European Airport PerformanceFramework
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European Airport PerformanceFramework

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Presented in the conference SPADE

Presented in the conference SPADE

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  • Transport Ministers adopted the ECAC Institutional strategy in 1997. It is based on two basic policy decisions: 1) Distributed Air Navigation service provision, as opposed to a Single Unified ATM system, and 2) focus on performance, with the creation of the Performance Review Commission, reporting at the level of DGCAs. The ambitious goal is for this distributed ATM system to be as safe and efficient as if it was run under a central authority. The decision making structure of the EUROCONTROL Organisation, comprising now 34 States with the accession of Ukraine and Poland, and soon 35 with Serbia-Montenegro, is that of the Revised EUROCONTROL Convention currently being ratified, with an Assembly at ministerial level, a Council at DGCA level, and the permanent Agency. 1 Overall cost of the European ATM system is roughly 7 billion euro per annum, of which Air traffic management is 88%. EUROCONTROL ’ s share is 5%. 2 Adoption of the Single European Sky regulations earlier this year and accession of the European Community to EUROCONTROL reinforces the action of EUROCONTROL in three ways: By an enforceable regulatory framework, By participation of the EU in EUROCONTROL decision making. In areas of exclusive competence, the European Commission will express the common position of EU States. This is a marked evolution from consensus based decision making. Finally, EUROCONTROL now acts not only in co-operative mode, but also under mandate from the EC.

European Airport PerformanceFramework European Airport PerformanceFramework Presentation Transcript

  • Definition and measure of KPAs/KPIs to monitor airside airport performance across Europe. SPADE Workshop Gran Canarias (Spain), 5 th November 2009 Jose Luis Garcia-Chico Performance Review Unit/Eurocontrol (Aena secondment to PRU) jose-luis.garcia-chico@eurocontrol.int / jlgchico@crida.es Tel. +32 474 123814
  • Acknowledgements
    • Work developed within ATMAP project by the Performance Review Unit of Eurocontrol in consultation with airport community
    • ATMAP Project manager: Francesco Preti
    • Main contributors to the framework from PRU staff: Heloise Cote, Luis Olmo, Holger Hegendorfer, Milen Dentchev, Philippe Enaud
    • … but also airports, airlines, slot coordinators, and ANSPs that, through ATMAP, have provided valuable comments and inputs to our framework
  • ATMAP aims at establishing a performance measuring framework of airport airside operations within the SES legislative context Institutional Framework
    • PRC/PRU monitor and report on ATM related performance parameters to EUROCONTROL
    • SES (2004) introduced the performance review function on ANS: requirements for monitoring and data collection
    • SES II (2009) introduced Performance Scheme: setting binding targets
    ATMAP Objectives ATMAP Approach Outcome
    • Develop a framework to measure ANS performance on the airport airside operations consistently and continuously across Europe
    • Identify a set of easy to understand relevant high-level performance indicators (KPIs)
    • Specify data requirements to feed the framework;
    • Developing understanding of the overall air transport at airports (focused on outcomes not accountability)
    • Identifying external factors to ANS that have a significant impact on performance
    • Validating the framework for high-level performance review with the project participants using various data sources
    • Leveraging previous PRU experience on ATM performance monitoring
    • Consistent approach to measure ANS airport performance across Europe: set of KPIs & common European data repository
    • Common understanding of performance indicators and associated definitions
    • Clear definition of data requirements to support performance based approach
  • Scope of the framework focuses on ANS around airports: airport airside-terminal environment
    • Network delivery in and out of the surrounding airport airspace
    • Interface between ground-handling and airside operations when impact ATM performance
      • No detailed analysis of local airport and airline turn-around processes
    • Airspace for arrival sequencing (0/40 and 0/100Nm)
    • Airport movement area (aprons, taxiways, runway)
    • Coordinated airports
    • Daily period 06:00– 21:59
    Runway system Taxiway system Gates/ turn-around TMA - arrival TMA – departure En-route En-route Entry fix Exit fix Network Network Airport Arrivals Departures Turn-around
