complexity study maastricht_upper_airspace

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The views expressed herein do not necessarily reflect the official views or policy of the Agency.

EUROPEAN ORGANISATION FOR THE SAFETY OF AIR NAVIGATION

EUROCONTROL EXPERIMENTAL CENTRE

A COMPLEXITY STUDY OF THE MAASTRICHT UPPER AIRSPACE CENTRE

EEC Report No. 403

Project COCA

Issued: February 2006

EUROCONTROL

REPORT DOCUMENTATION PAGE

Reference: EEC Report No. 403

Security Classification: Unclassified

Originator: EEC – NCD Network Capacity & Demand

Originator (Corporate Author) Name/Location: EUROCONTROL Experimental Centre Centre de Bois des Bordes B.P.15 F - 91222 Brétigny-sur-Orge Cedex FRANCE Telephone: +33 (0)1 69 88 75 00

Sponsor: EATM

Sponsor (Contract Authority) Name/Location: 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 Geraldine M Flynn

Claire Leleu (Isa Software) Brian Hilburn (Stasys)

Date 02/2006

Pages xii + 91

Figures 49

Tables 19

Annexes 7

References 7

EEC Contact

Project COCA

Task No. Sponsor

Period 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

Project COCA - Report No. 403 v

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.

EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre

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Project COCA - Report No. 403 vii

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


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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 Figure 19: Sector distribution by group and level within Complexity Cluster 2 .............................. 26 Figure 20: Location of the Cluster 2 sectors.................................................................................. 27 Figure 21: Sector distribution by group and level within Complexity Cluster 3 .............................. 28


Project COCA - Report No. 403 ix

Figure 22: Location of the Cluster 3 sectors.................................................................................. 29 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


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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


Project COCA - Report No. 403 xi

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.

http://www.eurocontrol.int/eec/public/standard_page/2004_note_04.html




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Project COCA - Report No. 403 1

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.


2 Project COCA - EEC Report No. 403

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.



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.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

BRUSSELS DECO HANNOVER

MUAC groups

ratio

(%)

OverflightsOverflights To/From Fringe AirportsInternalLandingDeparting

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:

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.

WEST

LUX OLNO



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.

WEST H KOKSY NICKY

REMBA REMBA



4.3.2. DECO Sector Group General Map

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.

Coastal

Delta



4.3.3. Hannover Sector Group

General Map

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.

Ruhr

Hamburg

Solling Munster



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) corresponds to the AIRAC cycle 255 and the 25th to 30th August 2004 (phase 2) corresponds to the AIRAC cycle 259.



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.

Minimum grade

Median grade

Maximum grade

Outlier

19

16 14

10

6

1

Mean grade



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.

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.

Traf

fic d

istri

butio

n si

mila

rity



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.

Figure 13: MUAC special and restricted areas

TRA-WESER

TRA-EH

TRA-North-B

TRA-CBA 1

TRA-16

TRA- Frankenal

TRA-TSA 22

TRA-TSA 20

TRA-Sachen

TRA-Melcken-burg 2

TRA-Lauter 2

TRA-NL2

TRA-EDD 100



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 military activity 62 37 39

Percentage of daily sectors affected by military activity (%) 81% 86% 63%

Military activity duration for the two phases (h) 117 250 42

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.



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.

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

Rel

ativ

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r of s

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rs

Brussels Deco HannoverGroup

>75% and <=100%>50% and <=75%>25% and <=50%>0% and <=25%

Volume not available



08:00 08:10 08:20 08:30 08:40 08:50 09:00 09:10 09:20 09:30 09:40 09:50ExcessiveHigh √ √ √Comfortable √ √ √ √ √ √ √ √ √RelaxedUnder 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 08:556 Extremely High √5 √ √ √ 4 √ √ √ √ √ 3 √ √ √21 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.



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

Hannover22%

Brussels78%

100%

0% 0%

Low

Low+High

High71%

14% 14%

Low

Low+High

High



Figure 18: Location of the Cluster 1 sectors

EBMABWH (WEST hi)

EBMALNL (OLNO lo)

EBMALUX (LUX)

EBMALXL (LUX lo)

EBMAWSL (WEST lo)

EBMAKOL (KOKSY lo)

EBMANIL (NICKY lo)

EDYSOLO (SOLLING lo)

EDYHALO (HAMBURG lo)



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).

Brussels50%

Hannover50%

0%

40%

60%

Low

Low+High

High

40%

60%

0%

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Low+H

ighHigh

Brussels50%

Hannover50%

0%

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Low+H

ighHigh





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.

EBMALNT (OLNO)

EBMABEH (OLN hi + LUX hi)

EDYRHLO (RUHR lo)

EDYYRHR (RUHR)

EDYYSOL (SOLLING)

EDYYMNS (MUNSTER)

EDYMNLO (MUNSTER lo)

EBMAWST (WEST)

EBMALNH (OLNO hi)

EBMABHN (WEST hi + OLNO hi)




Hannover36%

Brussels9%

Deco55%

0%

100%

0%

Low

Low+High

High

33%33%33%

Low

Low+High

High

0%

75%

25%

Low

Low+High

High




EDYCOHI (COASTAL hi)

EDYMURH (MUN + RUHR )

EDYYCST (COASTAL) EDYYHAM

(HAMBURG)

EHDELHI (DELTA hi) EBMAUCE

(OLNO + LUX)

EHDELTA (DELTA)

EDYYEST (HAM + SOL)

EDYESHI (HAM hi + SOL hi)

EDYCOLO (COASTAL lo)

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

was open, usually the sum of non consecutive periods. Expressed in hours and minutes.

09:30 The sector was opened for a total time of 9 hours and 30 minutes on the 28/08/2004.

Flight Interactions DIF per minute (#)

DIF stands for “Different Interacting Flows”. Captures interacting flows and respective numbers of flights: crossing, converging, etc.

0.25 It represents the average number of potential interactions a flight can have when crossing the sector. E.g. DIF=0.25 means that an aircraft is likely to be “involved” in 0.25 interactions or; on average, one interaction for every four flights.

Traffic Phase

Cruising traffic Percentage of aircraft that are in cruise.

59% On average, 59% of the traffic was in cruise.

Climbing traffic Percentage of aircraft that are in climb.

19% On average, 19% of the traffic was in climb.

Descending traffic

Percentage of aircraft that are in descent.

22% On average, 22% of the traffic was in descent.

Mix of traffic attitudes

Value to show the mix of traffic: the higher the value the more mixed the traffic.

57 The variety of the traffic mixture is moderate. A value between 0 and 100 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 Proximate Aircraft Pairs

Occasions when two aircraft (according to their filed flight paths) have approached within 10 nautical miles horizontally and 1000 ft vertically of each other. Expressed as a percentage.

8% On average, 8% of the flights have formed a “proximate pair”.

Along track Count of the Proximate Aircraft Pairs for which the angle between the two trajectories is less than 45°. Expressed as a percentage.

2% 2% of the flights have formed an “along track” type proximate pair.

Crossing Count of the Proximate Aircraft Pairs which are neither along track nor opposite. Expressed as a percentage.

4% 4% of the flights have formed a “crossing” type proximate pair.

Opposite Count of the Proximate Aircraft Pairs for which the angle between the two trajectories is more than 150°. Expressed as a percentage.

2% 2% of the flights have formed an “opposite” type proximate pair.

Traffic Evolution Nb levels crossed

Number of FL crossed on average by an aircraft (1FL=1000 feet).

1.98 An aircraft within the sector crossed, on average, almost 2FL.



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 vertical range. A sector is defined as a set of airblocks.

Density Total cell number (#)

Number of cells used to mesh the sector.

487 487 cells (cubes) were used to mesh Brussels East High.

Cells with more than 3 aircraft (#)

Percentage of cells with more than 3 aircraft.

7% At least three aircraft have been present in 7% of the Brussels East High cells (temporal and spatial aspects considered).

Mixture of Aircraft Types

Average Ground Speed

Average Ground Speed of all aircraft in the sector. Determined with respect to aircraft type, attitude and altitude using performance tables (BADA1). Expressed in knots.

433 On average, flights in this sector have an average speed of 433 knots.

Std Deviation of Avg Ground Speed

Captures the variability of the Ground Speed of all aircraft in the sector. Expressed in knots.

24 The speeds vary by +/- 24 knots from the average value. The speeds vary between 409kts and 457kts.

Sector Dimensions Total Volume Sector volume computed from

airblock2 volumes. Expressed in nm² * 100 ft.

715 121 The sector volume is 715 000 nm² * 100 ft.

Average volume not available

Percentage of the sector volume not available due to restricted areas or military activity (temporal aspect considered).

15% The military activity within Brussels East High, during the opening times, used 15% of the available sector volume.

Average Transit Time

Time spent on average by a flight within the sector. Expressed in minutes and seconds.

07:07 On average, a flight spends 7 minutes and 7 seconds in Brussels East High.

Traffic Rate Traffic throughput per 10 min

Average number of aircraft entering the sector during a 10 minute period.

8 On average, 8 aircraft entered Brussels East High during each 10 minute period.

Workload

Workload per flight

Average time for a controller to deal with a flight in the sector. Expressed in seconds.

50 The executive controller has to spend 50 seconds, on average, to handle a flight in Brussels East High.

Std Deviation of Workload per flight

Variability of average time for a controller to deal with a flight in the sector. Expressed in seconds

3 The workload per flight is variable at +/-3 seconds: the flights require between 47s and 53s to be handled.

2 Within the CFMU data an airblock defines a piece of airspace as a polygon with a max / min vertical range. A sector is defined as a set of airblocks.



8.2. SELECTED I/D CARD RESULTS

The examples below show one set of I/D cards for one sector from each Complexity Cluster and from each sector group. Each set has a card for weekdays6, Saturday and Sunday from phase 1 and phase 2. Complexity Cluster 1

Table 3: Brussels West Low / NICKY Low and KOKSY Low I/D Card

Sector Name Brussels West Low/Nicky Low and Koksy Low

Phase 1 Phase 2

Day Weekday Saturday Sunday Weekday Saturday Sunday Date 23-Apr-04 24-Apr-04 25-Apr-04 26-Aug-04 28-Aug-04 29-Aug-04Opening time Length (hh:mm) 7:19 4:49 6:40 10:19 8:49 9:00Flight Interactions DIF per minute 0.204 0.174 0.173 0.142 0.159 0.147Traffic Phase Cruising traffic (%) 17% 15% 12% 7% 9% 6%Climbing traffic (%) 47% 51% 56% 51% 49% 50%Descending traffic (%) 35% 34% 31% 42% 42% 44%Mix of traffic attitudes 94 93 89 98 98 99Presence of Proximate Aircraft Pairs Normalised Proximate Aircraft Pairs (%) 11% 4% 5% 9% 6% 6%Along track (%) 7% 3% 3% 5% 4% 5%Crossing (%) 3% 1% 2% 1% 1% 1%Opposite (%) 1% 0% 0% 2% 1% 1%Traffic Evolution Nb levels crossed 0.75 0.72 0.75 0.83 0.75 0.81Density Total cell number 390 390 390 446 446 446Cells with more than 3 aircraft (%) 7% 5% 6% 4% 6% 5%Mixture of Aircraft Types Average Ground Speed (knots) 413 415 418 412 414 415Std Deviation of Avg Ground Speed (knots) 30 30 24 28 24 20Sector Dimensions Total Volume (nm²*100ft) 572,309 572,309 572,309 654,943 654,943 654,943 Average volume not available (%) 38% 0% 0% 34% 0% 0%Average Transit Time (mm:ss) 07:07 06:25 06:56 08:02 07:41 07:15Traffic Rate Traffic throughput per 10 min 8 8 8 8 9 9 Workload Workload per flight (s) 55 52 52 55 53 54 Std Deviation of Workload per flight (s) 5 4 4 6 4 4 Comments

Brussels West Low (see Table 3) is typically a Complexity Cluster 1 sector because this sector belongs to “Low” (according to MUAC) airspace (between FL245 and FL335).

