0 0.0 0.0 0.0 0.0 0.0 12.0 84.3 9.6

DEN SEA 0.0 0.0 0.0 0.1 0.0 0.0 1.9 51.5 3.5

PDX PHX 0.0 0.0 0.0 0.0 0.0 0.0 3.0 2.3 2.3

Sum 568.4 53.7 271.4 15.3 11202.7 1312.2

Table 3. Annual fuel and time benefits by use case for 737-900. 

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) 

ORD SEA 0.0 0.0 0.0 4.8 0.0 0.0 72.2 3011.9 413.9

LAX SEA 0.0 0.0 0.0 0.0 0.0 0.0 133.0 1967.9 191.1

DFW SEA 0.0 0.0 0.0 0.0 0.0 0.0 39.0 1760.8 234.0

LAS SEA 0.0 0.0 0.0 0.0 0.0 0.0 83.0 1374.7 113.3

DEN SEA 0.0 0.0 0.0 0.6 0.0 0.0 18.4 489.0 33.5

PHX SEA 0.0 0.0 0.0 0.0 0.0 0.0 33.0 477.0 44.0

MSP SEA 0.0 0.0 0.0 2.2 68.0 2.2 8.8 468.1 30.8

SAN SEA 0.0 0.0 0.0 0.0 0.0 0.0 23.0 274.5 23.0

BOS SEA 0.0 0.0 0.0 0.0 0.0 0.0 4.0 209.1 36.3

SEA SFO 0.0 0.0 0.0 0.0 0.0 0.0 18.0 165.3 16.2

MCO SEA 0.1 29.6 2.8 0.1 3.8 0.4 0.8 89.6 8.6
13

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) 

SEA TUS 0.0 0.0 0.0 0.0 0.0 0.0 8.0 56.2 6.4

SEA STL 0.0 0.0 0.0 0.0 0.0 0.0 2.0 55.1 4.7

EWR SEA 0.0 0.6 0.1 0.1 0.6 0.1 0.9 54.4 7.9

PDX PHX 0.0 0.0 0.0 0.0 0.0 0.0 14.0 10.8 0.0

Sum 30.2 2.9 72.5 2.7 10464.5 1163.6

Table 4. Annual fuel and time benefits by use case for 737-800. 

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) 

DCA SEA 1.5 213.4 20.5 2.3 27.1 3.8 18.2 1429.1 137.3

MCO SEA 1.1 295.6 27.8 1.1 38.3 4.4 7.8 1010.5 85.6

SAN SEA 0.0 0.0 0.0 1.7 5.2 1.1 45.3 790.6 59.7

BOS SEA 0.0 0.0 0.0 0.0 0.0 0.0 13.0 763.9 117.8

ORD SEA 0.0 0.0 0.0 0.0 0.0 0.0 9.0 538.1 64.0

LAS SEA 0.0 0.0 0.0 0.5 5.7 0.5 31.5 526.0 39.8

DCA LAX 0.3 14.4 1.7 0.3 3.9 1.7 10.3 500.4 59.9

EWR SEA 0.2 4.9 0.7 0.5 5.1 0.5 7.3 495.1 63.5

FLL SEA 0.0 0.0 0.0 0.9 53.8 7.8 10.1 483.7 99.0

LAX SEA 0.0 0.0 0.0 0.6 0.0 0.0 37.4 447.2 36.3

DEN SEA 0.0 0.0 0.0 0.2 2.7 0.2 11.8 317.8 28.0

DCA PDX 0.0 0.0 0.0 0.0 0.0 0.0 11.0 238.5 33.0

DFW SEA 0.0 0.0 0.0 0.1 0.0 0.0 5.9 231.0 24.0

MSP SEA 0.0 0.0 0.0 0.7 3.0 0.7 7.3 223.9 27.6

PHX SEA 0.0 0.0 0.0 0.0 0.0 0.0 10.0 148.9 15.1

SEA SFO 0.0 0.0 0.0 0.0 0.0 0.0 26.0 102.6 10.4

SEA TUS 0.0 0.0 0.0 0.3 0.0 0.0 5.7 20.6 2.4

SEA STL 0.0 0.0 0.0 2.1 4.2 0.5 6.9 18.1 1.6

Sum 528.3 50.6 149.0 21.3 8286.0 905.2

Table 5. Annual fuel and time benefits by use case for 737-700. 

