Complexity study maastricht_upper_airspace

EUROPEAN ORGANISATION
FOR THE SAFETY OF AIR NAVIGATION

EUROCONTROL

EUROCONTROL EXPERIMENTAL CENTRE

A COMPLEXITY STUDY OF THE MAASTRICHT UPPER AIRSPACE CENTRE

EEC Report No. 403

Project COCA

Issued: February 2006

The information contained in this document is the property of the EUROCONTROL Agency and no part should
be reproduced in any form without the Agency’s permission.
The views expressed herein do not necessarily reflect the official views or policy of the Agency.

REPORT DOCUMENTATION PAGE

Reference: Security Classification:
EEC Report No. 403 Unclassified
Originator: Originator (Corporate Author) Name/Location:
EEC – NCD EUROCONTROL Experimental Centre
Network Capacity & Demand Centre de Bois des Bordes
B.P.15
F - 91222 Brétigny-sur-Orge Cedex
FRANCE
Telephone: +33 (0)1 69 88 75 00

Sponsor: Sponsor (Contract Authority) Name/Location:
EATM EUROCONTROL Agency
96, Rue de la Fusée
B - 1130 Brussels
BELGIUM
Telephone: +32 (0)2 729 90 11
WEB Site: www.eurocontrol.int

TITLE:
A COMPLEXITY STUDY OF THE MAASTRICHT UPPER AIRSPACE CENTRE

Authors Date Pages Figures Tables Annexes References
Geraldine M Flynn 02/2006 xii + 91 49 19 7 7
Claire Leleu (Isa Software)
Brian Hilburn (Stasys)

EEC Contact Project Task No. Sponsor Period
COCA 2004 - 2005
Distribution Statement:
(a) Controlled by: Head of NCD
(b) Special Limitations: None

Descriptors (keywords):
Complexity indicators, Complexity factors, Sectors classification, Sector I/D cards, Maastricht UAC,
Capacity indicators, Workload evaluation.

Abstract:

This report describes a complexity study performed on all the Maastricht UAC sectors. Particular focus
was put on the Brussels sectors in the vicinity of the REMBA navaid to assess if airspace changes made
in May 2004 resulted in a reduction of complexity. The study was conducted over two separate weeks;
one in April 2004 and the other in August 2004. The sectors were classified into three groups sharing
similar complexity characteristics. The results are presented in I/D cards for each sector; these contain
the quantitative values of the selected complexity indicators. The results of this study may be used to
support safety management processes in MUAC to reduce complexity and increase safety and to
support the MANTAS project.

A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL

ACKNOWLEDGEMENTS

The COCA project leader would like to thank the ATC experts of the Maastricht UAC for their
assistance and cooperation during the surveys. The COCA team highly appreciated their warm
welcome and their complete co-operation during the two data collection sessions (in April and
August 2004).
We would also like to thank those who participated in focus group and paired-comparison
sessions, as well as Stewart Mac Millan, Tina Braspennincx, James Kench. Special thanks should
go to Keith CARTMALE, Joachim BECKERS, Urs SCHOEKE and Rainer GRIMMER.

Project COCA - Report No. 403 v

EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre

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vi Project COCA - EEC Report No. 403


TABLE OF CONTENTS

LIST OF ANNEXES......................................................................................................... VIII

LIST OF FIGURES .......................................................................................................... VIII

LIST OF TABLES.............................................................................................................. IX

DEFINITIONS, ABBREVIATIONS AND ACRONYMS ....................................................... X

REFERENCES .................................................................................................................. XI

1. INTRODUCTION ...........................................................................................................1
1.1. STRUCTURE OF THE DOCUMENT ............................................................................. 1

2. BACKGROUND OF THE COCA PROJECT.................................................................2
2.1. THE COCA PROJECT ................................................................................................... 2

3. MUAC COMPLEXITY STUDY OBJECTIVES...............................................................3
3.1. GLOBAL DESCRIPTION OF THE METHOD................................................................. 3

4. OVERVIEW OF MUAC AIRSPACE AND SECTORS ...................................................5
4.1. DIRECTIONAL FLOWS.................................................................................................. 7
4.2. VERTICAL MOVEMENTS.............................................................................................. 7
4.3. OVERVIEW OF THE SECTOR GROUPS ................................................................... 10
4.3.1. Brussels Sector Group ....................................................................................10
4.3.2. DECO Sector Group........................................................................................12
4.3.3. Hannover Sector Group...................................................................................13

5. DATA USED IN STUDY..............................................................................................14
5.1. ELEMENTARY DATA .................................................................................................. 14
5.2. CONFIGURATION DATA............................................................................................. 14
5.3. DATA VALIDATION ..................................................................................................... 14
5.3.1. Elementary Data Validation .............................................................................14
5.3.2. Traffic Distribution Periods ..............................................................................16
5.3.3. Configuration Data – Military Impact ...............................................................18
5.4. DYNAMIC DATA .......................................................................................................... 20
5.4.1. Reported Workload Data .................................................................................20
5.4.2. Self-reported Complexity Factors ....................................................................21

6. CONTROLLER WORKLOAD CALCULATION ..........................................................22

7. COMPLEXITY CLUSTERS .........................................................................................23
7.1. COMPLEXITY CLUSTER 1: APPEAR TO BE HIGH COMPLEXITY SECTORS......... 23
7.2. COMPLEXITY CLUSTER 2: APPEAR TO BE MEDIUM COMPLEXITY SECTORS ... 26
7.3. COMPLEXITY CLUSTER 3: APPEAR TO BE LOW COMPLEXITY SECTORS ......... 27

8. RESULTS....................................................................................................................30
8.1. SECTOR I/D CARD EXAMPLE.................................................................................... 30
Project COCA - Report No. 403 vii


8.2. SELECTED I/D CARD RESULTS ................................................................................ 33
8.3. COMPARISON OF I/D CARD RESULTS BEFORE AND AFTER THE BRUSSELS
SECTOR CHANGE ...................................................................................................... 37
8.4. HOTSPOT MAPS ......................................................................................................... 41
8.5. WORKLOAD RESULTS ............................................................................................... 43
8.6. DYNAMIC RESULTS – REPORTED WORKLOAD RESULTS.................................... 44
8.6.1. Phase 1 ...........................................................................................................44
8.6.2. Phase 2 ...........................................................................................................46
8.7. COMPLEXITY FACTORS ASSOCIATED WITH HIGH WORKLOAD.......................... 51
8.7.1. Complexity Factors, by Sector Group..............................................................52
8.7.2. Complexity Factors, by Weekly Period............................................................53
8.8. COMPLEXITY PRECURSORS: FOCUS GROUP RESULTS...................................... 54

9. GENERAL SUMMARY AND CONCLUSIONS ...........................................................56

FRENCH TRANSLATION (TRADUCTION EN LANGUE FRANÇAISE............................57

LIST OF ANNEXES

ANNEX A - Centre configurations ................................................................................................... 65
ANNEX B - Civil and Military configuration sheets .......................................................................... 72
ANNEX C - Reported Workload Questionnaires ............................................................................. 75
ANNEX D - Macroscopic Workload Models..................................................................................... 77
ANNEX E - Classification Process .................................................................................................. 79
ANNEX F - Complexity Indicators ................................................................................................... 83
ANNEX G - Complexity Factor List.................................................................................................. 91