6 The weekday with the longest opening time was selected.



The high values for the DIF per minute indicator (related to the possible interactions between flights) can be explained by:

• The high number of aircraft within this sector (on average, 48 aircraft enter this sector per hour) which when combined with a short average transit time suggests a high workload.

• The high number of crossing flows within this sector (Northbound/Southbound: flights from/to Paris and from/to Amsterdam. Westbound/Eastbound: flights from/to London and from/to LUX, and south German airports in summer) combined with the small size of the sector.

The mix of traffic attitudes indicator is very high due to the low proportion of flights in cruise within this type of sector. The proportion of climbing flights is slightly higher than the proportion of descending flights because the north to south flow (flights departing from English airports) has more aircraft than the south to north one (flights arriving at English airports).

The number of proximate pairs is high during the weekday periods (about 10%) but lower during the weekend periods. This is certainly due to military inactivity during the weekends (and therefore more room for civil aircraft to manoeuvre).

The density of this sector is quite high (more than 4% of the cells with more than 3 aircraft). This can be explained by the small size of the sector (average transit time around 6.5 to 8 minutes) and the concentration of hotspots (busy navaids include BARTU, DENUT, GILOM, KEGIT).

The workload per aircraft value is high when compared to the sectors of other clusters. This reinforces the complexity of managing the flights within this sector: from 48 s to 61 s are required to handle a flight within Brussels West Low/ KOKSY NICKY Low sectors.



Complexity Cluster 2 Table 4: Solling I/D Card

Sector Name SOLLING

Phase 1 Phase 2

Day Weekday Saturday Sunday Weekday Saturday Sunday Date 23-Apr-04 24-Apr-04 25-Apr-04 26-Aug-04 28-Aug-04 29-Aug-04Opening time Length (hh:mm) 10:30 10:49 9:10 8:30 9:19 7:49Flight Interactions DIF per minute 0.062 0.058 0.047 0.098 0.075 0.081Traffic Phase Cruising traffic (%) 54% 59% 56% 51% 55% 51%Climbing traffic (%) 20% 17% 21% 24% 23% 24%Descending traffic (%) 26% 24% 23% 25% 22% 25%Mix of traffic attitudes 62 55 59 65 60 66Presence of Proximate Aircraft Pairs Normalised Proximate Aircraft Pairs (%) 5% 5% 5% 7% 7% 6%Along track (%) 1% 2% 1% 0% 1% 1%Crossing (%) 2% 2% 2% 4% 4% 3%Opposite (%) 2% 1% 2% 3% 2% 2%Traffic Evolution Nb levels crossed 0.41 0.36 0.53 0.41 0.38 0.45Density Total cell number 635 635 635 635 635 635Cells with more than 3 aircraft (%) 3% 2% 2% 2% 2% 1%Mixture of Aircraft Types Average Ground Speed (knots) 428 431 431 423 430 428Std Deviation of Avg Ground Speed (knots) 34 28 27 33 30 32Sector Dimensions Total Volume (nm²*100ft) 931,763 931,763 931,763 931,763 931,763 931,763 Average volume not available (%) 0% 0% 0% 0% 0% 0%Average Transit Time (mm:ss) 07:19 07:05 09:33 07:01 07:08 07:09Traffic Rate Traffic throughput per 10 min 8 7 7 9 8 8 Workload Workload per flight (s) 44 43 45 45 44 45 Std Deviation of Workload per flight (s) 5 4 6 5 4 5

Comments

In general, the complexity values for Complexity Cluster 2 are lower than those from Cluster 1.

Solling sector (see Table 4) is a “mixed” sector vertically extended over low and high airspace. It is typically a Complexity Cluster 2 sector because:

• The traffic level is as high as in Cluster 1 but includes significantly more cruising traffic. As a consequence,

- the DIF per minute indicator is lower than in Complexity Cluster 1 (less than 0.12), - the mix of traffic attitudes is lower than the values in Cluster 1 (about 61) due to the

higher presence of cruising flights in Solling,



- in general, the proportion of proximate pairs is lower and the values are quite balanced between weekdays and week-end days (no military impact in Solling).

• The Solling sector is larger than the Cluster 1 sectors for roughly the same number of flight entries (about 48 per hour),

• The workload per flight value is lower than in Cluster 1 with between 39 s and 51 s required to handle a flight.

Complexity Cluster 3

Table 5: Delta High I/D Card

Sector Name DELTA HIGH

Phase 1 Phase 2

Day Weekday Saturday Sunday Weekday Saturday Sunday Date 22-Apr-04 24-Apr-04 25-Apr-04 27-Aug-04 28-Aug-04 29-Aug-04Opening time Length (hh:mm) 6:49 6:30 4:30 11:30 8:10 5:30Flight Interactions DIF per minute 0.008 0.013 0.004 0.043 0.045 0.036Traffic Phase Cruising traffic (%) 69% 62% 61% 67% 58% 64%Climbing traffic (%) 19% 15% 22% 18% 16% 14%Descending traffic (%) 12% 23% 17% 15% 27% 22%Mix of traffic attitudes 41 51 52 44 56 48Presence of Proximate Aircraft Pairs Normalised Proximate Aircraft Pairs (%) 4% 6% 6% 8% 8% 8%Along track (%) 0% 1% 0% 3% 3% 1%Crossing (%) 1% 2% 3% 2% 3% 4%Opposite (%) 2% 3% 3% 3% 3% 3%Traffic Evolution Nb levels crossed 0.17 0.31 0.26 0.24 0.34 0.28Density Total cell number 1021 1021 1021 1022 1022 1022Cells with more than 3 aircraft (%) 2% 1% 1% 3% 2% 1%Mixture of Aircraft Types Average Ground Speed (knots) 439 434 438 437 434 437Std Deviation of Avg Ground Speed (knots) 26 28 26 29 28 32Sector Dimensions Total Volume (nm²*100ft) 1,498,421 1,498,421 1,498,421 1,499,376 1,499,376 1,499,376 Average volume not available (%) 16% 0% 0% 4% 0% 0%Average Transit Time (mm:ss) 13:25 13:52 13:49 13:31 13:45 13:09Traffic Rate Traffic throughput per 10 min 6 6 6 7 6 7 Workload Workload per flight (s) 39 41 40 41 43 42 Std Deviation of Workload per flight (s) 4 5 5 6 5 7



Comments

Delta High sector (see Table 5) belongs to Complexity Cluster 3 and is representative of the low complexity cluster for the following reasons:

• The strong presence of overflights in high level sectors explains:

- the very low number of possible interactions (DIF per minute value is less than 0.07),

- the very low number of levels crossed, - the low value of the mix of traffic attitudes indicator, - the high value of average ground speed.

• The large size of this sector is responsible for:

- the small value for cell density, - the high value for average transit time (more than 13 minutes), - the low workload value (between 35 s and 49 s to handle a flight).

8.3. COMPARISON OF I/D CARD RESULTS BEFORE AND AFTER THE BRUSSELS SECTOR CHANGE

To test the effect of the Brussels sector change, we conducted specific tests to compare the same day‘s traffic using phase 1 and phase 2 sector configurations. Although a direct comparison is not possible in absolute terms it was agreed that the same traffic sample would be superimposed upon the old and new sector dimensions. The selected date was, 25/08/2004.

Sector configuration 4 was selected to represent the phase 1 airspace (see Figure 23) and configuration 5.3 was selected to represent the phase 2 airspace (see Figure 24).

WST H LNO H LUX H4

WST L LNO L LUX L

EEBBMMAABBHHNN+ EEEBBBMMMAAALLLUUUXXX EEBBMMAAWWSSLL+EEBBMMAALLNNLL

Figure 23: Brussels Phase 1 configuration

KOK H NIK H LNO H LUX H5.3

KOK L NIK L LNO L LUX L

EEEBBBMMMAAABBBHHHNNN + EEEBBBMMMAAALLLUUUXXX

EEEBBBMMMAAAKKKOOOLLL+ EEEBBBMMMAAANNNIIILLL+ EEEBBBMMMAAALLLNNNLLL

Figure 24: Brussels Phase 2 configuration

Table 6 contains the I/D card results for the ‘before’ and ‘after’ configurations.



Table 6: Comparison of airspace before and after the Brussels sector change

COMPARISON Before/After Split

Sector Names OLNO and WEST HIGH /

OLNO, KOKSY and NICKY HIGH

OLNO LOW LUX TOTAL WEST LOW

KOKSY LOW

NICKY LOW

Env before after before after before after before after after AIRAC cycle 255 259 255 259 255 259 255 259 259Opening time Length (hh:mm) 7:00 7:00 7:00 7:00 7:00 7:00 7:00 7:00 7:00Flights interaction DIF (-) 0.13 0.13 0.16 0.15 0.13 0.12 0.22 0.23 0.22Traffic Phase Cruising traffic (%) 52% 43% 23% 21% 36% 36% 17% 18% 38%Climbing traffic (%) 31% 38% 41% 43% 25% 24% 52% 52% 37%Descending traffic (%) 18% 18% 36% 36% 39% 40% 31% 30% 24%Mix of traffic attitudes (%) 63 71 92 93 80 79 90 88 78Presence of Proximate Aircraft Pairs Normalised Proximate Aircraft Pairs (%) 15% 15% 4% 5% 11% 11% 8% 7% 8%Along track (%) 6% 7% 2% 2% 3% 4% 5% 6% 3%Crossing (%) 6% 6% 2% 4% 5% 4% 2% 1% 1%Opposite (%) 2% 2% 0% 0% 3% 3% 2% 1% 4%Traffic Evolution Nb levels crossed 0.27 0.33 0.69 0.73 0.53 0.52 0.76 0.78 0.54Density Total cell number 707 753 216 199 564 478 390 180 267Cells with more than 3 aircraft (%) 8% 10% 5% 5% 4% 3% 9% 10% 8%Mixture of aircraft types Average Ground Speed (knots) 434 430 414 414 420 421 417 421 417Std Deviation of Avg Ground Speed (knots) 25 28 38 35 32 31 29 20 39Sector dimensions Total Volume (nm²*100ft) 1037455 1104320 317570 292681 827225 701150 572309 263492 391452Average volume not available (%) 24% 27% 19% 17% 59% 54% 32% 21% 54%Average Transit Time (mm:ss) 13:48 14:03 5:10 05:08 07:48 07:43 07:18 06:05 06:30Traffic Rate Traffic throughput per 10 min 8 8 6 5 7 7 9 7 5Workload Workload per flight (s) 47 48 51 52 53 53 54 54 51Std Deviation of Workload per flight (s) 8 8 4 6 7 7 6 6 10


Project NCD-COCA - Report No. 403 39

It should be noted that this comparison is based on simulated traffic. The August traffic sample was superimposed on the April environment. It was agreed that this was a fair method to compare the effect of the sector changes.

The significant changes were identified on indicators related to:

• the sector dimensions: quite logically, because the sectors shapes have changed,

• the traffic phases: flights’ attitudes and number of levels crossed, are linked to the change in sector dimensions. The sectors still have the same min/max vertical limits but have changed in terms of surface area.

