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Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
Complexity study maastricht_upper_airspace
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  • 1. EUROPEAN ORGANISATION FOR THE SAFETY OF AIR NAVIGATION EUROCONTROL EUROCONTROL EXPERIMENTAL CENTRE A COMPLEXITY STUDY OF THE MAASTRICHT UPPER AIRSPACE CENTRE EEC Report No. 403 Project COCA Issued: February 2006The information contained in this document is the property of the EUROCONTROL Agency and no part should be reproduced in any form without the Agency’s permission. The views expressed herein do not necessarily reflect the official views or policy of the Agency.
  • 2. REPORT DOCUMENTATION PAGEReference: Security Classification:EEC Report No. 403 UnclassifiedOriginator: Originator (Corporate Author) Name/Location:EEC – NCD EUROCONTROL Experimental CentreNetwork Capacity & Demand Centre de Bois des Bordes B.P.15 F - 91222 Brétigny-sur-Orge Cedex FRANCE Telephone: +33 (0)1 69 88 75 00Sponsor: Sponsor (Contract Authority) Name/Location:EATM EUROCONTROL Agency 96, Rue de la Fusée B - 1130 Brussels BELGIUM Telephone: +32 (0)2 729 90 11 WEB Site: www.eurocontrol.intTITLE: A COMPLEXITY STUDY OF THE MAASTRICHT UPPER AIRSPACE CENTRE Authors Date Pages Figures Tables Annexes References Geraldine M Flynn 02/2006 xii + 91 49 19 7 7 Claire Leleu (Isa Software) Brian Hilburn (Stasys) EEC Contact Project Task No. Sponsor Period COCA 2004 - 2005Distribution Statement:(a) Controlled by: Head of NCD(b) Special Limitations: NoneDescriptors (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 focuswas put on the Brussels sectors in the vicinity of the REMBA navaid to assess if airspace changes madein 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 sharingsimilar complexity characteristics. The results are presented in I/D cards for each sector; these containthe quantitative values of the selected complexity indicators. The results of this study may be used tosupport safety management processes in MUAC to reduce complexity and increase safety and tosupport the MANTAS project.
  • 3. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL ACKNOWLEDGEMENTSThe COCA project leader would like to thank the ATC experts of the Maastricht UAC for theirassistance and cooperation during the surveys. The COCA team highly appreciated their warmwelcome and their complete co-operation during the two data collection sessions (in April andAugust 2004).We would also like to thank those who participated in focus group and paired-comparisonsessions, as well as Stewart Mac Millan, Tina Braspennincx, James Kench. Special thanks shouldgo to Keith CARTMALE, Joachim BECKERS, Urs SCHOEKE and Rainer GRIMMER.Project COCA - Report No. 403 v
  • 4. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre Page intentionally left blankvi Project COCA - EEC Report No. 403
  • 5. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL TABLE OF CONTENTSLIST OF ANNEXES......................................................................................................... VIIILIST OF FIGURES .......................................................................................................... VIIILIST OF TABLES.............................................................................................................. IXDEFINITIONS, ABBREVIATIONS AND ACRONYMS ....................................................... XREFERENCES .................................................................................................................. XI1. INTRODUCTION ...........................................................................................................1 1.1. STRUCTURE OF THE DOCUMENT ............................................................................. 12. BACKGROUND OF THE COCA PROJECT.................................................................2 2.1. THE COCA PROJECT ................................................................................................... 23. MUAC COMPLEXITY STUDY OBJECTIVES...............................................................3 3.1. GLOBAL DESCRIPTION OF THE METHOD................................................................. 34. 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...................................................................................135. 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 ....................................................................216. CONTROLLER WORKLOAD CALCULATION ..........................................................227. 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 ......... 278. RESULTS....................................................................................................................30 8.1. SECTOR I/D CARD EXAMPLE.................................................................................... 30Project COCA - Report No. 403 vii
  • 6. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre 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...................................... 549. GENERAL SUMMARY AND CONCLUSIONS ...........................................................56FRENCH TRANSLATION (TRADUCTION EN LANGUE FRANÇAISE............................57 LIST OF ANNEXESANNEX A - Centre configurations ................................................................................................... 65ANNEX B - Civil and Military configuration sheets .......................................................................... 72ANNEX C - Reported Workload Questionnaires ............................................................................. 75ANNEX D - Macroscopic Workload Models..................................................................................... 77ANNEX E - Classification Process .................................................................................................. 79ANNEX F - Complexity Indicators ................................................................................................... 83ANNEX G - Complexity Factor List.................................................................................................. 91 LIST OF FIGURESFigure 1: The three Maastricht sector groups................................................................................ 6Figure 2: Principle traffic flows related to MUAC (April 21st, 2004 from 07:00 to 19:00)................ 7Figure 3: Distribution of the flights in the vertical plane for the 21st April 2004 .............................. 8Figure 4: Influential airports that impact MUAC’s main traffic flows............................................... 9Figure 5: Location of the Brussels group within MUAC ............................................................... 10Figure 6: MUAC Brussels group: before sector change .............................................................. 11Figure 7: MUAC Brussels group: after sector change ................................................................. 11Figure 8: Location of the DECO group within MUAC................................................................... 12Figure 9: Location of the Hannover group within MUAC ............................................................. 13Figure 10: Analysis of the number of flights for the two phases .................................................... 15Figure 11: An annotated box-plot .................................................................................................. 16Figure 12: Similarity of the traffic distribution of the AIRAC cycle 259 and Saturday August, 28th17Figure 13: MUAC special and restricted areas .............................................................................. 18Figure 14: Impact of military activity on sectors per MUAC group................................................. 20Figure 15: Workload rating scale, phase 1 .................................................................................... 21Figure 16: Workload rating scale, phase 2 .................................................................................... 21Figure 17: Sector distribution by group and level within Complexity Cluster 1 .............................. 24Figure 18: Location of the Cluster 1 sectors .................................................................................. 25Figure 19: Sector distribution by group and level within Complexity Cluster 2 .............................. 26Figure 20: Location of the Cluster 2 sectors .................................................................................. 27Figure 21: Sector distribution by group and level within Complexity Cluster 3 .............................. 28viii Project COCA - EEC Report No. 403
  • 7. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLFigure 22: Location of the Cluster 3 sectors .................................................................................. 29Figure 23: Brussels Phase 1 configuration .................................................................................... 37Figure 24: Brussels Phase 2 configuration .................................................................................... 37Figure 25: Hotspots map for Brussels sectors between FL245 and FL335................................... 41Figure 26: Hotspots map for Brussels sectors between FL335 and FL450................................... 42Figure 27: Workload values per Complexity Cluster...................................................................... 44Figure 28: DECO Reported Workload distribution, phase 1 .......................................................... 45Figure 29: Reported workload (cumulative percent) across the three sector groups, phase 1 ..... 45Figure 30: Reported workload (cumulative percent) across the three sector groups, phase 2 ..... 46Figure 31: Reported Workload ratings for each sector group (cumulative percentage) ................ 47Figure 32: Brussels median workload for weekdays, Saturday and Sunday................................. 47Figure 33: DECO median workload for weekdays, Saturday and Sunday .................................... 48Figure 34: Hannover median workload for weekdays, Saturday and Sunday ............................... 48Figure 35: Reported workload as a function of time-of-day and traffic load, Brussels................... 49Figure 36: Reported workload as a function of time-of-day and traffic load, DECO ...................... 49Figure 37: Reported workload as a function of time-of-day and traffic load, Hannover................. 49Figure 38: Reported workload as a function of time-of-day and number of open sectors, Brussels........................................................................................................................ 50Figure 39: Reported workload as a function of time-of-day and number of open sectors, DECO. 50Figure 40: Reported workload as a function of time-of-day and number of open sectors, Hannover ...................................................................................................................... 