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Quantifying and Reducing Demand
Uncertainty in Ground Delay
Programs
Michael O. Ball
Thomas Vossen
University of Maryland
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Ground Delay Program (GDP)
delayed
departures
delayed departures
delayed arrivals/
no airborne holding
delayed departures
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Measuring Quality of GDP NEXTOR
Planning and Execution
origin
airports
reduced
arrival
capacity
(stochastic)
destination
airport
throughput
into airport
airborne
queue
airborne
delay
ground
delay
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Uncertainty in Arrival
Stream
slots w multiple flights
destination
airport
airborne
queue
open slots
Sources of uncertainty:
cancellations, pop-ups, drift
flights
Shift in Distribution of Cancellation
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Notification Time
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with CDM ave = 44
w/o CDM ave = - 49
Notification time given in minutes before OETD
(Original Estimated Time of Departure)
Airport = SFO
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Effects of Compression NEXTOR
Algorithm
AAL owns but cannot use
destination
airport
airborne
queue
AAL and UAL
swap slots
AAL moves
flight up
Net effect:
¥ win-win for airlines
¥ slots that may have gone unfilled are used
flights
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Questions
¥ What is cost of various forms of uncertainty
and what is value of reducing that
uncertainty?
¥ What is extent and nature of benefits of
compression?
¥ Can any changes in approach to planning
and controlling GDPs better deal with
uncertainty?
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Planned vs Actual
Airport Acceptance Rate
AAR: airport acceptanc
e rate — rate at
which flights landtaairport
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flights
Planned AAR (PAAR)
queue
destination
airport
Actual AAR
In order to fully utilize arrival
capacity, a queue must be
maintained to keep the pressure
on the airport
This is done by regulating the
value of the PAAR
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Models
¥ Integer Program
—considers only flight cancellations
—variables: PAAR in each time period, Pr{k flights in
airborne queue at end of period i}
—obj fcn: min total exp airborne delay
—inputs: flight cancellation prob, overall slot utilization,
AAR
¥ Simulation
—Considers cancellations, pop-ups, drift
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Airborne Delayvs
CancellationProb
(IP Model)
Exp Num
of Open Slots
in Program
Per-Flight
Air-Borne
Delay(in
min)
8
6
4.5
4
5
2
6
0
7
0
0.1
0.2
Cancellation Probability
0.3
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Marginal Effects of Uncertainty
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in Presence of Cancellations,
Pop-ups, Drift
(Sim Model)
(minutes/flt)
Airborne
Holding
Effect of Cancellation Probability
(lambda = 20, drift = [-5,15])
20
15
10
5
0
0.05
0.075
0.1
0.125
0.15
0.175
Cancellation Probability
Utilization = 0.97
Utilization = 0.96
Utilization = 0.95
Utilization = 0.94
0.2
Marginal Effects of Uncertainty
in Presence of Cancellations, NEXTOR
Pop-ups Drift
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(Sim Model)
(minutes/flt)
Airborne Holding
Effect of Drift (p_cnx = 0.1, lambda = 20)
30
20
10
0
[-15,25]
[-10,20]
[-5,15]
[-2.5,7.5]
Drift
Utilization = 0.97
Utilization = 0.96
Utilization = 0.95
Utilization = 0.98
[-1,4]
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Prelude to Understanding
Compression Impact:
Delay vs Arrival Times
scheduled
departure times
departure times
ground
delay
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arrival times
airborne
delay
Delay(f) = arr_time(f) —sched_arr_time(f)
Delay(f) = arr_time(f) —sched_dep_time(f) — dir_en_route_
time(f)
Tot_delay = Σarr_time(f) —Σsched_dep_time(f) —Σdir_en_route_time(f)
Ł As long as the overall set of arrival times (arrival slots used) remains the same
total delay remains the same
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Compression Benefits
Compression has filled in holes in the PAAR which
has reduced the amount of assigned ground delay
Possible system effects:
¥ Holes in AAR have been filled in and total delay
has been reduced
¥ There were no holes in AAR so reduced ground
delay has been replaced by airborne delay
¥ Uncertainty in the arrival stream has been
reduced enabling a reduction in the PAAR
required to achieve a given AAR and a reduction
in overall airborne delay
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Change in PAAR Policy??
40
35
PAAR
during
1800 Z
30
25
20
15
10
5
0
0
50
begin CDM
100
150
200
250
GDP Days 97-99
at SFO
300
350
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Typical PAARs Used Today
PAAR
35
34
33
32
31
30
29
28
27
1
2
Time Period
(hour)
3
4
5
6
7
1
2
3
4
5
6
7
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Sample PAARs
Recommended by IP Mode
l
PAAR
35
34
33
32
31
30
29
28
27
1
2
Time Period
(hour)
3
4
5
6
7
1
2
3
4
5
6
7