Presentation to the AGIFORS 2012 Operations Conference in Atlanta, Georgia. This presentation illustrates performance trends in the U.S. airline industry and identifies the impact of good weather on flight delays and cancellations during 2012. Prepared using the masFlight platform. masFlight is a leading analytics platform for aviation, combining global flight information with weather, airport, fleet and economic data. At the World Route Development Forum yesterday, masFlight and OAG, the market leader in airline schedule data, announced a new partnership to jointly develop operations data analysis tools to enable airlines and airports to understand their own and competitors’ operational performance.

1. August 22, 2012
Trends in U.S. Airline
On-Time Performance
AGIFORS Operations 2012 Presentation
Joshua Marks
Executive Director, AAI
Edmund Otubuah
Director of Aviation Products, masFlight
Ryan Leick, Ph.D.
Associate Professor, Utah Valley University
4833 RUGBY AVENUE SUITE 301 BETHESDA MD 20814

2. Study Overview
• 2012: outstanding operations performance
• Why is this happening?
• We test possible causes
– Weather conditions
– System capacity and demand
– Carrier airport and ops changes
– Strategic schedule padding vs. trimming
• Are the improvements sustainable?
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 2

3. Data Analysis & Methodology
Our study dataset combines DOT, FAA and
masFlight data for exploratory analysis
Our study covered the period from January 2009 through May 2012 and
incorporated 37 million flight records, hourly weather and FAA OPSNET data.
Data Set Contents Records Analyzed
Detailed information about flights
Flight Records 31.8 million (2009-2012)
ASQP + FLIFO + masFlight Direct Feeds
Airport Capacity Quarter-Hourly Airport Operational Detail 9.4 million (2009-2012)
Flight Schedules OAG airline schedules 42 months (2009-2012)
Weather Reports Hourly METAR and precipitation data 49 airports (2009-2012)
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 3

4. Observed Improvements
in On-Time Performance
SECTION 2
4
A M I FR I R S N 0 1V I A T IC H N I N S T I T U T E E N T 0 1 1 O N L R I G H T S R E S E R V E D
GE OCA 2 A 2 TE ON CAL PRES © 2ATI AL SLIDE 4

5. 2012 is clearly an anomaly – why?
Operational performance as measured by DOT
was significantly better than recent history.
DOT ASQP for 1Q 2012:
• 85.5% on-time mainline vs. 77-80% Q1 2009-2011
• 80.7% on-time regionals vs. 74-77% Q1 2009-2011
• Cancellations 1.2% vs. 1.9%-3.2% Q1 2009-2011
• Taxi-out times over 2 hours down 71% YOY
• Taxi-in times over 1 hour down 31% YOY
• Decrease of 4.7% in flights (reported pool change)
Source: masFlight
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 5

6. It’s not the industry gaming DOT
All carriers showed improvements in non-reported flight operations
Reported operations and non-reported operations each rose 3.4 pp
Airline Reported Flight Operations Non-Reported Flight Operations
OTD% % Rep Q1 2009 Q1 2012 Change Q1 2009 Q1 2012 Change
WN 100.0% 82.8% 84.6% +1.8 pp
DL/NW 61.3% 84.5% 88.7% +4.2 pp 83.0% 87.0% +4.0 pp
UA/CO 41.5% 81.2% 81.0% -0.2 pp 76.8% 78.1% +1.3 pp
AA 79.6% 79.5% 84.7% +5.2 pp 79.3% 83.7% +4.4 pp
US 45.3% 86.1% 90.0% +3.9 pp 78.6% 81.7% +3.1 pp
B6 87.4% 81.3% 83.0% +1.7 pp 82.6% 83.1% +0.5 pp
AS 51.3% 79.2% 87.3% +8.1 pp 81.3% 86.8% +5.5 pp
HA 95.3% 94.6% 94.3% -0.3 pp 59.3% 84.5% +25.2 pp
FL 96.5% 81.7% 92.7% +11.0 pp 57.4% 93.0% +35.6 pp
Group 64.1% 82.7% 86.1% +3.4 pp 78.7% 82.1% +3.4 pp
Reported flight operations are DOT Part 234 operations (domestic flights) operated by carriers
with at least 1% of domestic scheduled-passenger revenue – excludes key regional carriers;
Non-reported flights include international flights to/from the U.S. and most regional carriers Source: masFlight
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 6

7. Fewer flights were impacted this winter
There was a 23% decline in flights impacted by delays in Q1 2012.
But when flights were impacted, minutes of delay declined by just 1%.
Number of Delayed Flights And When Flights Impacted,
Dropped Across Categories Delay Minutes declined 1.1%
Q1 09-11 Average Q1 2012 Q1 09-11 Average Q1 2012
112.9
40.5 40.1
37.9 37.2
Minutes of Delay per Impacted Flight
Delays per 1,000 flights scheduled
91.7
87.3 32.2
82.1 30.5
70.8 72.3
24.7
23.0
11.8
8.6
Carrier Weather Airspace Late Carrier Weather Airspace Late
Inbound Inbound
Source: masFlight, DOT Part 234 Flight Data
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 7

