Combining Control by CTA and Dynamic En Route Speed ...
Transcript of Combining Control by CTA and Dynamic En Route Speed ...
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Combining Control by CTA and
Dynamic En Route Speed
Adjustment to Improve Ground
Delay Program Performance
James C. Jones, University of Maryland
David J. Lovell, University of Maryland
Michael O. Ball, University of Maryland
1
11th FAA/EUROCONTROL ATM R&D Seminar
Lisbon, Portugal
June 23-26, 2015
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Overview
• Motivation and Background
• Proposed changes to GDP planning
architecture
• New GDP planning integer programming
models
• Results
• Summary
2
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Motivating Ideas
• Control by CTA
– Klooster et al [2009], McDonald and Bronsvoort [2012], Nieuwenhuisen,
and Gelder [2012], Delta (Atilla), United Airlines (Heathrow)
• Speed Control
– GDP planning: Delgado and Prats [2011], [2013], [2014], Prats and
Hansen [2011], Jones [2014]
– Tactical planning: Neuman and Erzberger [1991], Knorr et al [2011],
Swenson et al [2011], Jones et al [2013], ATD-1, Extended Metering
• Collaborative Decision Making
– Wambsganns [1993], Ball et al [2001], Vossen and Ball [2006], [2006],
Fearing et al [2011]
• Hedging Under Uncertainty
– Capacity Uncertainty: Richetta and Odoni, [1993], Ball et al [2003],
Mukherjee and Hansen [2007], Ball et al [2010]
– Demand Uncertainty: Ball et al [2001]
3
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GDP Background
• When faced with inclement weather the capacity
of airports is often insufficient to meet demand
• To deal with these imbalances FAA managers
impose a Ground Delay Program (GDP)
• Since inclement weather will often clear prior to
the end of the GDP an exemption radius is set
– Removes delays to long haul flights
– Can reduce overall delay if the weather clears earlier
than expected
4
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Speed Control in Ground Delay Programs
• During GDPs, flight managers assign a controlled time of
departure (CTD) to flights
• Assigning Controlled Times of Arrival (CTAs) in lieu of
CTDs may offer a more attractive means of transferring
delay
– Provides carriers more flexibility and control
– Allows for system-wide trade-offs
5STD CTD CTASTA
GDTravel Time
Delay allocation under conventional GDP Planning
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Speed Control in Ground Delay Programs
• During GDPs, flight managers assign a controlled time of
departure (CTD) to flights
• Assigning Controlled Times of Arrival (CTAs) in lieu of
CTDs may offer a more attractive means of transferring
delay
– Provides carriers more flexibility and control
– Allows for system-wide trade-offs
6STD CTDmax
CTASTA
GDmin
Travel Time
Delay allocation under CTA based GDP Planning
CTDmin
Variable
Delay
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Speed Control in Ground Delay Programs
• During GDPs, flight managers assign a controlled time of
departure (CTD) to flights
• Assigning Controlled Times of Arrival (CTAs) in lieu of
CTDs may offer a more attractive means of transferring
delay
– Provides carriers more flexibility and control
– Allows for system-wide trade-offs
7STD CTDmax
CTASTA
GDmin
Travel Time
Delay allocation under CTA based GDP Planning
CTDmin
Variable
DelayFAA
Airlines
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Speed Control in Ground Delay Programs
• During GDPs, flight managers assign a controlled time of
departure (CTD) to flights
• Assigning Controlled Times of Arrival (CTAs) in lieu of
CTDs may offer a more attractive means of transferring
delay
– Provides carriers more flexibility and control
– Allows for system-wide trade-offs
8
GDP Planning Process
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Current Practice: RBS with Exemptions
• Flights are assigned to available slots based
on the order they appear in the schedule
• Exempt flights receive priority
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Shortcomings of Exemptions
• DAL490 and UA12 cannot make substitutions
since exempt flights are airborne
• Can add additional delay to flights or cause
additional cancellations10
Cancellation and Substitution process in current CDM framework.