For most of the sectors, the DIF per minute indicator, the proximate pairs distribution indicators and the mixture of aircraft types indicators remained stable.

The sectors least impacted are LUX, OLNO Low, then OLNO and West High collapsed. The most impacted is West Low which became the NICKY Low and KOKSY Low after the change.

Here, in detail, are the changes sector by sector, see Figure 6 and Figure 7 for maps of the change.

LUX.

This sector belongs to both low and high airspaces.

The sector has decreased in terms of volume (-15%) because the boundary with the West sector has been shifted to the east. Logically, the number of cells used to mesh the sector has decreased. The percentage of volume not available due to military activity has also decreased because the boundary shift reduced the superimposed volume of the military area TRA south B.

All the other indicators: DIF, traffic phases, traffic mixture and traffic rate and workload per flight remained stable.

To conclude, the reorganisation had no impact linked with the complexity on the LUX sector evaluated here.

OLNO Low.

The conclusions are exactly the same as for LUX sector (except the volume of OLNO Low decreased by a smaller proportion -8% only). The number of levels crossed increased slightly between April and August.

The proportion of crossing proximate pairs has slightly increased and may be due to the reduction of airspace volume.

The impact on the complexity of OLNO Low sector after the Brussels airspace reorganisation was small.


40 Project NCD-COCA - EEC Report No. 403

OLNO and West High collapsed.

This sector belongs to high airspace.

After the reorganisation, the volume increased by about 6%. In the same way, the total cell number and the average transit time increased. As a direct consequence:

• the average volume not available slightly increased (from 24% to 27%) because the boundary between West and LUX which was moved through a military sector (TRA south B). A larger proportion of this military area now belongs to the new sector,

• the proportion of cells with more than 3 aircraft increased by 2 percentage points (because the major flows - departing flights from English airports - stay in the sector longer).

The mix of traffic attitudes has increased from 63 to 71 to reflect the changes in the distribution of the flight phases. In phase 2 we observed that the percentage of flights in climb increased from 31% to 38% while the percentage of flights in cruise decreased from 52% to 43%. The percentage of flights in descent remained stable at 18%. The layer of airspace added to the sector (previously a part of LUX sector) may be responsible for the change in flight profiles within the airspace. As a consequence, the number of levels crossed increased in relation to the reduction in the number of flights in cruise.

West Low has been split into KOKSY Low and NICKY Low

These sectors belong to the lower airspace.

The West Low sector has been enlarged and split into KOKSY Low and NICKY Low.

Although absolute direct comparison between the sectors before and after the change is difficult due to the differences in coverage, it was deemed reasonable to compare the NICKY and WEST Low sectors as the controller tasks for the REMBA area are now the responsibility of the NICKY sector controller. If we compare the results for Brussels West Low sector with the new NICKY Low sector we can see a positive effect of the airspace reorganisation in the REMBA area: reduction of around 30% in the number of climbing a/c, a reduction of approx. 25% of descending a/c, and a decrease of around 30% of number of FL's crossed.

There are some other interesting results: KOKSY Low seems to have roughly the same properties as West Low. NICKY Low seems to have a lower complexity than West Low and KOKSY Low.

• The volume not available in West Low was 32%, it has decreased in KOKSY Low (21%) but increased in NICKY Low (54%);

• The throughput per 10 min in West Low was 9 and decreased for both sub sectors: it now equals 7 in KOKSY Low and 5 in NICKY Low. This is logical because there is less traffic in each of the two sectors replacing West Low;

• The DIF indicator remained stable over the three sectors;

• West Low and KOKSY Low have the same distribution over the three categories (climb/cruise/descent). NICKY Low has roughly two times more flights in cruise, and smaller proportion of flights in climb/descent;

• The number of levels crossed is similar between West Low and KOKSY Low. For NICKY Low, this number has decreased.



8.4. HOTSPOT MAPS

The hotspot maps may help to locate the zones where the interactions between flights are particularly high over a day. The following maps have been produced with the traffic from the 28/08/04. They represent projections on the horizontal plane of Brussels sectors between FL245 and FL335 (see Figure 25) and between FL335 and FL450 (see Figure 26). Each square (7.5 nm x 7.5 nm) represents the vertical projection of the cells used to mesh the sectors. The colour of the squares is linked with the maximum value of the DIF in each cell over all the flight levels and all the time period. The names within the squares refer to the navaid names identified as belonging to the cells. They have been intentionally written in the middle of each for better readability. When more than one navaid belongs to a cell, one of them has been randomly chosen.

(Y 1

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EXIP

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DIF 0DIF 1DIF 2DIF 3DIF 4DIF 5DIF 6DIF 7

KOKSY

OLNO

LUX

NICKY

Figure 25: Hotspots map for Brussels sectors between FL245 and FL335



EXIP

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DIF 0DIF 1DIF 2DIF 3DIF 4DIF 5DIF 6DIF 7

KOKSY

OLNO

LUX

NICKY

Figure 26: Hotspots map for Brussels sectors between FL335 and FL450

In the majority of Brussels sectors above FL335 (Figure 26), we observe that the interaction values are low (DIF=0) to moderate (DIF=2). The number of interactions is lower in the sectors labelled as “high” (belonging to airspace above FL335). This has already been discussed in paragraph 8.2, when explaining the complexity cluster specifics. Most of the Brussels “High” sectors belong to Complexity Cluster 2 and have low DIF per minute indicator values.

Higher numbers of interactions are located in the sectors belonging to “Low” airspace (between FL245 and FL335), as shown in Figure 25. The range of DIF values is well spread with specific concentration of high values in the NICKY sector. The REMBA “cell” appears to be free from interactions (grey square), which was one of the objectives of the space reorganisation. The high values of DIF are located in NICKY but far enough from the LUX and OLNO boundaries, which may give the controller more time to handle the flights before transferring them.



8.5. WORKLOAD RESULTS

Following the classification process described in Chapter 7, we applied the AMWM method (described in chapter 6) to obtain the workload coefficients.

Workload evaluation

After the optimisation process had been applied to each complexity cluster, the workload evaluation gave the coefficients (weights ω), shown in Table 7, which were used in Equation 1 for the two phases. It should be noted that these values cannot be interpreted as actual controller task durations. They are computed values that reflect differences in workload associated with diverse sector types.

Table 7: Complexity Cluster Coefficients

Phase 1

Complexity Cluster RoT LC CNF

1 43 10 49

2 38 10 47

3 36 10 47

Phase 2

Complexity Cluster RoT LC CNF

1 43 10 51

2 39 11 48

3 36 12 52

The resulting workload values computed for the sectors of each complexity cluster are represented in Figure 27. The workload values are spread according to the complexity of each cluster (from Cluster 1: high complexity/high workload to Cluster 3: low complexity/low workload). As seen in the clustering results, the workload values for Cluster 2 sectors are more variable than the other clusters because Cluster 2 is an intermediate class between high and low complexity.



Figure 27: Workload values per Complexity Cluster

8.6. DYNAMIC RESULTS – REPORTED WORKLOAD RESULTS

8.6.1. Phase 1

Preliminary data analysis of the phase 1 reported workload results showed two causes for concern. First was a “truncated” distribution, in which controller ratings were bunched around the middle (“comfortable”) level. This can be seen in Figure 28 which shows the distribution for DECO. There is not a single case of reported extreme (“excessive”) workload. One possibility was that controllers might be reluctant to rate workload as “excessive.” In any event, a rating scale that provides a narrow set of responses does not provide the richest data available.



A second concern was the interval between workload ratings. To minimise interruption, in phase 1 it was decided to set this at 20 minutes. Ultimately, it was decided that such a lengthy period between ratings risks insensitivity to complexity fluctuations, which might appear on a much more frequent basis.

0

20

40

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1 2 3 4 5

Rating

Cum

freq

uenc

y (n

r)

Figure 28: DECO Reported Workload distribution, phase 1

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50

Cum

ulat

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54321

workload rating (1-5)


Figure 29: Reported workload (cumulative percent) across the three sector groups, phase 1

Table 8: Reported workload (cumulative percent) across the three sector groups, phase 1


5 0.3 0.0 0.9 4 9.8 23.5 23.4 3 45.0 44.2 44.1 2 40.0 28.8 26.4 1 4.9 3.5 5.1



Notice in Figure 29 and Table 8 that the phase 1 distribution was very “peaked,” with a slight negative skew - that is, controllers reported the highest level of workload very infrequently (less than 1% of all reports). The pattern across sector groups showed no clear differences.

8.6.2. Phase 2

Data quality

Workload data seemed more robust from the phase 2 data collection. As shown in Figure 30 and Table 9 below, data distributions generally followed a more normal “bell curve” shape than they did in phase 1. Whereas DECO (n=1831) and Hannover (n=2565) appear quite normal in shape, Brussels sector group (n=3181) tended toward lower workload ratings. Obviously, these data should not be used to compare workload across sector groups.

0

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654321

Workload rating (1-6)


Figure 30: Reported workload (cumulative percent) across the three sector groups, phase 2

Table 9: Reported workload (cumulative percent) across the three sector groups, phase 2

BRUSSELS DECO HANNOVER6 0.3 1.0 1.1 5 2.3 5.8 9.1 4 12.4 20.5 20.1 3 29.8 35.3 35.4 2 40.1 26.9 27.8 1 15.5 10.3 6.6

Workload: Day-of-week differences across sector groups

It was clear that traffic load varied by day-of-week (see paragraph 5.3.2), and that three patterns could be clearly distinguished, corresponding to weekday, Saturday and Sunday periods. It was wondered whether a similar pattern would emerge for the reported workload data. Notice that there was no reason to suspect that this would have been the case. Traffic load, after all, is only one of a number of factors that drive the controllers’ experience of workload. Nonetheless, it seemed an interesting possibility to evaluate.



Figure 31 contains three graphs that show the reported workload ratings (in terms of cumulative percentage), for each of the three weekly periods. The way to interpret these graphs is that a given line (e.g. blue) captures all workload data from a given weekly period (Saturday, in this example). A line that is skewed rightward (i.e., with a peak toward the right) indicates a greater number of high workload ratings.

Figure 31: Reported Workload ratings for each sector group (cumulative percentage)

The graphs show no clear differences between the weekly periods.

Workload: Time-of-day fluctuations across sector groups

Another question was whether the reported workload would be sensitive to known and assumed fluctuations over the course of the day in traffic density/complexity. It is known that MUAC faces several peak periods a day, and it was hoped that the reported workload data would be sensitive to these.

Raw ratings were graphed as a function of time, for each of the three sector groups and for each weekly period; see Figure 32 to Figure 34. Notice that ratings are not standardised, and can differ from one controller to the next. Thus what one controller rates a “6” might be rated as a “5” by another controller.

Brussels median workload over the day, for the three weekly periods

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Figure 32: Brussels median workload for weekdays, Saturday and Sunday

Brussels sector group

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DECO median workload over the day, for the three weekly periods

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Figure 33: DECO median workload for weekdays, Saturday and Sunday

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In Figure 35 to Figure 37 the traffic load is superimposed on the reported workload results. The figures show that the workload peaks correspond fairly well to the increases in traffic at various times of day. Notice that the time on the x-axis runs from 0700 to 1900 local time in each graph. Further, the correspondence between the reported workload and traffic load differs across days of the week: weekday patterns are different from those of either Saturday or Sunday. For that reason, the time-of-day median workload values are presented separately for the three periods.

