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  1. NM USER FORUM 2014 Cooperative Traffic Management (CTM) Introduction Chris Bouman NM Head of Network Development 30/01/2014

  2. Cooperative Traffic Management NM implementation project to furtherreduce need for regulationand achieve important step towardstime based operations Improvements that directlyinteract and can not be addressed independently: • Use of Occupancy Counts by ANSP/FMPs to better assess demand and minimise need for regulation (e.g. Mandatory Cherry Picking, STAM Ph1, some ANSPs use since 2011) • System Supported ATFCM Coordination for all actors involved in establishing ATFCM measures • Predictability improvements by addressing tactical deviations from the filed Flight Plan (ongoing since 2009) • Target Time operations to enhance predictability and in support of arrival sequencing Interdependent!

  3. Presentations: • Marcel Richard (NM): • Use of Occupancy Counts for Short Term ATFM Measures • Mandatory Cherry Picking operations trial • STAM Phase 2: ensuring coordinated STAM Measures • Christian Faber (NM): • Flight Plan predictability: need and actions • Corinne Papier (DSNA) • Flight Plan predictability: example unpredictability impact and way ahead • Leo van der Hoorn (DSR) • Target Time trials: set-up and current findings

  4. NM USER FORUM 2014 Using occupancy counts for STAM & MCPTo further reduce the need for ATFCM measures Marcel Richard Senior ATC Expert 30/01/2014

  5. Use of Occupancy Counts – STAM and MCP Demand-Capacity balancing to identify needed ATFCM regulation => NMOC and FMP coordinate with AOs and Airports to reduce need & impact. Still used most: “hourly Entry Counts” => all flights that exceed declared capacity to be regulated. Wider use envisaged for benefit of AOs and network performance (at least EUR core by 2015, potentially all FMPs after that) => Basis for STAM Phase 2 (see later item) Occupancy Counts – more accurate demand picture => more focused solutions: only address specific flights. STAM Phase 1: local flow control measures (e.g. TONB, MIT, etc) based on Occupancy Counts to prevent /remove current regulations – in use by some FMPs with very good results 1 min

  6. Mandatory Cherry Picking (MCP) Enables limiting an ATFCM measure, addressing short peaks in ATC en-route sectors or Aerodromes, to only a few “cherry picked” flights instead of all flights that would normally be subject that regulation Onlythe flights subjects to that measure will receive a CTOT from NMOC. • all other flights that would normally be captured in the regulated period are excluded (i.e. no slot!). Results 2013 MCP trial (MUAC & Reims and NMOC, referred to earlier today): => 130 flights regulated instead of 2126 flights, saving 4666 delay minutes Towards permanent procedures for short term benefits.

  7. NM USER FORUM 2014 STAM Phase 2System Supported Coordination on ATFCM measures Marcel Richard Senior ATC Expert 30/01/2014

  8. STAM Phase 2 • STAM Editor with creation of What-if flights • Situation Awareness • Collaboration Forum for Coordination of the STAM Measures Complete the implementation of the STAM process

  9. STAM Measure Editor • Cherry Picked flights • Precise and focussed • Wider variety of measure type • Coordinated workflow • AU’s preferences

  10. STAM Collaboration Forum Querying and filtering area Incoming and outgoing Coordination request Detail of the item to be coordinated Topic area Hotspots and STAM measures Notifications Conversation history and Chat area

  11. STAM What Airspaces Users can see Flight subject to a STAM Measure Flights captures in a Hotspot Measure Kind Coordination Status

  12. STAM Phase 2Validation Exercise from 12 until 23rd May 2014 • Paris FMP • Aix en Provence FMP • Bordeaux FMP • Reims FMP • Brest FMP • Roissy FMP/TWR • Geneva FMP • Geneva TWR • Zurich FMP • Karlsruhe FMP • Roma/Padua FMP • UK FMP • Gatwick TWR • MUAC FMP After Validation STAM ready for deployment in CTM context

  13. NM USER FORUM 2014 PredictabilityReducing the gap between the planning and execution of flights Christian Faber ATFCM Expert 30/01/2014

  14. What is the problem? • Lack of pre-departure FPL updates can make the predicted flight trajectory invalid • Pilots are sometimes not informed about changes to the FPL such as a new RFL and so cannot implement these changes • The vertical profile or the route is not flown according to the FPL information held by the NMOC and ATC

  15. Actual profile FPL profile Why does it make a difference?

  16. What is the effect? • ATC sectors are entered that are not on the flight profile described by FPL • The demand ATC experiences can be significantly different from what was expected - including over deliveries! • Lack of certainty about the real level of demand can lead ATC to apply sector capacity ‘buffers’

  17. Why does it matter? • An independent study has estimated that improved predictability will provide the capability to increase sector monitoring valuesdelivering: • an increase of 5-10% in local sector capacities • a reduction in delaysof 20-30 % Note that flexibility remains to deviate from the FPL when tactically necessary

  18. File it – Fly it !!! How can aircraft operators and pilots help? • Update the FPL whenever “appropriate” • Inform pilots about all changes to the FPL affecting the conduct of the flight