LIST OF FIGURES

Figure 1: The three Maastricht sector groups................................................................................ 6
Figure 2: Principle traffic flows related to MUAC (April 21st, 2004 from 07:00 to 19:00)................ 7
Figure 3: Distribution of the flights in the vertical plane for the 21st April 2004 .............................. 8
Figure 4: Influential airports that impact MUAC’s main traffic flows............................................... 9
Figure 5: Location of the Brussels group within MUAC ............................................................... 10
Figure 6: MUAC Brussels group: before sector change .............................................................. 11
Figure 7: MUAC Brussels group: after sector change ................................................................. 11
Figure 8: Location of the DECO group within MUAC................................................................... 12
Figure 9: Location of the Hannover group within MUAC ............................................................. 13
Figure 10: Analysis of the number of flights for the two phases .................................................... 15
Figure 11: An annotated box-plot .................................................................................................. 16
Figure 12: Similarity of the traffic distribution of the AIRAC cycle 259 and Saturday August, 28th17
Figure 13: MUAC special and restricted areas .............................................................................. 18
Figure 14: Impact of military activity on sectors per MUAC group................................................. 20
Figure 15: Workload rating scale, phase 1 .................................................................................... 21
Figure 16: Workload rating scale, phase 2 .................................................................................... 21
Figure 17: Sector distribution by group and level within Complexity Cluster 1 .............................. 24
Figure 18: Location of the Cluster 1 sectors .................................................................................. 25
viii Project COCA - EEC Report No. 403


Figure 23: Brussels Phase 1 configuration .................................................................................... 37
Figure 24: Brussels Phase 2 configuration .................................................................................... 37
Figure 25: Hotspots map for Brussels sectors between FL245 and FL335................................... 41
Figure 26: Hotspots map for Brussels sectors between FL335 and FL450................................... 42
Figure 27: Workload values per Complexity Cluster...................................................................... 44
Figure 28: DECO Reported Workload distribution, phase 1 .......................................................... 45
Figure 29: Reported workload (cumulative percent) across the three sector groups, phase 1 ..... 45
Figure 30: Reported workload (cumulative percent) across the three sector groups, phase 2 ..... 46
Figure 31: Reported Workload ratings for each sector group (cumulative percentage) ................ 47
Figure 32: Brussels median workload for weekdays, Saturday and Sunday................................. 47
Figure 33: DECO median workload for weekdays, Saturday and Sunday .................................... 48
Figure 34: Hannover median workload for weekdays, Saturday and Sunday ............................... 48
Figure 35: Reported workload as a function of time-of-day and traffic load, Brussels................... 49
Figure 36: Reported workload as a function of time-of-day and traffic load, DECO ...................... 49
Figure 37: Reported workload as a function of time-of-day and traffic load, Hannover................. 49
Figure 38: Reported workload as a function of time-of-day and number of open sectors,
Brussels........................................................................................................................ 50
Figure 39: Reported workload as a function of time-of-day and number of open sectors, DECO. 50
Figure 40: Reported workload as a function of time-of-day and number of open sectors,
Hannover ...................................................................................................................... 50
Figure 41: Reported Workload Questionnaire, phase 1 ................................................................ 75
Figure 42: Reported Workload Questionnaire, phase 2 ................................................................ 76
Figure 43: MUAC sectors classification: Building of the binary tree from the data sample ........... 80
Figure 44: Horizontal view of a sector tiled by the mesh ............................................................... 83
Figure 45: Possible track values.................................................................................................... 84
Figure 46: Possible phase values.................................................................................................. 84
Figure 47: Graphical illustration of the mix of traffic attitudes indicator ......................................... 86
Figure 48: Proximate pairs: along track ......................................................................................... 87
Figure 49: Proximate pairs: opposite direction .............................................................................. 87

LIST OF TABLES

Table 1: Number of sectors affected by military activity within the MUAC groups ......................... 19
Table 2: How to read an I/D card ................................................................................................... 31
Table 3: Brussels West Low / NICKY Low and KOKSY Low I/D Card........................................... 33
Table 4: Solling I/D Card................................................................................................................ 35
Table 5: Delta High I/D Card.......................................................................................................... 36
Table 6: Comparison of airspace before and after the Brussels sector change ............................ 38
Table 7: Complexity Cluster Coefficients ....................................................................................... 43
Table 8: Reported workload (cumulative percent) across the three sector groups, phase 1 ......... 45
Table 9: Reported workload (cumulative percent) across the three sector groups, phase 2 ......... 46
Table 10: Brussels self-reported complexity factors associated with high workload (n=48) .......... 52
Table 11: DECO self-reported complexity factors associated with high workload (n=48) ............. 52
Table 12: Hannover self-reported complexity factors associated with high workload (n=100) ...... 53
Table 13: Weekday self-reported complexity factors associated with high workload .................... 53
Table 14: Saturday self-reported complexity factors associated with high workload ..................... 54
Table 15: Sunday self-reported complexity factors associated with high workload ....................... 54
Table 16: Table used to capture the sector configuration changes for the DECO group............... 73
Table 17: Table used to capture the military area activation for the Brussels group ..................... 73
Table 18: Classification results table ............................................................................................. 81
Table 19: Self–reported Airspace Complexity Factors................................................................... 91
Project COCA - Report No. 403 ix


DEFINITIONS, ABBREVIATIONS AND ACRONYMS
Abbreviation De-Code
AC AirCraft
ACC Area Control Centre
AIRAC Aeronautical Information Regulation and Control
AMWM Adapted Macroscopic Workload Model
ATC Air Traffic Control
ATFM Air Traffic Flow Management
ATM Air Traffic Management
Avg Average
BADA Base of Aircraft Data
CFMU Central Flow Management Unit
CNF Conflicts
COCA Complexity and Capacity
COLA Complexity Light Analyser
CTFM Current Tactical Flight Model
DIF Different Interacting Flows
DFS Deutsche Flugsicherung of Germany
EEC EUROCONTROL Experimental Centre
ETFMS Enhanced Tactical Flow Management System
FL Flight Level
Ft Feet
GAT General Air Traffic
GMT Greenwich Mean Time
I/D Identification
IFR Instrument Flight Rules
ISA Individual Self Assessment
LC Level Changes
LVNL Luchtverkeersleiding Nederland
MANTAS Maastricht ATC New Tools And Systems
MUAC Maastricht Upper Area Control centre
MWM Macroscopic Workload Model
NASA National Aeronautics and Space Administration
NCD Network Capacity and Demand Management
NM Nautical Miles
OAT Operational Air Traffic
R/T Radio Telephony
RoT Routine Tasks
RVSM Reduced Vertical Separation Minimum
TRA Temporary Reserved Airspace/Area
TSA Temporary Segregated Area
UAC Upper Area Control
UNL Unlimited

x Project COCA - EEC Report No. 403


REFERENCES

[1] Cognitive Complexity In Air Traffic Control, A Literature Review, B. Hilburn, EEC Note 04/04,
Web link: http://www.eurocontrol.int/eec/public/standard_page/2004_note_04.html

[2] Adaption of Workload Model by Optimisation Algorithms and Sector Capacity Assessment,
G. M. Flynn, A. Benkouar, R. Christien, EEC Note 07/05.
Web link: http://www.eurocontrol.int/eec/public/standard_page/2005_note_07.html

[3] RAMS Plus User Manual, Release 5.08, March 2004, Gate-To-Gate ATM Operations

[4] RAMS Plus Data Manual, Release 5.08, March 2004, Gate-To-Gate ATM Operations

[5] Air Traffic Complexity: Potential Impacts on Workload and Cost, T. Chaboud (EEC),
R. Hunter (NATS), J. C. Hustache (EEC), S. Mahlich (EEC), P. Tullett (NATS), EEC note
11/00. Web link: http://www.eurocontrol.int/eec/public/standard_page/2000_note_11.html

[6] Probabilités, analyse de données et statistique, G. Saporta, Editions Technip, 1990.