50Figure 41: Reported Workload Questionnaire, phase 1 ................................................................ 75Figure 42: Reported Workload Questionnaire, phase 2 ................................................................ 76Figure 43: MUAC sectors classification: Building of the binary tree from the data sample ........... 80Figure 44: Horizontal view of a sector tiled by the mesh ............................................................... 83Figure 45: Possible track values.................................................................................................... 84Figure 46: Possible phase values.................................................................................................. 84Figure 47: Graphical illustration of the mix of traffic attitudes indicator ......................................... 86Figure 48: Proximate pairs: along track ......................................................................................... 87Figure 49: Proximate pairs: opposite direction .............................................................................. 87 LIST OF TABLESTable 1: Number of sectors affected by military activity within the MUAC groups ......................... 19Table 2: How to read an I/D card ................................................................................................... 31Table 3: Brussels West Low / NICKY Low and KOKSY Low I/D Card........................................... 33Table 4: Solling I/D Card................................................................................................................ 35Table 5: Delta High I/D Card.......................................................................................................... 36Table 6: Comparison of airspace before and after the Brussels sector change ............................ 38Table 7: Complexity Cluster Coefficients ....................................................................................... 43Table 8: Reported workload (cumulative percent) across the three sector groups, phase 1 ......... 45Table 9: Reported workload (cumulative percent) across the three sector groups, phase 2 ......... 46Table 10: Brussels self-reported complexity factors associated with high workload (n=48) .......... 52Table 11: DECO self-reported complexity factors associated with high workload (n=48) ............. 52Table 12: Hannover self-reported complexity factors associated with high workload (n=100) ...... 53Table 13: Weekday self-reported complexity factors associated with high workload .................... 53Table 14: Saturday self-reported complexity factors associated with high workload ..................... 54Table 15: Sunday self-reported complexity factors associated with high workload ....................... 54Table 16: Table used to capture the sector configuration changes for the DECO group............... 73Table 17: Table used to capture the military area activation for the Brussels group ..................... 73Table 18: Classification results table ............................................................................................. 81Table 19: Self–reported Airspace Complexity Factors................................................................... 91Project COCA - Report No. 403 ix
  • 8. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre 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 Unlimitedx Project COCA - EEC Report No. 403
  • 9. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL REFERENCES[1] Cognitive Complexity In Air Traffic Control, A Literature Review, B. Hilburn, EEC Note 04/04, Web link: http://www.eurocontrol.int/eec/public/standard_page/2004_note_04.html[2] Adaption of Workload Model by Optimisation Algorithms and Sector Capacity Assessment, G. M. Flynn, A. Benkouar, R. Christien, EEC Note 07/05. Web link: http://www.eurocontrol.int/eec/public/standard_page/2005_note_07.html[3] RAMS Plus User Manual, Release 5.08, March 2004, Gate-To-Gate ATM Operations[4] RAMS Plus Data Manual, Release 5.08, March 2004, Gate-To-Gate ATM Operations[5] Air Traffic Complexity: Potential Impacts on Workload and Cost, T. Chaboud (EEC), R. Hunter (NATS), J. C. Hustache (EEC), S. Mahlich (EEC), P. Tullett (NATS), EEC note 11/00. Web link: http://www.eurocontrol.int/eec/public/standard_page/2000_note_11.html[6] Probabilités, analyse de données et statistique, G. Saporta, Editions Technip, 1990.[7] Air Traffic Complexity Indicators & ATC Sectors Classification, R. Christien, A. Benkouar, 5th USA/Europe Air Traffic Management R&D Seminar, June 2003, Budapest, Hungary.Project COCA - Report No. 403 xi
  • 10. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre Page Intentionally left blankxii Project COCA - EEC Report No. 403
  • 11. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL1. INTRODUCTIONA 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 highlightedincident ‘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 REMBAnavaid, located in the Brussels sector group. Safety monitoring processes show that the number ofincidents around REMBA has increased over the years. As a consequence, the airspace aroundREMBA 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 akey factor in the incidents, but, these data did not identify any common, quantifiable traffic and/orstatic airspace conditions.As a result, Maastricht Upper Airspace Centre (MUAC) safety managers and senior managementrequested the Complexity and Capacity (COCA) project to conduct a study to identify and measureairspace complexity factors existing in MUAC’s area of responsibility in general, and in the REMBAarea 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 phasesthe 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 managementinitiatives and processes at MUAC. In addition, it should be noted that this complexity study willsupport other EUROCONTROL initiatives, including the Performance Review Unit (PRU) ATMCost Effectiveness study and the Action Group for ATM Safety (AGAS) Session Service AccessPoint (SSAP) WorkPackage 06-01.1.1. STRUCTURE OF THE DOCUMENTThis document presents the method used and the results of the MUAC complexity study. Thestructure 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 genericsectors (dynamic re-sectorisation), mixed routes (gradually moving away from fixed routes to free routeairspace), no fixed sector groups, flexible use of airspace and voiceless Radar Control.Project COCA - Report No. 403 1
  • 12. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre2. BACKGROUND OF THE COCA PROJECT2.1. THE COCA PROJECTThe Complexity and Capacity (COCA) project was launched at the EUROCONTROL ExperimentalCentre (EEC) at the end of year 2000. Its main objective is to describe the relationship betweencapacity and complexity by means of accurate performance metrics. This objective is beingaddressed 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 notjust a function of the number of aircraft in a sector, it is also directly influenced by the interactionsbetween 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 tounderstand what factors or circumstances make the controllers’ work more complex and cause anincrease in workload.To gain a better understanding of the relationship between complexity, workload and capacity theCOCA 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 themethodology. During the development process, the COCA project built an elaborated complexitytoolbox named COCA Light Analyzer (COLA), and performed several macroscopic studies, theresults of which were validated by operational experts. The MUAC study has given COCA theopportunity to apply and test the methodology in the ‘real world’ (supported by subjective data),and to improve upon it.2 Project COCA - EEC Report No. 403
  • 13. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL3. MUAC COMPLEXITY STUDY OBJECTIVESThe 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 METHODA quantitative approach was used to evaluate operational complexity intrinsic to MUAC traffic flowsand airspace environment characteristics. This approach consisted of first defining the complexitymetrics 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 fixedaspects related to the airspace environment) and dynamic (e.g. operational behaviour, trafficvariability) data.The set of elicited metrics was systematically evaluated for all MUAC sectors in each sectorconfiguration that occurred during both data collection phases. The results provide quantitativemeasurements of the selected indicators and are used as the basis of the sector I/D cards. All theI/D cards are available onhttp://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 thethree 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 theCOLA 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;Project COCA - Report No. 403 3
  • 14. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre • 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 thoughtto 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 producevaluable results.The output from the simulations consisted of: • Values for the complexity indicators listed above; • Sector I/D cards; • Workload per flight.4 Project COCA - EEC Report No. 403
  • 15. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL4. OVERVIEW OF MUAC AIRSPACE AND SECTORSMaastricht Upper Airspace Centre (MUAC) is located within a dense region of airspace in the corearea of Europe. In 2003, MUAC handled more than 1.2 million flights (more than 5% growthcompared to the previous year). It is responsible for all upper airspace (i.e. above FL245) over theterritories of Belgium, the Netherlands, Luxembourg and northwest Germany, as well as theadjoining areas of the North Sea (see Figure 1). Lower airspace in the region is the responsibility ofthe Belgium national services (Belgocontrol), the Dutch national services (LVNL), and the Germannational services (DFS), through ACCs in Brussels, Amsterdam, Düsseldorf and Bremen.Several busy adjacent and subjacent European airports are located in the MUAC region andgenerate dense traffic streams from north to south, east to west and vice-versa. The traffic streamshave to be managed to accommodate other airspace users (e.g. military flights using and transitingto/from temporary restricted and segregated areas). MUAC is affected by a significant number oftemporary segregated airspace and restricted areas.Military/civil airspace sharing and coordination arrangements depend upon each individualcountry’s procedures. For example, in Belgian and Dutch airspace, there are reserved militaryareas and traffic is controlled by dedicated military units in the countries concerned. In Germanairspace, 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) andHannover sectors as shown in Figure 1. Each group is divided into subgroups which are describedhereafter.Project COCA - Report No. 403 5