8. Cause Analysis
SECTION 3
8
A G I FR I R S N 0 1V I A T IC H N I N S T I T U T E E N T 0 1 1 O N L R I G H T S R E S E R V E D
ME OCA 2 A 2 TE ON CAL PRES © 2ATI AL SLIDE 8

9. Possible Drivers of Improvement
We explore the following drivers:
• Weather events
• Maximum and realized runway capacity
• Aggregate flight schedules and demand
• Aircraft turn times and airport gate buffers
• Changes in block components (taxi-out, air-time, taxi-in)
Systemwide: we include reported and non-reported flights
Granular: we build data up from individual flight details
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 9

10. Winter – there wasn’t much of it!
Change in Snow Events
1H 2012 vs. 1H 2009-2011 • Worse winter at ANC, SEA, PDX
PDX 26% • Better winter everywhere else:
SEA 17%
MSP -18% NYC -72%; DC/PHL -30%;
ORD -20% Midwest -20%; SE almost none
IAD -25%
BWI -33% • Less impact from fog, mist, rain
MDW -33%
DTW -34% – Fog/mist: Houston -30%,
BOS -38% Atlanta -27%, Chicago -24%
SLC -41%
PHL -52% – Western U.S: 30-40% reduction in
LGA -67% rainy days and IFR impact
JFK -71%
EWR -78% • WX/NAS cancels down 85%
CLT -83%
IAH -100% with corresponding change
ATL -100% in on-time arrival numbers
Source: masFlight, NOAA METARs
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 10

11. Throughput Went Up Where It Counts
Change in Average Departure/Arrival Rates vs. Size of Airport
First Half 2012 vs First Half Average 2009-2011
20.0%
Bigger
TPA
MEM (+12.8%) CLT
Airports
(+11.2%)
(+10.9%)
IAH
More (+8.4%)
ORD ATL
Efficient DFW (+4.3%)
(+3.4%) (+3.6%)
0.0%
Less
FLL BWI Most airports saw significant improvement in
Efficient (-7.8%) (-8.6%)
realized runway capacity and throughput
-27% change in time when schedules exceeded capacity
CVG
(-15.1%) Implies less time waiting for departure at key hubs
and leads to lower taxi-out times
-20.0%
Airport Departure Demand, Low to High Source: masFlight
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 11

12. Taxi-out time and on-time arrivals
Comparing Change in Outbound OTA
(Percentage Points) vs. Change in
Taxi-Out Time, Q1 2009 vs. Q1 2012
Increase in • Logical relationship
outbound A14% between taxi-out time
and on-time arrivals
• System-wide decrease
10.4
10.3
9.2 in taxi-out times
8.5
8.3
6.3
6.2
3.5
• Some change in taxi-
out time likely due to
-2.0
-2.0
DOT 3-hour rule
-0.6
-1.2
-3.9
-2.0
-6.8
-6.8
Decrease in
Taxi-out (mins)
BOS CLT DCA EWR JFK LGA ORD PHL
Source: masFlight
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 12

13. Block Time Changes
• No evidence found of systemic block-time enhancement
across airlines that could explain the change in DOT metrics.
• Carriers showed definite block time changes (both positive & negative)
• Delta, JetBlue and American are clear outliers
DOMESTIC Block Time Block Time
MARKET Increased Decreased Airline Hub Comparison (Q1 2009 vs. Q1 2012)
Airline
PAIRS
Routes
Mean
Routes
Mean Routes with Routes with
ANALYZED Change Change Block Decrease Block Increase
DL 1,088 760 +4.5 m 328 -3.4 m AA - LAX
US 593 362 +3.5 m 231 -3.4 m AA - MIA
UA 1,186 614 +3.6 m 572 -3.8 m AA - ORD
AA - DFW
AS 202 113 +4.8 m 89 -2.5 m
DL - SLC
WN 800 392 +2.1 m 408 -2.5 m
DL - MSP
FL 218 95 +3.4 m 125 -3.4 m
DL - DTW
B6 195 62 +2.7 m 133 -4.3 m
DL - ATL
AA 575 211 +3.6 m 364 -3.2 m
-150 -100 -50 0 50 100 150
Markets Analyzed
Source: masFlight (Domestic marketed flights)
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 13

14. Flights are landing earlier vs. schedule
Comparing Landing Times and Scheduled Arrival Times
Q1 2009 (Blue) vs. Q1 2012 (Red), for U.S. Reporting Carriers
45,000
40,000 Flights are landing at destination
35,000
airports earlier relative to
Flights per Minute Early
Q1 2012 scheduled arrival times
30,000 Q1 2009
25,000
Suggests that block padding
20,000 could be partial driver
15,000
Earlier landing relative
10,000 to scheduled arrival time
5,000
Source: masFlight
0
0 5 10 15 20 25 30 35 40 45 50 55 60
Minutes Early - Runway "On" Time before Scheduled Gate Arrival Time
Q1 2009 Q1 2012
Source: masFlight
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 14

