evaluating the performance of nextgen using nasa’s ...evaluating the performance of nextgen using...
TRANSCRIPT
Evaluating the Performance of NextGen Using NASA’s Airspace
Concepts Evaluation System (ACES)
Frederick Wieland, Greg Carr, George Hunter, Alex Huang, Kris Ramamoorthy
Agenda
■ Approach■ NextGen Conops (Summer 2006)■Mapping Conops to Simulation
Parameters■ NextGen Performance Results■ Implications
Approach Used to Compute NextGenPerformance Improvements
Feb 19
May 10
Jul 27
0
2
4
6
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10
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14
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18
20
Number of Flights
Ave
rage
Del
ay/F
light Delays constant,
capacity increasedNumber offlights isconstant,delaysdecrease
Methods of Computing Benefits(Notional)
Approach for This Study (Notional)
0
10
20
30
40
50
60
Number of Flights
Ave
rage
Del
ay/F
light
Compute the delay reduction with NextGenimprovements
Baseline: Current system + OEP improvements, # flights
set at delay threshold for worst-case weather
Increase flights until worst-case delays reach an intolerable level then. . .
A Word About the Metric Used Herein for Performance Analysis
– Airline scheduled departure time explicit; scheduled arrival time ignored
– Provides the effect of the system on flights, without regard to schedule padding
■ Also. . .all flights are flown by the simulation, regardless of their delay– Allows realistic, meaningful comparisons between system configurations
– Avoids the issue of flight cancellation policy as a function of air carrier business model
Sched departuretime
ACES-computed minimumflight time
ACES-computedarrival time
Delay
Delay Metric:
NextGen CONOPS
En route
Surface
Terminal
taxi
landing
climb
descent
taxi
takeoff
Controllergate gate
ControllerDispatch Dispatch
voice
Controller Controller
Controller
NextGen Technologies
SMS4D gate-to-gate trajectoriesSWIM information enables better pre-departure plann ing
Moving map for low-viz taxiTime-based surface mgtRTSP, CDTI, ADS-B
Wake vortex predictionVMC departure rates in IMC
ADS-B surveillanceMoving map
RNAV routesWeather-savvy decision tools Continuous descent arrivals
RNP route from cruise to runway
Arrival scheduling/sequencing toolsRunway reconfiguration forecasts
High speed taxi exitsVariable touchdown pointsWake vortex detection
RNP routes to cruise
Wake diminishing airframe designs
Data-linked exchanged trajectories4D trajectory re-negotiationAuotmated separation assurance
Self-separation in some conditionsReduced arr/dep sep for closely-
spaced parallel runways
Simultaneous single-RW opsVMC arrival rates in IMC
Modeling Tools
EAD Integrated Modeling and Analysis Process
AvDemandTSAM
LMINET
BoeingCapacity
model
Feasible throughput
Segments 3, 7demand sets Trim
demand
ProbTFM
ACES
Wx-
impa
cted
enro
ute
capa
citie
s
NextGen OI performance
Sensitivity of performanceto wx predictability
Sensistivity of performanceto ATC capability
3-D VizTool
Animation showing enrouteand terminal-area effect of wx,
pre & post NGATS
EnvAnalysis
Capacity Benefit Calculation
AP caps for Segs 3, 7
ADSIM,RDSIM
To gate
ETMS
Today’s traffic
FAA benchmarkcapacities
Today’s capacities
EnrouteWeather
AirportWeather
Validation of ACES
Reference: Post, Joseph, James Bonn, Sherry Borener, Douglas Baart,Shahab Hasan, Alex Huang, “A Validation of Three Fa st Time Air Traffic Control Models,” Proceedings of the 5th ATIO Conference, September, 2005
Validation date: February 19, 2004
Modeling Assumptions:Translating NextGen Improvements into Airport and Enroute Modeling
Parameters
Estimating Airport Capacities for NextGenImprovements
45kts45 kts30 kts30 ktsAll WxROT (exit velocity)
SingleRunway
Constraints
Models
Same as VMCSame as VMCSame as VMCBaselineIMCDeparture / arrival separation
BaselineBaselineBaselineBaselineAll WxDeparture / departure separation
BaselineBaselineBaselineBaselineAll WxArrival / departure separation
4 touchdown points
2 touchdown points
2 touchdown points
BaselineAll WxArrival / arrival separation
3 nm3 nm3 nm5 nmMVMC
3 nm3 nm3 nm5 nmIMC
VMC
All Wx
IMC
MVMC
VMC
Wx
1 sec, 0 sec2 sec, 1 sec4 sec, 2 sec8 sec, 6 sec
1 sec, 0 sec2 sec, 1 sec4 sec, 2 sec8 sec, 6 sec
1 sec, 0 sec2 sec, 1 sec4 sec, 2 sec8 sec, 6 secMean departure release time & standard deviation
3 nm
6 sec
Segment7
3 nm
18 sec
Baseline
Final approach path length
Predictability at outer marker
Factor
3 nm
9 sec
Segment5
3 nm
12 sec
Segment3
Source: Monica Alcabin, Boeing Corporation