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) 

DEN SEA 0.0 0.0 0.0 0.0 0.0 0.0 134.0 5294.1 562.8
14

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) 

PHX SEA 2.3 207.9 7.6 5.3 216.7 8.3 60.4 2497.8 100.5

SAN SEA 0.0 0.0 0.0 0.0 0.0 0.0 45.0 1272.3 92.8

MSP SEA 0.0 0.0 0.0 0.0 0.0 0.0 34.0 1050.0 104.6

DFW SEA 0.0 0.0 0.0 0.0 0.0 0.0 15.0 830.2 74.5

LAX SEA 0.0 0.0 0.0 6.2 193.8 20.6 59.8 302.2 35.1

SEA SFO 0.0 0.0 0.0 3.0 36.5 3.0 60.0 284.5 25.5

PDX PHX 0.0 0.0 0.0 3.2 12.0 0.8 15.8 81.0 6.3

Sum 207.9 7.6 459.0 32.7 11612.0 1002.1

4.3 Estimating Annualized Cost Savings 

BTS Form 41i financial data was used to obtain fuel, maintenance, and depreciation costs 

in order to convert fuel and time savings to cost savings. 

Form 41 Schedule P-12(a) reported $111,391,459 cost for 34,199,887 gallons of fuel for 

Alaska Airlines in March 2014 for an average cost of $3.26/gallon. This cost of 

$3.26/gallon was multiplied by the sum of fuel savings and the number of aircraft of that 

type to obtain a total annual savings of $3.39 million per year as shown in Table 6. The 

annual fuel savings column adds the fuel savings for the three use cases reported in 

Tables 2 to 5 and rounds down to the nearest 1,000 gallons. The 737-800 is shown 

producing the lowest fuel savings (8,000 gallons per aircraft per year) but this was caused 

by only simulating 289 of an estimated 712 operations rather than any specific reason that 

would cause the aircraft to experience lower benefits if equipped with TASAR. For all 

aircraft, the fuel savings in Table 6 represents a lower bound that most likely 

underestimates benefits since not all aircraft pairs shown in Table 1 were analyzed. 

Table 6. Summary of fuel cost savings calculation. 

Aircraft 

Type 

Number of 

Aircraft of 

Type 

Annual Ops 

Simulated / 

Estimated 

Annual Ops ii

Annual Fuel 

Savings per 

Aircraft 

(gallons) 

Fuel Cost 

Fuel Cost 

Savings for All 

Aircraft of 

Type 

737-900ER 22 381/720 12,000 $3.26 $860,640

737-900 12 466/802 10,000 $3.26 $391,200

737-800 61 289/712 8,000 $3.26 $1,590,880

737-700 14 444/1,528 12,000 $3.26 $547,680

Sum $3,390,400

Form 41 Schedule P-5.2 reports the total maintenance, depreciation, and aircraft hours by 

aircraft type for Alaska Airlines and other large carriers. These figures were used to 

 

i

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

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

for each aircraft type causes difference in benefits. 
15

estimate maintenance and depreciation costs per minute by aircraft type. Alaska incurs 

other costs, including crew costs, but it was decided to exclude these from the analysis. 

The time savings may also result in increased customer satisfaction over time but no 

attempt was made to quantify that benefit. 

Table 7. 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 

737-900ER 22 1,300 $8.44 $241,384 $8.72 $249,392

737-900 12 1,100 $8.44 $111,408 $8.72 $115,104

737-800 61 900 $4.96 $272,304 $6.75 $370,575

737-700 14 1,000 $21.4 $299,600 $7.18 $100,520

Sum $924,696 Sum $835,591

The fuel, maintenance, and depreciation costs are added which results in a total cost 

savings of about $5.15 million annually ($3,390,400 + $924,696 + $835,591 = 

$5,150,687). 

These benefits were a result of lateral (44% of requests), vertical (5% of requests), and 

combination lateral and vertical TASAR requests (51% of requests). A breakdown of 

these percentages by aircraft type is included in Appendix B. 

5. ATC Impacts 

A total of 4,481 TASAR requests were simulated of which 285 (6%) were rejected due to 

conflicts (150) and other factors (135). 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 5,342 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 971 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 (285 + 971) / 

(4,481 + 971) = 23% would reasonably expected to be rejected. The (285 + 971) includes 

the original 285 rejections and the 971 requests not made since they were predicted by 

TASAR to contain conflicts and, without both surrounding traffic being equipped with 

ADS-B Out and TASAR ownship being equipped with 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 
16

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 requests were concentrated in sectors south of SEA due to significant 

Alaska north-south traffic between SEA and airports to the south and east (e.g, SFO, 

LAX, DEN, PHX). 23% of requests were found to occur in four high altitude (FL 240+) 

sectors: ZSE46, ZSE13, ZSE14, and ZOA31. 

