flight time components and their delays on us...

FLIGHT TIME COMPONENTS AND THEIR DELAYS ON US DOMESTIC ROUTES

G i Sk lGerasimos SkaltsasDr. Peter BelobabaProf. Amy Cohn

MIT GLOBAL AIRLINE INDUSTRY PROGRAMIAB/Industry Consortium Joint MeetingIAB/Industry Consortium Joint Meeting

November 4, 2010

Database

D t bDatabase

FAA Aviation System Performance Metrics detailed flight data for 77 Airportsg p 80% of all scheduled domestic commercial flights in 2009 88% of all domestic enplanements (based on the T‐100 Domestic Segment )

Final analyzed sample

2276 directional non‐stop routes between 72 ASPM Airports operated by 40 US carriers in daily basisoperated by 40 US carriers in daily basis 54% of the all scheduled domestic commercial flights in 2009.

2

Carrier‐Routes in the Sample

133659%

1400

1600

59926%

25011% 62 27400

600

800

1000

1200

umbe

r of rou

tes

11%3%

271%

20

200

400

1 2 3 4 5 6

Nu

Number of carriers

3679 distinct directional route‐carrier pairs.

59% of the routes are served only by one carrier, and 26 % are served bytwo In the remaining 15% operate more than 2 carrierstwo. In the remaining 15% operate more than 2 carriers.

We expect that competition affects the schedule padding practices,because the presence of the carriers in the ticket distribution systemsdepends often on their scheduled block time

3

depends often on their scheduled block time.

Schedule Padding by Time of Day(ORD – JFK, 2009)(ORD JFK, 2009)

140

150

160

minutes)

25 min

110

120

130

140

ed Block Tim

e (m

100

09‐10 10‐11 12‐13 13‐14 14‐15 15‐16 16‐17 17‐18 18‐19 19‐20 20‐21 21‐22 22‐23 23‐24Sche

dule

Scheduled Arrival Time

Airlines lengthen the scheduled block times of their flights to improve• the reliability of their schedules• their “on‐time arrival” statistics (a flight that arrives at the gate moretheir on time arrival statistics (a flight that arrives at the gate morethan 15 minutes later than scheduled is considered delayed)

The time that airlines add to their schedules is called BUFFER.

4

BUFFER = Scheduled Arrival Time – Delay‐Free Arrival Time.

Understanding theMeasurement of DelayMeasurement of Delay

5

Variability in Gate Delays

25

Newark (EWR) – Los Angeles (LAX), 200910%

10

15

20

ay (m

inutes)

4%

6%

8%

Freq

uency

17.4Average:Median:St. Deviation:

541.2

0

5

10

JAN FEB MAR APR MAY JUNE JULY AUG SEP OCT NOV DEC

Gate Dela

0%

2%

4%

‐20 ‐10 0 10 20 30 40 50 60 70

Relative

Gate Delay = Actual Gate Departure – Scheduled Gate Departure Time

‐20 ‐10 0 10 20 30 40 50 60 70

Gate Delay (minutes)

Causes: • Airport congestion at origin or destination (ceiling, visibility, winds)• Airspace congestion • Propagated delay (aircraft, crew)

6

• Airline operations ( boarding, catering, fueling etc.)

Variability in Taxi‐out Delays


2025303540

me (m

inutes)

6%

8%

e Freq

uency

14.8Average:Median: St. Deviation:

10.816.9

051015


Taxi ‐ou

t Tim

0%

2%

4%

Relative

Unimpeded Taxi‐out Average Taxi‐out

Taxi‐out Delay = Actual Taxi‐out Time – Unimpeded Taxi‐out Time

‐20 ‐10 0 10 20 30 40 50 60 70

Taxi‐out Delay (minutes)

Causes: • Airport congestion at origin or destination (ceiling, visibility, winds)• Airspace congestion • Delays during pushback

7

y g p

Variability in Taxi‐in Delays

14% 15

Newark (EWR) – Los Angeles (LAX), 2009

6%

8%

10%

12%

Freq

uency

2.5Average:Median:St. Deviation:

1.65.0 10

Time (m

inutes)

0%

2%

4%

6%

Relative

0

5

Taxi ‐in T

‐20 ‐10 0 10 20 30 40 50 60 70Taxi‐in Delay (minutes)