Calibrating Airport Capacities
FAA BenchmarkReport
ConstraintsIdentification
ConstraintsCalibration
SingleRunwayModel
BaselineConstraint
Values
BaselineAirport
Capacities
Arrival Rates
Departure Rates
Mixed Rates
FAABenchmarkCapacities
Fleet Mix
Runway Exits
Aircraft Parameters
RunwayConfigurations
CapacityEquations
ModifiedConstraints
Set
Airport Capacity
Model
NGATSAirport
CapacityBenefits
Assessment
BaselineCurrent
Operations
OperationalImprovement
Roadmap
Seg 3 Inputs
Seg 5 Inputs
Seg 7 Inputs
Segment 3 Capacities
Segment 5 CapacitiesSegment 7 Capacities
Source: Monica Alcabin, Boeing Corporation
Modeling Assumptions, Broad Area Precision Navigation + Aircraft Trajectory-Based Operations
■ Segment 3 Roadmap Enhancements– RNP routes are “available” everywhere
• Meaning they have been designed and published; not mandatory– Time-based trajectories available everywhere; arrival and departure sequencing and
spacing tools available only at OEP airports– All high-altitude (FL290+) flights managed by 4D trajectory, and exchanged via data-link
• No vectoring: whole trajectory is recomputed upon conflict
■ Modeling Approach, Segment 3– It is clear that RNP routes are not mandatory, hence there is still controller workload in
transition airspace– In reviewing benefits literature, we agreed to a 10% decrease in workload for Segment 3
• A similar workload decrease was observed during the introduction of URET: we hypothesize that the introduction of these procedures would have an impact at least as great as URET.
– Source: Kerns, Carol and Alvin McFarland, “Conflict Probe Operational Evaluation and Benefits Assessment, “ MITRE/CAASD MPW0000239
– Additionally, a partially-implemented datalink assumed to reduce overall controller workload by 15%� sector capacity increased by 15%
– Source: Center for Naval Analysis “CPDLC Benefits Story,” 2003 (Powerpoint presentation
Modeling Assumptions, Broad Area Precision Navigation + Aircraft Trajectory-Based Operations
■ Segment 7 Roadmap Enhancements– Time-based and metered RNP routes flown to and from all runway ends at top 100
airports�controller workload decreases dramatically– 4D gate-to-gate trajectories are filed and flown by flights arriving or departing from OEP
airports– All commercial and enroute traffic managed by 4D trajectories
■ Modeling Approach, Segment 7– RNP routes�aircraft become “invisible” to controllers in transition airspace for the top 100
airports– Based upon Eurocontrol experiments of pilot self-separation in the terminal area, the group
decided upon a 50% reduction in controller workload when aircraft are in tubes– Sources:
» Zingale, Carolina M., “Pilot-Based Separation and Spacing on Approach to Final: The Effect on Air Traffic Controller Workload and Performance,” DOT/FAA/CT-05/14, 2005.
» Grimaud, I., E. Hoffman, L. Rognin, and K. Zeghal, “Towards the use of spacing instructions to sequencing arrival flows,”Operational Datalink Panel Working Group presentation, 2003.
– Additionally, a fully-implemented datalink assumed to increase sector capacities by 30%– Source: Center for Naval Analysis “CPDLC Benefits Story,” 2003 (Powerpoint presentation
Modeling Assumptions, Minimize Applied Separation
■ Segment 3– Three mile separation standard applied to “new airspace”
• But not yet implemented
■ Segment 7– Aircraft performance variability further reduced through tighteraircraft performance standards
■ Modeling Approach, Segment 7– Three mile separation standard + tighter aircraft performance standards allows us to assume 3 mile enroute longitudinal separation
Not Modeled
■ Flexible Airspace:– Splitting/recombining sectors for workload management
• Mostly a cost issue anyway
■ General Aviation corridors in Class B airspace– Further reduces controller workload, in proportion to the amount of transiting
GA traffic
■ Effect of reduced performance variation on system performance– Except for reduced enroute and terminal-area spacing, which is modeled
Convective Weather
Three Weather Days
February 19, 2004 May 10, 2004
July 27, 2004
Dominated by a low pressure system
across the midwestinto the northeast
Mostly clear, some fog in the AM and some snow in the
mountainsMajor frontal system from the southeast to the northeast; heavy precipation and T-
storms in northeast
The Weather Information Integration Approach
■ Analyze traffic and weather data and forecasts
■ Unify all relevant demand information– Historical trends, flight plans, weather and
winds, TFM initiatives, etc.