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

simulation at once 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 Alaska flights between SEA and other continental US airports (192) derived from 

Table 1, the number of flights simulated (1,589), the number of TASAR requests by 

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

example, ZSE46 had 353 requests reported in the simulation so it was estimated that 

(353)(192/1589) = 43 requests per day occur in ZSE46. The requests not made (filtered) 

were used to approximate the number of requests if the aircraft was not equipped with 

ADS-B. 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 8 

for the ten sectors receiving the most TASAR requests. 

Table 8. 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) * (192 / 

1589) 

Requests per Day

without ADS-B 

In: (3) * (192 / 

1589) 

ZSE46 353 0 353 43 43

ZSE13 266 117 383 32 46

ZSE14 220 27 247 27 30

ZOA31 192 80 272 23 33

ZOA43 150 59 209 18 25

ZOA36 135 1 136 16 16

ZOA32 126 47 173 15 21

ZSE47 116 20 136 14 16

ZSE15 112 0 112 14 14

ZSE02 108 0 108 13 13

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

hours and scaling by requests per day (e.g., scale ZSE46 hourly results by 43/353) to 

account for the fact that flights were simulated across multiple days. Table 9 shows 

hourly results for the four sectors with the most requests which indicate that 4 to 8 

requests per sector occur during the peak hours of about 8 AM, 2 PM, and 9 PM. If 

necessary, the peak requests of 4 to 8 requests per sector per hour could potentially be 

managed through coordination with dispatchers or another procedure. 
17

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

Hour of 

Request 

(Pacific time) 

ZSE46 Average 

Requests in Hour

ZSE13 Average 

Requests in Hour

ZSE14 Average 

Requests in Hour

ZOA31 Average 

Requests in Hour

0 0.0 0.0 0.0 0.0

1 0.0 0.0 0.0 0.0

2 0.0 0.0 0.0 0.0

3 0.0 0.0 0.0 0.0

4 0.0 0.0 0.0 0.0

5 0.0 0.0 0.0 0.0

6 0.0 0.0 0.0 0.0

7 0.1 0.0 1.6 3.7

8 5.2 3.1 3.7 2.2

9 2.3 2.0 1.8 1.4

10 4.3 4.0 2.0 1.0

11 1.7 0.8 1.5 0.7

12 1.3 0.8 0.6 1.0

13 1.8 1.9 1.0 2.3

14 4.1 3.9 3.2 1.1

15 3.5 2.4 1.4 1.1

16 1.8 1.1 0.1 0.0

17 1.2 1.4 0.8 0.0

18 0.6 1.7 1.4 2.0

19 2.3 1.5 0.4 0.0

20 2.3 4.8 2.9 4.4

21 8.0 2.5 3.9 2.0

22 2.2 0.8 1.4 0.3

23 1.1 0.8 0.0 0.0

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 an 

Alaska 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.
18

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. The simulation platform uses the same aircraft model for 

the 737-900 and 737-900ER so the results for these aircraft were not separated. 

Table 10. 737-900, 737-900ER simulation results. 

Airport 

1 

Airport 

2 Use Case Flights Simulated

Time Savings 

(min) 

Fuel Savings 

(lbs) 