Figure 35: Reported workload as a function of time-of-day and traffic load, Brussels

Figure 36: Reported workload as a function of time-of-day and traffic load, DECO

Figure 37: Reported workload as a function of time-of-day and traffic load, Hannover

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During a review of the results, it was speculated that the number of open sectors might influence the reported workload. This interaction is shown in Figure 38 to Figure 40 which each contain three graphs (corresponding to weekday, Saturday and Sunday values, respectively). Number-of-open-sectors is superimposed on each graph, as a sliding hourly average over the day. There does, in fact, seem to be a relationship between workload and number-of-open-sectors, at least for weekdays (note that weekday averages are based on larger data sets).

Figure 38: Reported workload as a function of time-of-day and number of open sectors, Brussels

Figure 39: Reported workload as a function of time-of-day and number of open sectors, DECO

Figure 40: Reported workload as a function of time-of-day and number of open sectors, Hannover

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8.7. COMPLEXITY FACTORS ASSOCIATED WITH HIGH WORKLOAD

One chief aim in collecting workload data was to determine whether there was a systematic relationship between high workload, and certain specific complexity factors. For this reason, controllers were asked to identify, for any cases in which they rated workload either 5 or 6 (i.e. the top of the workload scale), all of the complexity factors that were relevant during that period. From the list of more than two dozen factors (plus provision for “others” to be identified), controllers could check all that applied7. This is evaluated below as a function of sector group (see section 8.7.1) and weekly period (8.7.2).

7 The initial list of candidate complexity factors was based on literature review and synthesis, and has been iteratively refined through controller interviews, etc. This current list appears in Annex G.



8.7.1. Complexity Factors, by Sector Group

Table 10 to Table 12 show the top ten factors identified more than once for high workload (i.e. 5 or 6) periods (irrespective of duration), for the three sector groups. Certain similarities appear across the sector groups. For instance, “mix of climbing and descending” aircraft is cited as either the #1 or #2 factor across all three sector groups. “High number of aircraft” and “Several traffic flows converging at the same point” were also often-cited factors across all three sector groups. There are, however, at least a few slight differences between the sector groups in terms of the factors cited. Brussels controllers tended to cite R/T congestion and crossing points close to sector boundaries, whereas Hannover and DECO identified military areas as critical factors.

Table 10: Brussels self-reported complexity factors associated with high workload (n=48)

Factor Rank

Mix of climbing and descending traffic flows 1 Several traffic flows converging at the same point 2 Traffic bunching 3 High number of aircraft 4 R/T congestion 5 Multiple crossing points in sector 6 Mix of climbing or descending flights in cruise 7 Crossing points close to boundaries 8 Merging of arrival flows 9 Mix of high and low performance aircraft 10

Table 11: DECO self-reported complexity factors associated with high workload (n=48)

Factor Rank

Mix of climbing & descending traffic flows 1 Several traffic flows converging at same point 2 High number of aircraft 3 Turbulence / weather 4 Merging of arrival flows 5 Military or other restricted area 6 Multiple crossing points in sector 7 R/T congestion 8 Mix of climbing or descending flights in cruise 9 Traffic bunching 10



Table 12: Hannover self-reported complexity factors associated with high workload (n=100)

Factor Rank

Mix of climbing and descending aircraft 1 High number of aircraft 2 Several traffic flows converging at the same point 3 Traffic bunching 4 Multiple crossing points in sector 5 Mix of climbing and descending traffic flows 6 R/T congestion 7 Pilots not listening/complying with R/T 8 Merging of arrival flows 9 Interface with next sector/centre 10

8.7.2. Complexity Factors, by Weekly Period

As shown in Table 13 to Table 158, when the factors are broken out by weekly period (i.e. either weekdays, Saturday or Sunday) the influence of the weekly period seemed minimal—the same factors generally appeared, and in the same order (e.g. notice that “mix of climbing and descending traffic flows” was the most-cited factor, across all weekly periods).

Table 13: Weekday self-reported complexity factors associated with high workload

WEEKDAYS Cumulative percent (%)

Mix of climbing and descending traffic flows 15.4

Several traffic flows converging at the same point 11.1

High number of aircraft 10.6

Traffic bunching 6.8

Multiple crossing points in sector 6.3

R/T congestion 5.7

Merging of arrival flows 5.3

Mix of climbing or descending flights in cruise 5.0

Pilots not listening to R/T 4.8

Turbulence/weather 4.1

8 The cumulative percent in Table 13 to Table 15 do not equal 100% because they only include the top ten self-reported factors.



Table 14: Saturday self-reported complexity factors associated with high workload

SATURDAY Cumulative percent (%)






Mix of high and low performance aircraft 7.8

R/T congestion 7.8


Crossing points close to boundaries 4.3


Table 15: Sunday self-reported complexity factors associated with high workload

SUNDAY Cumulative percent (%)







R/T congestion 7.0


Pilots not listening to R/T 6.1

Mix of high and low performance aircraft 5.2

8.8. COMPLEXITY PRECURSORS: FOCUS GROUP RESULTS

A focus group session was held at MUAC on 2 Oct 2004, using a subset of active MUAC controllers. Part of the aim of this session was to verify the candidate list of complexity factors identified thorough COCA and other (NASA) ongoing work. A secondary aim of this session was to identify critical factor combinations (i.e. combinations of two or more individual factors) that are sufficient to drive complexity to excessive levels.

On the basis of this session, it was concluded that there exists a core set of critical “precursor” factors for operations at MUAC. That is, these precursor factors, in combination with any other complexity factor (identified on the candidate list) are sufficient to raise complexity - and hence controller workload - above “redline.”



This list of precursor complexity factors is as follows:

• R/T congestion; • Mix climb/descent traffic; • High number aircraft; • Emergencies; • Military and other restricted areas (includes active military areas, and also shared civil/mil

airspace); • Weather/Turbulence; • Equipment (non-nominal) status.



9. GENERAL SUMMARY AND CONCLUSIONS

The study evaluated the operational complexity of all sectors within Maastricht airspace and developed I/D cards for each sector in every configuration that occurred during both phases of the study. The study also classified airspace sectors according to their complexity (either High, Medium or Low). The results demonstrate that the sector in which the REMBA navaid was sited was, and remains a High complexity sector, but the complexity values have been reduced significantly as a result of the airspace change.

The comparison between the West Low sector and the new NICKY Low sector where the REMBA navaid is located shows a reduction of approximately 30% in the proportion of climbing aircraft, a reduction of around 25% of flights in descent, and a reduction of approximately 30% in the number of FL's crossed. When these reductions are considered in combination with a lower value of workload per flight it suggests strongly that the airspace changes have successfully reduced complexity in the REMBA area.

The sector I/D cards provide objective measurements of complexity factors and will be useful to establish a baseline of complexity against which future airspace changes can be measured. They provide objective measurements of factors influencing controller workload linked to complexity. The workload per flight indicator shows how controller workload is influenced not only by the number of flights, but by the interaction between flights and the controllers' tasks in the context of the complexity of the sector in which they are performed.

The I/D cards and the calculation of workload have the potential to be used to identify the complexity variations that occur over a day, between different days of the week, weekdays and weekends, and between different sector configurations. Knowledge of these variations could assist managers in optimising controller resources linked directly to the variability of traffic demand and complexity fluctuations, and in the design of future sector changes. The objective and subjective data could be used by safety experts to establish and measure sector safety limits and to assess airspace changes in terms of increased safety. Given the projected increases in air traffic this could lead to the development of end-to-end processes to enable airspace designs to be evaluated and best practice methods based on complexity assessment to be implemented to ensure that future airspace changes are introduced safely.

However, further work could be conducted to validate the study of MUAC against other European Airspace service providers and introduce European complexity baseline figures.

The study showed that the method to calculate workload showed promise and, in many cases had a good correlation to the controllers' reported perceived workload. But, additional work is needed to identify those indicators and situations where the link is weakest, or missing, and incorporate them in an improved workload calculation. Further research should be taken to identify the point at which controllers' workload is no longer linked in a linear fashion to the number of flights, but is influenced by factors considered to be more complex and causing higher workload. Further, subjective results underscored the potentially critical role of factor combinations, especially interactions involving a subset of eight or so “precursor factors.” It might be instructive for future research to address such potential interactions, with an eye toward refining the COCA technique.

A future challenge is to continue to improve the method in parallel with an activity to develop a method and toolkit to predict short- and medium-term complexity in a real time environment.

Étude de Complexité du Centre de Maastricht EUROCONTROL

Projet COCA – Rapport CEE n° 403 57

TRADUCTION EN LANGUE FRANÇAISE

ÉTUDE DE COMPLEXITÉ DU CENTRE DE MAASTRICHT

1. INTRODUCTION

Une récente étude sur la sécurité au Centre de Maastricht, associée au rapport annuel de 2002 sur la sécurité, a mis en évidence la nécessité d’étudier la complexité de l’espace aérien dont le Centre a la charge. Cette étude a en effet mis en évidence des “foyers” d’incidents dans lesquels, d'après l’analyse des données recueillies a posteriori, la complexité pourrait avoir joué un rôle important. L’espace aérien à proximité de l’aide à la navigation REMBA, située dans le groupe de secteurs de Bruxelles, figure parmi les zones géographiques signalées dans l’étude. Des processus de suivi de la sécurité montrent que le nombre d’incidents autour de REMBA a augmenté au fil des ans. C’est pourquoi l’espace aérien en question a été modifié dans le cadre d'une stratégie visant à réduire le nombre d'incidents.

Comme indiqué ci-dessus, des rapports d’enquête sur les incidents ont laissé entendre que la complexité pourrait avoir joué un rôle important dans ces événements mais aucun élément commun quantifiable n’a pu être décelé dans le trafic ou les conditions statiques de l’espace aérien.

C’est pourquoi les gestionnaires de la sécurité et le management du Centre de Maastricht (MUAC) ont demandé qu’une analyse de complexité et de capacité (COCA) soit menée afin d'identifier et de mesurer les facteurs de complexité de l'espace aérien existant dans la zone de compétence générale du Centre de Maastricht, et la zone REMBA en particulier. L’étude a été menée en deux temps. La première phase s'est déroulée du 21 au 26 avril 2004, avant que des modifications ne soient apportées à l’espace, et la seconde du 25 au 30 août 2004, après modifications. Pendant les deux phases, l’équipe COCA a recueilli et compilé des données opérationnelles statiques et dynamiques entre 07h00 et 19h00 (heure locale) du mercredi au dimanche et entre 07h00 et 13h00 (heure locale) le lundi.

Les résultats de cette étude pourront venir en soutien du projet MANTAS9 ainsi que des initiatives et processus de gestion de la sécurité au Centre de Maastricht. On notera en outre que cette étude de complexité viendra également à l’appui d’autres initiatives prises par EUROCONTROL, telles que l’étude de l’efficacité économique de l’ATM, menée par le Bureau d’examen des performances et l’ensemble de tâches 06-01 relatif au Plan d’Action Stratégique pour la Sécurité (SSAP) du Groupe d’action pour la sécurité ATM (AGAS).

9 MANTAS est un nouveau concept opérationnel ATM créé en 2004 qui vise à établir des secteurs génériques (nouvelle sectorisation dynamique), des routes mixtes (abandon progressif des routes fixes au profit de l’espace aérien à itinéraire libre) et préconise l’absence de groupes de secteurs fixes, l’utilisation flexible de l’espace aérien et le contrôle radar sans communications vocales.