  • Conceptual framework links scheduling and observed operations with external factors/drivers Performance Airport Airlines ANSP Airport Airlines ANSP External factors Weather Environmental Airport layout Scheduling of Operations Observed activity the day of operations Traffic Mix ATC procedures Others
    • Scheduling practices and local external factors are drivers of performance
      • The airport scheduling process limits the utilisation ratio and reduce variability
    • Challenging task to develop a high-level framework affected by multiple factors
    • A clear allocation of causes and accountability to stakeholders is difficult
      • End-product results from complex interrelated systems
    • Current approach addresses the understanding the overall air transport performance at airports (Outcome)
  • Airport Airside Performance Framework Breakdown KPIs Breakdown KPAs Traffic Volume & Demand Capacity Punctuality Efficiency Predictability Flexibility Emissions
    • Handled Traffic
    • Coordinated Demand
    • Coordinated Cancelled Demand
    • Declared Capacity
    • Service Rate
    • Additional Time of Inbound Flow
    • Additional Time of Outbound Flow
    • Variability of Flight Segments
    • Variability of Arrival Flow Rate
    • TBD
    • TBD
    • On Time Arrivals & Departures
    • Early Arrivals
    • Departure Delay (including Causes)
    • Slot utilization: Handled Traffic /Declared Capacity
    • Ratio Service Rate /Peak Declared Capacity
    Consolidation Low High
  • KPI Handled Traffic: European traffic experienced a generalized decreased in both Winter (-8%) and Summer (-5%) seasons
    • Definition:
    • Number of flight movements served by an airport in a given time period
    • Breakdown:
    • total, daily rate, peak-month, by season, by calendar year,
    • IFR movements in the European area continued dropping during last year
    • Winter: -8%
    • Summer: -5%
    -4% -1% -4% -13% -7% -7% -17% -9% -5% -9% -10% -33% -10% -8% -2% -9% -6% -6% 0 200 400 600 800 1000 1200 1400 1600 Paris Charles-de-Gaulle London Heathrow Frankfurt Madrid Munich Rome Fiumicino Barcelona Vienna Zurich London Gatwick Paris Orly Brussels Milan Malpensa Dublin Palma Helsinki Prague Lisbon Airport Daily Movements [flt/day] Winter 07-08 Winter 08-09 data source : EUROCONTROL/CFMU
  • KPI Slot Utilization: European slot utilization decreased in both Winter (- 9,4%) and Summer (- 7,7%) seasons
    • Slot utilization is determined by the traffic served by the airport and the scheduling parameters (i.e., declared capacity)
    • This ratio illustrates how much the declared capacity is used, being higher during summer season. This difference is more significant at airports with seasonal demand
    • Slot utilization has decreased last summer in line with traffic drop
    Data source: EUROCONTROL/CFMU Sample of 18 ATMAP airports
  • KPI Service Rate as approximation of maximum airport throughput
    • Service rate is used as approximate measure of maximum airport throughput
    • This metric may be used to infer “operational capacity” only when the airport system is close to saturation:
      • Sufficient number of hours with peak demand
      • Traffic must experience certain level of delay
    • Metric is sensitive to demand variations
    Definition: 1 percentile of the distribution of observed throughput (mov/hour) during the peak month Data source: EUROCONTROL/CFMU Period 2008 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 7 10 13 16 19 22 25 28 31 34 37 1P Peak Month
  • KPI Punctuality is calculated based on comparison between actual block times at the stand with airline scheduled times Data source: EUROCONTROL/eCODA Period 2008
  • Both arrival (+7.9%) and departure (+7.5%) punctuality has improved in Europe in the last summer seasons Data source: EUROCONTROL/eCODA Sample of 18 ATMAP airports Summer 09 up to Oct 1st
    • There is a general improvement on arrival and departure performance (with both punctuality criteria: 3 and 15 minutes)
    • Early arrivals have increased: challenge for local resource allocation (e.g., stand allocation, ground handling)
    • Performance is consistent with decreased traffic demand
  • Conceptual framework: Efficiency is measured by indicators of additional times experienced by flights for each phase of flight Arrival Flow Departure (from airport ‘j’) En-route Flight Airport A Taxi-out Additional Time in Taxi-in Additional Time of Inbound Flow to Airport A Departure (at airport ‘A’) Engines-off Engines-on ANS-related holding at gate due to downstream Airport A (ATFM delays) Additional Time in ASMA (airborne) Additional Time of Outbound Flow to Airport A Engines-off Engines-on ANS-related holding at gate due to airport A (pre-departure delays) Additional Time in Taxi-out En-route Flight …