Unimpeded Taxi‐in Average Taxi‐in

Taxi‐in Delay = Actual Taxi‐in Time – Unimpeded Taxi‐in TimeTaxi in Delay = Actual Taxi in Time Unimpeded Taxi in Time

Causes: • Weather conditions at airport of destination, Runway configuration• Gate unavailability

8

Variability in Airborne Delays


320

330

340

350

Time (m

inutes)

4%

6%

8%

Freq

uency

22.2Average:Median: St. Deviation:

2019.2

290

300

310

JAN FEB MAR APR MAYJUNE JULY AUG SEP OCT NOV DEC

Airbo

rne T

0%

2%

‐20 ‐10 0 10 20 30 40 50 60 70

Relative

Average Airborne TimeNominal Airborne Time

Airborne Delay = Actual Airborne Time – Nominal Airborne Time

Airborne Delay based on Nominal Flight Time (minutes)

Actual Flight Time = Wheels On – Wheels OffNominal Airborne Time = 10th percentile of Actual Airborne Time Distribution

Causes: • Airspace congestion

9

• Winds (strength and direction)

Variability in Block Delays

400

)


350360370380390

k Time (m

inutes

4%

6%

8%

ive Freq

uency ‐3.8Average:

Median: St. Deviation:

‐624.1

320330340350


Block

A S h d l d Bl k Ti A A l Bl k Ti0

0%

2%

‐70 ‐50 ‐30 ‐10 10 30 50 70

Relati

Huge variability in block delays

The average block delay is very close to zero (most often negative)

Average Scheduled Block Times Average Actual Block Times Block Delay (minutes)

The average block delay is very close to zero (most often negative)

The average block delay is larger than the median, because there is a small percentage of flights with excessive delays that moves the average to the right.

10

The block delay distribution is almost symmetric around the median.

Distribution of Block Delays(January 2009)

6%7%8%

rier pairs

( a ua y 009)

1%2%3%4%5%

age of ro

ute‐carr

0%

Percen

ta

Average Block Delay (minutes)

74% of the 3679 studied route‐carrier pairs have negative average block delays 20% have average block delays between zero and five minutes,

l 6% h d l l h fi i

Average Block Delay (minutes)

only 6% have delays longer than five minutes In absence of gate delays, 92% of the flights would arrive on time (with gatedelays 80%)

Flights on most routes suffer lengthy gate delays (shown next) that are difficult

11

Flights on most routes suffer lengthy gate delays (shown next), that are difficultto be predicted. This results to significant arrival delays

Correlation between Block Delay and Delay Components

25%

30%

35%

rrier pa

irs Taxi Out and Block Delay

Airborne and Block DelayTaxi In and Block Delay

y p

0%

5%

10%

15%

20%

age of ro

ute‐car Taxi In and Block Delay

0%

Percen

t

Pearson product‐moment correlation coefficient

Very strong positive correlation between block delays and taxi‐out delays– Taxi‐out delays responsible for the biggest portion of the block delays

Strong positive correlation between block delays and airborne delaysg p y y– Smaller than the correlation of the taxi‐out delays

Small correlation between block delays and taxi‐in delays– Taxi‐in delays are usually very small compared to the taxi‐out and the

12

airborne delays

Arrival and Gate Delays

15

20

nutes)

Arrival Delay

20

25

)

Gate Delay ATL ‐ DFW

0

5

10

rrival Delay (m

in

5

10

15

Delay (m

inutes)

‐5

A

0

5

Gate

12

14

16

18 Arrival DelayGate Delay FLL ‐ ATL

2

4

6

8

10

Delay (m

inutes)

13

0D

Distribution of Gate Delays(January 2009)

6%

7%

8%

ier pa

irs

(January 2009)

1%

2%

3%

4%

5%

ge of rou

te‐carri

0%

1%

Percen

tag

Average Gate Delay in January 2009 (minutes)

The average gate delays (Jan 09) of 17% route‐carriers are longer than 15 min

The annual range of the monthly average gate delays can be very large and

Average Gate Delay in January 2009 (minutes)

there is no seasonality

It is very difficult for airlines to predict the gate delays

We expect that this will result to a strong correlation between the gate delay

14

We expect that this will result to a strong correlation between the gate delayand the arrival delay