■ Unify all relevant capacity information– All types of weather phenomena, SUAs,
security events, volcanic ash, etc.
■ Create system capacity and loading forecasts with probability distribution
■ Construct congestion forecast database
2000Z2100Z
2200Z2300Z
2400Z0100Z
Source: Ramamoorthy, K. and G. Hunter,“Modeling the Performance of the NAS in Inclement Weather,” Proceedings of the 6th ATIO Conference, 2006.
Dynamic Sector Capacity Changes ZDC 18
0
5
10
15
20
600 800 1000 1200 1400 1600 1800 2000
GMT Minutes
Sec
tor
Cap
acity
(M
AP
Val
ue)
NextGenImprovement
Results
0
10
20
30
40
50
60
70
80
Number of Flights
Ave
rage
Del
ay/F
light
NextGenperformancebaseline
ASPM subset shown here for reference only
1X NAS ~1.5X NAS
Determining the Performance Baseline
Effect of NextGen on NAS Performance
0
10
20
30
40
50
60
70
80
Ave
rage
Min
utes
of D
elay
Baseline
Seg 3
Seg 7
Baseline
Seg 3
Seg 7
July 27 th May 10th Feb 19th
Baseline
Seg 3Seg 7
February 19, 2004 baseline February 19, 2004 + Segment 3 NextGen
February 19, 2004 + Segment 7 NextGen
Delay Distribution
0
2
4
6
8
10
12
14
16
18
0.25 1.25 2.25 3.25 4.25 5.25 6.25 7.25 8.25 9.25
x = Minutes of delay (30 second bins)
y =
Flig
ht C
ount
(T
hous
ands
)
ACES Data
Power Law Distribution
y = 15,375 x -0.9958
0
5
10
15
20
25
0.25 1.25 2.25 3.25 4.25 5.25 6.25 7.25 8.25 9.25
x = Minutes of delay (30 second bins)
y =
Flig
ht C
ount
(T
hous
ands
)
ACES Data
Power Law Distribution
y = 20,356 x -1.23
0
5
10
15
20
25
30
35
0.25 1.25 2.25 3.25 4.25 5.25 6.25 7.25 8.25 9.25
x = Minutes of delay (30 second bins)
y =
Flig
ht C
ount
(T
hous
ands
)
ACES Data
Power Law Distribution
y = 32,611 x -1.52
0
5
10
15
20
25
0.25 1.25 2.25 3.25 4.25 5.25 6.25 7.25 8.25 9.25
x = Minutes of delay (30 second bins)
y =
Flig
ht C
ount
(T
hous
ands
)
ACES Data
Power Law Distribution
y = 20,356 x -1.23
February 19, 2004 +Segment 7 NextGen
Large Flight Delays (> 30 mins)
• Overall NAS performance largely influenced by those flights that experiencesignificant delays—i.e., the “tail” of the distribution
• NAS performance improvements should address what happens to the “abnormally delayed” flights, not just normal operations
ACES Data--Tail Behavior of Delay
0
20
40
60
80
100
120
140
30 34 38 42 46 50 54 58
x = Minutes of delay (30 second bins)
y =
Flig
ht C
ount
Delays����Benefits
■ ATA estimates cost of delays at approximately $50/minute
■With approximately 87,000 flights in the demand set, the delay savings in dollars is:– $52.2 million/day for the February weather day
– $282 million/day for the May weather day
– $239 million/day for the July weather day
Implications/Conclusions■ Modeling the system in various weather conditions is
useful in deriving performance information■ The leptokurtic (“fat-tailed”) power-law distribution
implies that a minority of the flights skew the average delay– Current system either lets them go (common for long-haul internationals) or
cancels them (esp. short-haul domestic when other flights are available)– NextGen end state performs well because the distribution “tightens,” i.e. there
are fewer very-long delays
■ Suggests that the highest payoffs involve ameliorating excessive delay, as opposed to reducing flights with average delay– System policies/procedures/rules should concentrate on the highest-delayed
flights– Reducing excessive delays also improves system predictability and air carrier’s
ability to plan and respond to the delays