    • Additional times are measured as difference between the actual length of a flight phase with respect to an unimpeded time ( reference ), which represents the time to typically complete the flight phase in period of low traffic
    • Indicators of efficiency are split on outbound / inbound flows of the studied airport
      • Engine-off delays: ATFM and pre-departure delays
      • Engine-on extra times in airborne holding and in taxi out phases
    • Additional time of taxi-in is not initially included in the framework (relative less influence)
  • Arrival inbound efficiency (1): ATFM arrival delay isolates ATFM regulations originated from the destination airport Data source: EUROCONTROL/CFMU Period 2008
    • ATFM delays due to CFMU regulations are isolated to account for restrictions originated at the destination airport
    • Airports are differently impacted by weather and capacity constrains. On average in 2008:
      • 32 % ATC & aerodrome capacity
      • 57 % weather
      • 11 % other
  • Arrival inbound efficiency (2): Arrival Sequencing and Metering Area additional time captures the arrival control strategies/inefficiencies
    • ASMA transit time is defined as the time between entering the circle 100NM and landing
    • Small variations of ASMA additional time among European airports
      • LHR strategy of holding aircraft on stacks close to airport
    • This metric is affected by several parameters such as type of aircraft, congestion level, airspace design, airport configuration, environmental restrictions
    Data source: EUROCONTROL/CFMU/eCODA Period 2008 ASMA additional time average: 3.3 min/flt 40 Nm 100 Nm Madrid March 6th 2008 Source CFMU
  • Departure outbound efficiency (1): pre-departure delays originated at the departure airport (normally do not generate AFTM) Data source: EUROCONTROL/eCODA Period 2008
    • Information based on airline submitted IATA delay codes
    • Isolate, among delay causes, those weather and congestion related restrictions at the airport of departure
      • 20 % departure congestion
      • 80 % weather departure
  • Departure outbound efficiency (2): taxi-out additional time as the period between take
    • Taxi-out is defined as the period between off-block and the time of taking off
    • Difference in taxi-out additional times are significant among European airports, as it depends of several factors: airport layout (distance stand-runway), type of stand, start-up process, apron congestion, de-icing procedures, and others.
    Taxi-out additional time average: 4,6 min/flt Data source: EUROCONTROL/CFMU Period 2008
  • There seems to be a link between the amount of slot utilization and quality of service in Europe
    • Link between inefficiencies (quality of service) and airport slot utilization (served traffic / available slots)
    • Trade-offs among indicators will help to understand performance
    • Other affecting performance factors must be accounted for at local and aggregate level (e.g., meteorology) that need to be incorporated into the framework
      • Algorithm to classify weather conditions
    Data source: EUROCONTROL/CFMU/Ecoda Period 2008
  • KPI Predictability: Variability of departure/arrival times and flight phase duration
    • Analysis from an airline scheduling point of view (same O&D, operator, STD)
    • Flight Phase predictability
    • Measured as the standard deviation or inter-percentile range of the distribution
    • Variability is mainly generated in the turn-around phase (or due to reactionary delays), but amplified by the airside operations
    • Delay is one driver, but not the only one
    Time of operation Number of observations (2) Schedule Arrival Average Actual Departure Average Actual Arrival Time of operation Time of operation Number of observations (2) Schedule Departure (2) Schedule Departure
  • Meteorology impact on ANS performance at airports
    • Breakdown weather conditions consistently across European airports based on transparent and common criteria (infrastructure, procedures, and congestion are not initially considered)
      • Nominal
      • Degraded
      • Disrupted
    • Definition of a severity scale (weather classes) for each weather phenomena
      • Type of weather phenomena (e.g., wind, visibility)
      • Duration
      • Combination
    • Algorithm initially applied to METAR reports
      • Wind by RWY
      • Visibility by RWY and cloud base
      • Precipitations
      • Freezing conditions (humidity and temperature)
      • Thunderstorms / Convective weather
      • Others (wind shear, wind aloft, etc.)