[7] Air Traffic Complexity Indicators & ATC Sectors Classification, R. Christien, A. Benkouar, 5th
USA/Europe Air Traffic Management R&D Seminar, June 2003, Budapest, Hungary.

Project COCA - Report No. 403 xi


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xii Project COCA - EEC Report No. 403


1. INTRODUCTION

A recent safety survey conducted at Maastricht, coupled with the annual safety report of 2002,
highlighted the need to study airspace complexity at the Unit. The safety survey highlighted
incident ‘hot-spots’ and post incident data inferred that complexity may have been a key factor.
One of the geographical areas highlighted in the survey was the airspace close to the REMBA
navaid, located in the Brussels sector group. Safety monitoring processes show that the number of
incidents around REMBA has increased over the years. As a consequence, the airspace around
REMBA was modified as part of a strategy to reduce the number of incidents.
As mentioned above, post-incident investigation reports implied that complexity may have been a
key factor in the incidents, but, these data did not identify any common, quantifiable traffic and/or
static airspace conditions.
As a result, Maastricht Upper Airspace Centre (MUAC) safety managers and senior management
requested the Complexity and Capacity (COCA) project to conduct a study to identify and measure
airspace complexity factors existing in MUAC’s area of responsibility in general, and in the REMBA
area in particular. The study was performed in two phases. The first phase ran from 21 - 26 April,
2004, prior to the airspace change, and the second from 25 - 30 August, 2004. During both phases
the COCA team collected and collated static and dynamic operational data between 0700-1900
(local) Wed-Sun and 0700-1300 (local) Monday.
The results of this study may be used to support the MANTAS1 project and the safety management
initiatives and processes at MUAC. In addition, it should be noted that this complexity study will
support other EUROCONTROL initiatives, including the Performance Review Unit (PRU) ATM
Cost Effectiveness study and the Action Group for ATM Safety (AGAS) Session Service Access
Point (SSAP) WorkPackage 06-01.

1.1. STRUCTURE OF THE DOCUMENT

This document presents the method used and the results of the MUAC complexity study. The
structure of the report is as follows:
Chapter 2 Background of the COCA project.
Chapter 3 The study objectives.
Chapter 4 General description of the MUAC airspace.
Chapter 5 Static and dynamic data collected and processed for this study.
Chapter 6 The method used to evaluate controller workload.
Chapter 7 Statistical Complexity Clustering analysis at sector level.
Chapter 8 Results obtained using both static and dynamic data.
Chapter 9 General Summary and Concluding remarks.

1 MANTAS, created in 2004, consists of a new operational and ATM concept: it aims to develop generic
sectors (dynamic re-sectorisation), mixed routes (gradually moving away from fixed routes to free route
airspace), no fixed sector groups, flexible use of airspace and voiceless Radar Control.

Project COCA - Report No. 403 1


2. BACKGROUND OF THE COCA PROJECT

2.1. THE COCA PROJECT

The Complexity and Capacity (COCA) project was launched at the EUROCONTROL Experimental
Centre (EEC) at the end of year 2000. Its main objective is to describe the relationship between
capacity and complexity by means of accurate performance metrics. This objective is being
addressed in two ways:

• Identifying and evaluating factors that constitute and capture complexity in air traffic
control;

• Validating and testing complexity factors and highlighting those linked with controller
workload.
The three terms “complexity”, “capacity” and “workload” are highly linked. Sector capacity is not
just a function of the number of aircraft in a sector, it is also directly influenced by the interactions
between the aircraft: the greater the number of interactions, the higher the complexity. Simply put,
complexity drives controller workload, and workload limits capacity. Hence, there is a need to
understand what factors or circumstances make the controllers’ work more complex and cause an
increase in workload.
To gain a better understanding of the relationship between complexity, workload and capacity the
COCA project’s specific objectives are to:

• Analyse the concept of ATM complexity at macroscopic and microscopic levels to include
elements such as route segments, airspace volumes, traffic flows, converging/crossing
points, etc. at various levels (sector, centre or state);

• Provide relevant complexity indicators and capacity evaluators for specific complexity
studies and other studies: ATFM, Airspace design, ATFM Performance and Efficiency,
Economical studies for ATM, etc.
Until now the COCA project has concentrated on macroscopic studies and development of the
methodology. During the development process, the COCA project built an elaborated complexity
toolbox named COCA Light Analyzer (COLA), and performed several macroscopic studies, the
results of which were validated by operational experts. The MUAC study has given COCA the
opportunity to apply and test the methodology in the ‘real world’ (supported by subjective data),
and to improve upon it.

2 Project COCA - EEC Report No. 403


3. MUAC COMPLEXITY STUDY OBJECTIVES
The main objectives identified for this complexity study were to:

• Evaluate the operational complexity of all sectors in Maastricht airspace with a particular
focus on the Brussels sectors;
• Establish a complexity baseline for Maastricht sectors against which future changes can
be measured to assess how sector complexity has changed;
• Derive a workload measure to be used throughout the analysis;
• Elicit relevant complexity factors from the controllers;
• Obtain reported controller workload assessments;
• Assess changes to complexity following airspace modifications in the REMBA area.
The outputs of the study were:

• I/D cards containing a list of complexity indicators and associated values for each sector;
• A classification of MUAC sectors according to shared complexity indicators;
• An operational complexity index based on workload per flight (presented in the I/D cards);
• A comparison of complexity metrics following airspace changes close to the REMBA
navaid.