15. Carrier strategies for gate utilization varied
Many factors drive the number of flights that land to occupied gates,
but we observed strategic and tactical differences across key hubs.
Percentage Point Change in Flights Percent of Flights (24-hour)
that Land to Occupied Gates That Land to an Occupied Gate
(March 2012 vs. March 2009) (Domestic, March 2012)
LAX 4.9 IAH 9.7%
SFO 4.8 LAX 9.3%
PHX 2.8 SFO 9.1%
SEA 2.0 DEN 7.4%
DTW 1.7 PHX 7.3%
CLT 1.4 DFW 7.1%
IAH 1.3 ATL 7.0%
LGA 0.8 LGA 5.7%
JFK 0.8 DTW 4.6%
EWR 0.7 CLT 4.6%
IAD 0.4 EWR 4.6%
PHL 0.1 JFK 4.4%
DEN 0.0 MSP 3.1%
MSP -0.9 SEA 2.9%
SLC -1.2 PHL 2.2%
ATL -4.6 IAD 2.0%
DFW -5.8 SLC 1.4%
Includes domestic flights by reporting and non-reporting carriers. Source: masFlight
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 15

16. Observations and Drivers
• Extremely mild and dry winter increased runway throughput
– Drove significant change in taxi-out times
– Little observable change in air-time, taxi-in times
• Block times show tactical adjustments by carrier
– Consistent across both reported and non-reported flights
– Differing strategies by airline
– Delta – consistent addition of block time vs. 2009, across routes
– JetBlue, American – reduction in block times
• Evidence of airport programs to reduce delays
– Delta – changed assignments and plans at ATL
– US Airways – continued emphasis on early push-back
– And then there’s United…
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 16

17. United’s anomaly was IT driven
UA aircraft turns at IAD, ORD, SFO, DEN
Distribution of Actual Minutes on Gate
Average Change after SHARES switch = +1.8 minutes
2,000
1,800 8 weeks Unique to s-UA hubs
after (s-CO actually shifted left
Turns per 100,000 Operations
1,600
SHARES
after March 3rd change)
1,400
8 weeks
before
1,200 Accounts for -2%
SHARES
1,000
change OTA%
800
600
The entire distribution shifts right
400
but only at s-UA hubs
200
-
100
105
110
115
120
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
Minutes on Gate
Source: masFlight
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 17

18. Statistical Analysis
SECTION 4
18
A M I FR I R S N 0 1V I A T IC H N I N S T I T U T E E N T 0 1 1 O N L R I G H T S R E S E R V E D
GE OCA 2 A 2 TE ON CAL PRES © 2ATI AL S L I D E 18

19. Statistical Analysis: Method and Results
• We used multivariate regression to identify drivers of performance
improvements across the 2009-2012 time period
– We analyzed a hybrid dataset of flight records and ASPM
– Dependent variables: delays, runway rates (ADR, AAR)
– Independent variables: efficiency, schedules, weather
• We identified basic relationships that confirmed our suspicion:
weather is the most significant explanation of delay changes.
– Weather is manifested in ADR, departure demand and efficiency
which explains 65% of the variation in taxi-time at airline hubs
– By airport: some weighted to ADR (ATL and LGA), others TFM (IAH and ORD)
• Per hour, these factors drove a 2.7 minute change in taxi-out time
and a 4.6 minute change in departure delays at the airport
– Q1 2012: 3 min of taxi change equals about +2% change in OTA performance
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 19

20. Weather Impact on Arrivals
• Departures: weather manifests via ADR, demand, efficiency
• Arrivals: weather and demand are inextricably connected
– Increased arrival demand exacerbates the impact of weather
– Arrival demand queues as capacity and efficiency degrade
– Arrival demand, AAR and weather explain 59% of arrival delay at hubs
– Arrival delay explains 56% of departure delays
What we conclude:
1. Weather impacts inbound capacity, forcing arrival queues
2. Queues cause peaked demand, inbound delays, departure delays
3. Changes in departure demand don’t explain delay reduction.
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 20

21. Conclusions
SECTION 5
21
A M I FR I R S N 0 1V I A T IC H N I N S T I T U T E E N T 0 1 1 O N L R I G H T S R E S E R V E D
GE OCA 2 A 2 TE ON CAL PRES © 2ATI AL S L I D E 21

22. What we found
• What drove the improvement in Q1 2012?
• We find no compelling evidence of:
– Carriers gaming DOT reporting metrics
– Systemic block time padding (but varies by carrier)
– Day-of operational changes during IRROPS
• But we do observe:
– Dry and warm weather over key hubs
– Low weather impact on inbound flows,
so flights land at destinations earlier relative to schedule
– Low inbound queues, increased gate buffer times
 higher D0 performance and virtuous cycle
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 22

23. Is it sustainable?
• Partially sustainable
• Winter cancellations are safety-, tarmac fine-driven and
will directly correlate to winter severity
• But D0 focus, gate buffers, and tactical schedule padding
should improve winter performance relative to 2009-2011
time period
• We expect the IT issues at United will be reduced through
agent training, turn time spacing
AGIFORS 2012 TECHNICAL PRESENTATION SLIDE 23