KPHX KSEA Cx Reroute TMI 0 0.0 0.0

KPHX KSEA Weather 0 0.0 0.0

KPHX KSEA Wind 3 ‐1.3 ‐98.9

KPHX KSEA All 3 ‐1.3 ‐98.9

KSAN KSEA Cx Reroute TMI 0 0.0 0.0

KSAN KSEA Weather 0 0.0 0.0

KSAN KSEA Wind 5 ‐1.0 ‐81.6

KSAN KSEA All 5 ‐1.0 ‐81.6

KMSP KSEA Cx Reroute TMI 0 0.0 0.0

KMSP KSEA Weather 1 ‐1.0 ‐211.4

KMSP KSEA Wind 4 ‐3.5 ‐363.9

KMSP KSEA All 5 ‐3.0 ‐333.4

KDEN KSEA Cx Reroute TMI 0 0.0 0.0

KDEN KSEA Weather 1 0.0 65.3

KDEN KSEA Wind 33 ‐1.8 ‐181.4

KDEN KSEA All 34 ‐1.8 ‐174.1

KLAS KSEA Cx Reroute TMI 0 0.0 0.0

KLAS KSEA Weather 0 0.0 0.0

KLAS KSEA Wind 52 ‐1.4 ‐113.3

KLAS KSEA All 52 ‐1.4 ‐113.3

KPDX KPHX Cx Reroute TMI 0 0.0 0.0

KPDX KPHX Weather 0 0.0 0.0

KPDX KPHX Wind 2 0.0 ‐5.3

KPDX KPHX All 2 0.0 ‐5.3

KSEA KSFO Cx Reroute TMI 0 0.0 0.0

KSEA KSFO Weather 0 0.0 0.0

KSEA KSFO Wind 10 ‐0.9 ‐62.8

KSEA KSFO All 10 ‐0.9 ‐62.8

KLAX KSEA Cx Reroute TMI 0 0.0 0.0

KLAX KSEA Weather 0 0.0 0.0

KLAX KSEA Wind 87 ‐1.4 ‐101.2

KLAX KSEA All 87 ‐1.4 ‐101.2

KSEA KSTL Cx Reroute TMI 0 0.0 0.0

KSEA KSTL Weather 0 0.0 0.0

KSEA KSTL Wind 3 ‐2.3 ‐188.5

KSEA KSTL All 3 ‐2.3 ‐188.5
19

Airport 

1 

Airport 

2 Use Case Flights Simulated

Time Savings 

(min) 

Fuel Savings 

(lbs) 

KORD KSEA Cx Reroute TMI 0 0.0 0.0

KORD KSEA Weather 1 0.0 15.7

KORD KSEA Wind 15 ‐5.7 ‐285.4

KORD KSEA All 16 ‐5.4 ‐266.6

KDFW KSEA Cx Reroute TMI 0 0.0 0.0

KDFW KSEA Weather 0 0.0 0.0

KDFW KSEA Wind 3 ‐6.0 ‐308.8

KDFW KSEA All 3 ‐6.0 ‐308.8

KSEA KTUS Cx Reroute TMI 0 0.0 0.0

KSEA KTUS Weather 0 0.0 0.0

KSEA KTUS Wind 5 ‐0.8 ‐48.1

KSEA KTUS All 5 ‐0.8 ‐48.1

Table 11. 737-800 simulation results. 

Airport 

1 

Airport 

2 Use Case Flights Simulated

Time Savings 

(min) 

Fuel Savings 

(lbs) 

KPHX KSEA Cx Reroute TMI 0 0.0 0.0

KPHX KSEA Weather 0 0.0 0.0

KPHX KSEA Wind 35 ‐1.5 ‐101.9

KPHX KSEA All 35 ‐1.5 ‐101.9

KSAN KSEA Cx Reroute TMI 0 0.0 0.0

KSAN KSEA Weather 3 ‐0.7 ‐21.4

KSAN KSEA Wind 82 ‐1.3 ‐119.3

KSAN KSEA All 85 ‐1.3 ‐115.8

KMSP KSEA Cx Reroute TMI 0 0.0 0.0

KMSP KSEA Weather 1 ‐1.0 ‐28.6

KMSP KSEA Wind 10 ‐3.8 ‐210.6

KMSP KSEA All 11 ‐3.5 ‐194.1

KDEN KSEA Cx Reroute TMI 0 0.0 0.0

KDEN KSEA Weather 1 ‐1.0 ‐78.6

KDEN KSEA Wind 50 ‐2.4 ‐184.8

KDEN KSEA All 51 ‐2.4 ‐182.7

KDCA KSEA Cx Reroute TMI 2 ‐13.5 ‐961.9

KDCA KSEA Weather 3 ‐1.7 81.5

KDCA KSEA Wind 24 ‐7.5 ‐536.9

KDCA KSEA All 29 ‐7.3 ‐502.2

KFLL KSEA Cx Reroute TMI 0 0.0 0.0

KFLL KSEA Weather 2 ‐8.5 ‐401.6

KFLL KSEA Wind 22 ‐9.8 ‐328.1

KFLL KSEA All 24 ‐9.7 ‐334.3

KDCA KLAX Cx Reroute TMI 1 ‐5.0 ‐295.4

KDCA KLAX Weather 1 ‐5.0 ‐79.3
20

Airport 

1 

Airport 

2 Use Case Flights Simulated

Time Savings 

(min) 

Fuel Savings 

(lbs) 