3.1. GLOBAL DESCRIPTION OF THE METHOD

A quantitative approach was used to evaluate operational complexity intrinsic to MUAC traffic flows
and airspace environment characteristics. This approach consisted of first defining the complexity
metrics which could best describe the factors contributing to the complexity of MUAC sectors.
These factors have been defined considering both static (sector configuration and specific fixed
aspects related to the airspace environment) and dynamic (e.g. operational behaviour, traffic
variability) data.
The set of elicited metrics was systematically evaluated for all MUAC sectors in each sector
configuration that occurred during both data collection phases. The results provide quantitative
measurements of the selected indicators and are used as the basis of the sector I/D cards. All the
I/D cards are available on
http://www.eurocontrol.int/eec/public/standard_page/2006_report_403.html#ID_CARDS.
In this report we will present a set of I/D cards showing the results for one sector from each of the
three MUAC sector groups. Each I/D card set comprises three cards: one card for Monday-Friday
(weekdays), and separate cards for Saturday and Sunday. The analysis was performed using the
COLA fast-time complexity simulator.
The inputs to the simulations were the:

• Flight plan data describing individual aircraft trajectories (IFR flights) – for all MUAC
sectors – covering a 12 hour period (0700-1900 local);
• Sector descriptions and dimensions;
• Sector configurations for the traffic sample for each day of both phases and the
corresponding Aeronautical Information Regulation And Cycle (AIRAC) notice;



• Geographical environments of the military zones;
• Military activation/deactivation times for the sample dates, and
• Parameters required for the selected complexity indicators.
Following several meetings between MUAC and the COCA team, the complexity indicators thought
to be most relevant to the MUAC sectors were selected:

• Interactions between flights (DIF);
• Sector volume;
• Airspace available;
• Occurrences of proximate pairs;
• Number of flight levels crossed;
• Spatial traffic distribution, (density);
• Mixture of aircraft types and performance;
• Numbers of flights per hour and per 10 min period (avg);
• Traffic mixture in relation to flights in climb, cruise and descent.
The workload calculation using the Macroscopic Workload Model was also expected to produce
valuable results.
The output from the simulations consisted of:

• Values for the complexity indicators listed above;
• Sector I/D cards;
• Workload per flight.



4. OVERVIEW OF MUAC AIRSPACE AND SECTORS
Maastricht Upper Airspace Centre (MUAC) is located within a dense region of airspace in the core
area of Europe. In 2003, MUAC handled more than 1.2 million flights (more than 5% growth
compared to the previous year). It is responsible for all upper airspace (i.e. above FL245) over the
territories of Belgium, the Netherlands, Luxembourg and northwest Germany, as well as the
adjoining areas of the North Sea (see Figure 1). Lower airspace in the region is the responsibility of
the Belgium national services (Belgocontrol), the Dutch national services (LVNL), and the German
national services (DFS), through ACCs in Brussels, Amsterdam, Düsseldorf and Bremen.
Several busy adjacent and subjacent European airports are located in the MUAC region and
generate dense traffic streams from north to south, east to west and vice-versa. The traffic streams
have to be managed to accommodate other airspace users (e.g. military flights using and transiting
to/from temporary restricted and segregated areas). MUAC is affected by a significant number of
temporary segregated airspace and restricted areas.
Military/civil airspace sharing and coordination arrangements depend upon each individual
country’s procedures. For example, in Belgian and Dutch airspace, there are reserved military
areas and traffic is controlled by dedicated military units in the countries concerned. In German
airspace, a DFS military unit is co-located within the MUAC control room.
The Maastricht centre is divided into 3 broad sector groups: Brussels, Delta & Coastal (DECO) and
Hannover sectors as shown in Figure 1. Each group is divided into subgroups which are described
hereafter.



Figure 1: The three Maastricht sector groups

MUAC airspace projects onto a surface area equivalent to 76,000 sq nautical miles. It is currently
ranked 15th amongst all the European centres in terms of surface size. In terms of traffic numbers
MUAC controllers handle an average of 3,400 flights per day (based on 2003 data). The
distribution of the traffic is well balanced between the three sector groups: Brussels handles on
average 39% of the traffic, Hannover 34% and DECO 26%.



4.1. DIRECTIONAL FLOWS

Figure 2 shows the four main traffic flows handled by MUAC: the northbound and southbound
flows between the northern European airports and Paris or the southern European airports and the
eastbound and westbound flows between London and German or central European airports.

Figure 2: Principle traffic flows related to MUAC (April 21st, 2004 from 07:00 to 19:00)

The black triangles symbolize navaids. The yellow flows represent northbound/westbound
and the red ones southbound/eastbound.

4.2. VERTICAL MOVEMENTS

Figure 3 shows the breakdown of the vertical movements computed for the 21st of April 2004. By
vertical movements we mean the proportion of flights in climb/descent subdivided into the following
categories: Internal, Departing, Landing, and Overflights.
Internal flights are those which have departed from and landed at airports located beneath each
sector group’s geographical boundaries.
Departing and Landing flights are those which have either departed from or landed at airports
located beneath each sector group’s geographical boundaries.



Overflights are those flights that have passed through the sector group and did not depart from or
land at an airfield located subjacent to the group’s area of responsibility. The overflights are divided
into two categories: “pure” overflights, and overflights to/from fringe airports. The fringe airports are
defined as being within a radius of 150 NM from the sector group’s area of responsibility.
The proportion of Internal/Landing/Departing flights does not exceed 20% for Brussels and DECO,
and is around 37% for Hannover. These low percentages are explained by the fact that MUAC
operates in the upper airspace only. Moreover, the three sector groups have very few or no
Internal flights: Brussels and DECO have no internal flights and Hannover has only 2% Internal
flights - flights between Hamburg and Köln or Düsseldorf.
Nevertheless, MUAC airspace sits over a number of major European airports: Amsterdam,
Brussels, Düsseldorf, Köln, Luxembourg and Hamburg. The flights departing from or landing at
these airports are generally in a “transition” phase when they enter the MUAC sectors.
Dealing with the overflights, all the groups---and particularly Brussels and DECO---are clearly
affected by traffic to/from fringe airports. As we can see in Figure 4, MUAC is located in the middle
of the core area and is surrounded (150 NM fringe) by numerous important airports such as
London, Paris, Frankfurt, Copenhagen, Frankfurt, Basel, Zurich, Munich and Berlin.

100%

90%
Overflights
Overflights To/From Fringe Airports
80%
Internal
Landing
70% Departing

60%
ratio (%)

50%

40%

30%

20%

10%

0%
BRUSSELS DECO HANNOVER
MUAC groups

Figure 3: Distribution of the flights in the vertical plane for the 21st April 2004



Figure 4: Influential airports that impact MUAC’s main traffic flows



4.3. OVERVIEW OF THE SECTOR GROUPS

4.3.1. Brussels Sector Group

General Map:

WEST
OLNO
LUX

Figure 5: Location of the Brussels group within MUAC

The Brussels group is divided into three parts: LUX sectors, OLNO sectors and WEST sectors as
shown in Figure 5.
The airspace around the REMBA navaid in the Brussels sector group was identified as an incident
hotspot. In recent years, the number of incidents close to this navaid has increased. As a
consequence, the sector design in the REMBA area was changed in mid-2004, (see Figure 6 and
Figure 7) as part of a strategy to reduce incidents and increase capacity.
The airspace changes were:

• Moving the eastern boundary between the West sector (and adjacent sectors) further
east to increase the distance from REMBA and adjacent sectors.

• Splitting longitudinally the former West Low sector (FL245 to FL335) to form two new low
sectors; KOKSY Low and NICKY Low.

• Similarly, splitting the West High sector (FL335 to UNL) into KOKSY High and NICKY
High. NICKY High is never used on its own but always combined with different sectors.