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Advantage of Adding Speed Control
• With speed control Delta and United can
reassign airborne flights and achieve better
metrics
11
Cancellation and Substitution process without exemptions. Delta and
United can substitute and improve their on-time performance.
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Fuel Usage Characteristics
• Fuel efficiency is a concave
function of speed
• General Characteristics:– Cost curves are relatively flat
– Cruise speeds can often exceed the
maximum range
– Slowing down during cruise can
increase specific range
• Airlines could adjust speeds
of flights to reduce ground
delays during GDPs at a
relatively small fuel penalty
Maximum
Range
Specific
Range
NM/ton
Mach
Number
Range of
Operation
vmin vmax
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New GDP Architecture
• FAA procedural modifications– Replace the use of CTDs with CTAs in GDP planning
– Remove the exemption radius
– Allow en route speed changes by carriers
• Airline decision making modifications– Incorporate speed changes into substitution and
cancellation process
– Introduce a new stochastic optimization model to
support airline decision-making with substitutions and
cancellations
• Model matches flights to assigned capacity
• Allows airlines to hedge for early weather clearance13
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AA
Conventional GDP Planning
14
Airborne Flights:
Flight Positions and
ETAs, AC speeds
Flight on Ground:
Flight Schedule
Assign
CTDs to
Airlines
DB-
RBS
FCFS
Substitute
and Cancel
Flights
Compression
FAA FAAAirlines
Data
Algorithms
Allocations
Airline Decisions
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AA
Conventional GDP Planning
15
Assign
CTDs to
Airlines
DB-
RBS
FCFS
Substitute
and Cancel
Flights
Compression
Departure
Time
Changes
Flight
Cancellations
FAA FAAAirlines
Airborne Flights:
Flight Positions and
ETAs, AC speeds
Flight on Ground:
Flight Schedule
Data
Algorithms
Allocations
Airline Decisions
Mechanisms
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AA
Conventional GDP Planning
16
Assign
CTDs to
Airlines
DB-
RBS
FCFS
Substitute
and Cancel
Flights
Compression
Departure
Time
Changes
Data
Algorithms
Allocations
Airline Decisions
Mechanisms
FAA FAAAirlines
Airborne Flights:
Flight Positions and
ETAs, AC speeds
Flight on Ground:
Flight Schedule
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AA
Conventional GDP Planning
17
Assign
CTDs to
Airlines
DB-
RBS
FCFS
Substitute
and Cancel
Flights
Compression
FAA FAAAirlines
• System has limited measures for recourse in the
presence of unplanned conditions
• Slow to react in the presence of early clearance of
weather
• Cannot take full advantage of opportunities for
potential trades
Airborne Flights:
Flight Positions and
ETAs, AC speeds
Flight on Ground:
Flight Schedule
Data
Algorithms
Allocations
Airline Decisions
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AA
CTA Based GDP Planning
18
Assign
CTAs to
Airlines
RBS
FCFS
Substitute
and Cancel
Flights+ + Compression
FAA FAAAirlines
Airborne Flights:
Flight Positions and
ETAs, AC speeds
Flight on Ground:
Flight Schedule
Data
Algorithms
Allocations
Airline Decisions
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AA
CTA Based GDP Planning
19
Assign
CTAs to
Airlines
RBS
FCFS
Substitute
and Cancel
Flights+ + Compression
FAA FAAAirlines
Airborne Flights:
Flight Positions and
ETAs, AC speeds
Flight on Ground:
Flight Schedule
Data
Algorithms
Allocations
Airline Decisions
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AA
CTA Based GDP Planning
20
Assign
CTAs to
Airlines
RBS
FCFS
Substitute
and Cancel
Flights+ + Compression
Departure
Time
Changes
HedgingFlight
Cancellations
Speed
Control
FAA FAAAirlines
Airborne Flights:
Flight Positions and
ETAs, AC speeds
Flight on Ground:
Flight Schedule
Data
Algorithms
Allocations
Airline Decisions
Mechanisms
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AA
CTA Based GDP Planning
21
Assign
CTAs to
Airlines
RBS
FCFS
Substitute
and Cancel
Flights+ + Compression
Departure
Time
Changes
Speed
Control
FAA FAAAirlines