KDCA KLAX Wind 31 ‐5.5 ‐404.2

KDCA KLAX All 33 ‐5.5 ‐391.0

KEWR KSEA Cx Reroute TMI 1 ‐3.0 ‐147.9

KEWR KSEA Weather 2 ‐1.0 ‐76.7

KEWR KSEA Wind 32 ‐8.7 ‐463.0

KEWR KSEA All 35 ‐8.1 ‐431.9

KLAS KSEA Cx Reroute TMI 0 0.0 0.0

KLAS KSEA Weather 2 ‐1.0 ‐77.1

KLAS KSEA Wind 125 ‐1.3 ‐114.2

KLAS KSEA All 127 ‐1.3 ‐113.6

KBOS KSEA Cx Reroute TMI 0 0.0 0.0

KBOS KSEA Weather 1 4.0 437.2

KBOS KSEA Wind 31 ‐9.1 ‐401.9

KBOS KSEA All 32 ‐8.7 ‐375.7

KSEA KSFO Cx Reroute TMI 0 0.0 0.0

KSEA KSFO Weather 0 0.0 0.0

KSEA KSFO Wind 30 ‐0.4 ‐27.0

KSEA KSFO All 30 ‐0.4 ‐27.0

KMCO KSEA Cx Reroute TMI 1 ‐25.0 ‐1819.5

KMCO KSEA Weather 1 ‐4.0 ‐235.6

KMCO KSEA Wind 7 ‐11.0 ‐888.7

KMCO KSEA All 9 ‐11.8 ‐919.5

KLAX KSEA Cx Reroute TMI 0 0.0 0.0

KLAX KSEA Weather 1 0.0 0.0

KLAX KSEA Wind 67 ‐1.0 ‐81.7

KLAX KSEA All 68 ‐1.0 ‐80.5

KDCA KPDX Cx Reroute TMI 0 0.0 0.0

KDCA KPDX Weather 0 0.0 0.0

KDCA KPDX Wind 1 ‐3.0 ‐148.3

KDCA KPDX All 1 ‐3.0 ‐148.3

KSEA KSTL Cx Reroute TMI 0 0.0 0.0

KSEA KSTL Weather 4 ‐0.3 ‐13.6

KSEA KSTL Wind 13 ‐0.2 ‐17.9

KSEA KSTL All 17 ‐0.2 ‐16.9

KORD KSEA Cx Reroute TMI 0 0.0 0.0

KORD KSEA Weather 3 ‐2.0 40.3

KORD KSEA Wind 43 ‐7.1 ‐409.0

KORD KSEA All 46 ‐6.8 ‐379.7

KDFW KSEA Cx Reroute TMI 0 0.0 0.0

KDFW KSEA Weather 1 1.0 75.5

KDFW KSEA Wind 44 ‐4.1 ‐269.3

KDFW KSEA All 45 ‐4.0 ‐261.6

KSEA KTUS Cx Reroute TMI 0 0.0 0.0

KSEA KTUS Weather 1 0.0 2.7
21

Airport 

1 

Airport 

2 Use Case Flights Simulated

Time Savings 

(min) 

Fuel Savings 

(lbs) 

KSEA KTUS Wind 19 ‐0.4 ‐25.7

KSEA KTUS All 20 ‐0.4 ‐24.3

Table 12. 737-700 simulation results. 

Airport 

1 

Airport 

2 Use Case Flights Simulated

Time Savings 

(min) 

Fuel Savings 

(lbs) 