  • 16. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre Figure 1: The three Maastricht sector groupsMUAC airspace projects onto a surface area equivalent to 76,000 sq nautical miles. It is currentlyranked 15th amongst all the European centres in terms of surface size. In terms of traffic numbersMUAC controllers handle an average of 3,400 flights per day (based on 2003 data). Thedistribution of the traffic is well balanced between the three sector groups: Brussels handles onaverage 39% of the traffic, Hannover 34% and DECO 26%.6 Project COCA - EEC Report No. 403
  • 17. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL4.1. DIRECTIONAL FLOWSFigure 2 shows the four main traffic flows handled by MUAC: the northbound and southboundflows between the northern European airports and Paris or the southern European airports and theeastbound 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 MOVEMENTSFigure 3 shows the breakdown of the vertical movements computed for the 21st of April 2004. Byvertical movements we mean the proportion of flights in climb/descent subdivided into the followingcategories: Internal, Departing, Landing, and Overflights.Internal flights are those which have departed from and landed at airports located beneath eachsector group’s geographical boundaries.Departing and Landing flights are those which have either departed from or landed at airportslocated beneath each sector group’s geographical boundaries.Project COCA - Report No. 403 7
  • 18. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentreOverflights are those flights that have passed through the sector group and did not depart from orland at an airfield located subjacent to the group’s area of responsibility. The overflights are dividedinto two categories: “pure” overflights, and overflights to/from fringe airports. The fringe airports aredefined 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 MUACoperates in the upper airspace only. Moreover, the three sector groups have very few or noInternal flights: Brussels and DECO have no internal flights and Hannover has only 2% Internalflights - 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 atthese 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 clearlyaffected by traffic to/from fringe airports. As we can see in Figure 4, MUAC is located in the middleof the core area and is surrounded (150 NM fringe) by numerous important airports such asLondon, Paris, Frankfurt, Copenhagen, Frankfurt, Basel, Zurich, Munich and Berlin. 100% 90% Overflights Overflights To/From Fringe Airports 80% Internal Landing 70% Departing 60% ratio (%) 50% 40% 30% 20% 10% 0% BRUSSELS DECO HANNOVER MUAC groups Figure 3: Distribution of the flights in the vertical plane for the 21st April 20048 Project COCA - EEC Report No. 403
  • 19. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL Figure 4: Influential airports that impact MUAC’s main traffic flowsProject COCA - Report No. 403 9
  • 20. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre4.3. OVERVIEW OF THE SECTOR GROUPS4.3.1. Brussels Sector GroupGeneral Map: WEST OLNO LUX Figure 5: Location of the Brussels group within MUACThe Brussels group is divided into three parts: LUX sectors, OLNO sectors and WEST sectors asshown in Figure 5.The airspace around the REMBA navaid in the Brussels sector group was identified as an incidenthotspot. In recent years, the number of incidents close to this navaid has increased. As aconsequence, the sector design in the REMBA area was changed in mid-2004, (see Figure 6 andFigure 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.10 Project COCA - EEC Report No. 403
  • 21. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL KOKSY NICKY WEST H REMBA REMBAFigure 6: MUAC Brussels group: before sector change Figure 7: MUAC Brussels group: after sector changeDuring the first phase, 8 different sector configurations were used with a maximum of 5 sectorsopen at the same time. After the reorganisation, the number of configurations (observed duringphase 2) increased to 9 (out of 18 possible) and the maximum number of sectors open at the sametime 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 thevolume of Brussels was used2 for military purposes.2The term ‘used’ reflects occupancy in both a temporal and a spatial sense.Project COCA - Report No. 403 11
  • 22. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre4.3.2. DECO Sector GroupGeneral Map Coastal Delta Figure 8: Location of the DECO group within MUACThis 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 4sectors open at the same time. The possible combinations of sectors are shown in a sector blockdiagram 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. Themajor flow in the Coastal sectors is towards the southwest (London) and northeast towardsScandinavia and eastern Europe. In the southerly region of the Delta sectors, the major flow iseastbound-westbound (Atlantic flights and eastern Europe). For both lower and upper sectors, themajor flow is oriented between westbound and north-eastbound.Military activity has a significant affect on this group: on weekdays, on average, 17% of the volumeof the DECO group was used2 for military purposes.12 Project COCA - EEC Report No. 403
  • 23. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL4.3.3. Hannover Sector GroupGeneral Map Hamburg Munster Solling Ruhr Figure 9: Location of the Hannover group within MUACThis group is divided into four parts: Ruhr, Munster, Hamburg and Solling sectors as shown onFigure 9.During the first and the second phases, 8 different configurations were used, with a maximum of 6sectors open simultaneously. The possible combinations of sectors are shown in a block diagramin 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/southdirections.In the Ruhr and Hamburg sectors, the major flow is oriented between northwestbound andsouth-eastbound.Military activity does not have a great impact on this group: on weekdays, on average, 6% of thevolume of the Hannover airspace was used2 for military purposes.Project COCA - Report No. 403 13
  • 24. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre5. DATA USED IN STUDYThe data used for the study fall into two broad categories, Static and Dynamic data. Static data isdivided into two sub-sets referred to as Elementary and Configuration data; Configuration data isoperationally updated Elementary data. The static data are used to compute the complexityindicators, 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. Thesedata 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 bothConfiguration 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 eachphase.5.1. ELEMENTARY DATATo perform a simulation, traffic sample data describing flight plan aircraft trajectories andenvironment data were required for each week of the two phases. The traffic flight plan andenvironment 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 CurrentTactical Flight Model (CTFM) data. The ETFMS system uses the message received on an airborneflight to update the CTFM. The CTFM is updated if the actual position deviates from the plannedprofile by more than 20 nm laterally, 700 ft vertically and 5 minutes in time. Updates to the CTFMare suspended when the flight is less than 30 nm from the arrival airport.5.2. CONFIGURATION DATATo 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 VALIDATION5.3.1. Elementary Data ValidationThe two weeks of CMFU traffic data were validated before processing the traffic complexityindicators to ensure that the weeks selected for the study were representative of the expectedtraffic 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 eachweek of the sample (April/August) and for each sector group the box-plots show the numbers offlights (from 0700 to 1900). The traffic volume is significantly higher in August than in April: about7% more flights for each group.14 Project COCA - EEC Report No. 403
  • 25. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLThe figure also shows the mean number of flights (red dot) and the standard deviation (pink arrowsextending above and below the red dots) for the corresponding AIRAC3 cycles. The traffic samplesfor 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 phases3 The environment and traffic data are organised by AIRAC cycles (28 days per cycle). The 21st to the 26th April 2004 week (phase 1) th thcorresponds to the AIRAC cycle 255 and the 25 to 30 August 2004 (phase 2) corresponds to the AIRAC cycle 259.Project COCA - Report No. 403 15
  • 26. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre Maximum grade 19 16 14 Median grade Mean grade 10 6 Minimum grade 1 Outlier Figure 11: An annotated box-plotFigure 11 is a box-plot showing examination results for a group of students. The following textdescribes how to read the figure.The solid black line within the yellow box is the median value (14) of the sample. The median valueis the middle value of a distribution: half of the students’ scores are above the line and half arebelow 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 14and 16, and 25% students scored between 10 and 14.The scores 6 and 19 are respectively, the minimum and the maximum scores achieved in theexamination.The values outside the yellow box, but inside the min/max limits represent the other half of thesample. In effect, 25% of the students have a grade between 16 and 19 and 25% of the studentshave a grade between 6 and 10.5.3.2. Traffic Distribution PeriodsThe 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 differsignificantly. 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.16 Project COCA - EEC Report No. 403
  • 27. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL It is generally acknowledged that there are differences in the traffic distribution between weekdays and weekends. Before aggregating the data it was necessary to assess if these differences were evident in the two weeks of data and if they were significant. We observed that the traffic distributions on Saturdays and Sundays were significantly different to the other days of the week and that the day with the greatest variability was a Saturday (see Figure 12). We noticed that the link between weekdays of the AIRAC cycle is usually quite high but the strongest link is not necessarily between days having the same name. The tests identified three distinct periods: • Weekdays, • Saturdays, • Sundays.Traffic distribution similarity Figure 12: Similarity of the traffic distribution of the AIRAC cycle 259 and Saturday August, 28th Figure 12 shows an example of the test results used to determine if the traffic distributions were significantly different. Please note that Figure 12 is an example of one statistical test. All results are available on web link http://www.eurocontrol.int/eec/public/standard_page/2006_report_403.html#PHASE_1. The figure compares Saturday, August 28th to the other days of AIRAC cycle 259. The comparison day (Sat) is shown in red and other days of phase 2 are represented in blue. The days of the AIRAC cycle are represented in black. Project COCA - Report No. 403 17