KOKSY NICKY
WEST H REMBA REMBA

Figure 6: MUAC Brussels group: before sector change Figure 7: MUAC Brussels group: after sector change

During the first phase, 8 different sector configurations were used with a maximum of 5 sectors
open at the same time. After the reorganisation, the number of configurations (observed during
phase 2) increased to 9 (out of 18 possible) and the maximum number of sectors open at the same
time was 6. The possible combinations of sectors are shown in a sector block diagram in Annex A.
The major airports located below the Brussels sectors are: Antwerpen, Brussels, Charleroi,
Luxembourg and Maastricht. Other major adjacent airports are Amsterdam, Dusseldorf, Frankfurt,
Koln, Paris-CDG, Stuttgart and London airports.
Military activity has a significant impact upon this group: on weekdays, on average, 24% of the
volume of Brussels was used2 for military purposes.

2The term ‘used’ reflects occupancy in both a temporal and a spatial sense.



4.3.2. DECO Sector Group
General Map

Coastal

Delta

Figure 8: Location of the DECO group within MUAC

This group is divided into two parts: Coastal and Delta sectors, as shown in Figure 8.
During the first and the second phases, 4 different configurations were used, with a maximum of 4
sectors open at the same time. The possible combinations of sectors are shown in a sector block
diagram in Annex A.
The major airports located below the DECO sectors are: Amsterdam, Groningen and Rotterdam.
The other major influencing airports are Brussels, Copenhagen, Düsseldorf, Frankfurt, Hamburg,
London, Manchester, Oslo, and Paris.
For both Coastal and Delta one of the influential flows is oriented Southbound-Northbound. The
major flow in the Coastal sectors is towards the southwest (London) and northeast towards
Scandinavia and eastern Europe. In the southerly region of the Delta sectors, the major flow is
eastbound-westbound (Atlantic flights and eastern Europe). For both lower and upper sectors, the
major flow is oriented between westbound and north-eastbound.
Military activity has a significant affect on this group: on weekdays, on average, 17% of the volume
of the DECO group was used2 for military purposes.



4.3.3. Hannover Sector Group

General Map

Hamburg

Munster
Solling
Ruhr

Figure 9: Location of the Hannover group within MUAC

This group is divided into four parts: Ruhr, Munster, Hamburg and Solling sectors as shown on
Figure 9.
During the first and the second phases, 8 different configurations were used, with a maximum of 6
sectors open simultaneously. The possible combinations of sectors are shown in a block diagram
in Annex A.
The major airports located below the Hannover sectors are Düsseldorf, Essen, Hamburg,
Hannover and Köln. Other major influencing airports are Amsterdam, Basel, Berlin, Copenhagen,
Frankfurt, London, Munich and Zurich.
In the Munster and Solling sectors, the main streams are oriented along east/west and north/south
directions.
In the Ruhr and Hamburg sectors, the major flow is oriented between northwestbound and
south-eastbound.
Military activity does not have a great impact on this group: on weekdays, on average, 6% of the
volume of the Hannover airspace was used2 for military purposes.



5. DATA USED IN STUDY
The data used for the study fall into two broad categories, Static and Dynamic data. Static data is
divided into two sub-sets referred to as Elementary and Configuration data; Configuration data is
operationally updated Elementary data. The static data are used to compute the complexity
indicators, while dynamic data are used to evaluate and assess the complexity indicators.
Dynamic data were collected in-situ from airspace managers and controllers in real time. These
data include the controllers’ perception of workload called “reported workload data”.
During both phases, the COCA team members were in the MUAC control room to collect both
Configuration and Dynamic data.
Data collection times were 0700-1900 (local) 21-25 April 2004 and 25-29 August, 2004, and 0700-
1300 (local) on 26 April and 30 August. In total, 132 hours of data were collected; 66 hours in each
phase.

5.1. ELEMENTARY DATA

To perform a simulation, traffic sample data describing flight plan aircraft trajectories and
environment data were required for each week of the two phases. The traffic flight plan and
environment data were provided by the CFMU. The Enhanced Tactical Flow Management System
(ETFMS) produces flight plan data updated with the current trajectory of the flights called Current
Tactical Flight Model (CTFM) data. The ETFMS system uses the message received on an airborne
flight to update the CTFM. The CTFM is updated if the actual position deviates from the planned
profile by more than 20 nm laterally, 700 ft vertically and 5 minutes in time. Updates to the CTFM
are suspended when the flight is less than 30 nm from the arrival airport.

5.2. CONFIGURATION DATA

To compensate for the lack of accuracy of the CFMU data the following data were collected in-situ.

• Civil activity: all the sector configuration changes and activation times in each sector
group (see Annex B for data collection forms);
• Military activity: all the activation/deactivation times of special and restricted areas;
• Military zones not described in Elementary CFMU data.

5.3. DATA VALIDATION

5.3.1. Elementary Data Validation

The two weeks of CMFU traffic data were validated before processing the traffic complexity
indicators to ensure that the weeks selected for the study were representative of the expected
traffic demand and flow: i.e. the weeks were not exceptional.
The box-plot in Figure 10 shows that the flows were relatively stable from day-to-day. For each
week of the sample (April/August) and for each sector group the box-plots show the numbers of
flights (from 0700 to 1900). The traffic volume is significantly higher in August than in April: about
7% more flights for each group.



The figure also shows the mean number of flights (red dot) and the standard deviation (pink arrows
extending above and below the red dots) for the corresponding AIRAC3 cycles. The traffic samples
for the two weeks did not show any outlier values which could introduce bias in the data.

Figure 10: Analysis of the number of flights for the two phases

3 The environment and traffic data are organised by AIRAC cycles (28 days per cycle). The 21st to the 26th April 2004 week (phase 1)
th th
corresponds to the AIRAC cycle 255 and the 25 to 30 August 2004 (phase 2) corresponds to the AIRAC cycle 259.



Maximum grade
19

16
14 Median grade
Mean grade
10

6
Minimum grade

1 Outlier

Figure 11: An annotated box-plot

Figure 11 is a box-plot showing examination results for a group of students. The following text
describes how to read the figure.
The solid black line within the yellow box is the median value (14) of the sample. The median value
is the middle value of a distribution: half of the students’ scores are above the line and half are
below the line.
The black square represents the arithmetic mean value (often called the average).
The yellow box represents 50% of the students’ scores: 25% of the students scored between 14
and 16, and 25% students scored between 10 and 14.
The scores 6 and 19 are respectively, the minimum and the maximum scores achieved in the
examination.
The values outside the yellow box, but inside the min/max limits represent the other half of the
sample. In effect, 25% of the students have a grade between 16 and 19 and 25% of the students
have a grade between 6 and 10.

5.3.2. Traffic Distribution Periods

The objective was to find representative patterns in terms of traffic distribution4 throughout a week.
To do this we used a statistical test (Kolmogorov-Smirnov) which determines if two datasets differ
significantly. We compared the traffic distributions from each day studied (phase 1 and phase 2)
against all the days of the corresponding AIRAC cycle.