KPHX KSEA Cx Reroute TMI 3 ‐3.3 ‐627.5

KPHX KSEA Weather 7 ‐1.6 ‐280.2

KPHX KSEA Wind 80 ‐1.7 ‐282.7

KPHX KSEA All 90 ‐1.7 ‐294.0

KSAN KSEA Cx Reroute TMI 0 0.0 0.0

KSAN KSEA Weather 0 0.0 0.0

KSAN KSEA Wind 16 ‐2.1 ‐193.4

KSAN KSEA All 16 ‐2.1 ‐193.4

KMSP KSEA Cx Reroute TMI 0 0.0 0.0

KMSP KSEA Weather 0 0.0 0.0

KMSP KSEA Wind 13 ‐3.1 ‐211.2

KMSP KSEA All 13 ‐3.1 ‐211.2

KDEN KSEA Cx Reroute TMI 0 0.0 0.0

KDEN KSEA Weather 0 0.0 0.0

KDEN KSEA Wind 5 ‐4.2 ‐270.2

KDEN KSEA All 5 ‐4.2 ‐270.2

KLAS KSEA Cx Reroute TMI 0 0.0 0.0

KLAS KSEA Weather 0 0.0 0.0

KLAS KSEA Wind 1 2.0 110.7

KLAS KSEA All 1 2.0 110.7

KPDX KPHX Cx Reroute TMI 0 0.0 0.0

KPDX KPHX Weather 4 ‐0.3 ‐26.0

KPDX KPHX Wind 20 ‐0.4 ‐35.0

KPDX KPHX All 24 ‐0.4 ‐33.5

KSEA KSFO Cx Reroute TMI 0 0.0 0.0

KSEA KSFO Weather 2 ‐1.0 ‐83.2

KSEA KSFO Wind 40 ‐0.4 ‐32.4

KSEA KSFO All 42 ‐0.5 ‐34.8

KLAX KSEA Cx Reroute TMI 0 0.0 0.0

KLAX KSEA Weather 3 ‐3.3 ‐214.2

KLAX KSEA Wind 29 ‐0.6 ‐34.6

KLAX KSEA All 32 ‐0.8 ‐51.4

KDFW KSEA Cx Reroute TMI 0 0.0 0.0

KDFW KSEA Weather 0 0.0 0.0

KDFW KSEA Wind 28 ‐5.0 ‐378.6

KDFW KSEA All 28 ‐5.0 ‐378.6
22

Appendix B: TASAR Request Trajectory Change Types 

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

Trajectory Change 

Type 737-900/ER 737-800 737-700 All 

Lateral 323 1268 71 1,866

Vertical Lower 4 102 0 109

Vertical Higher 2 79 3 88

Lateral and Lower 78 602 212 1,120

Lateral and Higher 73 509 214 1,013

Sum 480 2560 500 4,196

Lateral (%) 67.3% 49.5% 14.2% 44.5%

Vertical Lower (%) 0.8% 4.0% 0.0% 2.6%

Vertical Higher (%) 0.4% 3.1% 0.6% 2.1%

Lateral and Lower (%) 16.3% 23.5% 42.4% 26.7%

Lateral and Higher (%) 15.2% 19.9% 42.8% 24.1%

Sum 100.0% 100.0% 100.0% 100.0%
24

Appendix C: Summary Presentation

The following presentation summarizes the Alaska Airlines 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

Alaska Airlines Traffic Aware Strategic Aircrew 

Requests (TASAR) Benefits Assessment

Jeff Henderson

Engility Corporation

David Wing

NASA Langley

Seattle, WA

October 2014
25

2

Overview of benefits assessment

•TASAR use-cases analyzed

•Method used to estimate fuel and time benefits

•Results

– Benefits for Alaska 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/
26

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 predicted winds. Aircraft 

flies historical sensed (e.g., 

aircraft report) winds that have 

been fused into RAP analysis 

winds.

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
27

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

ORD

Aircraft location after reaching cruise altitude (current location following

historical trajectory)

Aircraft follow historical trajectories within 200 nmi of airport

SEA

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

All named waypoints inside bounding box used to generate alternative trajectories*

* Note: Additional alternative waypoints not shown in figure.
28

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
29

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

– 737-900ER

– 737-900

– 737-800

– 737-700

•Summary of annual fuel and time benefits:

– by aircraft type, and

– across all 737s (excluding 737-400s) 
30

12

737-900ER benefits by airport pair

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

• TASAR is expected to have highest benefit for 737-900ER operations between Seattle 

(SEA) and Newark (EWR)

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

13

Airport pairs with highest expected annual fuel 

benefits for 737-900ER when using TASAR

Airport 1 Airport 2 Per Operation 

Benefit

Annual Benefit

Fuel 

(gal)

Time

(min)

Ops per 737-

900ER

Fuel 

(gal)

Time 

(min)

Newark (EWR) Seattle (SEA) 60.5 8.7 38 2,268 326

Boston (BOS) Seattle (SEA) 58.8 9.1 30 1,764 272

Orlando (MCO) Seattle (SEA) 119.3 11.0 13 1,550 143

Las Vegas (LAS) Seattle (SEA) 16.6 1.4 78 1,292 107

Dallas (DFW) Seattle (SEA) 45.1 6.0 31 1,120 149

Los Angeles (LAX) Seattle (SEA) 14.8 1.4 61 903 88

Chicago (ORD) Seattle (SEA) 41.7 5.7 19 617 85

Total Annual Benefit (Wind Use Case) 381* 11,203 1,312

Total Annual Benefit (Wind, Convective Wx, and expired 

TMI)

381* 12,042 1,381

• 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: Underestimates annual benefit since typical aircraft can perform at least 700 ops per year. Not all 

airport pairs shown.