  • 28. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentreThe x-axis represents the days of the AIRAC cycle 259 in chronological order. We observe that theSaturdays 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 ImpactThe impact of military airspace was evaluated by computing the percentage of civil airspaceaffected by the presence of military activity. This percentage varied not only with respect to thesectors 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 affectMUAC, which are not shown on the map, were gathered during the two data collection phases. TRA-EDD 100 TRA-EH TRA-Melcken- burg 2 TRA-WESER TRA-NL2 TRA-North-B TRA- Sachen TRA-CBA 1 TRA-16 TRA-Lauter 2 TRA- Frankenal TRA-TSA 22 TRA-TSA 20 Figure 13: MUAC special and restricted areas18 Project COCA - EEC Report No. 403
  • 29. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLTable 1 shows the total number of daily5 sectors that were open and the corresponding number ofdaily sectors affected by military activity for the two phases of the sample. The actual activationand de-activation times were recorded in-situ at MUAC. As there was no military activity during theweekends the data relate only to weekdays. Table 1: Number of sectors affected by military activity within the MUAC groups Brussels DECO Hannover Total number of daily sectors opened 77 43 62 Total number of daily sectors affected by 62 37 39 military activity Percentage of daily sectors affected by 81% 86% 63% military activity (%) Military activity duration for the two 117 250 42 phases (h) Average volume not available (%) 24 17 6The table shows that the percentage of daily sectors affected by military activity varied between63% 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 wasunavailable. The Brussels group is most affected by military activity, followed by DECO thenHannover. This can be explained by the fact that the number of military zones in Brussels is veryhigh and the pure “civil” volume of this group is small (i.e. volume where no military activity cantake place). As a consequence, 81% of the daily sectors in Brussels are affected by militaryactivity. Around 60% of those sectors have their volume reduced by more than 25% (25%-50% and50%-75% “volume not available”). The military presence is both strong and evenly spread over thegroup.The DECO group has the longest military activity duration of the three groups. The group has avery high number of military zones with most concentrated in the north-west corner. This isreflected in the 86% of daily sectors which are affected by military activity. Of those sectors, around70% are unable to use up to 25% of their volume while another 25% cannot use between 25% and50% 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 othergroups. As a consequence, some sectors are never affected by military activity (elementarysectors within Ruhr and Solling sub-groups). However, the majority (95%) of daily sectors inHannover that are affected by military activity have less than 25% of their volume unavailable. Thistranslates into an average percentage of volume not available of 6%; compared to 24% forBrussels 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 throughoutthe two phases then it will count as 5 daily sectors.Project COCA - Report No. 403 19
  • 30. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre 1 0,9 0,8 0,7 Volume Relative number of sectors not available 0,6 >75% and <=100% >50% and <=75% 0,5 >25% and <=50% >0% and <=25% 0,4 0,3 0,2 0,1 0 Brussels Deco Hannover Group Figure 14: Impact of military activity on sectors per MUAC group5.4. DYNAMIC DATAThe 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 DataAlthough such factors as fatigue, skill, strategies etc. can influence the workload a given controllerexperiences, controller workload remains the best criterion we have against which to assess theinfluence 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 thepurposes of evaluating workload in operational centres, subjective methods have a number ofbenefits, including ease of administration and data collection, and minimal task disruption.Phase 1Reported workload was elicited and evaluated using paper-and-pencil workload rating scales. Inphase 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 usedextensively in operational and simulated ATC environments, and has shown itself fairly intuitiveand non-intrusive to use, as well as robust and valid in the data it provides.20 Project COCA - EEC Report No. 403
  • 31. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL 08:00 08:10 08:20 08:30 08:40 08:50 09:00 09:10 09:20 09:30 09:40 09:50 Excessive High √ √ √ Comfortable √ √ √ √ √ √ √ √ √ Relaxed Under utilised Figure 15: Workload rating scale, phase 1Phase 2On the basis of phase 1 results, a number of modifications were made to the workload ratingtechnique, including: • Shorter rating intervals, with ratings collected using five minute time slices (to minimise interruption, controllers were asked to provide two (five-minute) ratings, once every ten minutes); • Workload was rated on a six-point scale (i.e., with no midpoint to force ratings either above or below the middle value), see Figure 16; • Reworded data labels with “non-judgmental” end points (e.g. “Extremely High” in place of “Excessive,”) and no text labels for intermediate values. 08:00 08:05 08:10 08:15 08:20 08:25 08:30 08:35 08:40 08:45 08:50 08:55 6 Extremely High √ 5 √ √ √ 4 √ √ √ √ √ 3 √ √ √ 2 1 Extremely Low Figure 16: Workload rating scale, phase 25.4.2. Self-reported Complexity FactorsWhen the controllers reported high workload, either 5 or 6, they were asked to identify all thecomplexity factors that were relevant during that period. As shown in Annex C, a list of factors wasprovided (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 ofself-reported complexity factors. The former consists of quantitative variables derived directly fromthe airspace (e.g. average crossing angle), whereas the latter is built on factors that controllersreport 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.Project COCA - Report No. 403 21
  • 32. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre6. CONTROLLER WORKLOAD CALCULATIONFor this study, the executive controllers’ task workload was computed using the AdaptedMacroscopic Workload Model (AMWM) developed by the COCA project. This model relies on theMacroscopic 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 controllertasks are considered. Adapted (in AMWM) refers to a classification process which creates clustersof sectors with similar complexity characteristics. Workload values are then evaluated for eachcluster.The MWM has been built to evaluate ACC workload, and is based on the workload used in theRAMS Plus fast time simulator. This model is described in references [3], [4] and [5]. The MWMstates 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 Plusbut some examples of these tasks include: Routine tasks – R/T tasks to and by the pilot for firstand last call on frequency, flight progress data management tasks, route clearances, etc. Levelchange tasks include controller radar monitoring (or aircraft report) of flight leaving current leveland reaching assigned level as well as associated flight data management tasks. Conflict tasksinclude 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 FormulaWhere:ωRoT, ωLC and ωCNF are respectively the times (expressed in seconds) needed to execute routinetasks, level change tasks, and conflict tasks and nAC, nLC and nCNF are respectively the number ofaircraft, 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 everycircumstance, or in different sector types: hence, controller task workload is context related. TheAMWM is an endeavour to take account of the context of sector types by applying different weightsto the same task dependant upon the sector type. To do this, sectors were first grouped intoclusters sharing similar complexity properties. Following classification, an optimisation process isapplied to weight the controller tasks according to the sector type (so as to evaluate the ωRoT, ωLCand 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 canbe found in Annex D.22 Project COCA - EEC Report No. 403
  • 33. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL7. COMPLEXITY CLUSTERSMUAC sectors have been classified using the complexity metrics contained in the I/D cards. Threedifferent complexity clusters were identified: 1- high complexity, 2 – medium complexity and 3 - lowcomplexity.The classification process applied to MUAC sectors (phase 1 and phase 2) is fully detailed inAnnex E.As its name indicates, MUAC airspace belongs to upper airspace. But, as many sectors arelabelled “Low” (e.g. EBMAWSL for West Low sector of Brussels) we have defined a very simplefunctional sector typology based on the minimum and maximum levels of the sectors as defined inthe 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 SECTORSGenerally, 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 toBrussels group (78%). The rest (22%) come from Hannover group. Figure 18 shows thegeographical location of the sectors.Low level sectors account for a very high proportion of the total airspace within this cluster. Thisvalidates 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.Project COCA - Report No. 403 23
  • 34. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre Hannover 22% 100% 0% h 0% w h ig ig Lo H H 71% w+ Lo 14% 14% h w h ig ig Lo H H w+ Lo Brussels 78% Figure 17: Sector distribution by group and level within Complexity Cluster 124 Project COCA - EEC Report No. 403
  • 35. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL EBMABWH EBMALNL EDYHALO (WEST hi) (OLNO lo) (HAMBURG lo) EBMAWSL (WEST lo) EDYSOLO (SOLLING lo) EBMAKOL (KOKSY lo) EBMANIL (NICKY lo) EBMALUX (LUX) EBMALXL (LUX lo) Figure 18: Location of the Cluster 1 sectorsProject COCA - Report No. 403 25
  • 36. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre7.2. COMPLEXITY CLUSTER 2: APPEAR TO BE MEDIUM COMPLEXITY SECTORSGenerally, 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 belongto the Hannover group and 50% of the sectors belong to the Brussels group. Figure 20 shows thegeographical location of the sectors.It is the most varied cluster in terms of group and type distribution. This result is quite logical in thesense that this cluster contains the “medium” complexity sectors and includes sectors which are onthe “borderline” of the other two clusters (high complexity and/or low complexity sectors). 60% 60% Hannover 40% 40% 50% Brussels 50% 0% 0% h w h ig ig Lo h w h H ig H ig Lo H w+ H w+ Lo Lo Figure 19: Sector distribution by group and level within Complexity Cluster 226 Project COCA - EEC Report No. 403