Airborne Flights:
Flight Positions and
ETAs, AC speeds
Flight on Ground:
Flight Schedule
Data
Algorithms
Allocations
Airline Decisions
Mechanisms
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AA
CTA Based GDP Planning
22
Assign
CTAs to
Airlines
RBS
FCFS
Substitute
and Cancel
Flights+ + Compression
FAA FAAAirlines
• System is more flexible in accommodating
unplanned conditions
• More effective delay in the presence of early
clearance of weather
• Allows additional substitution opportunities
• Adjustments can be performed to react to both major
and minor perturbations
Airborne Flights:
Flight Positions and
ETAs, AC speeds
Flight on Ground:
Flight Schedule
Data
Algorithms
Allocations
Airline Decisions
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AA
CTA Based GDP Planning
23
Assign
CTAs to
Airlines
RBS
FCFS
Substitute
and Cancel
Flights+ + Compression
FAA FAAAirlines
Model 1: Airline Substitution and Cancellation Model
• Hedging
• Incorporates Speed Control into Decisions
Model 2: FAA Compression Model
• Adds speed control to current compression
framework
Model 1 Model 2
Airborne Flights:
Flight Positions and
ETAs, AC speeds
Flight on Ground:
Flight Schedule
Data
Algorithms
Allocations
Airline Decisions
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GDP Timeline
Evolution of a GDP
24
Cstandard
CGDP
GDP
Capacity
Goal: We want to account for the possibility of
early clearance when assigning flights.
tstart tendtc1 tc2 tc3 tc4
GDP
Activity
GDP Timelinetstart tendtc1 tc2 tc3 tc4
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Evolution of a GDP
25
tc1
tc2
tc3
tc4
tend
Yes Yes Yes Yes Yes
Scenario TreeGDP Timelinetstart tendtc1 tc2 tc3 tc4
GDP
Activity
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Model 1: Airline Substitution and Cancellation
26
Flight
DAL53:5:03
Arrival Time
S1-5:10
S2-5:12
S3-5:14
S4-5:16
S5-5:18
S6-5:20
Stage 1:
Initial
Assignment
DAL201:5:06
DAL142:5:10
DAL
Other Airlines
Slots
Allocated
with RBS
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Model 1: Airline Substitution and Cancellation
27
Flight
DAL53:5:03
Arrival Time
S1-5:10
S2-5:12
S3-5:14
S4-5:16
S5-5:18
S6-5:20
Stage 1:
Initial
Assignment
DAL201:5:06
DAL142:5:10
DAL
Other Airlines
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Model 1: Airline Substitution and Cancellation
28
Flight
DAL53:5:03
Arrival Time
S1-5:10
S2-5:12
S3-5:14
S4-5:16
S5-5:18
S6-5:20
Stage 1:
Initial
Assignment
DAL201:5:06
DAL142:5:10
DAL
Other Airlines
Cancelled Flights
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Model 1: Airline Substitution and Cancellation
29
Flight
DAL53:5:03
Arrival Time
S1-5:10
S2-5:12
S3-5:14
S4-5:16
S5-5:18
S6-5:20
Stage 1:
Initial
Assignment
DAL201:5:06
DAL142:5:10
DAL
Other Airlines
Cancelled Flights
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Model 1: Airline Substitution and Cancellation
30
Flight
DAL53:5:03
Arrival Time
S1-5:10
S2-5:12
S3-5:14
S4-5:16
S5-5:18
S6-5:20
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Feasible
Range
without
Holding
Stage 1:
Initial
Assignment
Stage 2:
Revised
Assignment
Feasible
Range
with Holding
Cancelled Flights
DAL201:5:06
DAL142:5:10
DAL
Other Airlines
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Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Model 1: Airline Substitution and Cancellation
31
Flight
DAL53:5:03
Arrival Time
S1-5:10
S2-5:12
S3-5:14
S4-5:16
S5-5:18
S6-5:20
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Stage 1:
Initial
Assignment
Stage 2:
Revised
Assignment
Cancelled Flights
DAL201:5:06
DAL142:5:10
DAL
Other Airlines
Incorporates
Planning over
Multiple
Scenarios
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Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Model 1: Airline Substitution and Cancellation
32
Flight
DAL53:5:03
Arrival Time
S1-5:10
S2-5:12
S3-5:14
S4-5:16
S5-5:18
S6-5:20
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Stage 1:
Initial
Assignment
Stage 2:
Revised
Assignment
Cancelled Flights
DAL201:5:06
DAL142:5:10
DAL
Other Airlines
Airlines can choose to
remain in original slot if
they expect no early
weather clearance in
scenario.