TASAR fuel 

savings is at 

least 12,000 gal 

per 737-900ER 

per year
31

14

Trajectories between SEA and EWR (highest 

annual 737-900ER TASAR fuel benefit)

Historically Flown ASA Lateral Trajectories

ASA Lateral Trajectories with TASAR Requests EWR

SEA

EWR

SEA

TASAR requests not simple directs. Larger spread result of TASAR taking advantage of changing atmospheric conditions and ATC restrictions.

15

Altitudes between SEA and EWR (highest 

annual 737-900ER TASAR fuel benefit)

•On average TASAR requests result in aircraft cruising at 

lower altitudes between SEA and EWR

•Pure altitude changes are least frequent TASAR solution

– TASAR requests generally lateral or combination lateral and vertical

– Similar results for other airport pairs

0%

10%

20%

30%

40%

50%

60%

70%

FL320 FL340 FL360 FL380

Percentage of Time at 

Cruising Altitude

Cruising Altitude Range between SEA and EWR

Historical

TASAR

0%

20%

40%

60%

80%

Lateral Lower Higher Lateral 

and 

Higher

Lateral 

and 

Lower

Percentage of 737-900ER 

Requests

TASAR Request Trajectory Change Type
32

16

737-900 benefits by airport pair

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

• TASAR is expected to have highest benefit for 737-900 operations between Seattle 

(SEA) and Chicago (ORD)

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

17

Airport pairs with highest expected annual fuel 

benefits for 737-900 when using TASAR

Airport 1 Airport 2 Per Operation 

Benefit

Annual Benefit

Fuel 

(gal)

Time

(min)

Ops per 737-

900

Fuel 

(gal)

Time 

(min)

Chicago (ORD) Seattle (SEA) 41.7 5.7 77 3,012 414

Los Angeles (LAX) Seattle (SEA) 14.8 1.4 133 1,968 191

Dallas (DFW) Seattle (SEA) 45.1 6.0 39 1,761 234

Las Vegas (LAS) Seattle (SEA) 16.6 1.4 83 1,375 113

Denver (DEN) Seattle (SEA) 26.5 1.8 19 489 34

Phoenix (PHX) Seattle (SEA) 14.5 1.3 33 477 44

Minneapolis (MSP) Seattle (SEA) 53.2 3.5 11 168 31

Total Annual Benefit (Wind Use Case) 466* 10,465 1,164

Total Annual Benefit (Wind, Convective Wx, and expired 

TMI)

466* 10,567 1,169

* Note: Underestimates annual benefit since typical aircraft can perform at least 700 ops per year. Not all 

airport pairs shown.

TASAR fuel 

savings is at 

least 10,000 gal 

per 737-900 per 

year
33

18

737-800 benefits by airport pair

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

• TASAR is expected to have highest benefit for 737-800 operations between Seattle 

(SEA) and Washington National (DCA)

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

19

Airport pairs with highest expected annual fuel 

benefits for 737-800 when using TASAR

Airport 1 Airport 2 Per Operation 

Benefit

Annual Benefit

Fuel 

(gal)

Time

(min)

Ops per 

737-900

Fuel 

(gal)

Time 

(min)

Washington (DCA) Seattle (SEA) 78.5 7.5 18 1,429 137

Orlando (MCO) Seattle (SEA) 129.9 11.0 8 1,010 86

San Diego (SAN) Seattle (SEA) 17.4 1.3 45 791 60

Boston (BOS) Seattle (SEA) 58.8 9.1 13 764 118

Chicago (ORD) Seattle (SEA) 59.8 7.1 9 538 64

Las Vegas (LAS) Seattle (SEA) 16.7 1.3 31 526 40

Washington (DCA) Los Angeles (LAX) 48.4 5.8 10 500 60

Newark (EWR) Seattle (SEA) 67.7 8.7 7 495 64

Total Annual Benefit (Wind Use Case) 289* 8,286 905

Total Annual Benefit (Wind, Convective Wx, and expired TMI) 289* 8,963 977

* Note: Underestimates annual benefit since typical aircraft can perform at least 700 ops per year. Not all 

airport pairs shown.