EUROCONTROL Étude de Complexité du Centre de Maastricht

58 Projet COCA - Rapport CEE n° 403

1.1. STRUCTURE DU DOCUMENT

Le présent document expose la méthode utilisée pour mener l’étude de complexité du Centre de Maastricht et les résultats qui en sont ressortis. Le document est structuré comme suit :

Chapitre 2 Historique du projet COCA

Chapitre 3 Objectifs de l’étude

Chapitre 4 Description générale de l’espace aérien du Centre de Maastricht

Chapitre 5 Données statiques et dynamiques recueillies et traitées pour la présente étude

Chapitre 6 Méthode employée pour évaluer la charge de travail des contrôleurs

Chapitre 7 Analyse statistique par groupe de complexité au niveau des secteurs

Chapitre 8 Résultats obtenus avec les données tant statiques que dynamiques

Chapitre 9 Synthèse générale et conclusions

2. OBJECTIFS DE L’ETUDE DE COMPLEXITE DU CENTRE DE MAASTRICHT

Les principaux objectifs de l'étude de complexité étaient les suivants :

• Evaluer la complexité opérationnelle de tous les secteurs de l’espace aérien relevant du Centre de Maastricht, et plus particulièrement des secteurs de Bruxelles.

• Etablir une base de référence de la complexité des secteurs de Maastricht à partir de laquelle les changements futurs pourront être mesurés, ce qui permettra d’évaluer l’évolution de la complexité des secteurs.

• Etablir une mesure de la charge de travail qui servira pour toute l’analyse.

• Obtenir des contrôleurs des facteurs pertinents de complexité.

• Obtenir des évaluations officielles de la charge de travail des contrôleurs.

• Evaluer l’évolution de la complexité à la suite de la réorganisation de l’espace aérien dans la région REMBA.

L‘étude a produit les résultats suivants :

• Des cartes d’identité de complexité comprenant une liste des indicateurs de complexité et les valeurs correspondantes pour chaque secteur.

• Une classification des secteurs du Centre de Maastricht selon les indicateurs de complexité communs.



• Un indice de complexité opérationnelle reposant sur la charge de travail par vol (présenté avec les cartes d’identité).

• Une comparaison des mesures de complexité à la suite de la réorganisation de l’espace aérien à proximité de REMBA.

2.1. DESCRIPTION GENERALE DE LA METHODE

L'évaluation de la complexité opérationnelle inhérente aux flux de trafic du Centre de Maastricht et aux caractéristiques environnementales de l'espace aérien s'est faite selon une approche quantitative qui a consisté, dans un premier temps, à définir les mesures de complexité de nature à refléter au mieux les facteurs contribuant à la complexité des secteurs du Centre de Maastricht. Ces facteurs ont été définis en tenant compte à la fois des données statiques (configuration des secteurs et aspects figés, propres à l’environnement de l’espace aérien) et des données dynamiques (comportement opérationnel, variabilité du trafic).

L’ensemble de mesures obtenu a été systématiquement évalué pour tous les secteurs du Centre de Maastricht, dans chaque configuration (de secteur) rencontrée au cours des deux phases de collecte de données. C'est ainsi qu'ont été obtenues des mesures quantitatives pour les indicateurs sélectionnés, qui servent de base aux cartes d'identité de secteurs.

Toutes les cartes d’identité peuvent être consultées sur http://www.eurocontrol.int/eec/public/standard_page/2006_report_403.html .

Dans le présent rapport, nous présenterons une série de cartes d’identité illustrant les résultats pour un secteur de chacun des trois groupes de secteurs que compte le Centre de Maastricht. Chaque ensemble de cartes en compte trois : une carte pour les jours de semaine (du lundi au vendredi) et deux cartes distinctes pour le samedi et le dimanche. L’analyse a été réalisée sur le simulateur en temps accéléré de complexité COLA.

Les données suivantes ont été utilisées pour les simulations :

• données des plans de vol décrivant les trajectoires individuelles des aéronefs (vols IFR) – pour tous les secteurs de MUAC – pendant une période de 12 heures (07h00 – 19h00 heure locale) ;

• description et dimensions des secteurs ;

• configurations des secteurs pour l’échantillon de trafic, pour chaque jour des deux phases, et pour le cycle AIRAC (Aeronautical Information Regulation and Control) correspondant ;

• environnement géographique des zones militaires ;

• heures d’activation/de désactivation des zones militaires aux dates de l'échantillon ;

• paramètres requis pour les indicateurs de complexité sélectionnés.



À l’issue de plusieurs réunions entre le Centre de Maastricht et l’Équipe COCA, les indicateurs de complexité jugés les plus pertinents pour le Centre ont été sélectionnés :

• interactions entre vols (DIF) ;

• volume des secteurs ;

• espace aérien disponible ;

• cas de « paires d’aéronefs en proximité » ;

• nombre de niveaux de vol franchis ;

• répartition spatiale du trafic (densité) ;

• mélange de catégories et de performances d’aéronef ;

• nombre de vols par heure et par fraction de 10 mn (en moyenne) ;

• mélange de trafic en rapport avec les vols en montée, en croisière et en descente.

Le calcul de la charge de travail à l’aide du modèle macroscopique devait également produire des résultats utiles.

Les résultats des simulations ont été les suivants :

• valeurs définies pour les indicateurs de complexité énumérés ci-dessus ;

• cartes d’identité de secteur ;

• charge de travail par vol.

3. SYNTHESE GENERALE ET CONCLUSIONS

L'étude a permis d'évaluer la complexité opérationnelle de tous les secteurs situés dans l'espace aérien de Maastricht et d'établir des cartes d’identité pour chaque secteur dans toutes les configurations qui se sont présentées au cours des deux phases de l’étude. Une classification des secteurs selon leur complexité (élevée, moyenne ou faible) a en outre été réalisée. Les résultats de l’étude montrent que le secteur dans lequel est situé l’aide à la navigation REMBA était, et demeure, un secteur de complexité élevée, mais les valeurs de complexité ont été sensiblement réduites à la suite du réaménagement de l'espace aérien.

La comparaison entre le secteur West Low et le nouveau secteur NICKY Low, où l’aide à la navigation REMBA est située, fait apparaître une diminution d’environ 30% du nombre d’aéronefs en montée et d’environ 25% des vols en descente, ainsi qu’une baisse d’environ 30% du nombre de niveaux de vol franchis. Ces réductions, conjuguées à une charge de travail par vol moins élevée, donnent clairement à penser que la réorganisation de l’espace aérien a permis de réduire la complexité dans la zone REMBA.

Les cartes d’identité par secteur, qui fournissent des mesures objectives des facteurs de complexité, serviront à établir une base de référence de la complexité pour l’évaluation des modifications futures de l'espace aérien. Elles donnent des mesures objectives des facteurs ayant une incidence sur la charge de travail des contrôleurs en rapport avec la complexité. L’indicateur de charge de travail par vol montre comment la charge de travail des contrôleurs est influencée non seulement par le nombre de vols mais aussi par l’interaction entre les vols et les tâches assignées au contrôleur dans le contexte de la complexité du secteur considéré.



Les cartes d’identité de complexité et le calcul de la charge de travail pourraient servir à déceler les variations de complexité au cours d'une journée, entre les différents jours de la semaine et entre les jours de semaine et de week-end, et entre les diverses configurations de secteur. La connaissance de ces variations aiderait les gestionnaires à optimiser les ressources en contrôleurs, qui sont directement liées à la variabilité de la demande de trafic et aux fluctuations de la complexité, et à concevoir les futures modifications des secteurs. Les experts en sécurité pourraient mettre à profit les données objectives et subjectives recueillies pour définir et mesurer les limites de sécurité des secteurs et évaluer les changements à apporter à l’espace aérien en vue du renforcement de la sécurité. Compte tenu de l’essor prévu du trafic aérien, il pourrait être possible de concevoir des processus de bout en bout, qui permettraient d’évaluer l’organisation de l’espace aérien et les meilleures pratiques à adopter sur la base d'une évaluation de la complexité ; solutions à mettre en œuvre pour s’assurer que les futurs aménagements de l'espace aérien sont apportés en toute sécurité.

Toutefois, le travail effectué pourrait être complété par une validation de l’étude du Centre de Maastricht par rapport aux résultats d’autres prestataires européens de service de navigation aérienne ainsi que par la prise en compte de données de référence européennes sur la complexité de l'espace aérien.

L’étude a montré que la méthode de calcul de la charge de travail est prometteuse et présente souvent une bonne corrélation avec la charge de travail telle qu’elle est perçue par les contrôleurs. Un complément d’étude est cependant nécessaire pour mettre en évidence les indicateurs et les situations où le lien est le plus faible, voire absent, et les intégrer dans une méthode améliorée de calcul de la charge de travail. Il faudrait en outre rechercher le point à partir duquel la charge de travail des contrôleurs n’est plus liée de façon linéaire au nombre de vols mais subit l’influence de facteurs jugés plus complexes, qui font accroître ladite charge. Par ailleurs, les résultats subjectifs ont mis en évidence le rôle potentiellement critique des combinaisons de facteurs, notamment les interactions associant une série d'environ huit "facteurs précurseurs". Dans le cadre de recherches futures, il pourrait être intéressant d’étudier ces interactions potentielles, en s’attachant plus particulièrement à perfectionner la technique COCA.

Un des défis à relever sera de poursuivre l’amélioration de la méthode tout en oeuvrant, en parallèle, à l'élaboration d'une méthode et d'une panoplie d'outils destinés à prévoir la complexité à court et moyen termes dans un environnement en temps réel.



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Project COCA - EEC Report No. 403 63

ANNEXES


64 Project COCA – EEC Report No. 403

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ANNEX A - CENTRE CONFIGURATIONS

The configurations that have been used during the two phases are highlighted (configuration number highlighted). The percentage of use represents the percentage the configuration has been used (separated according to the three period weekdays (We) /Saturday (Sa) /Sunday (Su)) during the time we were there.

