Presentation on theme: "NM USER FORUM 2014 Cooperative Traffic Management (CTM) Introduction Chris Bouman NM Head of Network Development 30/01/2014."— Presentation transcript:
NM USER FORUM 2014 Cooperative Traffic Management (CTM) Introduction Chris Bouman NM Head of Network Development 30/01/2014
2 Cooperative Traffic Management Improvements that directly interact 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! NM implementation project to further reduce need for regulation and achieve important step towards time based operations
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
NM USER FORUM 2014 Marcel Richard Senior ATC Expert 30/01/2014 Using occupancy counts for STAM & MCP To further reduce the need for ATFCM measures
5 Use of Occupancy Counts – STAM and MCP 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) 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. 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 Only the 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.
NM USER FORUM 2014 Marcel Richard Senior ATC Expert 30/01/2014 STAM Phase 2 System Supported Coordination on ATFCM measures
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 AUs preferences
10 STAM Collaboration Forum Detail of the item to be coordinated Conversation history and Chat area Incoming and outgoing Coordination request Notifications Topic area Hotspots and STAM measures Querying and filtering area
11 STAM What Airspaces Users can see Flights captures in a Hotspot Flight subject to a STAM Measure Measure Kind Coordination Status
12 STAM Phase 2 Validation Exercise from 12 until 23 rd 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
NM USER FORUM 2014 Christian Faber ATFCM Expert 30/01/2014 Predictability Reducing the gap between the planning and execution of flights
14 What is the problem? Lack of updates Lack of pre-departure FPL updates can make the predicted flight trajectory invalid Pilotsnot informed Pilots are sometimes not informed about changes to the FPL such as a new RFL and so cannot implement these changes profile is not flown The vertical profile or the route is not flown according to the FPL information held by the NMOC and ATC
15 Why does it make a difference? Actual profile FPL profile
16 What is the effect? ATC sectors are entered that are not on the flight profile described by FPL demandsignificantly different The demand ATC experiences can be significantly different from what was expected - including over deliveries! capacity buffers 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 values delivering: increase of 5-10% capacities an increase of 5-10% in local sector capacities reductiondelays % a reduction in delays of % flexibility remains Note that flexibility remains to deviate from the FPL when tactically necessary
18 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 File it – Fly it !!!
NM USER FORUM 2014 Predictability issues, impact on ATC How 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
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 24 Due to Flight plan non adherence ETOT/CTOT non adherence AO reactivity when receiving a CTOT (even with 0mn of delay) Fancy routings FMPs and ATC are daily facing unpredictable and dangerous situations.
26 Daily case AND massive effect Typical peak hour summer time : KR flight list, from 10h to 12h 60 flights/26 intruders Typical peak hour summer time : KR flight list, from 10h to 12h 60 flights/26 intruders
27 Intruders: A safety Issue 27 Final action = ATC clearance NMOC Crew AO Ops ANSPs Over-delivery Overload Over-delivery Overload
28 Our Objective: Safety, fluidity, efficiency, dynamicity, equity 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 Flight Plan is not just a flying ticket! It should be mutual commitment and responsibility for safety and more efficiency.
NM USER FORUM 2014 Leo van der Hoorn Validation Manager, SESAR Network Operations 30/01/2014 Results of SESAR Target Time (TT) Trials Validating an important step towards Time Based Operations
30 From CTOT to TT – Concept in a nutshell Now: Use (only) CTOT for time-based ATFCM Entry Time congestion CTOT dep Time-based ATFCM measure 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 Issues: New: Target Time congestion CTOT dep Time-based ATFCM measure Use Target Time at congestion For trials: Target Time +/- 3 minutes Flight Crew aim to meet Target Times Arrival Regulations => input to sequencing Over-regulation or Over-delivery, unpredictability
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 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 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