TASAR fuel 

savings is at 

least 8,000 

gal per 737-

800 per year
34

20

737-700 benefits by airport pair

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

• TASAR is expected to have highest benefit for 737-700 operations between Seattle 

(SEA) and Denver (DEN)

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

21

Airport pairs with highest expected annual fuel 

benefits for 737-700 when using TASAR

Airport 1 Airport 2 Per Operation 

Benefit

Annual Benefit

Fuel 

(gal)

Time

(min)

Ops per 737-

900

Fuel 

(gal)

Time 

(min)

Denver (DEN) Seattle (SEA) 39.5 4.2 134 5,294 563

Phoenix (PHX) Seattle (SEA) 41.3 1.7 60 2,498 100

San Diego (SAN) Seattle (SEA) 28.3 2.1 45 1,272 93

Minneapolis (MSP) Seattle (SEA) 30.9 3.1 34 1,050 105

Dallas (DFW) Seattle (SEA) 55.3 5.0 15 830 74

Los Angeles (LAX) Seattle (SEA) 5.1 0.6 60 302 35

San Francisco (SFO) Seattle (SEA) 4.7 0.4 60 284 26

Total Annual Benefit (Wind Use Case) 444* 11,612 1,002

Total Annual Benefit (Wind, Convective Wx, and expired 

TMI)

444* 12,279 1,042

* Note: Underestimates annual benefit since typical aircraft can perform at least 700 ops per year. Not all 

airport pairs shown.

TASAR fuel 

savings is at 

least 12,000 gal 

per 737-700 per 

year
35

22

Fuel and Time Savings Summary

Aircraft Type Annual TASAR Fuel 

Benefit

Annual TASAR 

Time Benefit

Airport Pair with 

Highest TASAR 

Benefit

737-900ER 12,000 gallons/aircraft 1,300 min/aircraft EWR – SEA

737-900 10,000 gallons/aircraft 1,100 min/aircraft ORD – SEA

737-800 8,000 gallons/aircraft 900 min/aircraft DCA – SEA

737-700 12,000 gallons/aircraft 1,000 min/aircraft DEN – SEA 

All aircraft types 

excluding 737-

400s

1,040,000 gallons* 110,700 min* ORD – SEA

* Assumes 22 737-900ERs, 12 737-900s, 61 737-800s, 14 737-700s as of August 2014. 

Additional 737-900ERs on order.

23

Estimated Cost Savings Summary

Aircraft Type Fuel Cost/Gallon

(March 2014)*

Maintenance 

Cost/min

(2013)*

Depreciation 

Cost/Min

(2013)*

737-900ER $3.26 $8.44 $8.72

737-900 $3.26 $8.44 $8.72

737-800 $3.26 $4.96 $6.75

737-700 $3.26 $21.40 $7.18

Total Cost 

Savings by 

Category

$3,390,000/year** $924,000/year** $835,000/year**

Total Cost 

Savings for all 

Categories

$5,150,000/year**

* Obtained from BTS Form 41 data

** Applies fuel and time savings from previous slide.
36

24

ATC Impacts

•Average of 3.4 requests per flight

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

other reasons

– With ADS-B IN – 6% 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

25

ATC Impacts

•23% of Alaska TASAR 

requests 

concentrated in four 

high altitude (FL 

240+) sectors south 

of Seattle (SEA)

SFO

SEA ZSE46 With ADS-B: 43 TASAR requests/day Without ADS-B: 43 TASAR requests/day (no change) ZSE14 With ADS-B: 27 TASAR requests/day Without ADS-B: 30 TASAR requests/day

ZSE13 With ADS-B: 32 TASAR requests/day Without ADS-B: 46 TASAR requests/day

ZOA31 With ADS-B: 23 TASAR requests/day Without ADS-B: 33 TASAR requests/day
37

26

ATC Impacts

• Peak Alaska TASAR requests are about 4 to 8 requests per hour 

per sector

• Could be managed through dispatcher coordination or other 

procedure

0

1

2

3

4

5

6

7

8

9

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 during Hour

Local Hour (Pacific Time)

ZSE46

ZSE13

ZSE14

ZOA31

Peak requests at about 

8 AM, 2 PM, and 9 PM 

Pacific.

27

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 an Alaska revenue flight 

after flight trial

•Validation method, controller observations, and Alaska 

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 Alaska Airlines 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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Unclassified 

Subject Category 03

Availability: NASA STI Program (757) 864-9658

19a. NAME OF RESPONSIBLE PERSON

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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 Alaska Airlines. The simulation compares historical trajectories without TASAR to 

trajectories developed with TASAR and evaluated by controllers against their objectives. It was estimated that between 8,000 

and 12,000 gallons of fuel and 900 to 1,300 minutes could be saved annually per aircraft. These savings were applied 

fleet-wide to produce an estimated annual cost savings to Alaska Airlines 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. Alaska TASAR requests peaked at four to eight requests per hour in 

high-altitude Seattle center sectors south of Seattle-Tacoma airport. 

15. SUBJECT TERMS

Alaska airlines; Benefits; Electronic flight bag; Route optimization; Traffic aware strategic Aircrew request; User requests

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(757) 864-9658

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