Phase 1

BRUSSELS Percentage of use Config No Grouping together of sectors Configuration composition We Sa Su

WST H LNO H LUX H 1

WST L LNO L LUX L EEBBMMAAAASSUU 1% 0% 0%

WST H LNO H LUX H 2

WST L LNO L LUX L EEBBMMAAWWSSTT+EEBBMMAAUUCCEE 4% 4% 29%

WST H LNO H LUX H 3

WST L LNO L LUX L EEBBMMAAWWSSTT+EEBBMMAALLNNTT+EEBBMMAALLUUXX 10% 37% 3%

WST H LNO H LUX H 4

WST L LNO L LUX L

EEBBMMAABBHHNN+EEBBMMAALLUUXX

EEBBMMAAWWSSLL+EEBBMMAALLNNLL 62% 24% 43%

WST H LNO H LUX H 4.1

WST L LNO L LUX L

EEBBMMAAWWSSTT+EEBBMMAABBEEHH

EEBBMMAALLNNLL+EEBBMMAALLXXLL 12% 17% 5%


WST L LNO L LUX L

EEBBMMAABBWWHH+EEBBMMAALLNNTT+EEBBMMAALLUUXX

EEBBMMAAWWSSLL 3% 0% 15%

WST H LNO H LUX H 5

WST L LNO L LUX L

EEBBMMAABBWWHH+EEBBMMAABBEEHH

EEBBMMAAWWSSLL+EEBBMMAALLNNLL+EEBBMMAALLXXLL 9% 17% 0%


WST L LNO L LUX L

EEBBMMAABBWWHH+EEBBMMAALLNNHH+EEBBMMAALLUUXX EEBBMMAAWWSSLL+EEBBMMAALLNNLL

0% 0% 5%



DECO

Percentage of use Configuration number

Grouping together of sectors

Configuration composition We Sa Su

DEL H COA H 1

DEL L COA L EEDDYYYYDDCCOO 2% 4% 0%

DEL H COA H 2

DEL L COA L EEHHDDEELLTTAA+EEDDYYYYCCSSTT 24% 42% 63%

DEL H COA H 3

DEL L COA L

EEHHDDEELLHHII+EEDDYYYYCCSSTT

EEHHDDEELLMMDD

60% 54% 38%

DEL H COA H 4

DEL L COA L

EEHHDDEELLHHII+EEDDYYCCOOHHII

EEHHDDEELLMMDD+EEDDYYCCOOLLOO 13% 0% 0%

HANNOVER

Percentage of use Config No Grouping together of sectors Configuration

composition We Sa Su

RHR H MNS H SOL H HAM H1

RHR L MNS L SOL L HAM L EEDDYYMMRRHHSS 0% 0% 1%


RHR L MNS L SOL L HAM L EEDDYYMMUURRHH+EEDDYYYYEESSTT 1% 3% 0%


RHR L MNS L SOL L HAM L EEDDYYMMUURRHH+EEDDYYYYSSOOLL+EEDDYYYYHHAAMM 27% 35% 27%

RHR H MNS H SOL H HAM H3.1

RHR L MNS L SOL L HAM L EEDDYYYYRRHHRR+EEDDYYYYMMNNSS+ EEEDDDYYYYYYEEESSSTTT 0% 0% 0%




RHR L MNS L SOL L HAM L

EEDDYYYYRRHHRR+EEDDYYYYMMNNSS+ EEDDYYYYSSOOLL+EEDDYYYYHHAAMM 56% 38% 45%

RHR H MNS H SOL H HAM H4.2


EEDDYYMMUURRHH+EEDDYYEESSHHII EEDDYYSSOOLLOO+EEDDYYHHAALLOO 0% 6% 0%



EEDDYYYYRRHHRR+EEDDYYYYMMNNSS+EEDDYYEESSHHII

EEDDYYSSOOLLOO+EEDDYYHHAALLOO 13% 0% 26%

RHR H MNS H SOL H HAM H5West


EEEDDDYYYYYYHHHHHHWWW+EEDDYYYYSSOOLL+EEDDYYYYHHAAMM

EEDDYYRRHHLLOO+EEDDYYMMNNLLOO 0% 19% 0%



EEDDYYYYHHHHWW +EEDDYYEESSHHII EEDDYYRRHHLLOO++EEDDYYMMNNLLOO++ EEDDYYSSOOLLOO++EEDDYYHHAALLOO

3% 0% 0%

Phase 2

BRUSSELS The KOKSY and NICKY sectors have been introduced. This operational change was called for following safety concerns in the REMBA area, known to be particularly complex. In order to provide more space to resolve conflicts in this area, the new sector boundaries were extended eastwards.

Percentage of use Config No Grouping together of sectors Configuration composition

We Sa Su

KOK H NIK H LNO H LUX H 1

KOK L NIK L LNO L LUX L EEBBMMAAAASSUU 0% 0% 0%


KOK L NIK L LNO L LUX L EBMAWST+EEBBMMAAUUCCEE 5% 3% 1%

KOK H NIK H LNO H LUX H 3.1

KOK L NIK L LNO L LUX L EEBBMMAAWWSSTT+EEBBMMAALLNNTT+EEBBMMAALLUUXX 4% 14% 24%




KOK L NIK L LNO L LUX L EEBBMMAAKKOOKK+EEBBMMAANNIIKK+EEBBMMAAUUCCEE 1% 0% 0%



EEBBMMAABBWWHH + EEBBMMAAUUCCEE



KOK L NIK L LNO L LUX L EBMAKOK+EBMANIK+EBMALNT+EBMALUX 0% 0% 0%



EEBBMMAAWWSSTT + EEBBMMAABBEEHH

EEBBMMAALLNNLL + EEBBMMAALLXXLL 0% 0% 0%



EEBBMMAAWWSSTT+EEBBMMAALLNNHH+EEBBMMAALLUUXX

EEBBMMAALLNNLL 0% 0% 0%



EEBBMMAABBHHNN + EEBBMMAALLUUXX

EEBBMMAAWWSSLL + EEBBMMAALLNNLL 55% 47% 40%








EEBBMMAAKKOOLL+ EEBBMMAANNIILL 0% 0% 0%



EEBBMMAAKKOOKK+EEBBMMAABBHHMM+EEBBMMAALLUUXX

EEBBMMAANNIILL+EEBBMMAALLNNLL 0% 0% 0%



EEBBMMAABBHHNN + EEBBMMAALLUUXX

EEBBMMAAKKOOLL+EEBBMMAANNIILL+EEBBMMAALLNNLL 22% 10% 0%





EEBBMMAAKKOOKK+EEBBMMAANNIIKK+EEBBMMAABBEEHH

EEBBMMAALLNNLL +++ EEBBMMAALLXXLL 0% 0% 0%



EEBBMMAABBWWHH+EEBBMMAALLNNHH+EEBBMMAALLUUXX

EEBBMMAAWWSSLL + EEBBMMAALLNNLL 0% 11% 3%



EEBBMMAAKKOOKK + EEBBMMAABBNNEE EEBBMMAANNIILL+EEBBMMAALLNNLL+EEBBMMAALLXXLL

0% 0% 0%




EEBBMMAAWWSSLL+EEBBMMAALLNNLL+EEBBMMAALLXXLL 9% 15% 33%




EEBBMMAAKKOOLL+EEBBMMAANNIILL+EEBBMMAALLNNLL +EEBBMMAALLXXLL 2% 0% 0%

DECO

Percentage of use Configuration number

Grouping together of

sectors Configuration composition

We Sa Su

DEL H COA H 1

DEL L COA L EEDDYYYYDDCCOO 0% 0% 4%

DEL H COA H 2

DEL L COA L EEHHDDEELLTTAA + EEDDYYYYCCSSTT 6% 31% 50%

DEL H COA H 3.1

DEL L COA L

EEHHDDEELLHHII + EEDDYYYYCCSSTT

EEHHDDEELLMMDD 76% 69% 38%

DEL H COA H 3.2

DEL L COA L

EEHHDDEELLTTAA + EEDDYYCCOOHHII

EEDDYYCCOOLLOO 0% 0% 0%

DEL H COA H 4

DEL L COA L

EEHHDDEELLHHII + EEDDYYCCOOHHII

EEHHDDEELLMMDD + EEDDYYCCOOLLOO 18% 0% 8%



HANNOVER Percentage of use Config

No Grouping together of sectors Configuration composition We Sa Su

RHR H MNS SOL H HAM H 1

RHR L MNS L SOL L HAM L EEEDDDYYYYYYMMMRRRHHHSSS 0% 0% 0%

RHR H MNS H SOL H HAM H 2

RHR L MNS L SOL L HAM L EEEDDDYYYMMMUUURRRHHH + EEEDDDYYYYYYEEESSSTTT 0% 3% 10%

RHR H MNS H SOL H HAM H 3.1

RHR L MNS L SOL L HAM L EEEDDDYYYMMMUUURRRHHH + EEEDDDYYYYYYSSSOOOLLL +

EEEDDDYYYYYYHHHAAAMMM 16% 23% 24%


RHR L MNS L SOL L HAM L EEEDDDYYYYYYRRRHHHRRR + EEEDDDYYYYYYMMMNNNSSS +

EEEDDDYYYYYYEEESSSTTT 0% 18% 8%

3.3 RHR H MNS H SOL H HAM H


EEEDDDYYYYYYHHHHHHWWW + EEEDDDYYYYYYEEESSSTTT

EEEDDDYYYYYYHHHLLLWWW 0% 0% 0%



EEEDDDYYYYYYMMMUUURRRHHH + EEEDDDYYYEEESSSHHHIII

EEEDDDYYYYYYHHHLLLEEE 0% 0% 0%



EEEDDDYYYYYYRRRHHHRRR+ EEEDDDYYYYYYMMMNNNSSS+ EEEDDDYYYYYYSSSOOOLLL+ EEEDDDYYYYYYHHHAAAMMM 59% 54% 42%



EEEDDDYYYMMMUUURRRHHH + EEEDDDYYYEEESSSHHHIII

EEEDDDYYYSSSOOOLLLOOO +++ EEEDDDYYYHHHAAALLLOOO 1% 0% 8%



EEEDDDYYYMMMUUURRRHHH+ EEEDDDYYYSSSOOOHHHIII+ EEEDDDYYYYYYHHHAAAMMM

EEEDDDYYYSSSOOOLLLOOO

0% 0% 0%





EEEDDDYYYYYYHHHHHHWWW + EEEDDDYYYYYYEEESSSTTT

EEEDDDYYYRRRHHHLLLOOO + EEEDDDYYYMMMNNNLLLOOO 0% 0% 0%



EEEDDDYYYYYYRRRHHHRRR+++EEEDDDYYYMMMNNNHHHIII+ EEEDDDYYYYYYEEESSSTTT

EEEDDDYYYMMMNNNLLLOOO 0% 0% 0%



EEEDDDYYYYYYHHHHHHWWW+ EEEDDDYYYYYYSSSOOOLLL+ EEEDDDYYYYYYHHHAAAMMM

EEEDDDYYYRRRHHHLLLOOO+ EEEDDDYYYMMMNNNLLLOOO 0% 0% 0%



EEEDDDYYYYYYRRRHHHRRR+ EEEDDDYYYMMMNNNHHHIII+ EEEDDDYYYEEESSSHHHIII

EEEDDDYYYMMMNNNLLLOOO+ EEEDDDYYYYYYHHHLLLEEE 0% 0% 0%



EEEDDDYYYYYYRRRHHHRRR+ EEEDDDYYYYYYMMMNNNSSS+ EEEDDDYYYEEESSSHHHIII

EEEDDDYYYSSSOOOLLLOOO+ EEEDDDYYYHHHAAALLLOOO 23% 0% 8%



EEEDDDYYYYYYHHHHHHWWW+ EEEDDDYYYSSSOOOHHHIII+ EEEDDDYYYYYYHHHAAAMMM

EEEDDDYYYYYYHHHLLLWWW+ EEEDDDYYYSSSOOOLLLOOO 0% 0% 0%



EEEDDDYYYYYYRRRHHHRRR+ EEEDDDYYYMMMNNNHHHIII+ EEEDDDYYYYYYSSSOOOLLL+EEEDDDYYYYYYHHHAAAMMM

EEEDDDYYYMMMNNNLLLOOO 0% 1% 0%



EEEDDDYYYYYYHHHHHHWWW + EEEDDDYYYEEESSSHHHIII

EEEDDDYYYRRRHHHLLLOOO+ EEEDDDYYYMMMNNNLLLOOO+ EEEDDDYYYSSSOOOLLLOOO+ EEEDDDYYYHHHAAALLLOOO

0% 0% 0%



EEEDDDYYYYYYRRRHHHRRR+ EEEDDDYYYMMMNNNHHHIII+++EEEDDDYYYEEESSSHHHIII

EEEDDDYYYMMMNNNLLLOOO+ EEEDDDYYYSSSOOOLLLOOO+ EEEDDDYYYHHHAAALLLOOO 1% 0% 0%



EEEDDDYYYYYYRRRHHHRRR+ EEEDDDYYYMMMNNNHHHIII+ EEEDDDYYYSSSOOOHHHIII+ EEEDDDYYYYYYHHHAAAMMM

EEEDDDYYYMMMNNNLLLOOO + EEEDDDYYYSSSOOOLLLOOO

0% 0% 0%



ANNEX B - CIVIL AND MILITARY CONFIGURATION SHEETS

Table 16 is an example of the forms that were used in-situ to collect information on the civil sector configurations.