  • 37. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL EDYYMNS (MUNSTER) EBMALNT (OLNO) EDYMNLO EBMALNH (MUNSTER lo) (OLNO hi) EBMABHN (WEST hi + OLNO hi) EBMAWST (WEST) EDYYSOL (SOLLING) EDYYRHR EBMABEH (RUHR) (OLN hi + LUX hi) EDYRHLO (RUHR lo) Figure 20: Location of the Cluster 2 sectors7.3. COMPLEXITY CLUSTER 3: APPEAR TO BE LOW COMPLEXITY SECTORSGenerally, 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 orHigh 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 mostof them are sectors in upper airspace. In effect, 27% of Cluster 3 sectors are pure High sectorsand 55% are of type Low+High.Project COCA - Report No. 403 27
  • 38. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre 100% Brussels 9% Hannover 0% 0% 36% h w h ig ig Lo H H w+ 75% Lo 25% 0% h w h ig ig Lo H 33% 33% 33% H w+ Lo h w h ig ig Lo H H w+ Lo Deco 55% Figure 21: Sector distribution by group and level within Complexity Cluster 328 Project COCA - EEC Report No. 403
  • 39. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL EDYYCST (COASTAL) EDYYHAM (HAMBURG) EDYCOHI (COASTAL hi) EDYCOLO (COASTAL lo) EDYYEST (HAM + SOL) EDYESHI (HAM hi + SOL hi) EHDELTA (DELTA) EDYMURH (MUN + RUHR ) EHDELHI (DELTA hi) EBMAUCE (OLNO + LUX) EHDELMD (DELTA lo) Figure 22: Location of the Cluster 3 sectorsProject COCA - Report No. 403 29
  • 40. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre8. RESULTSThis section presents the I/D card results. Please note that all the I/D card results are available onweb 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 sectionsprovide a sample ID card from each sector group and complexity cluster.8.1. SECTOR I/D CARD EXAMPLEThe 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 howto read an I/D card. A hash (#) in the name column indicates that the metric has been computedusing a mesh. Further details of the mesh and the indicator calculation methods can be found inAnnex F.30 Project COCA - EEC Report No. 403
  • 41. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL Table 2: How to read an I/D card Name Description Example Explanation Sector Name A_Sector Sector name. Brussels East High Day Date Day considered. 28/08/04 Data from 28/08/204 Opening time Length Length of time when the sector 09:30 The sector was opened for a total time of was open, usually the sum of non 9 hours and 30 minutes on the consecutive periods. 28/08/2004. Expressed in hours and minutes. Flight Interactions DIF per minute DIF stands for “Different 0.25 It represents the average number of (#) Interacting Flows”. Captures potential interactions a flight can have interacting flows and respective when crossing the sector. E.g. DIF=0.25 numbers of flights: crossing, means that an aircraft is likely to be converging, etc. “involved” in 0.25 interactions or; on average, one interaction for every four flights. Traffic Phase Cruising traffic Percentage of aircraft that are in 59% On average, 59% of the traffic was in cruise. cruise. Climbing traffic Percentage of aircraft that are in 19% On average, 19% of the traffic was in climb. climb. Descending Percentage of aircraft that are in 22% On average, 22% of the traffic was in traffic descent. descent. Mix of traffic Value to show the mix of traffic: 57 The variety of the traffic mixture is attitudes the higher the value the more moderate. A value between 0 and 100 mixed the traffic. indicates the “level” of mixture. 0 means all traffic are either in cruise, in climb or in descent and 100 means that half of the flights are in climb and half in descent. Presence of Proximate Aircraft Pairs Normalised Occasions when two aircraft 8% On average, 8% of the flights have Proximate (according to their filed flight formed a “proximate pair”. Aircraft Pairs paths) have approached within 10 nautical miles horizontally and 1000 ft vertically of each other. Expressed as a percentage. Along track Count of the Proximate Aircraft 2% 2% of the flights have formed an “along Pairs for which the angle between track” type proximate pair. the two trajectories is less than 45°. Expressed as a percentage. Crossing Count of the Proximate Aircraft 4% 4% of the flights have formed a “crossing” Pairs which are neither along type proximate pair. track nor opposite. Expressed as a percentage. Opposite Count of the Proximate Aircraft 2% 2% of the flights have formed an Pairs for which the angle between “opposite” type proximate pair. the two trajectories is more than 150°. Expressed as a percentage. Traffic Evolution Nb levels Number of FL crossed on average 1.98 An aircraft within the sector crossed, on crossed by an aircraft (1FL=1000 feet). average, almost 2FL.Project COCA - Report No. 403 31
  • 42. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre Density Total cell Number of cells used to mesh the 487 487 cells (cubes) were used to mesh number (#) sector. Brussels East High. Cells with Percentage of cells with more 7% At least three aircraft have been present more than 3 than 3 aircraft. in 7% of the Brussels East High cells aircraft (#) (temporal and spatial aspects considered). Mixture of Aircraft Types Average Average Ground Speed of all 433 On average, flights in this sector have an Ground Speed aircraft in the sector. Determined average speed of 433 knots. with respect to aircraft type, attitude and altitude using performance tables (BADA1). Expressed in knots. Std Deviation Captures the variability of the 24 The speeds vary by +/- 24 knots from the of Avg Ground Ground Speed of all aircraft in the average value. The speeds vary between Speed sector. Expressed in knots. 409kts and 457kts. Sector Dimensions Total Volume Sector volume computed from 715 121 The sector volume is 715 000 nm² * 100 airblock2 volumes. Expressed in ft. nm² * 100 ft. Average Percentage of the sector volume 15% The military activity within Brussels East volume not not available due to restricted High, during the opening times, used available areas or military activity (temporal 15% of the available sector volume. aspect considered). Average Time spent on average by a flight 07:07 On average, a flight spends 7 minutes Transit Time within the sector. Expressed in and 7 seconds in Brussels East High. minutes and seconds. Traffic Rate Traffic Average number of aircraft 8 On average, 8 aircraft entered Brussels throughput per entering the sector during a 10 East High during each 10 minute period. 10 min minute period. Workload Workload per Average time for a controller to 50 The executive controller has to spend 50 flight deal with a flight in the sector. seconds, on average, to handle a flight in Expressed in seconds. Brussels East High. Std Deviation Variability of average time for a 3 The workload per flight is variable at +/-3 of Workload controller to deal with a flight in seconds: the flights require between 47s per flight the sector. Expressed in seconds and 53s to be handled. 1 Base of Aircraft Data - a database of aircraft performance data. 2 Within the CFMU data an airblock defines a piece of airspace as a polygon with a max / min 2 Within the CFMU data an airblock defines a piece of airspace as a polygon with a max / min vertical range. vertical range. A sector is defined as a set of airblocks. A sector is defined as a set of airblocks.32 Project COCA - EEC Report No. 403
  • 43. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL8.2. SELECTED I/D CARD RESULTSThe examples below show one set of I/D cards for one sector from each Complexity Cluster andfrom each sector group. Each set has a card for weekdays6, Saturday and Sunday from phase 1and phase 2.Complexity Cluster 1 Table 3: Brussels West Low / NICKY Low and KOKSY Low I/D CardSector Name Phase 1 Phase 2Brussels West Low/Nicky Low and Koksy LowDay Weekday Saturday Sunday Weekday Saturday SundayDate 23-Apr-04 24-Apr-04 25-Apr-04 26-Aug-04 28-Aug-04 29-Aug-04Opening timeLength (hh:mm) 7:19 4:49 6:40 10:19 8:49 9:00Flight InteractionsDIF per minute 0.204 0.174 0.173 0.142 0.159 0.147Traffic PhaseCruising 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 PairsNormalised 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 EvolutionNb levels crossed 0.75 0.72 0.75 0.83 0.75 0.81DensityTotal cell number 390 390 390 446 446 446Cells with more than 3 aircraft (%) 7% 5% 6% 4% 6% 5%Mixture of Aircraft TypesAverage Ground Speed (knots) 413 415 418 412 414 415Std Deviation of Avg Ground Speed (knots) 30 30 24 28 24 20Sector DimensionsTotal Volume (nm²*100ft) 572,309 572,309 572,309 654,943 654,943 654,943Average 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 RateTraffic throughput per 10 min 8 8 8 8 9 9WorkloadWorkload per flight (s) 55 52 52 55 53 54Std Deviation of Workload per flight (s) 5 4 4 6 4 4CommentsBrussels West Low (see Table 3) is typically a Complexity Cluster 1 sector because this sectorbelongs to “Low” (according to MUAC) airspace (between FL245 and FL335).6 The weekday with the longest opening time was selected.Project COCA - Report No. 403 33
  • 44. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentreThe high values for the DIF per minute indicator (related to the possible interactions betweenflights) 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 withinthis type of sector. The proportion of climbing flights is slightly higher than the proportion ofdescending flights because the north to south flow (flights departing from English airports) hasmore 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 duringthe weekend periods. This is certainly due to military inactivity during the weekends (and thereforemore 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). Thiscan be explained by the small size of the sector (average transit time around 6.5 to 8 minutes) andthe 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. Thisreinforces the complexity of managing the flights within this sector: from 48 s to 61 s are requiredto handle a flight within Brussels West Low/ KOKSY NICKY Low sectors.34 Project COCA - EEC Report No. 403
  • 45. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLComplexity Cluster 2 Table 4: Solling I/D CardSector Name Phase 1 Phase 2SOLLINGDay Weekday Saturday Sunday Weekday Saturday SundayDate 23-Apr-04 24-Apr-04 25-Apr-04 26-Aug-04 28-Aug-04 29-Aug-04Opening timeLength (hh:mm) 10:30 10:49 9:10 8:30 9:19 7:49Flight InteractionsDIF per minute 0.062 0.058 0.047 0.098 0.075 0.081Traffic PhaseCruising 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 PairsNormalised 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 EvolutionNb levels crossed 0.41 0.36 0.53 0.41 0.38 0.45DensityTotal cell number 635 635 635 635 635 635Cells with more than 3 aircraft (%) 3% 2% 2% 2% 2% 1%Mixture of Aircraft TypesAverage Ground Speed (knots) 428 431 431 423 430 428Std Deviation of Avg Ground Speed (knots) 34 28 27 33 30 32Sector DimensionsTotal Volume (nm²*100ft) 931,763 931,763 931,763 931,763 931,763 931,763Average 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 RateTraffic throughput per 10 min 8 7 7 9 8 8WorkloadWorkload per flight (s) 44 43 45 45 44 45Std Deviation of Workload per flight (s) 5 4 6 5 4 5CommentsIn 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 istypically 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,Project COCA - Report No. 403 35