  19. NM USER FORUM 2014 Predictability issues, impact on ATCHow lack of predictability affects ANSP attempts to reduce need for regulation and may lead to safety issue. Corinne Papier DSNA -Head of ATFCM Division 30/01/2014

  20. Our Objective: Safety, fluidity, efficiency, dynamicity, equity • By • Proposing evolution in Airspace Structure, associated with better capacities • Selecting optimum ATC sector configuration based on Traffic Demand and ATC staffing 20

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  22. Our Objective: Safety, fluidity, efficiency, dynamicity, equity • By • Proposing evolution in Airspace Structure, associated with better capacities • Building ATC sector configuration based on AO demand and ATC staff • From planning phase to real time phase, cooperating with military partners 22

  23. Our Objective: Safety, fluidity, efficiency, dynamicity, equity • By • Proposing evolution in Airspace Structure, associated with better capacities • Building ATC sector configuration based on AO demand and ATC staffing • From planning phase to real time phase, cooperating with military partners • Identifying excessive workload • Acting on few selected flights to smooth the traffic (amount of flights and complexity) DSNA is a path finder in Dynamic ATFCM process which allows a gain of capacity while maintaining high level of safety towards our customers. BUT…. 23

  24. Our Objective: Safety, fluidity, efficiency, dynamicity, equity • Due to • Flight plan non adherence • ETOT/CTOT non adherence • AO reactivity when receiving a CTOT (even with 0’mn of delay) • Fancy routings • FMPs and ATC are daily facing unpredictable and dangerous situations. 24

  25. Our Objective: Safety, fluidity, efficiency, dynamicity, equity Display at 11:07 Display at 11:50 • UnexpectedSectorsOpening • Emergency action • or • Overload on an elementarysector • > Safety Issue 25

  26. Daily case AND massive effect Typical peak hour summer time : KR flight list , from 10h to 12h 60 flights/26 intruders Intruders

  27. Intruders: A safety Issue Over-delivery Overload Final action = ATC clearance ANSPs NMOC Crew AO Ops 27

  28. Our Objective: Safety, fluidity, efficiency, dynamicity, equity Flight Plan is not just a flying ticket! It should be mutual commitment and responsibility for safety and more efficiency. • ANSP reactions: • Decrease capacity • Take capacity buffer • Over-Regulate on all layers sectors • ATC reluctance to apply STAM measures • Misjudgement on CFPS system • Loss of Cooperation between ATC and AO • Is it a good solution ? NO 28

  29. NM USER FORUM 2014 Results of SESAR Target Time (TT) TrialsValidating an important step towards Time Based Operations Leo van der Hoorn Validation Manager, SESAR Network Operations 30/01/2014

  30. From CTOT to TT – Concept in a nutshell Use (only) CTOT for time-based ATFCM Now: Time-based ATFCM measure Entry Time CTOT dep congestion Issues: • Assumed profile not always the actual profile • Objective of CTOT not managed after take-off • Actual trajectory and sector entry time can significantly deviate from intended ATFCM measure Over-regulation or Over-delivery, unpredictability New: Use Target Time at congestion Time-based ATFCM measure Target Time • For trials: • Target Time +/- 3 minutes • Flight Crew aim to meet Target Times • Arrival Regulations => input to sequencing CTOT dep congestion

  31. From CTOT to TT – Expected Benefits • For ALL Network actors: increase predictability • More effective regulations • Potential for capacity increase  decrease of regulations • For airspace users: flexibility & flight efficiency • Operational flight plan adapted to airline needs, meeting TT • Effective regulations  Better use of capacity  Less holding, less ATC actions (e.g. vectoring, separation,…) • For ATC (en-route/airports): potential local TT preferences exchange with NM • Optimising local operations, based on local business rules (e.g. arrival sequence, link to AMAN, XMAN) • In collaboration with Airspace Users Potential drawbacks to be considered • Workload for AO dispatch & pilots • Impact on flight efficiency

  32. From CTOT to TT – SESAR Validation Trials Live trials using real airport regulations • TT Trial Palma June 2013 • 3 Airlines (Airberlin, EasyJet, Air Europa) • 129 measured flights under TTA • Validated also the integration of AOP and NOP: TT optimised to respond to airport business needs • Fair Stream TT Trial May-October 2013 • 3 Airlines (Air France, Lufthansa, Swiss) • CDG/DSNA - Munich/DFS - Zurich/Skyguide • 800+ measured flights under TTA • Validated preliminary AMAN integration at CDG

  33. Validation Trials – Main Conclusions and further research Ops procedures for TT sharing between NM/APT/AOC/Flight Crew: Acceptable and applicable in real operational conditions Network provided TT for airport regulations: Can be used for airport impact assessment And adjusted to optimise airport operations • Some lessons learned – Objectives for future trials • Adherence to TT reduced by: DEP time fluctuation, Delta Plan/Execution, ATC involvement • Clear Predictability increase has been measured, but…overall network impact & benefits to individual airlines still to be addressed (mid-2014) • Predictability at TTA fix not propagated to landing time predictability, reducing benefits for AO – May be solved by integration with AMAN