Investigation of NASA’s - ATM Seminar › seminarContent › seminar10 › ...10’th USA/Europe ATM R&D Seminar 22 CLT Fuel Burn •Departures Ramp Fuel Burn •Arrivals Percent

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Investigation of NASA’s

Spot and Runway Departure Advisor Concept

at PHL, CLT, and LAX Airports

Stephen Atkins, Brian Capozzi, Andrew Churchill, Alicia Fernandes, and Chris Provan

Mosaic ATM, Inc., Leesburg, VA, USA

Tenth USA/Europe Air Traffic Management Research and Development Seminar

Chicago, IL

June 10-13, 2013

2 10’th USA/Europe ATM R&D Seminar

Motivation / Objective

Motivation

• NASA has developed and is studying the Spot and Runway

Departure Advisor (SARDA)

– Initial research focused on operations at Dallas/Fort Worth

(DFW) airport

– Continuing research includes studying SARDA’s application at

other airports

Objective

• Study NASA’s SARDA concept at three new airports


SARDA Benefit Mechanisms

• Decision support automation to be used by Ramp, Ground

and Local Controllers

• Departure reservoir metering

− Plans TOBT and TMAT times to manage departure

queue length

• Departure runway sequencing

− Plans TMAT times and departure queue assignments to

achieve efficient departure runway sequences

• Expedite runway crossing

− Plans departure runway crossings as part of runway

schedule


SARDA Notional Architecture

• TMAT and TOBT

for metering and

to effect sequence

at runway

• Uncertainty buffer

accounts for:

− Actual taxi time

exceeding

expected un-

delayed time

− Target

departure

queue

Local Controller

Ramp Controller

Ground Controller

(Dynamic Program)


Approach

• Fast-time simulations comparing baseline and modeled

SARDA operations at three airports

– Philadelphia International Airport (PHL)

– Charlotte/Douglas International Airport (CLT)

– Los Angeles International Airport (LAX)

• Human factors study of SARDA concept at three airports

– Structured controller interviews


PHL

• Arrival Runways: 27R, 35

• Departure Runways: 27L, 35 @ K


CLT

• Arrival Runways: 18R, 23

• Departure Runways: 18C, 18L


LAX

• Arrival Runways: 24R, 25L

• Departure Runways: 24L, 25R


• Fast-time simulation for evaluating ATM

algorithms, concepts, and procedures

• Simulates aircraft movements and pilot/controller decisions, on airport

surface and airborne

• Easy integration of new automation concepts and procedures; configure

geographic region simulated

• Metrics collection, post-analysis, visualization infrastructure

Metroplex Simulation Environment


Baseline Modeling

• Generic models for controllers and pilots; adaptation data defines procedures

for each airport

̶ Standard routes (when they exist)

̶ Typical logic for controller decisions (assignments, sequence)

• Simulation starts/stops

modeling arrivals/departures

at fixes

• Adapted taxi paths more realistic

than shortest-path/time


SARDA Model

• Modeled SARDA concept

− Did not have access to NASA’s real-time implementation of SARDA

• Modeled how Ramp, Ground, and Local Controllers would use SARDA outputs

• Spots 1, 2 and 3 for 18C

• Spots 8, 10, 11 and 12 for 18L

• Spots 4, 5, 6, and 7 for arrivals

• Aircraft hold short of first instance

of spot until cleared to proceed

through entire intersection


Validation

• SARDA simulation vs. baseline simulation (not simulation vs. actual data)

• Validation on per-aircraft basis

̶ Compared simulated operations against realistic aircraft behavior

̶ Did not validate aggregate simulation metrics against real-world data

• Examples

̶ Single occupancy of runways

̶ No aircraft collisions

̶ Realistic spacing between taxiing and queued flights

̶ Realistic runway and taxi route assignments

̶ Appropriate aircraft taxi and flying speeds

̶ Realistic time to cross a runway

̶ Realistic pushback and engine start times


Experiment Design

6 Traffic Scenarios at

each Airport

PHL, CLT, LAX Airports


Human Factors Study

• Structured interviews with Subject Mater Experts

– 6 retired air traffic controllers: PHL (1), CLT (2), LAX (2), MCO (1)