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Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Model 1: Airline Substitution and Cancellation
33
Flight
DAL53:5:03
Arrival Time
S1-5:10
S2-5:12
S3-5:14
S4-5:16
S5-5:18
S6-5:20
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Stage 1:
Initial
Assignment
Stage 2:
Revised
Assignment
Cancelled Flights
DAL201:5:06
DAL142:5:10
DAL
Other Airlines
Airlines can also fly
faster if they expect the
weather to clear early in
the scenario.
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Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Model 1: Airline Substitution and Cancellation
34
Flight
DAL53:5:03
Arrival Time
S1-5:10
S2-5:12
S3-5:14
S4-5:16
S5-5:18
S6-5:20
Arrival Time
S1-5:08
S2-5:10
S3-5:12
S4-5:14
S5-5:18
S22-5:42
Stage 1:
Initial
Assignment
Stage 2:
Revised
Assignment
Cancelled Flights
DAL201:5:06
DAL142:5:10
DAL
Other Airlines
Objective: Each airline
minimizes the total expected
cost of assignments and
cancellations over all
scenarios.
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Model 2: Compression
35
Compression
Flight
UA53-5:00
UA183-5:05
Airport
S1-5:00
S2-5:04
S3-5:06
S4-5:09
S5-5:12
S6-5:15
AA561-4:58
DAL12-5:03
DAL490-5:06
AA321-5:11
Flight
UA53-5:00
UA183-5:05
Airport
S1-5:00
S2-5:04
S3-5:06
S4-5:09
S5-5:12
S6-5:15
AA561-4:58
DAL12-5:03
DAL490-5:06
AA321-5:11
RBS++ : Vossen and Ball [2006] with
speed control
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Impact of Changes on Airline Costs
• The proposed changes have potential to reduce
airline costs
– Airlines can choose whether to prioritize cancellations
or delay
– End result is fewer missed connections and/or better
customer service
• Computational experiment was conducted to
evaluate potential cost savings
– GDP cancellation probabilities and amount of ground
delay recovered was taken from Inniss and Ball1
– Examined GDP cost savings under various early
weather clearance scenarios361Inniss, T.R., Ball, M.O., 2004. Estimating one-parameter airport arrival capacity distributions for air traffic flow management.
Air Traffic Control Quarterly 12, 223–251.