Table 16: Table used to capture the sector configuration changes for the DECO group

DECO configuration Date :

Local time

07:0

0

10:0

0

13:0

0

…

CNF1 x x CNF2 x x x x x CNF3 x x x CNF4 x CNF5 x x

Table 17 is an example of the forms that were used in-situ to collect information on the activation and deactivation of military areas.

Table 17: Table used to capture the military area activation for the Brussels group

BRUSSELS Military Zones Date:

Local Time

07:0

0

10:0

0

13:0

0

16:0

0

…

CBA1ABC

EBTRANB

EBTRASB

EDR305B

G1

LFTSA20

LIPPEN

LIPPEN2

NL

NL2

PINOT



ANNEX C - REPORTED WORKLOAD QUESTIONNAIRES Phase 1 Figure 41 shows an example of the questionnaires that were filled in by the controllers during phase 1.

CFMU Sector Name Local Sector Name

Date Start Finish Time Local/GMT

08:00 08:20 08:40 09:00 09:20 09:40 10:00 10:20 10:40 11:00

Excessive High Comfortable × × Relaxed × × × × × Under utilised × ×

Flows Mix of climbing and descending traffic flows Mix of climbing or descending streams and flights in cruise. Several traffic flows converge at same point. Mix of OAT/GAT Crossing points Multiple crossing points in sector Single crossing point for converging routes Crossing points close to boundaries Traffic mix Mix of high and low performance aircraft Co-ordination/Communication High co-ordination workload Controlling traffic while it is within the limits of another sector Monitoring traffic in your sector while it is under the control of another sector Late transfer of communications to control sector R/T congestion Interface with next sector / centre Traffic Volume Traffic bunching Restrictions Aircraft flight profile restricted Military or other restricted area FL’s not available for use Lack of holding areas

Figure 41: Reported Workload Questionnaire, phase 1



Phase 2 Figure 42 shows an example of the phase 2 questionnaires that were filled in by the controllers.

Ident Date

Sector

Start Time (LOCAL) 08:0

0

08:1

0

08:2

0

08:3

0

08:4

0

08:5

0

6 Extremely High 5 × × × × 4 × 3 × 2 × 1 Extremely Low Flows Mix of climbing and descending traffic flows Mix of climbing or descending flights in cruise

Several traffic flows converge at the same point × Mix of OAT/GAT Merging of arrival flows Crossing points Multiple crossing points in sector × Crossing points close to boundaries × Traffic Mix Mix of high and low performance aircraft × Co-ordination Controlling traffic in another sector In-sector traffic controlled by another sector Late transfer of communications to control sector Interface with next sector/centre × RT R/T congestion Blocked frequency Pilots not listening to R/T Pilots not complying Traffic Volume Traffic bunching High number of aircraft Restrictions Aircraft flight profile restricted Military or other restricted area FL’s not available for use Lack of holding areas Opening/closing of a sector. Change to non RVSM Etc Turbulence – weather Coaching

If you have other comments then please turn over….. Other

Figure 42: Reported Workload Questionnaire, phase 2



ANNEX D - MACROSCOPIC WORKLOAD MODELS

In this Annex, we give a detailed description of the Adapted Macroscopic Workload Model (AMWM). As written in Chapter 6, the AMWM is based on the Macroscopic Workload Model (MWM).

Macroscopic Workload Model

The MWM states that 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. Two steps are required to obtain a workload value for a sector for a day:

Step 1 Computation of the occurrences (n) of the 3 macro-tasks (routine, level changes, conflicts) for the sector for the day.

The evaluation of these occurrences is performed using COLA:

• AC: the number of aircraft per 10 minutes corresponds to the traffic throughput,

• LC: the number of level changes corresponds to the number of level crossed,

• CNF: the number of conflicts corresponds to the number of proximate pairs, independent of their type and calculated within a cylinder of 5 nm radius and 1000 ft high.

Step 2 Determination of macro-task durations (ω): the set of macro-task durations are determined with input from operational experts (the choice is described in reference [5]). In the MWM, the macro-task durations are identical for each sector of the study.

Adapted Macroscopic Workload Model

Indicators other than number of flights, number of level changes and number of conflicts are evaluated at sector level. Clearly, the interactions and influence of these indicators on the controller workload varies amongst ATC sector types. As a consequence, there is a need to adapt the macroscopic workload model according to the complexity characteristics of sector types.

In classifying sectors, we can identify groups of sectors sharing similar complexity indicators. From these groups of indicators, an adapted macroscopic workload model (AMWM) was built (see reference [2]). Four steps are required to obtain a workload value for a sector for a day:

Step 1 Identification of complexity indicators: combination of ATC operational advice with statistical analysis to compile a list of relevant complexity indicators.



Step 2 Computation of the complexity indicators identified (including the occurrences (n) of the 3 macro-tasks) for the sector for the day.

Step 3 Classification of the sectors into an appropriate number of homogenous groups, or complexity clusters to arrive at sector types, see Annex E.

Step 4 Determination of macro-task durations (ω) for each group: the set of macro-task durations is determined via an optimisation process described in reference [2]. In the AMWM, the macro-task durations are identical for each of the sectors within a complexity cluster.



ANNEX E - CLASSIFICATION PROCESS

General method

Classification methods (see reference [6]) are used to build groups of objects which share the same properties. The groups are characterised on the basis of discriminating variables selected to highlight relevant properties of the objects. In this study the goal was to classify the sectors into complexity clusters. The number of complexity clusters has to be determined.

As explained in Annex D, step 3 of the workload calculation involves the classification of the sectors. This step is particularly important for two reasons: first, the chosen classification method will pull together the sectors sharing the same properties. Then, the workload evaluation will be completed for each different complexity cluster and therefore, the workload coefficients will be adapted to the types of the sectors.

To classify the sectors into complexity clusters we used the DIVAF technique. DIVAF is a hierarchical method which gives, at the end, a decision tree. The method has already been used by the COCA team for several studies and is documented in reference [7]. The method can be briefly summarised. At the beginning, all the sectors are considered to belong to a unique cluster (root of the tree). During the hierarchy building process, each single cluster is divided into two sub-clusters. The division is obtained by the selection of a complexity indicator. A corresponding question (binary type) is associated to the selected indicator which makes it possible to distribute the elements of the cluster into the two sub-clusters. By repeating this process until getting a satisfactory final number of clusters (leaves), a decision tree is built. Advantages of this method are that it is easy to interpret as well as allowing for operational input on the selection of the discriminating indicator.

Four steps are necessary to carry out the classification:

1. Select a representative sample of sectors to build the decision tree;

2. Normalise and aggregate the complexity values of the selected sample;

3. Build the decision tree from the sample: identify the complexity indicator which best divides the sample into two sub-clusters and repeat the process until a suitable number of clusters has been obtained;

4. Classify the remaining sectors according to branches of the tree (after having normalised their complexity values as in step 2.).

Classification process on MUAC sectors

The four steps necessary to carry out the classification have been carefully followed using, as input, the set of complexity indicators (I/D cards) evaluated for each daily sector. The successive results are described hereafter. For information, there are 34 sectors to be classified.

Step 1:

The global set of data consists of 283 elements which correspond to the number of daily sectors for which an I/D card has been computed. When using the term “daily sector”, we understand the complexity information of one sector according to a specific day. The sample chosen to build the tree corresponds to the set of “daily sectors” which have been opened for minimum 4 hours per day. It contains 136 elements.



Step 2:

What we want at the end is to be able to classify the sectors into complexity clusters independently of a specific day. Therefore, we need to reduce the sample.

Firstly, we normalised the complexity data of the sample by the length of the opening time. The longer the sector has been opened, the more reliable the corresponding complexity data.

Then, we aggregated the information at sector level. For each different sector we have averaged the complexity data at daily level.

The normalised and aggregated sample is made of 20 elements10 (9 sectors from Brussels, 6 sectors from DECO and 5 sectors from Hannover).

The sample is made of 58% of the total elements (20 sectors within the sample out of 34 to be classified).

It was agreed that three complexity clusters are sufficient to draw a fair analysis. In effect, we were in search of one group of high complexity sectors, one group of medium complexity sectors and a last group of low complexity sectors.

Step 3:

The indicator which discriminates the sample the most is the DIF indicator. The two first sub-clusters are determined by whether the DIF value is greater or less than 0.12. Then, the same indicator again (DIF) discriminates one of the first sub-cluster into two other sub-sub-clusters (whether DIF is greater or less than 0.07). The distribution of the clusters using the sample of data is represented in Figure 43.

Figure 43: MUAC sectors classification: Building of the binary tree from the data sample 10 These elements are highlighted in blue in the classification results table (see Table 18).

Sample of data

(20 sectors)

Rest of the sample of data

(13 sectors)

Cluster 1 (7 sectors)



DIF ≥ 0.12 DIF < 0.12

DIF ≥ 0.07 DIF < 0.07

High complexity sector Medium complexity sector Low complexity sector



Step 4:

There are 14 remaining sectors to be classified according to the binary tree built in step 3.

Ten of them have been dispatched through the leaves of the binary tree according to their DIF values. The other four cannot be classified because of the “lack” of information (e.g. some of them were only opened for one day and for less than 30 minutes which is not sufficient for a systematic classification process). Two of them belong to Brussels: KOKSY total (EBMAKOK) and NICKY total (EBMANIK), one belongs to Hannover: Munster High (EDYMNHI) and the last one belongs to DECO: Delta and Coastal collapsed (EDYYDCO).

The classification process of the whole MUAC sectors lead to the distribution between the three Complexity Clusters shown in Table 18.

Table 18: Classification results table

COMPLEXITY CLUSTER 1 COMPLEXITY CLUSTER 2 COMPLEXITY CLUSTER 3

BRUSSELS WEST HIGH BRUSSELS OLNO AND WEST HIGH BRUSSELS OLNO AND LUX COLLAPSED

BRUSSELS KOKSY LOW BRUSSELS ONLO AND LUX HIGH DECO COASTAL HIGH BRUSSELS OLNO LOW BRUSSELS OLNO HIGH DECO COASTAL LOW BRUSSELS LUX TOTAL BRUSSELS OLNO TOTAL DECO COASTAL TOTAL

BRUSSELS LUX LOW BRUSSELS KOKSY AND NICKY COLLAPSED DECO DELTA HIGH

BRUSSELS NICKY LOW HANNOVER MUNSTER LOW DECO DELTA LOW BRUSSELS WEST LOW HANNOVER RUHR LOW DECO DELTA TOTAL

HANNOVER HAMBURG LOW HANNOVER MUNSTER TOTAL HANNOVER HAMBURG AND SOLLING HIGH

HANNOVER SOLLING LOW HANNOVER RUHR TOTAL HANNOVER MUNSTER AND RUHR COLLAPSED

HANNOVER SOLLING TOTAL HANNOVER HAMBURG AND SOLLING COLLAPSED

HANNOVER HAMBURG TOTAL



ANNEX F - COMPLEXITY INDICATORS

About the Mesh

As mentioned in chapter 8.1, some of the indicators were evaluated using a mesh. This involves dividing the MUAC airspace into identical 4D cells, collecting data in each cell and then using the data to calculate the indicators at a sector level.