  • 46. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre - 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 CardSector Name Phase 1 Phase 2DELTA HIGHDay Weekday Saturday Sunday Weekday Saturday SundayDate 22-Apr-04 24-Apr-04 25-Apr-04 27-Aug-04 28-Aug-04 29-Aug-04Opening timeLength (hh:mm) 6:49 6:30 4:30 11:30 8:10 5:30Flight InteractionsDIF per minute 0.008 0.013 0.004 0.043 0.045 0.036Traffic PhaseCruising 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 PairsNormalised 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 EvolutionNb levels crossed 0.17 0.31 0.26 0.24 0.34 0.28DensityTotal cell number 1021 1021 1021 1022 1022 1022Cells with more than 3 aircraft (%) 2% 1% 1% 3% 2% 1%Mixture of Aircraft TypesAverage Ground Speed (knots) 439 434 438 437 434 437Std Deviation of Avg Ground Speed (knots) 26 28 26 29 28 32Sector DimensionsTotal Volume (nm²*100ft) 1,498,421 1,498,421 1,498,421 1,499,376 1,499,376 1,499,376Average 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 RateTraffic throughput per 10 min 6 6 6 7 6 7WorkloadWorkload per flight (s) 39 41 40 41 43 42Std Deviation of Workload per flight (s) 4 5 5 6 5 736 Project COCA - EEC Report No. 403
  • 47. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLCommentsDelta High sector (see Table 5) belongs to Complexity Cluster 3 and is representative of the lowcomplexity 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 CHANGETo test the effect of the Brussels sector change, we conducted specific tests to compare the sameday‘s traffic using phase 1 and phase 2 sector configurations. Although a direct comparison is notpossible in absolute terms it was agreed that the same traffic sample would be superimposed uponthe 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) andconfiguration 5.3 was selected to represent the phase 2 airspace (see Figure 24). WST H LNO H LUX H EBMABHN+EBMALUX EBMALUX EBMALUX 4 WST L LNO L LUX L EBMAWSL+EBMALNL E Figure 23: Brussels Phase 1 configuration KOK H NIK H LNO H LUX H EBMABHN + EBMALUX EBMABHN EBMALUX EBMABHN EBMALUX 5.3 KOK L NIK L LNO L LUX L EBMAKOL+EBMANIIIL+EBMALNL EBMAKOL EBMAN L EBMALNL EBMAKOL EBMAN L EBMALNL Figure 24: Brussels Phase 2 configurationTable 6 contains the I/D card results for the ‘before’ and ‘after’ configurations.Project COCA - Report No. 403 37
  • 48. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre Table 6: Comparison of airspace before and after the Brussels sector change COMPARISON Before/After Split OLNO and WEST HIGH / WEST KOKSY NICKY Sector Names OLNO, KOKSY and NICKY OLNO LOW LUX TOTAL LOW LOW LOW HIGH Env before after before after before after before after after AIRAC cycle 255 259 255 259 255 259 255 259 259 Opening time Length (hh:mm) 7:00 7:00 7:00 7:00 7:00 7:00 7:00 7:00 7:00 Flights interaction DIF (-) 0.13 0.13 0.16 0.15 0.13 0.12 0.22 0.23 0.22 Traffic 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 78 Presence 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 0.54 Density Total cell number 707 753 216 199 564 478 390 180 267 Cells with more than 3 aircraft (%) 8% 10% 5% 5% 4% 3% 9% 10% 8% Mixture of aircraft types Average Ground Speed (knots) 434 430 414 414 420 421 417 421 417 Std Deviation of Avg Ground Speed (knots) 25 28 38 35 32 31 29 20 39 Sector dimensions Total Volume (nm²*100ft) 1037455 1104320 317570 292681 827225 701150 572309 263492 391452 Average 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 06:30 Traffic Rate Traffic throughput per 10 min 8 8 6 5 7 7 9 7 5 Workload Workload per flight (s) 47 48 51 52 53 53 54 54 51 Std Deviation of Workload per flight (s) 8 8 4 6 7 7 6 6 1038 Project COCA - EEC Report No. 403
  • 49. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLIt should be noted that this comparison is based on simulated traffic. The August traffic sample wassuperimposed on the April environment. It was agreed that this was a fair method to compare theeffect 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 andthe mixture of aircraft types indicators remained stable.The sectors least impacted are LUX, OLNO Low, then OLNO and West High collapsed. The mostimpacted 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 thechange.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 sectorhas 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 theboundary 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 flightremained stable.To conclude, the reorganisation had no impact linked with the complexity on the LUX sectorevaluated here.OLNO Low.The conclusions are exactly the same as for LUX sector (except the volume of OLNO Lowdecreased by a smaller proportion -8% only). The number of levels crossed increased slightlybetween April and August.The proportion of crossing proximate pairs has slightly increased and may be due to the reductionof airspace volume.The impact on the complexity of OLNO Low sector after the Brussels airspace reorganisation wassmall.Project NCD-COCA - Report No. 403 39
  • 50. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentreOLNO 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 numberand 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 ofthe flight phases. In phase 2 we observed that the percentage of flights in climb increased from31% to 38% while the percentage of flights in cruise decreased from 52% to 43%. The percentageof flights in descent remained stable at 18%. The layer of airspace added to the sector (previouslya 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 thenumber of flights in cruise.West Low has been split into KOKSY Low and NICKY LowThese 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 difficultdue to the differences in coverage, it was deemed reasonable to compare the NICKY and WESTLow sectors as the controller tasks for the REMBA area are now the responsibility of the NICKYsector controller. If we compare the results for Brussels West Low sector with the new NICKY Lowsector we can see a positive effect of the airspace reorganisation in the REMBA area: reduction ofaround 30% in the number of climbing a/c, a reduction of approx. 25% of descending a/c, and adecrease of around 30% of number of FLs crossed.There are some other interesting results: KOKSY Low seems to have roughly the same propertiesas 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.40 Project NCD-COCA - EEC Report No. 403
  • 51. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL8.4. HOTSPOT MAPSThe hotspot maps may help to locate the zones where the interactions between flights areparticularly high over a day. The following maps have been produced with the traffic from the28/08/04. They represent projections on the horizontal plane of Brussels sectors between FL245and FL335 (see Figure 25) and between FL335 and FL450 (see Figure 26). Each square (7.5 nm x7.5 nm) represents the vertical projection of the cells used to mesh the sectors. The colour of thesquares is linked with the maximum value of the DIF in each cell over all the flight levels and all thetime period. The names within the squares refer to the navaid names identified as belonging to thecells. They have been intentionally written in the middle of each for better readability. When morethan one navaid belongs to a cell, one of them has been randomly chosen. TOV R ATLO ID ESI R IN IS PEVAD GOR LO QU R AY AN C OR R IVER KAN D Y LEKKO SILVO LOPIK AR KON OD IN O SU VOX XAMAN R EFSO IN KET METR O N APSI FOXTO MEV KOKSY NICKY DIF 0 C KO LOGAN OD R OB R IMBU D IBR U BESTI R U MER KOMOT DIF 1 ER IN G GILTI SASKI GOESW TOLEN PESER BATAK TILVU GEMTI VELN I DIF VALSU 2 DIF 3 TEBR A R APIX D IBLI KEGIT ALIN A PU TTY BEKEM BABIX R U SAL BU D IP U BOR O D ISMO DIF 4 ABA DIF 5 VABIK BU LAM D EN U T H ELEN LU TOM W ILMA D IBIR D EXON R U D EL DIF 6 TIM OR C AR LA MAD U X SON D I ELSIK BR OGY SOR AT EVOSA MILGI LEBTI DIF ELD OM 7 LIPM ABE D IPKA BAR TU BOGR U GOBN O GESBI ELD AR TR AC A FER D I R OD R I D EN OX AKU XO BIBO AKOVI LER VO GILOM H OR TA ER IGO BEMTI N AVAK AGEN I KEN U M D EPU K ABAX (Y 1unit=7.5 NM) TU K IBER U SISGA N IVOR R EMBA TER LA BATTY POD AT LEN D O GEBSU D OSEL AD U TO AR VOL SU LIS BU LU X PELIX KOGES IBESA LIKN O R ASOK D IPER BAR LI LU MIL VEKIN D EN IN MED IL ID OKO R U D IX LAR EP D ITEL BEN AK OLNO D ELOM GIR EL SOPOK AR C KY ER POL N EKIR POBIX AKIGO W EZEL OSM U D A ABN U R MATIX MOPIL IN GU L D ID U S KEN AK D EPAX VIBOM MOS N EBR U BELD I SU LEX VER MA KEN AP FAMEN N OR PA D EMU L AD U SU R ALAM BITBU N OSPA AD EN U OBIGA LU KA LUX SU D OPALE N ITAR XOR BI N U R MO BILGO R OKR O AR D EN D IN AN N U D R I R ATU M N IKLU AMOGA LESD O ELVES R EMGO ID OSA BETEX PITES ID AR O KOMEL VAKER SOMTU VED U S KU D IN TALU D TILVI LIMGO D ISKI BER GE KOPOR KOVIN AN AR U R EN SA LIPN I IBER A MOSET MAPIG D ID OR GIMER SON U R MED OX AKELU KU R H O TOMPI SOR BU N OR POD U K VELER SOLBA SU IPE J AR N Y SABEX LAR PO D IVED N APIX D IKOL R AN U X SOR AL MAKOT EXIP MOSU D LAU R A KELU D U TELA BATAG KELON LASIV N AN C Y Figure 25: Hotspots map for Brussels sectors between FL245 and FL335Project NCD-COCA - Report No. 403 41
  • 52. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre XAMAN R EFSO IN KET METR O N APSI FOXTO MEV KOKSY NICKY DIF 0 C KO LOGAN OD R OB R IMBU D IBR U BESTI R U MER KOMOT DIF 1 ER IN G GILTI SASKI GOESW TOLEN PESER BATAK TILVU GEMTI VELN I DIF VALSU 2 DIF 3 TEBR A R APIX D IBLI KEGIT ALIN A PU TTY BEKEM BABIX R U SAL BU D IP U BOR O D ISMO DIF 4 ABA DIF 5 VABIK BU LAM D EN U T H ELEN LU TOM W ILMA D IBIR D EXON R U D EL DIF 6 TIM OR C AR LA MAD U X SON D I ELSIK BR OGY SOR AT EVOSA MILGI LEBTI DIF ELD OM 7 LIPM ABE D IPKA BAR TU BOGR U GOBN O GESBI ELD AR TR AC A FER D I R OD R I D EN OX AKU XO BIBO AKOVI LER VO GILOM H OR TA ER IGO BEMTI N AVAK AGEN I KEN U M D EPU K ABAX TU K IBER U SISGA N IVOR R EMBA TER LA BATTY POD AT LEN D O GEBSU D OSEL AD U TO AR VOL SU LIS BU LU X PELIX KOGES IBESA LIKN O R ASOK D IPER BAR LI LU MIL VEKIN D EN IN MED IL ID OKO R U D IX LAR EP D ITEL BEN AK OLNO D ELOM GIR EL SOPOK AR C KY ER POL N EKIR POBIX AKIGO W EZEL OSM U D A ABN U R MATIX MOPIL IN GU L D ID U S KEN AK D EPAX VIBOM MOS N EBR U BELD I SU LEX VER MA KEN AP FAMEN N OR PA D EMU L AD U SU R ALAM BITBU N OSPA AD EN U OBIGA LU KA LUX SU D OPALE N ITAR XOR BI N U R MO BILGO R OKR O AR D EN D IN AN N U D R I R ATU M N IKLU AMOGA LESD O ELVES R EMGO ID OSA BETEX PITES ID AR O KOMEL VAKER SOMTU VED U S KU D IN TALU D TILVI LIMGO D ISKI BER GE KOPOR KOVIN AN AR U R EN SA LIPN I IBER A MOSET MAPIG D ID OR GIMER SON U R MED OX AKELU KU R H O TOMPI SOR BU N OR POD U K VELER SOLBA SU IPE J AR N Y SABEX LAR PO D IVED N APIX D IKOL R AN U X SOR AL MAKOT EXIP MOSU D LAU R A KELU D U TELA BATAG KELON LASIV N AN C Y Figure 26: Hotspots map for Brussels sectors between FL335 and FL450In the majority of Brussels sectors above FL335 (Figure 26), we observe that the interaction valuesare 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 toComplexity Cluster 2 and have low DIF per minute indicator values.Higher numbers of interactions are located in the sectors belonging to “Low” airspace (betweenFL245 and FL335), as shown in Figure 25. The range of DIF values is well spread with specificconcentration of high values in the NICKY sector. The REMBA “cell” appears to be free frominteractions (grey square), which was one of the objectives of the space reorganisation. The highvalues of DIF are located in NICKY but far enough from the LUX and OLNO boundaries, whichmay give the controller more time to handle the flights before transferring them.42 Project NCD-COCA - EEC Report No. 403
