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

2. Cooperative Traffic Management
NM implementation project to further reduce need for regulation and achieve important step towards time based operations
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!
Examples for local TT preferences
Palma , using airport impact assessment based on turn around times and avoidance of reactionary delays augmented with airspace user preferences

3. Presentations: Marcel Richard (NM): Christian Faber (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. Marcel Richard Senior ATC Expert 30/01/2014
NM USER FORUM 2014
Using occupancy counts for STAM & MCP To 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.
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
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)

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.

7. Marcel Richard Senior ATC Expert 30/01/2014
NM USER FORUM 2014
STAM Phase 2 System 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
Flights captures in a Hotspot
Flight subject to a STAM Measure
Measure Kind
Coordination Status

12. STAM Phase 2 Validation 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. Christian Faber ATFCM Expert 30/01/2014
NM USER FORUM 2014
Predictability Reducing 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. 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
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 values delivering:
an increase of 5-10% in local sector capacities
a reduction in delays of %
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 !!!

19. 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, equityBy
Proposing evolution in Airspace Structure, associated with better capacities
Selecting optimum ATC sector configuration based on Traffic Demand and ATC staffing 20

21. 21

22. Our Objective: Safety, fluidity, efficiency, dynamicity, equityBy
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, equityBy
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
Unexpected Sectors Opening
Emergency action or
Overload on an elementary sector
> 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
Final action = ATC clearance
NMOC
Crew
AO Ops
ANSPs
Over-delivery
Overload 27

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
Flight Plan is not just a flying ticket!
It should be mutual commitment and responsibility for safety and more efficiency. 28

29. NM USER FORUM 2014
Results of SESAR Target Time (TT) Trials Validating 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
Now:
Use (only) CTOT for time-based ATFCM
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
Intersecting flights either “classical regulations” or time based STAM
Explain very briefly the main eroding factors on the assumptions
Prediction models vs operational flight plans
Meteo
ATC
DCT , SID short cut
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
Examples for local TT preferences
Palma , using airport impact assessment based on turn around times and avoidance of reactionary delays augmented with airspace user preferences
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