Each cell has a regular size in terms of both space and time. A cell belongs to a sector if and only if its centre is included within the sector’s boundaries.

Figure 44: Horizontal view of a sector tiled by the mesh

To prevent boundary effects, a spatial displacement of the mesh is applied. In this study, for each computation phase, the mesh is shifted four times both in latitude and longitude. When computing an indicator, the evaluation in each cell is performed four times; once for each grid displacement. The cell value is the mean of these four values.

For this study, the cell parameters are:

• Spatial: ∆lat = 7.5 nm, ∆long=7.5 nm, ∆alt= 3000 ft

• Temporal: ∆t =10 min

The mesh is shifted randomly both in latitude and longitude, at maximum ∆lat/2 and ∆long/2.

The cell size (7.5 nm x 7,5 nm x 3 000 ft) was chosen to reflect the level of the study (sector level).

The number of displacements of the grid has been set to four which is a trade-off between the time consumed in computations and the level of accuracy of the results.



The ‘spatial mesh’ refers to the dimensions of the cells, so in Figure 44, if we consider the mesh to be 1 layer thick then the spatial mesh relating to the sector contains 19 cells.

The ‘temporal mesh’ takes into account the 10 minute time step, so over one hour there are 6 ten minute time steps, and therefore 6 x 19 = 114 cells.

Detailed Descriptions of the Indicators

This section provides detailed descriptions of the indicators used in the I/D cards shown in chapters 8.1 to 8.3.

Flight Interactions

DIF per minute

• Handling traffic on crossing flows and traffic flying in different attitudes is more complex than handling a unique flow of aircraft. The DIF indicator has been developed to capture and quantify this aspect of complexity.

• In each cell, every flight is allocated a “behaviour” composed of a track and a phase. The track refers to the horizontal vector of the flight and the phase refers to its vertical attitude. There are eight possible options for the track; one for each of the half quadrants shown in Figure 45. There are three possible options for the phase, as shown in Figure 46.

Let “long”, “lat” and “alt” be respectively “x”, “y” and “z”.

Figure 45: Possible track values

Figure 46: Possible phase values

NEN

NW

E W

SW S

SE

x

y

climbing

cruising

descending

x

y

z



Let n_types be the total number of possible behaviours of a flight. For each cell, the phase and track of each aircraft is determined when it enters the cell. Then the number of flights Ni belonging to each behaviour is counted (i.e.: for i=1 to n_types).

In each cell, the DIF indicator is given by the following formula:

∑ ∑= >=

=typesn

jtkjtk

typesn

jiiitk CNCNCDIF

_

1,,

_

,1, )()()(

Where Ck,t denotes the cell k at time step t. This DIF indicator depends on time and space.

To aggregate the results we take the data from each cell for each time step. Therefore six sets of data are extracted from each cell over an hour; one for each 10 minute time step.

To aggregate DIF(Ck,t) at the spatial level, we compute the sum over the spatial mesh. To aggregate DIF(Ck,t) at time level, we compute the sum over the temporal mesh. The corresponding DIF value per day for a sector made of Ncells is given by:

∑∑= =

=T

t

N

ktk

cell

CDIFDIF0 1

, )(

where T denotes the number of time steps.

At sector level, the result is the normalised value of the DIF per minute flown:

∑ ∑= =

=T

t

N

ktk

cells

d

DIFDIFperMin

0 1,

where dk,t is the total time flown by the aircraft present in cell k for the time period t expressed in minutes.

Traffic Phase

• Cruising/Climbing/Descending (%)

As explained for the DIF indicator there are three possible phases that can be attributed to any aircraft: climb/cruise/descent. For each sector, the attitude of each aircraft is determined at sector entry. The results are the percentages of aircraft climbing, descending and cruising.

• Mix of traffic attitudes

This represents the variety of aircraft attitudes within the sector. Let cl be the percentage of climbing flights and de the percentage of descending flights, the mix indicator is given by the following formula:

))5113216()5113216((9

200),( 2323 ++−+++−×= dedededeclclclcldeclMIX

This indicator ranges from 0 to 100.



100

50

0

Z

0.0

0.5

1.0Y 1.0

0.50.0

X

Figure 47: Graphical illustration of the mix of traffic attitudes indicator

In Figure 47 the x-axis represents the percentage of climbing flights (cl) and the y-axis represents the percentage of descending flights (de). This figure is a plot of the surface defined by the external function MIX(cl,de).

This function reaches its maximum value (100) when both cl and de equal 50%. The minimum value (0) of this function is reached for 4 specific cases:

• both cl and de equal 0% (i.e. cruising (cr) equals 100%); • cl equals 0% and de equals 100%; • de equals 0% and cl equals 100%; • both cl and de equal 100%.

It should be noted that the last case cannot exist when considering the MIX function because the sum of the 3 possible attitudes must equal 100% (cl+cr+de=1).

Presence of Proximate Aircraft Pairs

• Normalised Proximate Aircraft Pairs This indicator measures the likelihood of the close approach of flight paths: occasions when two aircraft (according to their filed flight paths) approach within a cylinder of 10 nm radius and 1000 ft high. When two flights have formed a proximate pair, we consider that the same two flights will not form another along the rest of their flight paths.

This value is divided by the total number of aircraft within the sector and expressed as a percentage.



• Along track / Crossing / Opposite (%)

We divide the proximate pairs into three classes.

• 'along track': an along track proximate pair is detected if the angle between the two trajectories is less than 45° (see Figure 48),

• 'opposite direction': an opposite direction proximate pair is detected if the angle α between the two trajectories is less than -30° (see Figure 49),

• 'crossing': a crossing proximate pair is detected when the two trajectories form neither an along track nor an opposite track proximate pair.

Each type of proximate pair is counted in each sector, normalised by the total number of aircraft in the sector and expressed as a percentage.

45° α

Figure 48: Proximate pairs: along track

-30° α

Figure 49: Proximate pairs: opposite direction

Traffic Evolution

The extent of vertical movements of flights can be important to capture the traffic complexity relating to flight profiles.

• Nb levels crossed

For each aircraft within a sector, the absolute difference between its altitude at sector entry and at sector exit is calculated. The altitudes are expressed in thousands of feet; as a consequence, differences of less than one thousand feet are not recorded. For example, if a flight enters the sector at FL310 and exits at FL314, the difference in levels will be 0.

Finally, the number of level crossed within a sector is the sum of the absolute differences for each aircraft going through the sector divided by the total number of aircraft within the sector.



Density

• Total cell number

This is the total number of cells covering the sector of interest. It is the average number of cells over the four meshes used. This indicator characterizes the area of the sector. It only uses the spatial mesh.

• Cells with more than 3 aircraft

For each sector this is the number of cells where more than 3 aircraft have entered during each 10 minutes period. The result corresponds to the percentage of cells with more than 3 aircraft with respect to the total number of cells in the temporal mesh.

Mixture of Aircraft Types

Each aircraft is associated with an altitude, an attitude and a type. The altitude is determined at sector entry according to the flight plan information; the attitude is determined by the sign of the difference between the altitude at sector entry and the altitude at sector exit. Finally, the aircraft type information is extracted from the flight plan.

For each aircraft, these three attributes are correlated with a Base of Aircraft Data (BADA) performance table11. For each aircraft type, the performance tables specify the true air speed, rate of climb/descent and fuel flow for conditions of climb, cruise and descent at various flight levels. The performance figures contained within the tables are calculated based on a total-energy model and BADA 3.6 performance coefficients.

In our analysis, true airspeed and ground speed are considered to be equivalent because of the lack of data on wind speed, wind direction, etc.

Each type of aircraft not present in the BADA files is associated with a “similar” aircraft type - sharing the same properties in terms of performance - and we can get the corresponding parameters (airspeed) from a synonym table. From our statistics, about 98 % of the aircraft types were covered in the samples processed for this study.

• Average Ground Speed:

According to BADA tables, each aircraft present in the sector of interest is associated with a true airspeed. The average ground speed indicator is the mean value of all the aircraft speeds identified in the sector. This indicator is expressed in knots.

• Std Deviation of Avg Ground Speed

This is the standard deviation of the aircraft speed. It is expressed in knots.

11 This database (current version is BADA 3.6) provides a set of ASCII files containing performance and operating procedure data for 295 different aircraft types. BADA is being maintained and developed by the EEC.



Sectors Dimensions

• Total Volume

This is the sum of the volumes of the airblocks that constitute the sector. It is a surface (in nm²) multiplied by the number of available FL covered by the airblocks. The sector volume above FL450 is not taken into account. This indicator is expressed in nm² * 100 feet.

• Average Volume Not Available:

The volume of the each restricted area is evaluated in each sector (using the sum of the airblock volumes). By taking into account the time each restricted area has been opened and comparing it to the time the considered sector has been opened, a ratio of airspace non-availability is determined. This indicator is expressed as a percentage.

• Average Transit Time

This is the time spent, on average, by a flight within the sector. It is simply the ratio of the total minutes controlled to the total number of flights within the sector. This indicator is expressed in minutes and seconds.

Traffic Rate:

• Traffic throughput per 10 min

The traffic throughput corresponds to the average number of aircraft that entered the sector per 10 minute time step. The throughput per hour can be derived by multiplying this value by 6.

Workload

• Workload per flight

The workload calculation has been fully described in Annex D. For each sector, the workload indicator is evaluated per 10 minute period. Using a sliding window, the workload per hour is derived. Then, this workload per hour value is normalized by the number of flights present in the sector during the corresponding hour. Finally, the average workload per aircraft is the mean of the workload per hour per aircraft values. This indicator is expressed in seconds.

• Std Deviation of Workload per flight

This is a measure of the variability of the workload per flight value.



ANNEX G - COMPLEXITY FACTOR LIST

For clarity, the factors in Table 19 are clustered into groups that are ordered from local (pilot) to more global (e.g. aircraft, airspace, etc) perspectives. Table 19: Self–reported Airspace Complexity Factors

COMMS WITH PILOTS AIRCRAFT TRAFFIC AIRSPACE OTHER SECTORS OTHER

1 R/T congestion 5 Restricted flight profile

6 Mix of climbing and descending traffic

18 Military or other restricted area

25 Controlling traffic in another sector 29 Staffing

2 Blocked frequency 7 Traffic flows converging at same point

19 FLs not available for use

26 Late transfer of communications to control sector

30 On-the-job training (OJT)

3 Pilots not listening / complying with R/T 8 Mix of OAT/GAT

20 Lack of holding areas

27 Interface with another sector / centre

31 Nr required procedures ++

4 Pilot requests 9 Multiple crossing points in sector

21 Change to non RVSM

28 Opening / closing of a sector

32 Equipment status** / ++

10 Crossing points close to sector boundaries

22 Turbulence / Weather

33 Other

11 Mix of high and low performance aircraft* 23 Sector volume

12 Traffic bunching 24 nr aerodromes ++ 13 High number of aircraft

14 Merging / crossing aircraft at narrow angles

15 Emergencies 16 Special flights 17 Nr of path changes ++

* encompasses both airspeed and climb performance ** fully-functional versus degraded ++ added from NASA’s list of “dynamic density” factors

Airspace complexity factors, self-reported

complexity study maastricht_upper_airspace

Technology

major influencing

workload rating

reported workload

muac control

major airports

gate atm operations

reported complexity

selected complexity