• Qualitative, alternative investigation into how SARDA would operate

at these airports

• Identify issues that might not be observed through the simulations

• Emphasis on use of SARDA during off-nominal conditions and

differences from DFW

• Results intended to inform SARDA concept refinements and

algorithmic requirements


CLT – Queue Reduction Example

Baseline SARDA

Controller Comments

• Ground controller has formed sequence by the time aircraft leaves the ramp

• What information is presented to which user needs to vary based on airport

geometry


PHL – Queue Reduction Example

Baseline SARDA

Controller Comments

• Ensure TOBTs avoid conflicts due to pushback from adjacent gates

• Consider arrival gate demand when calculating TOBTs and TMATs


Nearing hold short

point to cross 24L

Inbound

aircraft held

up by queue

Outbound

aircraft to be

inserted in

sequence

Taking off on 24R

LAX – Queue Reduction Example

Baseline

SARDA

Controller Comments

• Limited ramp space shared by arrivals and departures prevents significant

holding at spots

• Schedule arrival spot usage with departure spot crossings


PHL Departure Queue Reduction

• SARDA reduced

average Departure

Queue Duration by

75%

• Saved almost 4

minutes per

departure (on

average)

• Saved 34 aircraft-

hours of departure

queue time per

day

Maximum runway 27L queue was between 15 and 18

in the six baseline simulations and was 3 or 4 in the

SARDA simulations


PHL Taxi Time Reduction

SARDA Benefits

• Departure ramp

duration reduced on

average by 60

seconds

• Departure AMA

Movement Time

reduced by average

of 40 seconds

• Reduced number of

taxi stops by

departures (by

about 70%) as well

as total duration of

stops

Histogram of Baseline and SARDA Departure Total

Taxi Times (time engines running on surface)

