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Lessons Learned from FAA Free Flight's En Route Modeling Workshop with a CPDLC Modeling Case Study
Dave Knorr & Mike BennettFAA Free Flight Office
4 November, 2003
Need for En Route Modeling
• Future demand will begin to increase delays related to en route sector capacity constraints
• Need capability to assess the magnitude of the en route problem
• Need modeling to compare the System level value of alternative solutions (datalink vs. ERAM vs. airspace redesign vs. XXX)
• Need to be able to develop credible $$ benefit projections
En Route Modeling Workshop Overview
• FAA Free Flight Office held a modeling workshop in March of 2003
• Goals were:– understand modeling options for en route sector
capacity increases – Insight into consistency of model results – Assess FACET as potential model engine
• Attendees– Boeing, NASA, CENA, Mitre CAASD, LMI, Metron, SAIC,
CSSI, Aerospace, NEXTOR, FAA Tech Center, etc.
Modeling Workshop Findings
• No single model met all of the needs• List of functionalities of models• No understanding of the relative validity of
results• Need for establishing a standardized set of inputs
for model comparisons
What should a model include? A case study
• From a user’s perspective, what capabilities should a model have?
• What a model needs to do:– Quantify benefits of tool to airspace user
• Delay reduction, fuel savings– Estimate benefits over lifetime of tool (10 years)
• Breakdown by Center, by Year
• Consider Controller-Pilot Data Link Communications (CPDLC) Investment Analysis as a case study
Controller Pilot Data Link Communications (CPDLC)
CPDLC Benefit Analysis Steps
• Determine Workload impact of CPDLC– Performed with voice tape analysis, HITL simulation
• Estimate Sector Capacity Increase (High vs. Low Sectors)• Model decrease in delay associated with
– capacity increase– increased trajectory efficiency
CPDLC Reduced Voice Occupancy
Increased En Route Capacity Increased Voice Channel Availability
More efficient conflict
resolution
Reduced delay in good weather
Reduced delay in severe weather
NASPAC & DPAT CSSI TTT
Voice Channel Utilization with CPDLC
• CPDLC– Enhances En Route Controller productivity by automating
communication which increases sector capacity– Reduced workload improves controllers’ efficiency in vectoring for
traffic
Total Voice Channel Utilization
01020304050607080
0 50 100 150
Fraction of MAP
Per
cent
Voi
ce C
hann
el
Utili
zatio
n
ZDV 25ZDV 38ZDV 3ZDV 14ZDV 30ZTL 6ZTL 4ZTL 3ZFW 28
Voice Channel Utilization for CPDLC Messages
01020304050607080
0 50 100 150
Fraction of MAP
Perc
ent V
oice
Cha
nnel
Ut
iliza
tion
ZDV 25ZDV 38ZDV 3ZDV 14ZDV 30ZTL 6ZTL 4ZTL 3ZFW 28
Model for Sector Capacity Increase
0%
20%
40%
60%
80%
100%
Number of flights in sector
Perc
ent W
orkl
oad
Non-communications tasks
CommunicationsWith CPDLC
No CPDLC With CPDLCMaximum sector capacity
CommunicationsNo CPDLC
Modeled Delay from NASPAC
• Advantages of NASPAC/DPAT Model– Captures sector capacity constraints– Allows full NAS Modeling– Supports Growth in Demand
NASPAC Daily En Route DelayHigh Sectors
01000200030004000500060007000
Baseline 5% 15% 20% 30%Capacity Increase
Ave
rage
Dai
ly
Del
ay (m
in)
200220102020
NASPAC Daily En Route DelayLow Sectors
01000200030004000500060007000
Baseline 5% 15% 20% 30%Capacity Increase
Ave
rage
Dai
ly
Del
ay (m
in) 2002
20102020
Good Weather Using Southern Region Routing
Modeling Problems Encountered
• Issues Within NASPAC/DPAT– TRACON Capacity– Severe Weather
• Issues with Input Assumptions– Routing Assumptions Sensitivity
• Issues not Addressed by NASPAC/DPAT– Efficiency when Demand less than Capacity
Problems encountered: TRACON Capacity
• In reality, airport congestion affects en route airspace
• Assigning finite capacity to TRACON airspace in model allows this “backup” to be simulated
• Tricky to get right– No well-defined standard for what that capacity should
be– Most modelers decouple airport constraints from
enroute constraints
Implementing TRACON capacity
Modeled delay with and without Tracon Capacity
0
5000
10000
15000
20000
2002 2010 2020
Tota
l dai
ly e
nrou
te d
elay
(m
inut
es)
Unlimited29% of AAR
Modeled delay with and without Tracon capacity
0100020003000400050006000700080009000
Baseline 5% 15% 20% 30%Increase in Enroute Sector Capacity
Tota
l Dai
ly E
nrou
te
Dela
y (m
inut
es)
Unlimited29% of AAR
Tracon Capacity, Major Airports March 7, 2002
0%10%20%30%40%50%60%70%
ATL
BO
SB
WI
CLE CLT
CV
GD
CA
DE
ND
FWD
TWE
WR
FLL
IAD
IAH
JFK
LAS
LAX
LGA
MC
OM
DM
IAM
SP
OR
DP
DX
PH
LP
HX
PIT
SA
NS
EA
SFO SLC STL
TPA
Num
ber o
f AC
in T
raco
n (%
of h
ourly
AA
R) max
95th %ile
Average of 95th
percentile:29%
Severe Weather Modeling
• Routing around severe weather increases number of areas where demand exceeds capacity
• Available models did not allow for routing around affected areas
• We used actual tracks as input trajectories– Weather has already been routed around, which may not
have been necessary if more capacity had been available
NASPAC Bad Weather En Route DelayHigh Sectors
0
5000
10000
15000
20000
Baseline 5% 15% 20% 30%Capacity Increase
Bad
Wx
Dai
ly D
elay
(m
in)
200220102020
NASPAC Bad Weather En Route DelayLow Sectors
0
5000
10000
15000
20000
25000
30000
Baseline 5% 15% 20% 30%Capacity Increase
Bad
Wx
Dai
ly D
elay
(m
in)
200220102020
Bad Weather based on tracks “as flown”
Sensitivity to Routing Assumptions
• This is not necessarily a modeling issue per se– Underlines necessity for standardized scenarios
• Observed delay for given capacity in future scenarios is very sensitive to
– Routing assumed• Great circles, pref routes, RNAV routes
– Details of future scheduling• Forecast (TAF) gives ops counts, but not city-pairs• What about rolling hubs? More point-to-point?