4 By “traffic distribution”, we mean the number of flight per 10 minutes throughout the day.



It is generally acknowledged that there are differences in the traffic distribution between weekdays
and weekends. Before aggregating the data it was necessary to assess if these differences were
evident in the two weeks of data and if they were significant.
We observed that the traffic distributions on Saturdays and Sundays were significantly different to
the other days of the week and that the day with the greatest variability was a Saturday (see Figure
12).
We noticed that the link between weekdays of the AIRAC cycle is usually quite high but the
strongest link is not necessarily between days having the same name. The tests identified three
distinct periods:

• Weekdays,
• Saturdays,
• Sundays.
Traffic distribution similarity

Figure 12: Similarity of the traffic distribution of the AIRAC cycle 259 and Saturday August, 28th

Figure 12 shows an example of the test results used to determine if the traffic distributions were
significantly different. Please note that Figure 12 is an example of one statistical test. All results are
available on web link
http://www.eurocontrol.int/eec/public/standard_page/2006_report_403.html#PHASE_1.
The figure compares Saturday, August 28th to the other days of AIRAC cycle 259. The comparison
day (Sat) is shown in red and other days of phase 2 are represented in blue. The days of the
AIRAC cycle are represented in black.



The x-axis represents the days of the AIRAC cycle 259 in chronological order. We observe that the
Saturdays of the AIRAC cycle are similar to Saturday, August 28th (the value is close to 1 on the y-
axis).

5.3.3. Configuration Data – Military Impact

The impact of military airspace was evaluated by computing the percentage of civil airspace
affected by the presence of military activity. This percentage varied not only with respect to the
sectors but also with respect to the days of the sample.
The daily military activity configurations that were collected in-situ were used in the calculations.
Figure 13 shows the principle military areas affecting MUAC. Data on other areas that affect
MUAC, which are not shown on the map, were gathered during the two data collection phases.

TRA-EDD 100

TRA-EH
TRA-Melcken-
burg 2

TRA-WESER

TRA-NL2

TRA-North-B

TRA-
Sachen
TRA-CBA 1

TRA-16

TRA-Lauter 2
TRA-
Frankenal

TRA-TSA 22
TRA-TSA 20

Figure 13: MUAC special and restricted areas



Table 1 shows the total number of daily5 sectors that were open and the corresponding number of
daily sectors affected by military activity for the two phases of the sample. The actual activation
and de-activation times were recorded in-situ at MUAC. As there was no military activity during the
weekends the data relate only to weekdays.

Table 1: Number of sectors affected by military activity within the MUAC groups

Brussels DECO Hannover
Total number of daily sectors opened 77 43 62
Total number of daily sectors affected by
62 37 39
military activity
Percentage of daily sectors affected by
81% 86% 63%
military activity (%)
Military activity duration for the two
117 250 42
phases (h)
Average volume not available (%) 24 17 6

The table shows that the percentage of daily sectors affected by military activity varied between
63% and 86% of the total number of daily sectors opened; these are substantial proportions.
Figure 14 shows how much of the volume of the daily sectors affected by military activity was
unavailable. The Brussels group is most affected by military activity, followed by DECO then
Hannover. This can be explained by the fact that the number of military zones in Brussels is very
high and the pure “civil” volume of this group is small (i.e. volume where no military activity can
take place). As a consequence, 81% of the daily sectors in Brussels are affected by military
activity. Around 60% of those sectors have their volume reduced by more than 25% (25%-50% and
50%-75% “volume not available”). The military presence is both strong and evenly spread over the
group.
The DECO group has the longest military activity duration of the three groups. The group has a
very high number of military zones with most concentrated in the north-west corner. This is
reflected in the 86% of daily sectors which are affected by military activity. Of those sectors, around
70% are unable to use up to 25% of their volume while another 25% cannot use between 25% and
50% of their volume.
In Hannover, the number of military / restricted zones and other racetrack activities are limited
(geographically speaking), and the military activation duration is short compared to the other
groups. As a consequence, some sectors are never affected by military activity (elementary
sectors within Ruhr and Solling sub-groups). However, the majority (95%) of daily sectors in
Hannover that are affected by military activity have less than 25% of their volume unavailable. This
translates into an average percentage of volume not available of 6%; compared to 24% for
Brussels and 17% for DECO.

5 A ‘daily’ sector refers to the complexity information relating to one sector for one day, so if one sector is open for 5 days throughout
the two phases then it will count as 5 daily sectors.



1

0,9

0,8

0,7 Volume
Relative number of sectors

not available

0,6
>75% and <=100%
>50% and <=75%
0,5
>25% and <=50%
>0% and <=25%
0,4

0,3

0,2

0,1

0
Brussels Deco Hannover
Group

Figure 14: Impact of military activity on sectors per MUAC group

5.4. DYNAMIC DATA

The dynamic data includes:

• The configuration data collected in-situ from airspace managers concerning the actual
sector configuration schemes and military activity activation/deactivation times, see
section 5.2.

• The reported workload data collected from controllers in real time; see below.

5.4.1. Reported Workload Data

Although such factors as fatigue, skill, strategies etc. can influence the workload a given controller
experiences, controller workload remains the best criterion we have against which to assess the
influence of airspace complexity. There are various means of assessing workload, from objective
(e.g. behavioural or even physiological) indicators to subjective “self-report” techniques. For the
purposes of evaluating workload in operational centres, subjective methods have a number of
benefits, including ease of administration and data collection, and minimal task disruption.
Phase 1
Reported workload was elicited and evaluated using paper-and-pencil workload rating scales. In
phase 1, workload was rated using a variation of the Individual Self Assessment (ISA) instrument,
a 5-point rating scale on which workload was rated at twenty-minute intervals from “Under utilised”
to “Excessive”, see Figure 15. The workload form is reproduced in Figure 15. ISA has been used
extensively in operational and simulated ATC environments, and has shown itself fairly intuitive
and non-intrusive to use, as well as robust and valid in the data it provides.



08:00 08:10 08:20 08:30 08:40 08:50 09:00 09:10 09:20 09:30 09:40 09:50
Excessive
High √ √ √
Comfortable √ √ √ √ √ √ √ √ √
Relaxed
Under utilised

Figure 15: Workload rating scale, phase 1

Phase 2
On the basis of phase 1 results, a number of modifications were made to the workload rating
technique, including:

• Shorter rating intervals, with ratings collected using five minute time slices (to minimise
interruption, controllers were asked to provide two (five-minute) ratings, once every ten
minutes);
• Workload was rated on a six-point scale (i.e., with no midpoint to force ratings either
above or below the middle value), see Figure 16;
• Reworded data labels with “non-judgmental” end points (e.g. “Extremely High” in place of
“Excessive,”) and no text labels for intermediate values.

08:00 08:05 08:10 08:15 08:20 08:25 08:30 08:35 08:40 08:45 08:50 08:55
6 Extremely High √
5 √ √ √
4 √ √ √ √ √
3 √ √ √
2
1 Extremely Low

Figure 16: Workload rating scale, phase 2

5.4.2. Self-reported Complexity Factors

When the controllers reported high workload, either 5 or 6, they were asked to identify all the
complexity factors that were relevant during that period. As shown in Annex C, a list of factors was
provided (with provision for “others” to be identified) and the controllers ticked all that applied.
Please note the distinction between the list of computed complexity indicators, and the set of
self-reported complexity factors. The former consists of quantitative variables derived directly from
the airspace (e.g. average crossing angle), whereas the latter is built on factors that controllers
report as complexity drivers. These were identified through literature review (see reference [1]),
and the candidate list refined through repeated face-to-face sessions with controllers.



