incorporating gate variability in airline block planning
DESCRIPTION
Presentation at AGIFORS SSP May 21, 2013. Reviews variability of gate taxi-out time and how on-time performance improvements can be driven by incorporating taxi variability into block plans.TRANSCRIPT
AGIFORS SSP 2013
Incorporating Gate Operations into Schedule Planning
Joshua Marks, CEO+1 703 994 0000 Mobile [email protected]
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