  • 53. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL8.5. WORKLOAD RESULTSFollowing the classification process described in Chapter 7, we applied the AMWM method(described in chapter 6) to obtain the workload coefficients.Workload evaluationAfter the optimisation process had been applied to each complexity cluster, the workloadevaluation gave the coefficients (weights ω), shown in Table 7, which were used in Equation 1 forthe two phases. It should be noted that these values cannot be interpreted as actualcontroller task durations. They are computed values that reflect differences in workloadassociated with diverse sector types. Table 7: Complexity Cluster Coefficients Phase 1 Phase 2 Complexity Complexity RoT LC CNF RoT LC CNF Cluster Cluster 1 43 10 49 1 43 10 51 2 38 10 47 2 39 11 48 3 36 10 47 3 36 12 52The resulting workload values computed for the sectors of each complexity cluster are representedin Figure 27. The workload values are spread according to the complexity of each cluster (fromCluster 1: high complexity/high workload to Cluster 3: low complexity/low workload). As seen in theclustering results, the workload values for Cluster 2 sectors are more variable than the otherclusters because Cluster 2 is an intermediate class between high and low complexity.Project NCD-COCA - Report No. 403 43
  • 54. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre Figure 27: Workload values per Complexity Cluster8.6. DYNAMIC RESULTS – REPORTED WORKLOAD RESULTS8.6.1. Phase 1Preliminary 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. Thereis not a single case of reported extreme (“excessive”) workload. One possibility was that controllersmight be reluctant to rate workload as “excessive.” In any event, a rating scale that provides anarrow set of responses does not provide the richest data available.44 Project NCD-COCA - EEC Report No. 403
  • 55. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLA second concern was the interval between workload ratings. To minimise interruption, in phase 1it was decided to set this at 20 minutes. Ultimately, it was decided that such a lengthy periodbetween ratings risks insensitivity to complexity fluctuations, which might appear on a much morefrequent basis. 120 100 Cum frequency (nr) 80 60 40 20 0 1 2 3 4 5 Rating Figure 28: DECO Reported Workload distribution, phase 1 50 Cumulative percent (%) 40 30 20 10 0 1 2 3 4 5 workload rating (1-5) Brussels DECO Hannover 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 Brussels DECO Hannover 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 5.1Project NCD-COCA - Report No. 403 45
  • 56. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentreNotice in Figure 29 and Table 8 that the phase 1 distribution was very “peaked,” with a slightnegative skew - that is, controllers reported the highest level of workload very infrequently (lessthan 1% of all reports). The pattern across sector groups showed no clear differences.8.6.2. Phase 2Data qualityWorkload data seemed more robust from the phase 2 data collection. As shown in Figure 30 andTable 9 below, data distributions generally followed a more normal “bell curve” shape than they didin 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 datashould not be used to compare workload across sector groups. 50 Cumulative percent (%) 40 30 20 10 0 1 2 3 4 5 6 Workload rating (1-6) Brussels DECO Hannover 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 HANNOVER 6 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 6.6Workload: Day-of-week differences across sector groupsIt was clear that traffic load varied by day-of-week (see paragraph 5.3.2), and that three patternscould be clearly distinguished, corresponding to weekday, Saturday and Sunday periods. It waswondered whether a similar pattern would emerge for the reported workload data. Notice that therewas no reason to suspect that this would have been the case. Traffic load, after all, is only one of anumber of factors that drive the controllers’ experience of workload. Nonetheless, it seemed aninteresting possibility to evaluate.46 Project NCD-COCA - EEC Report No. 403
  • 57. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLFigure 31 contains three graphs that show the reported workload ratings (in terms of cumulativepercentage), for each of the three weekly periods. The way to interpret these graphs is that a givenline (e.g. blue) captures all workload data from a given weekly period (Saturday, in this example). Aline that is skewed rightward (i.e., with a peak toward the right) indicates a greater number of highworkload ratings. Brussels sector group DECO sector group Hannover sector group 45 45 45 Sat Sat Sat Sun 40 Sun 40 Sun 40 Weekdays Weekdays Weekdays 35 35 35 Cumulative percent (% Cumulative percent (%) Cumulative percent (%) 30 30 30 25 25 25 20 20 20 15 15 15 10 10 10 5 5 5 0 0 0 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 Workload rating (1-6) Workload rating (1-6) Workload rating (1-6) 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 groupsAnother question was whether the reported workload would be sensitive to known and assumedfluctuations over the course of the day in traffic density/complexity. It is known that MUAC facesseveral peak periods a day, and it was hoped that the reported workload data would be sensitive tothese.Raw ratings were graphed as a function of time, for each of the three sector groups and for eachweekly period; see Figure 32 to Figure 34. Notice that ratings are not standardised, and can differfrom one controller to the next. Thus what one controller rates a “6” might be rated as a “5” byanother controller. Brussels median workload over the day, for the three weekly periods 5 Median workload rating (1-6) 4 3 2 1 0 0700- 0800- 0900- 1000- 1100- 1200- 1300- 1400- 1500- 1600- 1700- 1800- 0800 0900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 time-of-day (local) weekday Sat Sun Figure 32: Brussels median workload for weekdays, Saturday and SundayProject NCD-COCA - Report No. 403 47
  • 58. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre DECO median workload over the day, for the three weekly periods 5 Median workload rating (1-6) 4 3 2 1 0 0700- 0800- 0900- 1000- 1100- 1200- 1300- 1400- 1500- 1600- 1700- 1800- 0800 0900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 time-of-day (local) weekday Sat Sun Figure 33: DECO median workload for weekdays, Saturday and Sunday Hannover median workload over the day, for the three weekly periods 5 Median workload rating (1-6) 4 3 2 1 0 0700- 0800- 0900- 1000- 1100- 1200- 1300- 1400- 1500- 1600- 1700- 1800- 0800 0900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 time-of-day (local) weekday Sat Sun Figure 34: Hannover median workload for weekdays, Saturday and Sunday48 Project NCD-COCA - EEC Report No. 403
  • 59. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLIn Figure 35 to Figure 37 the traffic load is superimposed on the reported workload results. Thefigures show that the workload peaks correspond fairly well to the increases in traffic at varioustimes 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 ofthe 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. WL WL WL Brussels - Weekday Brussels - Saturday Brussels - Sunday traffic load traffic load traffic load 5 140 5 140 5 140 120 120 120 4 4 4 Median WL rating (1-6) Median WL rating (1-6) Median WL rating (1-6) 100 100 100 Avg nr flights / hr Avg nr flights / hr Avg nr flights / hr 3 3 3 80 80 80 60 60 60 2 2 2 40 40 40 1 1 1 20 20 20 0 0 0 0 0 0 time of day (0700-1900) time of day (0700-1900) time of day (0700-1900) Figure 35: Reported workload as a function of time-of-day and traffic load, Brussels WL WL WL DECO - Weekday DECO - Saturday DECO - Sunday traffic load traffic load traffic load 5 140 5 140 5 140 120 120 120 4 4 4 Median WL rating (1-6) Median WL rating (1-6) Median WL rating (1-6) 100 100 100 Avg nr flights / hr Avg nr flights / hr Avg nr flights / hr 3 3 3 80 80 80 60 60 60 2 2 2 40 40 40 1 1 1 20 20 20 0 0 0 0 0 0 time of day (0700-1900) time of day (0700-1900) time of day (0700-1900) Figure 36: Reported workload as a function of time-of-day and traffic load, DECO WL WL WL Hannover - Weekday Hannover - Saturday Hannover - Sunday traffic load traffic load traffic load 5 140 5 140 5 140 120 120 120 4 4 4 Median WL rating (1-6) Median WL rating (1-6) Median WL rating (1-6) 100 100 100 Avg nr flights / hr Avg nr flights / hr Avg nr flights / hr 3 3 3 80 80 80 60 60 60 2 2 2 40 40 40 1 1 1 20 20 20 0 0 0 0 0 0 time of day (0700-1900) time of day (0700-1900) time of day (0700-1900) Figure 37: Reported workload as a function of time-of-day and traffic load, HannoverProject NCD-COCA - Report No. 403 49
  • 60. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentreDuring a review of the results, it was speculated that the number of open sectors might influencethe reported workload. This interaction is shown in Figure 38 to Figure 40 which each contain threegraphs (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, infact, seem to be a relationship between workload and number-of-open-sectors, at least forweekdays (note that weekday averages are based on larger data sets). WL WL WL Brussels - Weekday Brussels - Saturday Brussels - Sunday nr open sector nr open sector nr open sector 5 6 5 6 5 6 5 5 5 4 4 4 Median WL rating (1-6) Median WL rating (1-6) Median WL rating (1-6) Nr open sectors, Nr open sectors, 4 Nr open sectors, 4 4 hourly avg hourly avg hourly avg 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 0 0 0 0 0 0 time of day (0700-1900) time of day (0700-1900) time of day (0700-1900) Figure 38: Reported workload as a function of time-of-day and number of open sectors, Brussels WL WL WL DECO - Weekday DECO - Saturday DECO - Sunday nr open sector nr open sector nr open sector 5 6 5 6 5 6 5 5 5 4 4 4 Median WL rating (1-6) Median WL rating (1-6) Median WL rating (1-6) Nr open sectors, Nr open sectors, 4 Nr open sectors, 4 4 hourly avg hourly avg hourly avg 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 0 0 0 0 0 0 time of day (0700-1900) time of day (0700-1900) time of day (0700-1900) Figure 39: Reported workload as a function of time-of-day and number of open sectors, DECO WL WL WL Hannover - Weekday Hannover - Saturday Hannover - Sunday nr open sector nr open sector nr open sector 5 6 5 6 5 6 5 5 5 4 4 4 Median WL rating (1-6) Median WL rating (1-6) Median WL rating (1-6) Nr open sectors, Nr open sectors, 4 Nr open sectors, 4 4 hourly avg hourly avg hourly avg 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 0 0 0 0 0 0 time of day (0700-1900) time of day (0700-1900) time of day (0700-1900) Figure 40: Reported workload as a function of time-of-day and number of open sectors, Hannover50 Project NCD-COCA - EEC Report No. 403