Strong Correlation between Gate and Arrival DelaysGate and Arrival Delays

1573

35%

40%

45%

carrier

67 129317

713842

10%

15%

20%

25%

30%

age of ro

ute‐c

pairs

38 67 129

0%

5%

[0.3, 0.7) [0.7, 0.75) [0.75, 0.8) [0.8, 0.85) [0.85, 0.9) [0.9, 0.95) [0.95, 1)Percen

t

Pearson product‐moment correlation coefficient

Very strong positive correlation

A delayed pushback results most often to a delayed arrival

Gate delays are caused mostly by stochastic factors, such as propagateddelays (aircraft, crew) and airline operations (boarding, catering, fuelingetc.). Therefore, airlines can not predict them and schedule without taking

h b l d l

15

into account the variability in gate delays

Destination can affect the Taxi out Times

354045

es)

ORD – LGA ORD – MCO

1015202530

out T

ime (m

inute

05

Taxi‐o

Unimpeded Taxi‐out Average Taxi‐out

Flights from the same airport (ORD) and by the same carrier (AmericanAirlines) but with different destinations (LGA and MCO) do not follow the samedistribution of taxi‐out times

The annual range of monthly average taxi‐out times is• 11.3 minutes on the ORD‐LGA route• 2.6 minutes on the ORD‐MCO route

16

We expect that this happens due to the existence of Ground Delays Programsthat hold the flights that are destined to LGA on the ground

Destination can affect the Taxi out Times

50%

60%

70%

35

40

45

hat were

Program

es)

ORD – LGA by AAL

20%

30%

40%

50%

10

15

20

25

30

tage of flights th

Groun

d Delay

ut Tim

e (m

inute

0%

10%

0

5

10

Percen

tun

der a

Taxi‐ou

Unimpeded Taxi‐out Average Taxi‐outUnimpeded Taxi out g

The taxi‐out times of the flights from ORD to LGA are affected by the GDPs

May and June have the highest average taxi‐out times and the highesty g g gpercentage of flights that were held on the ground by GDPs

September has the smallest average taxi‐out times and the smallestpercentage of held flightsp g g

Only 3 flights that were destined to MCO were held in 200917

Buffer in Schedule increases with Distancewith Distance

8090

100

es)

January 2009

304050607080

Buffer (m

inute

y = 0.0878x + 27.387R² = 0.3767

01020

0 50 100 150 200 250 300 350 400Nominal Airborne Time (minutes)

Each point corresponds to the average buffer time and nominal airbornetime for a route‐carrier pair in January 2009

Some linearity between buffer and distance.y

Carriers hide more delays in the long‐haul flights to handle the increaseduncertainty in the airborne times (winds).

We expect that the extensive padding of some short haul flights

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We expect that the extensive padding of some short‐haul flightscompensates the gate delays and the taxi‐out, taxi‐in delays rather than theairborne delays.

Conclusions

The average block delay is most of the times negative, and close to zero.Taxi out delays airborne delays and taxi in delays are very effectively hiddenTaxi‐out delays, airborne delays and taxi‐in delays are very effectively hiddenin the schedule

Very strong Correlation between gate delays and arrival delays. Carriersschedule without taking into account the variability in gate delays becauseschedule without taking into account the variability in gate delays becauseits difficult to predict them

Large seasonality in taxi‐out and airborne times. This makes necessary amonth basis analysis of the schedule padding practicesmonth basis analysis of the schedule padding practices

Limited seasonality and variability in taxi‐in times

The congestion in the arrival airport affects the gate delays and the taxi‐out times through the Ground Delay Programs

Linearity between buffer and distance. Long‐haul flights have in averagelarger delays than short‐haul flights

19

Future Research

1. For two selected months , use• linear regression methods • non parametric regression trees• non‐parametric regression treesto study the relationship between the buffer time and the

• fight components • distance • ground hold times

2. Measure quantitatively and qualitatively the benefits and the costs of

• time of the day • route competition • carrier type (LCC vs. NLC)

padding for the carriers and the airports.

For airlines:• Crew costs

For airports:• Delays

• Utilization costs• Recovery costs/ delay propagation• Presence in ticket distribution systems

y• Level of Service• Traffic• Revenues

• Reliability – on time performance • Cost for investments in terminals, runways, ATC technologies

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