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Analysis of Demand Uncertainty in Ground Delay Programs. Mike Ball University of Maryland. Uncertainty in Arrival Stream. flights. slots w multiple flights. destination airport. airborne queue. open slots. Sources of uncertainty: cancellations, pop-ups, drift. Planned vs Actual

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slide2

Uncertainty in Arrival Stream

flights

slots w multiple flights

destination

airport

airborne

queue

open slots

Sources of uncertainty:

cancellations, pop-ups, drift

slide3

Planned vs Actual

Airport Acceptance Rate

AAR: airport acceptance rate – rate at

which flights land at airport

flights

Planned AAR (PAAR)

queue

destination

airport

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

Actual AAR

models
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
marginal effects of uncertainty in presence of cancellations pop ups drift sim model
“Marginal” Effects of Uncertainty in Presence of Cancellations, Pop-ups, Drift (Sim Model)
marginal effects of uncertainty in presence of cancellations pop ups drift sim model1
“Marginal” Effects of Uncertainty in Presence of Cancellations, Pop-ups Drift (Sim Model)
prelude to understanding compression impact delay vs arrival times
Prelude to Understanding Compression Impact: Delay vs Arrival Times

scheduleddeparture times

departure times

arrival times

airborne

delay

ground

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 = arr_time(f) – sched_dep_time(f) – dir_en_route_time(f)

  • As long as the overall set of arrival times (arrival slots used) remains the same

total delay remains the same

compression benefits
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  • There were no holes in AAR so reduced ground delay has been replaced by airborne delay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

Of course, each individual airline generally derives substantial benefits based on internal cost reductions.

analysis
Analysis
  • Looked at PAAR vs Actual AAR (arrivals) for 1st program hr, 2nd program hr, etc. for three airports; tried to group like GDPs, e.g. only morning GDPs at SFO
    • ATL and BOS: used command center logs for PAAR and AAR
    • SFO: used command center logs for PAAR, tower counts for AAR.
  • Important Questions:
    • Is PAAR or AAR increasing or decreasing (improvement = increase in AAR)
    • Is deviation of PAAR from AAR changing (improvement: PAAR and AAR closer together)
slide11

SFO

PAAR

Actual AAR

slide12

PAAR

ATL

Actual AAR

slide13

PAAR

BOS

Actual AAR

typical paars used today
Typical PAARs Used Today

PAAR

35

34

33

32

31

30

29

28

27

1 2 3 4 5 6 7

1 2 3 4 5 6 7

Time Period

(hour)

sample paars recommended by ip model
Sample PAARs Recommended by IP Model

PAAR

35

34

33

32

31

30

29

28

27

1 2 3 4 5 6 7

1 2 3 4 5 6 7

Time Period

(hour)

conclusions
Conclusions
  • Reducing uncertainty is extremely important
    • Pop-ups
    • Cancellations w/o notification
    • Wide departure windows (may be most important)
  • No evidence found (yet) that improved prediction accuracy of CDM has been transformed into actual uncertainty reduction benefits (more analysis necessary)
  • Command Center (and FSM) should consider change in manner of setting AARs