Your SlideShare is downloading. ×
0
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
AGIFORS Operations Conference Presentation
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

AGIFORS Operations Conference Presentation

311

Published on

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 …

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.

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
311
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
7
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. August 22, 2012Trends in U.S. AirlineOn-Time PerformanceAGIFORS Operations 2012 PresentationJoshua MarksExecutive Director, AAIEdmund OtubuahDirector of Aviation Products, masFlightRyan Leick, Ph.D.Associate Professor, Utah Valley University4833 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 & MethodologyOur study dataset combines DOT, FAA andmasFlight data for exploratory analysisOur study covered the period from January 2009 through May 2012 andincorporated 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 4A 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 DOTwas 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: masFlightAGIFORS 2012 TECHNICAL PRESENTATION SLIDE 5
  • 6. It’s not the industry gaming DOTAll carriers showed improvements in non-reported flight operationsReported 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: masFlightAGIFORS 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 InboundSource: masFlight, DOT Part 234 Flight DataAGIFORS 2012 TECHNICAL PRESENTATION SLIDE 7
  • 8. Cause Analysis SECTION 3 8A 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 ImprovementWe 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 flightsGranular: we build data up from individual flight detailsAGIFORS 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 METARsAGIFORS 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 ATLEfficient DFW (+4.3%) (+3.4%) (+3.6%) 0.0% Less FLL BWI Most airports saw significant improvement inEfficient (-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: masFlightAGIFORS 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: masFlightAGIFORS 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: masFlightAGIFORS 2012 TECHNICAL PRESENTATION SLIDE 14
  • 15. Carrier strategies for gate utilization variedMany 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: masFlightAGIFORS 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: masFlightAGIFORS 2012 TECHNICAL PRESENTATION SLIDE 17
  • 18. Statistical Analysis SECTION 4 18A 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 performanceAGIFORS 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 21A 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 cycleAGIFORS 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 spacingAGIFORS 2012 TECHNICAL PRESENTATION SLIDE 23

×