6. CONTROLLER WORKLOAD CALCULATION
For this study, the executive controllers’ task workload was computed using the Adapted
Macroscopic Workload Model (AMWM) developed by the COCA project. This model relies on the
Macroscopic Workload Model (MWM) which is described in reference [2]. As its name indicates,
the workload evaluation is performed at a macroscopic level. That is to say, only a few controller
tasks are considered. Adapted (in AMWM) refers to a classification process which creates clusters
of sectors with similar complexity characteristics. Workload values are then evaluated for each
cluster.
The MWM has been built to evaluate ACC workload, and is based on the workload used in the
RAMS Plus fast time simulator. This model is described in references [3], [4] and [5]. The MWM
states that every controller task can be placed in one of three macro task categories:

• Routine tasks (RoT);
• Level change tasks (LC);
• Conflict tasks (CNF).
The list of tasks associated with the three macro task categories are those defined in RAMS Plus
but some examples of these tasks include: Routine tasks – R/T tasks to and by the pilot for first
and last call on frequency, flight progress data management tasks, route clearances, etc. Level
change tasks include controller radar monitoring (or aircraft report) of flight leaving current level
and reaching assigned level as well as associated flight data management tasks. Conflict tasks
include identification, resolution and monitoring of conflicts.
Thus, an estimate of workload can be obtained from the following formula:

MWM = ωRoT * nAC + ωLC * nLC + ω CNF * nCNF

Equation 1: Macroscopic Workload Formula

Where:

ωRoT, ωLC and ωCNF are respectively the times (expressed in seconds) needed to execute routine
tasks, level change tasks, and conflict tasks and nAC, nLC and nCNF are respectively the number of
aircraft, flight levels crossed and the conflict search/resolutions.

These different parameters (ω and n) are estimated at sector level.

It is recognised that controller tasks (and associated durations) may not be the same in every
circumstance, or in different sector types: hence, controller task workload is context related. The
AMWM is an endeavour to take account of the context of sector types by applying different weights
to the same task dependant upon the sector type. To do this, sectors were first grouped into
clusters sharing similar complexity properties. Following classification, an optimisation process is
applied to weight the controller tasks according to the sector type (so as to evaluate the ωRoT, ωLC
and ωCNF weights). Table 7 in results section 8.5 contains the weighting coefficients that were used.
The classification results are presented in the following chapter. Further details on the AMWM can
be found in Annex D.



7. COMPLEXITY CLUSTERS
MUAC sectors have been classified using the complexity metrics contained in the I/D cards. Three
different complexity clusters were identified: 1- high complexity, 2 – medium complexity and 3 - low
complexity.
The classification process applied to MUAC sectors (phase 1 and phase 2) is fully detailed in
Annex E.
As its name indicates, MUAC airspace belongs to upper airspace. But, as many sectors are
labelled “Low” (e.g. EBMAWSL for West Low sector of Brussels) we have defined a very simple
functional sector typology based on the minimum and maximum levels of the sectors as defined in
the data environment. We then identified three functional sector types for the vertical plane:

• Low for sectors located above FL245 and below FL335;
• High for sectors located above FL335 (no upper limit);
• Low+High for sectors located above FL245 (no upper limit).

7.1. COMPLEXITY CLUSTER 1: APPEAR TO BE HIGH COMPLEXITY SECTORS

Generally, sectors that were classified as high complexity have the following characteristics:

• High value for the DIF indicator;
• Mix of attitudes (highest percentage of climbing traffic then descending and cruising
traffic);
• Rate of conflict (proximate pairs) higher than average;
• Volume reserved for military activity higher than average;
• Small sectors and short average transit time.
Complexity Cluster 1 is made up of 9 sectors. As shown in Figure 17, most of the sectors belong to
Brussels group (78%). The rest (22%) come from Hannover group. Figure 18 shows the
geographical location of the sectors.
Low level sectors account for a very high proportion of the total airspace within this cluster. This
validates the hypothesis that sectors in Complexity Cluster 1 (high complexity indicators)
correspond to sectors in the lower airspace: 89% of Cluster 1 sectors are low-level sectors,
including 78% of pure low-level sectors.



Hannover
22%
100%

0% h 0%
w

h

ig
ig
Lo

H
H

71%
w+
Lo

14% 14%

h
w

h

ig
ig
Lo

H
H
w+
Lo

Brussels
78%

Figure 17: Sector distribution by group and level within Complexity Cluster 1



EBMABWH EBMALNL EDYHALO
(WEST hi) (OLNO lo) (HAMBURG lo)

EBMAWSL
(WEST lo)

EDYSOLO
(SOLLING lo)
EBMAKOL
(KOKSY lo)

EBMANIL
(NICKY lo)

EBMALUX
(LUX)

EBMALXL
(LUX lo)

Figure 18: Location of the Cluster 1 sectors



7.2. COMPLEXITY CLUSTER 2: APPEAR TO BE MEDIUM COMPLEXITY SECTORS

Generally, sectors that were classified as medium complexity have the following characteristics:
• Moderate value for DIF indicator;
• Higher percentage of traffic in cruise than in climb or descent;
• Average rate of proximate pairs equally spread across the 3 categories;
• Traffic density lower than in the Cluster 1 sectors;
• Lower proportion of airspace volume reserved for military activity;
• Larger sector size than in Cluster 1 and longer average transit time.
Complexity Cluster 2 is made up of 10 sectors. As shown in Figure 19, 50% of the sectors belong
to the Hannover group and 50% of the sectors belong to the Brussels group. Figure 20 shows the
geographical location of the sectors.
It is the most varied cluster in terms of group and type distribution. This result is quite logical in the
sense that this cluster contains the “medium” complexity sectors and includes sectors which are on
the “borderline” of the other two clusters (high complexity and/or low complexity sectors).

60%
60%
Hannover 40%
40%
50% Brussels
50%
0%
0%
h
w

h

ig
ig
Lo

h
w

h
H

ig
H

ig
Lo

H
w+

H
w+
Lo

Lo




EDYYMNS
(MUNSTER)
EBMALNT
(OLNO)
EDYMNLO
EBMALNH (MUNSTER lo)
(OLNO hi)

EBMABHN
(WEST hi + OLNO hi)

EBMAWST
(WEST)
EDYYSOL
(SOLLING)

EDYYRHR
EBMABEH (RUHR)
(OLN hi + LUX hi)
EDYRHLO
(RUHR lo)


7.3. COMPLEXITY CLUSTER 3: APPEAR TO BE LOW COMPLEXITY SECTORS

Generally, sectors that were classified as low complexity have the following characteristics:

• Higher percentage of cruising traffic.
• Average rate of proximate pairs with slightly more opposite proximate pairs than the other
two clusters.
• High average speed of aircraft.
• Low proportion of airspace volume reserved for military activity.
• Large sectors with longer average transit time.
This cluster is made up of 11 sectors. As shown in Figure 21, most of them belong to DECO (55%)
then Hannover (36%) and the rest (9%) belong to Brussels. They are mainly of type Low+High or
High and rarely of type Low. Figure 22 shows the geographical location of the sectors.
As the sectors of Complexity Cluster 3 show low complexity properties, it is not surprising that most
of them are sectors in upper airspace. In effect, 27% of Cluster 3 sectors are pure High sectors
and 55% are of type Low+High.