Routing Scenario 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Southern Region routing 0 0 212 445 639 831 987 1333 1679 2042 2418 2852 3462 3901All Great Circle routes 0 0 2546 6697 10254 14678 18291 27840 37307 46835 56629 67232 79364 90873Most likely value 0 0 424 891 1277 1661 1974 2665 3358 4084 4835 5704 6924 7803
YearModeled Daily Delay Savings in 8 CPDLC ARTCCs (minutes per day)
Modeled Sector En Route Daily Delay
2012
Capacity constraints in en route airspace will become more of a
problem in the future
Many en route sectors are currently capacity constrained
High Sectors
2002
Trajectory Efficiency
• Queue-based enroute models apply no efficiency penalty when demand < capacity
• In practice, efficiency of conflict resolution and descent/ascent profiles vary with sector load
• CSSI (P. Lucic, S. Mondoloni, W. Weiss) constructed an ad hoc model built on TTT to estimate benefit
High and Superhigh Sectors
y = 0.0762x + 1.4182
02468
1012
0 10 20 30 40 50
Voice Channel Occupancy (%)
Exce
ss D
ista
nce
(nm
i)
Distance at lower alt vs voice occupancy
y = 15.673x + 1.5912
0
5
10
15
20
25
0 0.2 0.4 0.6 0.8 1
% of time voice channel occupied
dist
ance
trav
eled
(nm
)
En Route Efficiency Pool – ‘Excess’ Distance
Efficiency measure
Here’s what we’d like to see a model do…
Modeling Airspace Performance
If sector load > capacity,
• Allow to enter and adjust dwell time, OR
• Delay
If delay is excessive, adjust trajectory—HOW???
If sector load < capacity,
Adjust dwell time
Excess Distance vs. Sector Load
0
0.5
11.5
2
2.5
3
0 20 40 60 80 100 120 140
Percent Load (1 Min Bins)
Avg.
Exc
ess
Dist
ance
Modeled aircraft approaches new sector
Modeling Airspace Performance
How to do route adjustment to avoid excessive delay?
Dynamic:Route around congested area
Iterative:Limited set of alternate full trajectories
Modeling Airspace Performance
What about terminal delay that can’t be routed around?
In the case of excessive terminal delay, crossing traffic is affected.
This can happen, but not always and is hard to model.
Hold on ground,Then release on same trajectory?
Hold
Backup Slides
What is CPDLC?
• CPDLC allows digital text messaging between enroutecontrollers and pilots
– Replace the voice messages:• Initial Communications• Transfer of Communications• Altimeter Setting
– Also transmits locally-defined menu of common messages• Benefits Include:
– Reduced voice channel occupancy• Greater communications availability More efficient maneuvers• Decreased controller workload Increased sector capacity
– Increased communications accuracy• Reduced readback errors• Reduced OD/OE
Model Needs 1: Input
• A model should compute trajectories of aircraft from input files:– Parse flight plans to produce route of flight– Include wind effects in modeled trajectory– Use individual airplane characteristics to model aircraft
performance– Compute pierce and dwell times for all airspace
elements for each aircraft– Allow linkage of different flight legs
Model Needs 2: Airspace
• A model should simulate the behavior of the full airspace:– Include enroute centers, terminal areas and runways– Include capacity limits on airspace elements,
specifically sector and TRACON flight count limits and runway operation rates for airports
– Include demand-dependent airspace performance based on real-world data (i.e. increase in excess distance in enroute sectors when busy, change in runway service times at different demand levels)
Model Needs 3: Dynamic Adjustments
• A model should allow dynamic adjustments to aircraft trajectories based on the current state of the airspace:– Include the ability to reroute aircraft at different levels of
control• Automatic rerouting around congested sectors, to emulate
behavior of controllers and traffic management units• Strategic rerouting as part of traffic flow management initiatives
– Include ability to implement traffic flow management initiatives, such as ground delay programs
– External perturbations to the airspace, such as convective weather or security issues, must the handled by the model
Model Needs 4: Output
• A model should generate detailed reports, both summary (full-run) and at selected sampled times:
• Sector and TRACON loads• Aircraft trajectories• Aircraft delay