AGIFORS SSP 2013

Incorporating Gate Operations into Schedule Planning

Joshua Marks, CEO+1 703 994 0000 Mobile josh@masflight.com

W W W . M A S F L I G H T . C O M

We used masFlight’s analytics platformAll 2012 flight operations for U.S. mainline carriers

Gate Characteristics in Schedule PlanningNew technology makes it possible to incorporate gate variability into schedules

Big data can highlight where:

• Obstructions or distance drive significantly longer taxi-out times

• Other gate factors drive variabilitythat impacts on-time performance

Block planning is an art based on review of:

Taxi-out time history

Flight time history

Taxi-in history

One-time factors

Big data enables a more scientific approach with:

Departure and arrival gates

Competitive dynamics

Intra-seasonal weather

Tail number differences

5 13 21 29 37 45 53 61 69 77 85 93 101

109

117

125

133

141

149

157

165

173

181

189

197

205

213

221

229

237

0

50

100

150

200

250

Taxi-Out, Runway Landing and Gate In Distribution

Delta LGA-ATL 2012

Gate Out

Landing Time

Gate In

Minutes After Gate Departure

Co

un

t o

f F

lig

hts

Block Time Planning: From Art to ScienceMuch more is possible today than just taxi and air time analysis

ModalTaxi Out23 min

ModalGate Arrival2h 28m

0

500

1000

1500

2000

2500

3000

3500

4000

United Mainline Taxi-Out from SFO GatesCalendar Year 2012 – All Flights

Taxi-Out Time In Minutes

United Taxi-Out from SFO (2012)Domestic (Blue) vs. Int’l (Red)

Domestic

International

Taxi-Out Time in MInutes

Multiple Factors Affect Taxi-Out VariabilityBlocked rampTugs and tow barsGround personnelPush-back distance

Distance to runwayTaxiway factorsConstructionATC and pilot skills

Taxi speedConcurrent runwaysTraffic managementWeather

Flig

hts

Flight Type Alone Doesn’t Reveal Underlying DriversParsing by flight or mission (domestic, international, fleet) doesn’t tell the full story

Parsing operations by

flight type doesn’t reveal

the drivers

Gate Variability in Taxi Out Time is SignificantUnited’s SFO Operation: 5 min difference in average taxi-out times by pier

West International(Odd gates 91-99)

23.5 minutes

East International(Even gates 90-100)

21.3 minutes

West Base Domestic(Gates 72-75)

21.0 minutes

East Base Domestic(Gates 68-71)

18.1 minutes

Outer Domestic Pier(Gates 76-77 and 80, 82, 84, 88)

18.6 minutes

Inner Domestic Pier(Gates 81, 83, 85, 87, 89)

20.7 minutes

Data from 2012 All UA SFO Operations

Gate Assignments Matter! UA 760 SFO-JFK 10:45am Departure

Gate 80*19 min

Gate 8420 min

Gate 8122 min

Gate 8523 min

At hubs, gate choices drive 5 min differences in taxi-out.Gate choice can determine an on-time arrival for 10% of flights.

Gate 8218 min

Gate 8323 min

Now consider taxi-out averages at the gate level UA 760 SFO-JFK 10:45am Departure (2012 departures)

Airport teams think about operations…But from a passenger-centric perspective.

joshrushing.com

Many teams plan for consistency in gating,but operational demands shift assignments.

Evidence supports that real improvements can result from collaboratively integrating gate allocation into block forecasts.

LAX

MIA

ORD

DFW

JFK

54%

48%

36%

31%

21%

American Hubs

LGA

DTW

ATL

MSP

SLC

43%

42%

34%

33%

22%

Delta HubsSFO

ORD

IAH

IAD

EWR

LAX

DEN

62%

45%

35%

33%

27%

24%

12%

United Hubs

BOS

JFK

LGB

FLL

23%

13%

9%

9%

JetBlue Focus Cities

Significant Variance in Taxi Time by Gate Creates an Opportunity to Improve OTPWe reviewed the difference in average taxi-out time at key hubs, measuring the spread between the fastest and slowest gates.

BWI

LAX

MDW

DAL

PHL

LAS

PHX

46%

31%

22%

21%

21%

20%

17%

Southwest Focus Cities

Narrow ramps, tight piers and intersections are key drivers of gate-level taxi out variability.

Less significant variability observed for Alaska, Frontier, and Virgin America hubs

Variability + Delays = OpportunityBecause behavioral change is needed, focus on hubs where gate adjustments can drive maximum OTP gains.

