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Schedule Delay

Initial Delay

Crews

Gate Space

Aircraft

Passengers

Propagates into Large Delay

Non-linear Response to Capacity Reduction

Reducing the airport capacity to 50%, generates over 65,000 minutes of delay at the airport and 139,000 minutes system wide.

System delay

0

50000

100000

150000

200000

250000

300000

100 96 92 88 83 80 75 72 67 63 58 55 50 47 42

Available Airport Capacity (Percentage)

Del

ay

Airport delay

Current CDM GDP Process

FAA/Airline Evaluation Demand Vs.

Capacity

GDP ModelingSend Proposed GDP

Advisory

Airline Response(cancellations)

Is GDP still required?

Issue GDP Airline Response(Substitutions & Cancellations)

CompressionGDP Revision/Extension

Yes

End

No

Exit loop when program expires or is cancelled.

(Ration by schedule)

TFM Dependencies

Although, the capacity at an airport is reduced and ATC delay has been applied, users may cancel and delay flights as well as notify the FAA of earliest feasible departure times of flights.

FAA Actions

Cancellations

Airline Delays beyond ATC delay

Earliest Departure/Arrival Time updates

Airline Actions

Issue ATC delay

Generate Demand

Reduce ATC delay

Passengers

60

116

93

87

87

Total 443

P-DELAY = #PSGR X min DLYD

P-Delay min = 26580

P-Delay min =3600

Original P-Delay is

738% greater than revised

Passengers

0 CNX

176 (60 late)

93

87

87

Airline and FAA Actions can Reduce Delays

What Do We Measure?

• Event Predictability (also referred to as compliance) ETA predictability– Departure Compliance– ETE Fluctuation– Arrival Compliance

• Arrival rate delivery• Rate Control Index (RCI)

• What could have been done to improve?• What are the impacts of new changes

(e.g., +_5 minute window)?

What Do We Measure?• Departure Compliance

What Do We Measure?• ETE Fluctuation

What Do We Measure?

• Arrival Compliance

What Do We Measure?Arrival Rate Delivery

• Rate Control Index (RCI)– Used to quantify the deviations in the actual hourly

arrival counts from the planned hourly arrival counts– Can be used to compare one program to any other – Perfect score = 100%

• Aggregate RCI– Looks only at total number of flights in each hour

• Nominal RCI– Looks at which flights arrived in each hour– Nominal RCI < Aggregate RCI

What Could Have Been Done to Improve?

• More frequent revisions• Tighter enforcement of departure times• Higher standards for data quality to remove:

– Time-Out cancellations– Time-Out delays– Cancel but flew

• Re-evaluated treatment of Pop Up flights

What Do We Measure?+/- 5 minute Compliance Test

• Controlled flights departing to a GDP are required to depart within +/- 5 minutes of their Expected Departure Clearance Time (EDCT)

• Flights that fail to meet the compliance window must request a new EDCT from the ATCSCC.

• Miles In Trail restrictions are lifted for flights destined for 7 airports; ATL, DFW, ORD, EWR, STL, SFO, PHL.

• Goal of improved departure compliance.

Impact of New Changes• Departure

Standard DeviationBefore test = 32.14During test = 15.90

Impact of New Changes• En Route

Standard DeviationBefore test = 12.85During test = 10.00

Impact of New Changes• Arrivals

Standard DeviationBefore test = 33.32During test = 38.12

Impact of New Changes• RCI

Average Aggregate RCI 1/1/01-4/2/02: 87.94

Average Aggregate RCI 4/3/02-4/21/02: 96.75

RCI has improved by: 10%

The Need for Revision

• As the demand changes, the need for revisions increases.

• The arrival demand would have been smoothed out if a revision had been run at 1758Z during this PHL program.

• Solid bars = current demand

• Hashed bars = revised demand

The Fallacy of Binning

• Measuring demand in one-hour bins does not describe how that demand is distributed throughout the hour.

• Each hour bin below has the same total demand. However, the demand distributions vary dramatically.

=19.4 =8.66 =0

Solution to Binning:Arrival Flow Rates

Viewing the demand in one-hour bins does not show the underlying fluctuations that exist.

Viewing the demand as a Flow Rate shows the actual demand fluctuations,forecasting the potential for airborne holding – viewed as a moving window average

Images from the Flow Rate Analysis Tool

Equity Measures

• Affected Average Delay

• Proportions from carrier statistics

• EMF/EMA from power run

Equity Measures• Affected Average Delay

Equity Measures• Proportion of Flights Affected by Carrier

Equity Metric for Airlines (EMA) and Equity Metric for Flights (EMF):

• EMA/EMF: Compares the amount of imposed ground delay to the baseline of airborne holding

• 2-8: Good Equity

• 9-16: Significant deviations from good equity

• 16+: Poor equity

The Many Facets of Equity

• Long haul Vs. Short haul flights• GA Vs. Scheduled Carriers• Regional Vs. Majors• Overhead stream Vs. Departures• Equity across flights, carriers, ARTCCs,

airports,...

• No single vantage point serves for all occasions

Distance-based GDPs• Set inclusion for ground

delay based on distance from GDP airport

• As GDP radius is increased, delay per flight goes down but (risk of) unrecoverable delay goes up

• Find optimal tradeoff

Delay Graphic

Air HoldOuch

220

200

180

160

140

120

100

80

60

40

20

Minutes

Unrecoverable Delay

Average Delay

200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800

Miles

Air HoldUnrecov DelayAverage DelayMax Delay

160

58

37

What Do Airlines Measure?

• Air En Route Times

• Fuel Burn

• Diversions

• Taxi-Out Times

• Passenger Load Factor

As the time of the event approaches, certainty goes up but possible options are reduced.

Time Before Event

OptionsCertainty

Time Before event

There is Always a Trade-off in Delaying Actions

Options Decrease While Knowledge Increases

• Objective – increase knowledge while options remain open.• Essential to explore tactical relationships

Op

tion

s

Certainty

Increase knowledge earlier

Keep options open longer