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Airline Cost Model
• Adopted cost model used by Vakili and Ball (ATM seminar 2009)
• Approach assumes block time is free during first 15 min, then $32 per
min on the ground and $64 in the air
• We assume equal cost of ground and air delay of $40 after first 15 min
• Model also assumes airlines take delay cost of $0.1 per min
• Updating to 2013 airline costs reach $0.125 per min
• Parameters
– P: Number of passengers of aircraft
– x: Minutes of flight delay
– Mp: The delay threshold beyond which it becomes cost effective to cancel the flight
37
pp
p
MxMP
MxxP
x
PxC
15125.040
1515125.040
150
),(
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Experimental Test Conditions
• Data Source: TFMS and ASDX files
• Airport: ATL
• Date of Flights: May 1, 2011
• 5-hour GDP
• Aircraft speeds ranged from Mach 0.72-0.85
• Capacity reduced to 40 flights per hour
• Load Factor of 0.8
• Flight cancellation costs equal delay costs at 120 min
• Exemption Radius of 1000 nm for Conventional GDPs
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Cost Savings under Early Cancellation
39
• CTA assignment leads to significant cost savings
when the weather clears sufficiently early
• Earlier departure times allow for greater delay
recovery
0.00
50,000.00
100,000.00
150,000.00
200,000.00
250,000.00
300,000.00
350,000.00
0 1 2 3 4 >=5
Co
st (
$)
Clearance Time
Conventional Winter Spring Spring
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Cost Savings under Early Cancellation
40
• CTA assignment leads to significant cost savings
when the weather clears sufficiently early
• Earlier departure times allow for greater delay
recovery
0 19.56 22.32 22.32
1 19.46 22.21 22.21
2 17.47 19.87 19.09
3 5.51 6.25 5.72
4 1.75 1.72 1.23
>=5 1.57 1.54 1.06
Cancellation
HourWinter Spring Summer
Percentage Cost Savings
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Experimental Test Conditions
• Data Source: TFMS and ASDX files
• Airport: ATL
• Date of Flights: May 1, 2011
• 5-hour GDP
• Aircraft speeds ranged from Mach 0.72-0.85
• Capacity reduced to 40 flights per hour
• Load Factor of 0.8
• Flight cancellation costs equal delay costs at 90 min
• Exemption Radius of 1000 nm for Conventional GDPs
• Uniform Probabilities for scenarios
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Cancellations and Substitutions
• Assumed up to 60 minutes of early GDP clearance
• Proposed changes lead to fewer cancellations and
more delay
42
Conventional
GDPCTA Based GDP
Conventional
GDPCTA Based GDP
Conventional
GDPCTA Based GDP
Delta (DAL) 27.78 24.07 11.14 25.4 108 108
Air Tran (TRS) 32.14 28.57 12.37 18.02 28 28
American
Southeast
(ASQ)
27.59 17.24 18.29 19.24 58 58
American
(AAL)33.33 33.33 46.5 25 3 3
Pinnacle
(FLG)25 0 29.25 37.75 4 4
Percentage of Flights Cancelled Passenger Delay Number of Flights
Airline
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Cancellations and Substitutions
• Assumed up to 30 minutes of early GDP clearance
• Proposed changes lead to fewer cancellations and
more delay
Percentage of Cancellations Minutes of Delay per Flight
0
10
20
30
40
50
DAL TRS ASQ AAL FLG
Min
ute
s o
f D
elay
Conventional CTA Based
0
5
10
15
20
25
30
35
DAL TRS ASQ AAL FLG
Conventional CTA Based
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Effect of Exemption Radius
• Used integer programming models seeded from DB-RBS
instead of RBS
• The addition of the exemption radius transfers delay from
DAL and TRS to regional Airlines ASQ
44
Percentage of Cancellations Minutes of Delay per Flight
0
10
20
30
40
50
60
DAL TRS ASQ AAL FLG
Min
ute
s o
f D
elay
With Radius Without Radius
0
5
10
15
20
25
30
35
DAL TRS ASQ AAL FLG
With Radius Without Radius
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Impact of Early Cancellation
• Early cancellation is more helpful to
dominant carriers with more long haul
flights
45
Minutes of Delay Recovered
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Impact of Early Cancellation
• Early cancellation is more helpful to
dominant carriers with more long haul
flights
46
Minutes of Delay Recovered per Flight
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
DAL TRS ASQ AAL FLG
Min
ute
s o
f D
ela
y p
er F
ligh
t
0 15 30 45 60
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Summary and Future Work
• Proposed a new strategy for managing GDPs
– Control by CTA
– En Route Speed Control
– Eliminated Exemption Radius
• Strategy provides opportunities for significant cost savings
• Strategy may lead to airline behavioral changes
– Fewer cancellations
– More delay
• Near Term Challenges
– Compliance
– Modifying current procedures
• Long Term Challenges
– Need to reconcile flexibility with Trajectory Based Operations
– Managing flights with dynamic adjustment
47