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%

5.0%

6.0%

7.0%

8.0%

9.0%

10.0%

11.0%

12.0%

13.0%

14.0%

15.0%

AA-DFW

AA-JFK

AA-LAX

AA-ORD

AA-MIA

AS-PDX

AS-SEA

B6-BOSB6-FLL

B6-JFK

B6-LGBDL-ATL DL-DTW

DL-LGA

DL-MSPDL-SLC

UA-DEN

UA-EWR

UA-IAD

UA-IAH

UA-LAX

UA-ORD

UA-SFO

US-CLT

US-DCA

US-PHL

US-PHXWN-BWI

WN-DAL

WN-LAX

WN-LAS

WN-MDW

WN-PHL

WN-PHX

FL-ATL

F9-DEN

NK-FLLVX-LAX

VX-SFO

2012 Comparison: Variability of Departure Taxi-Out Time Among Gatesvs. Percent of Critical Flights (Arrival 10-20 mins after scheduled time)

TAXI OUT VARIABILITY AMONG DEPARTURE GATES (Average taxi-out difference, fastest vs. slowest gates)

CR

ITIC

AL

FL

IGH

TS

(S

AT

+10

TO

+20

)

Box 3:Block-gate coordination matters:

High impact + High variability

Box 1:Gates don’t matter (much)

Box 2:General Scheduling Issues

Possible solutions:

Taxi faster from the problem gates.Incentives, fuel and maintenance, etc.

Allocate flights to gates during planning.Reduce airport discretion in managing flows.

Both are viable – but both require cross-functional coordination in the airline

Food for Thought #1: Taxi Faster!

Coordinating with flight operations to increase taxi-out speed when departing specific gates.

Southwest does it!

Delta

US Airways

Alaska

United

Spirit

American

JetBlue

Frontier

Virgin America

Southwest

-40% -30% -20% -10% 0% 10% 20% 30% 40%

-23%

-12%

-7%

-7%

-4%

-2%

-1%

4%

19%

31%

Relative Taxi Speed (Narrowbodies Only) at U.S. Stations (2012)

Versus other airlines at each airport

WN averages 31% faster

at each airport they serve

While Delta lags significantly

behind

You can change flight and ground behavior

• AirTran pilots adopting Southwest practices?

• Delta’s taxi-out improvement initiatives are focused on this

• Surgical approach

BWI-FLL 2009 2010 2011 2012

Average Taxi-Out 13.5 min 13.4 min 13.0 min 12.4 min

Food for Thought #2: Gate Arbitrage?

If you are willing to restrict flights to groups of gates...Then can you shift block time from fast to slow gates?

The Concept

Divide airport gates into three buckets based on taxi-out times and variability

Fastest third: Reduce block times for departuresMiddle third: No change for departuresSlowest third: Increase block times

Objective is to keep overall block times neutraland benefit from “fitting the curve” of taxi-out times

Food for Thought #2: Gate Arbitrage?

Method: Restrict flight assignments to specific color boxes – and adjust block times for each color set accordingly.

Gate 41Increase

Gate 43Increase

Gate 45No Change

Gate 47ANo Change

Gate 47BNo Change

Gate 49ANo Change

Gate 48ASubtract

Gate 46ANo Change

Gate 42BNo Change

Gate 42AIncrease

Gate 40Increase

Gate 48BSubtract

Gate 44Subtract

AA LAX T-4

Food for Thought #2: Gate Arbitrage?

If you are willing to restrict flights to groups of gates...Then can you shift block time from fast to slow gates?

What we found from applying this method

Los Angeles (American)

Denver (Frontier)

San Francisco (United)

29,000 flights in sample set

Low variance of taxitimes across gates

Shifting 1 minute from best gates to worst drove marginal (< 0.1%)

change in on-time arrivals

No material benefits.Without variability,

no impact.

23,100 flights in sample set

Moderate variance of taxi-out across gates

Shifting 5 minutes from best gates to worst drove small shift (0.5%)

in on-time arrivals

Small but tangible gain,but may not be worth the effort.

36,500 flights in sample set

High variance of taxitimes across gates

Same 0.5% gain from neutral block, but surgical block adds

can drive 1% gain in OTP

Adding block by gate set drives a

material gain.

Food for Thought #2: Gate Arbitrage?

If you are willing to restrict flights to groups of gates...Then can you shift block time from fast to slow gates?

Conclusions

If you have high variability of taxi-out timesacross gates at a hub, particularly within the same pier

And if the number of flights where assigning gates will make a material difference in on-time performance

Then assigning flights to specific gate sets and adjusting blocks can potentially drive OTP gains

Communication is Key

How do you persuade…

Airport teams to change gate assignment priorities?

Flight operations to focus on taxi speed and routes?

Management to embrace how big data visibility can address small issues that add up to big OTP changes?

It takes focus to coordinate and prioritize at the cross-department level required.

Conclusions

Big Data focuses gate performance

Multiyear analysis can focus attention on

specific gate problems

Define controllable factors & parties

Gate issues can be addressed but require cross-functional input

Gate taxi variability is material in OTP

Five-minute average differences in taxi time

in the same pier

Low-hanging fruitat key hubs

Many critical flights + high gate variability =

OTP opportunity

Increasing taxi-outspeed is one way…

Encourage ground and pilot actions to speed

push & taxi

… block adjustment by gate is another

Folding taxi-out time into gate allocation

drives improvements

Schedule planning should visualize the potential and seek buy-in across flight and airport operations