    Data source: METAR reports Period Jan-March 2008
  • Weather classification varies across European airports: reaction to weather situations are expected to vary too
    • Analyze performance drivers in different weather conditions
      • Nominal days to analyze general airport performance
      • Degraded days to assess ANS airport reaction to weather phenomena
      • Disrupted are assumed to be exceptional (performance drops dramatically)
    Data source: METAR reports Period Jan-March 2008
  • A dashboard of indicators evolution may help to identify drivers and understand performance at European level
  • Conclusions and future work
    • An approach to measure and analyze airport-terminal joint system performance consistently across European airports
    • The airport scheduling process is linked to the quality of service delivered.
      • Working close to the limits makes the system more sensitive to variability
    • Last year traffic evolution seem to have lessened pressure into the ATM system, increasing quality of service
    • Trade-off among objectives are key to understand performance: efficiency, punctuality, predictability, environment and others
    • Future steps are needed to complete the picture: Consolidating the framework, addressing external affecting factors, assessing trade-offs
  • Jose Luis Garcia-Chico Performance Review Unit/Eurocontrol (Aena secondment to PRU) jose-luis.garcia-chico@eurocontrol.int / jlgchico@crida.es Tel. +32 474 123814 www.eurocontrol.int/prc
  • BACK UP SLIDES
  • Controlling variability of air transport operations TMA Arrival airport Departure airport En-route ATFM delays Airport ATFM delays Airborne holding Reactionary delays Network delivery (volume and variability of TMA entry flow) Airport scheduling (utilisation ratio) Management of arrival flows Landing interval (actual throughput) Local turnaround delays Arrival time variability Departure time variability Pre-departure delays ATM Weather
    • The airport scheduling process determines the utilisation ratio (e.g. scheduled capacity / peak capacity) and the corresponding quality of service (i.e. delays). It also tries to reduce variability
    • On the day of operation, measures such as local airborne holdings or ATFM regulations are applied in order to balance variations in demand with variations in actual throughput;
    • The way traffic inbound flows are managed at airports on the day of operation is an important factor to control variability and airport utilisation
    STD STA
  • Decrease on Service Rate due to drop of demand
    • Service rate is an approximate measure of maximum airport throughput
    • The metric varies with traffic demand
    • 10-minute rolling hours may introduce same stability on the calculation
  • Calculation of unimpeded and additional times in the taxi-out phase is derived by statistical analysis of historic data
  • CODA: Central Office for Delay Analysis
    • Baseline: IFR flights
    • European coverage of IFR flights > 60%
    • Data coverage by airport upto 90%
    • > 100 data partners (majority airlines, but also ANSP’s & airports
    • Feed of flight-by-flight operational data direct from Airlines since 2000:
      • AC-registration
      • Callsign
      • City-pair
      • Scheduled Times
      • OOOI-Times
      • Delay reasons (IATA delaycodes) and times
  • METAR contents EBBR 010420Z 310 31 G51 KT 270V340 3000 R25L/P1500N R25R/P1500N R02/1400N +SHRAGR TS SCT008 BKN012 CB 05 /04 9999 m 9999 m – 5000 m 4900 m – 3000 m 2900 m – 1500 m 1490 m – 750 m 750 m – 350 m 350 m – 0 m Visibility >1000 m 600 m – 400 m 400 m – 300 m 300 m – 200 m 200 m – 100 m 100 m – 50 m Cloud base -RA +RA -FZRA +FZRA Precipi- tations 0 kt – 5 kt 5 kt – 8 kt 8 kt – 10 kt 10 kt – 15 kt >15 kt Wind speed CB TCU Convective weather Temperature/ Dewpoint +3°C to -10°C Freezing Conditions
    • METAR: observed average weather conditions measured over the preceding 10-minute period each 30 minutes
    • Example:
    Weather phenomena Cloud & Ceiling Gusts Wind speed Wind direction CB/TCU Visibility RVR Values (m) T° (°C°)
  • Example of weather classification: Wind class for steady wind direction
    • Head wind component ( severity )
      • 0 to 5 Kts; No severity; Code 1
      • 6 to 10 kts; moderate severity ; Code 2
      • 10kts to 20kts; medium severity ; Code 3
      • >20Kts; high severity ; Code 4
    • Degraded day when duration is:
      • Code 2 for more than 3 consecutive hours
      • Code 3 for more than 2 consecutive hours
      • Code 4 for 1 consecutive hour or more