100% Brussels
9%

Hannover 0% 0%
36%

h
w

h

ig
ig
Lo

H
H
w+
75%

Lo
25%

0%
h
w

h

ig
ig
Lo

H

33% 33% 33%
H
w+
Lo

h
w

h

ig
ig
Lo

H
H
w+
Lo

Deco
55%




EDYYCST
(COASTAL)
EDYYHAM
(HAMBURG)
EDYCOHI
(COASTAL hi)

EDYCOLO
(COASTAL lo)
EDYYEST
(HAM + SOL)

EDYESHI
(HAM hi + SOL hi)

EHDELTA
(DELTA) EDYMURH
(MUN + RUHR )
EHDELHI
(DELTA hi) EBMAUCE
(OLNO + LUX)
EHDELMD
(DELTA lo)




8. RESULTS
This section presents the I/D card results. Please note that all the I/D card results are available on
web link http://www.eurocontrol.int/eec/public/standard_page/2006_report_403.html#PHASE_2.
The following section provides an explanation of how to read an I/D card. The subsequent sections
provide a sample ID card from each sector group and complexity cluster.

8.1. SECTOR I/D CARD EXAMPLE

The computed complexity metrics are presented in an “I/D card” and encompass the following:
• Interactions between flights.
• Traffic mixture.
• Proximate Pairs.
• Number of levels crossed.
• Density.
• Mixture of aircraft types.
• Sector dimensions.
• Workload per flight.
All computed metrics have been calculated at sector level. Table 2 provides an explanation of how
to read an I/D card. A hash (#) in the name column indicates that the metric has been computed
using a mesh. Further details of the mesh and the indicator calculation methods can be found in
Annex F.



Table 2: How to read an I/D card

Name Description Example Explanation
Sector Name
A_Sector Sector name. Brussels
East High
Day
Date Day considered. 28/08/04 Data from 28/08/204
Opening time
Length Length of time when the sector 09:30 The sector was opened for a total time of
was open, usually the sum of non 9 hours and 30 minutes on the
consecutive periods. 28/08/2004.
Expressed in hours and minutes.
Flight Interactions
DIF per minute DIF stands for “Different 0.25 It represents the average number of
(#) Interacting Flows”. Captures potential interactions a flight can have
interacting flows and respective when crossing the sector. E.g. DIF=0.25
numbers of flights: crossing, means that an aircraft is likely to be
converging, etc. “involved” in 0.25 interactions or; on
average, one interaction for every four
flights.
Traffic Phase
Cruising traffic Percentage of aircraft that are in 59% On average, 59% of the traffic was in
cruise. cruise.
Climbing traffic Percentage of aircraft that are in 19% On average, 19% of the traffic was in
climb. climb.
Descending Percentage of aircraft that are in 22% On average, 22% of the traffic was in
traffic descent. descent.
Mix of traffic Value to show the mix of traffic: 57 The variety of the traffic mixture is
attitudes the higher the value the more moderate. A value between 0 and 100
mixed the traffic. indicates the “level” of mixture. 0 means
all traffic are either in cruise, in climb or in
descent and 100 means that half of the
flights are in climb and half in descent.
Presence of Proximate Aircraft Pairs
Normalised Occasions when two aircraft 8% On average, 8% of the flights have
Proximate (according to their filed flight formed a “proximate pair”.
Aircraft Pairs paths) have approached within 10
nautical miles horizontally and
1000 ft vertically of each other.
Expressed as a percentage.
Along track Count of the Proximate Aircraft 2% 2% of the flights have formed an “along
Pairs for which the angle between track” type proximate pair.
the two trajectories is less than
45°. Expressed as a percentage.
Crossing Count of the Proximate Aircraft 4% 4% of the flights have formed a “crossing”
Pairs which are neither along type proximate pair.
track nor opposite. Expressed as
a percentage.
Opposite Count of the Proximate Aircraft 2% 2% of the flights have formed an
Pairs for which the angle between “opposite” type proximate pair.
the two trajectories is more than
150°. Expressed as a percentage.
Traffic Evolution
Nb levels Number of FL crossed on average 1.98 An aircraft within the sector crossed, on
crossed by an aircraft (1FL=1000 feet). average, almost 2FL.



Density
Total cell Number of cells used to mesh the 487 487 cells (cubes) were used to mesh
number (#) sector. Brussels East High.
Cells with Percentage of cells with more 7% At least three aircraft have been present
more than 3 than 3 aircraft. in 7% of the Brussels East High cells
aircraft (#) (temporal and spatial aspects
considered).
Mixture of Aircraft Types
Average Average Ground Speed of all 433 On average, flights in this sector have an
Ground Speed aircraft in the sector. Determined average speed of 433 knots.
with respect to aircraft type,
attitude and altitude using
performance tables (BADA1).
Expressed in knots.
Std Deviation Captures the variability of the 24 The speeds vary by +/- 24 knots from the
of Avg Ground Ground Speed of all aircraft in the average value. The speeds vary between
Speed sector. Expressed in knots. 409kts and 457kts.
Sector Dimensions
Total Volume Sector volume computed from 715 121 The sector volume is 715 000 nm² * 100
airblock2 volumes. Expressed in ft.
nm² * 100 ft.
Average Percentage of the sector volume 15% The military activity within Brussels East
volume not not available due to restricted High, during the opening times, used
available areas or military activity (temporal 15% of the available sector volume.
aspect considered).
Average Time spent on average by a flight 07:07 On average, a flight spends 7 minutes
Transit Time within the sector. Expressed in and 7 seconds in Brussels East High.
minutes and seconds.
Traffic Rate
Traffic Average number of aircraft 8 On average, 8 aircraft entered Brussels
throughput per entering the sector during a 10 East High during each 10 minute period.
10 min minute period.

Workload
Workload per Average time for a controller to 50 The executive controller has to spend 50
flight deal with a flight in the sector. seconds, on average, to handle a flight in
Expressed in seconds. Brussels East High.

Std Deviation Variability of average time for a 3 The workload per flight is variable at +/-3
of Workload controller to deal with a flight in seconds: the flights require between 47s
per flight the sector. Expressed in seconds and 53s to be handled.

1 Base of Aircraft Data - a database of aircraft performance data.
2 Within the CFMU data an airblock defines a piece of airspace as a polygon with a max / min
2 Within the CFMU data an airblock defines a piece of airspace as a polygon with a max / min vertical range.
vertical range. A sector is defined as a set of airblocks.
A sector is defined as a set of airblocks.


Complexity study maastricht_upper_airspace

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