• SARDA reduced number

of departures with long

taxi times

• Less congestion reduces

taxi movement times

• Arrivals spent an average of 49 seconds less time to reach their parking gate

after landing


PHL Benefit Consistency

• Benefits consistent across traffic scenarios

SARDA Benefit PHL-1 PHL-2 PHL-3 PHL-4 PHL-5 PHL-6

Reduction in Average Total

Taxi Time (seconds) 303 299 335 542 341 227

Total Taxi Time Savings

(minutes) 2962 3001 1268 1998 1261 776

Percent Reduction in

Surface Fuel Burn PHL-1 PHL-2 PHL-3 PHL-4 PHL-5 PHL-6

Arrivals 18% 12% 18% 37% 30% 14%

Departures 36% 36% 36% 51% 36% 32%

24 hours 6 peak hours


Impact of Uncertainty

• Goal: Maintain small departure queue with no instances of zero runway demand

• Uncertainty in actual taxi times and departure rate

• Tradeoff between

queue reduction

benefit and

runway

throughput impact

• Solutions

− Reduce

uncertainty

− Adjust target

queue length

based on

uncertainty


CLT Fuel Burn

• Departures

• Arrivals


Fuel Burn Resulting from

SARDA

CLT-1 CLT -2 CLT -3 CLT -4 CLT -5 CLT -6

Ramp Fuel Burn 7% 4% 14% 9% 9% 6%

AMA Fuel Burn 5% -1% 14% 23% 25% 21%

Departure Queue Fuel

Burn 79% 84% 82% 85% 84% 75%

Total Fuel Burn 23% 23% 28% 30% 29% 21%

Total Fuel Saved (kg) 9218 9350 4313 4659 4689 2879


Fuel Burn Resulting from

SARDA

CLT-1 CLT -2 CLT -3 CLT -4 CLT -5 CLT -6

Total Fuel Burn 2% -1% 2% 1% 2% 2%

• Reduced queue length and taxi movement times

• Average gate hold about 4 minutes


LAX Fuel Burn

• Departures

• Arrivals

Percent Reduction in Fuel

Burn Resulting from SARDA LAX-1 LAX -2 LAX-3 LAX -4 LAX -5 LAX -6

Ramp Fuel Burn 1% 1% 2% 1% 2% -1%

AMA Fuel Burn 1% 2% 44% 4% 38% 1%

Departure Queue Fuel Burn 77% 49% 41% 49% 38% 14%

Total Fuel Burn 16% 6% 25% 8% 22% 1%

Total Fuel Savings (kg) 16,647 4739 8529 1650 7106 126

Percent Reduction in Fuel

Burn Resulting from SARDA LAX-1 LAX -2 LAX-3 LAX -4 LAX -5 LAX -6

Total Fuel Burn -1% -1% 17% 0% 14% 0%

• Benefits varied across scenarios depending on extent

of departure queuing in baseline simulation


Runway Throughput – PHL

• Baseline LC model opportunistically optimizes sequence from flights at front of

each queue

• 95% large aircraft; limited opportunity to optimize sequence

• No measured benefit

• Significant benefit may

come when there are a

lot of TMIs


Runway Throughput – CLT

• No sequencing opportunity

− Almost all large aircraft

− Modeled 100% RNAV

departures which have

common initial segment

• No measured benefit

• 18L Departures dependent

on runway 35 arrivals

Controller Comments

• Controllers would not trust automation to advise whether there is enough

space for a departure or runway crossing before the next arrival


Runway Throughput – LAX

• Simulation did not show measurable benefit

• Poor runway sequence compliance

− Controlling only gate and spot times may not be sufficient to achieve

planned runway sequence with single departure queue

− Additional control required to implement sequence

Controller Comments

• Questioned ability to handle non-standard situations

• Flexible runway assignments could cause challenge


Runway Crossing

• Did not find runway crossing efficiency benefits

• PHL

̶ No departure runways were crossed

• CLT

̶ Departure runway 18C crossed by 18R arrivals on taxiway Sierra

̶ No opportunity to stage multiple crossing

̶ No Heavy departures

Controller Comments

• Aircraft crossing [CLT] 5/23 at R and G, and crossing 18L/36R at A,

contribute to congestion at Hot Spot 1. If SARDA can predict congestion, it

could alert controllers to wait to allow crossing until hot spot clears

• When the flight will be ready to cross is too uncertain to plan whether it will

be before or after the next flight to takeoff or land on the runway being

crossed – what exit it takes, how fast it taxis, etc.

• Don’t sequence crossings until you really know what the order should be

and that there is enough time for the crossing (before an arrival)

• CLT 18C standard procedure

is “launch one, cross one”


Runway Crossing – LAX

• Most arrivals cross a departure runway

̶ Limited ability to hold arrivals between north-complex runways

̶ Cannot schedule far in advance due to uncertainty in runway exits used

• Southern runways – nominal taxi routes modeled; no queuing at multiple

crossing points

̶ Plan taxi routes and crossing sequence together

Controller Comments

• There is significant individual variability in the number of aircraft a Local

Controller is able to cross at once

• Identify crossing opportunities and let controllers decide how to use them

• If runway is too efficient, need to build crossing opportunities by purposely

sequencing Heavies or back-to-back departures to the same fix


Conclusions

• SARDA concept applies well to PHL, CLT, and LAX

• SARDA concept for departure reservoir metering (DRM) achieves

benefits at all 3 airports – reductions in departure queue length and

surface congestion

• Distinct airport geometries resulted in potential concept extensions to

expand benefits

• Human factors study identified potential new requirements to address

unique situations at some airports

• Results may be applicable to other DRM concepts


Future Work

• Currently studying SARDA concept in presence of uncertainty

− EOBT accuracy

− Unknown departure runway assignments

− Dynamic TFM restrictions

− Taxi movement time forecast errors

− Runway throughput

• Prediction errors result in early or late delivery to runway

• Adjust target queue length based on level of uncertainty

Documents

Investigation of NASA’s - ATM Seminar › seminarContent › seminar10 › ...10’th USA/Europe ATM R&D Seminar 22 CLT Fuel Burn •Departures Ramp Fuel Burn •Arrivals Percent