  • 61. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL8.7. COMPLEXITY FACTORS ASSOCIATED WITH HIGH WORKLOADOne chief aim in collecting workload data was to determine whether there was a systematicrelationship 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. thetop of the workload scale), all of the complexity factors that were relevant during that period. Fromthe list of more than two dozen factors (plus provision for “others” to be identified), controllers couldcheck 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 throughcontroller interviews, etc. This current list appears in Annex G.Project NCD-COCA - Report No. 403 51
  • 62. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre8.7.1. Complexity Factors, by Sector GroupTable 10 to Table 12 show the top ten factors identified more than once for high workload (i.e. 5 or6) periods (irrespective of duration), for the three sector groups. Certain similarities appear acrossthe sector groups. For instance, “mix of climbing and descending” aircraft is cited as either the #1or #2 factor across all three sector groups. “High number of aircraft” and “Several traffic flowsconverging at the same point” were also often-cited factors across all three sector groups. Thereare, however, at least a few slight differences between the sector groups in terms of the factorscited. Brussels controllers tended to cite R/T congestion and crossing points close to sectorboundaries, 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 1052 Project NCD-COCA - EEC Report No. 403
  • 63. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL 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 108.7.2. Complexity Factors, by Weekly PeriodAs shown in Table 13 to Table 158, when the factors are broken out by weekly period (i.e. eitherweekdays, Saturday or Sunday) the influence of the weekly period seemed minimal—the samefactors generally appeared, and in the same order (e.g. notice that “mix of climbing anddescending traffic flows” was the most-cited factor, across all weekly periods). Table 13: Weekday self-reported complexity factors associated with high workload Cumulative WEEKDAYS 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 4.18 The cumulative percent in Table 13 to Table 15 do not equal 100% because they only include the top ten self-reported factors.Project NCD-COCA - Report No. 403 53
  • 64. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre Table 14: Saturday self-reported complexity factors associated with high workload Cumulative SATURDAY percent (%) Mix of climbing and descending traffic flows 13.0 Mix of climbing or descending flights in cruise 10.4 Several traffic flows converging at the same point 9.6 Multiple crossing points in sector 9.6 Traffic bunching 8.7 Mix of high and low performance aircraft 7.8 R/T congestion 7.8 High number of aircraft 7.8 Crossing points close to boundaries 4.3 Merging of arrival flows 3.5 Table 15: Sunday self-reported complexity factors associated with high workload Cumulative SUNDAY percent (%) Mix of climbing and descending traffic flows 13.0 Several traffic flows converging at the same point 10.4 High number of aircraft 10.4 Multiple crossing points in sector 8.7 Mix of climbing or descending flights in cruise 7.8 Traffic bunching 7.8 R/T congestion 7.0 Merging of arrival flows 6.1 Pilots not listening to R/T 6.1 Mix of high and low performance aircraft 5.28.8. COMPLEXITY PRECURSORS: FOCUS GROUP RESULTSA focus group session was held at MUAC on 2 Oct 2004, using a subset of active MUACcontrollers. Part of the aim of this session was to verify the candidate list of complexity factorsidentified thorough COCA and other (NASA) ongoing work. A secondary aim of this session was toidentify critical factor combinations (i.e. combinations of two or more individual factors) that aresufficient 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 othercomplexity factor (identified on the candidate list) are sufficient to raise complexity - and hencecontroller workload - above “redline.”54 Project NCD-COCA - EEC Report No. 403
  • 65. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLThis 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.Project NCD-COCA - Report No. 403 55
  • 66. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre9. GENERAL SUMMARY AND CONCLUSIONSThe study evaluated the operational complexity of all sectors within Maastricht airspace anddeveloped I/D cards for each sector in every configuration that occurred during both phases of thestudy. 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 sitedwas, and remains a High complexity sector, but the complexity values have been reducedsignificantly as a result of the airspace change.The comparison between the West Low sector and the new NICKY Low sector where the REMBAnavaid is located shows a reduction of approximately 30% in the proportion of climbing aircraft, areduction of around 25% of flights in descent, and a reduction of approximately 30% in the numberof FLs crossed. When these reductions are considered in combination with a lower value ofworkload per flight it suggests strongly that the airspace changes have successfully reducedcomplexity in the REMBA area.The sector I/D cards provide objective measurements of complexity factors and will be useful toestablish a baseline of complexity against which future airspace changes can be measured. Theyprovide objective measurements of factors influencing controller workload linked to complexity. Theworkload per flight indicator shows how controller workload is influenced not only by the number offlights, but by the interaction between flights and the controllers tasks in the context of thecomplexity 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 thecomplexity variations that occur over a day, between different days of the week, weekdays andweekends, and between different sector configurations. Knowledge of these variations could assistmanagers in optimising controller resources linked directly to the variability of traffic demand andcomplexity fluctuations, and in the design of future sector changes. The objective and subjectivedata could be used by safety experts to establish and measure sector safety limits and to assessairspace changes in terms of increased safety. Given the projected increases in air traffic this couldlead to the development of end-to-end processes to enable airspace designs to be evaluated andbest practice methods based on complexity assessment to be implemented to ensure that futureairspace changes are introduced safely.However, further work could be conducted to validate the study of MUAC against other EuropeanAirspace service providers and introduce European complexity baseline figures.The study showed that the method to calculate workload showed promise and, in many cases hada good correlation to the controllers reported perceived workload. But, additional work is needed toidentify those indicators and situations where the link is weakest, or missing, and incorporate themin an improved workload calculation. Further research should be taken to identify the point at whichcontrollers workload is no longer linked in a linear fashion to the number of flights, but is influencedby factors considered to be more complex and causing higher workload. Further, subjective resultsunderscored the potentially critical role of factor combinations, especially interactions involving asubset of eight or so “precursor factors.” It might be instructive for future research to address suchpotential 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 amethod and toolkit to predict short- and medium-term complexity in a real time environment.56 Project NCD-COCA - EEC Report No. 403
  • 67. Étude de Complexité du Centre de Maastricht EUROCONTROL TRADUCTION EN LANGUE FRANÇAISE ÉTUDE DE COMPLEXITÉ DU CENTRE DE MAASTRICHT1. INTRODUCTIONUne récente étude sur la sécurité au Centre de Maastricht, associée au rapport annuel de 2002sur la sécurité, a mis en évidence la nécessité d’étudier la complexité de l’espace aérien dont leCentre a la charge. Cette étude a en effet mis en évidence des “foyers” d’incidents dans lesquels,daprès l’analyse des données recueillies a posteriori, la complexité pourrait avoir joué un rôleimportant. L’espace aérien à proximité de l’aide à la navigation REMBA, située dans le groupe desecteurs de Bruxelles, figure parmi les zones géographiques signalées dans l’étude. Desprocessus de suivi de la sécurité montrent que le nombre d’incidents autour de REMBA aaugmenté au fil des ans. C’est pourquoi l’espace aérien en question a été modifié dans le cadredune stratégie visant à réduire le nombre dincidents.Comme indiqué ci-dessus, des rapports d’enquête sur les incidents ont laissé entendre que lacomplexité pourrait avoir joué un rôle important dans ces événements mais aucun élémentcommun quantifiable n’a pu être décelé dans le trafic ou les conditions statiques de l’espaceaérien.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 didentifier etde mesurer les facteurs de complexité de lespace aérien existant dans la zone de compétencegénérale du Centre de Maastricht, et la zone REMBA en particulier. L’étude a été menée en deuxtemps. La première phase sest déroulée du 21 au 26 avril 2004, avant que des modifications nesoient apportées à l’espace, et la seconde du 25 au 30 août 2004, après modifications. Pendantles deux phases, l’équipe COCA a recueilli et compilé des données opérationnelles statiques etdynamiques entre 07h00 et 19h00 (heure locale) du mercredi au dimanche et entre 07h00 et13h00 (heure locale) le lundi.Les résultats de cette étude pourront venir en soutien du projet MANTAS9 ainsi que des initiativeset processus de gestion de la sécurité au Centre de Maastricht. On notera en outre que cetteétude 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 desperformances 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 secteursgénériques (nouvelle sectorisation dynamique), des routes mixtes (abandon progressif des routes fixes auprofit de l’espace aérien à itinéraire libre) et préconise l’absence de groupes de secteurs fixes, l’utilisationflexible de l’espace aérien et le contrôle radar sans communications vocales.Projet COCA – Rapport CEE n° 403 57
  • 68. EUROCONTROL Étude de Complexité du Centre de Maastricht1.1. STRUCTURE DU DOCUMENTLe présent document expose la méthode utilisée pour mener l’étude de complexité du Centre deMaastricht et les résultats qui en sont ressortis. Le document est structuré comme suit :Chapitre 2 Historique du projet COCAChapitre 3 Objectifs de l’étudeChapitre 4 Description générale de l’espace aérien du Centre de MaastrichtChapitre 5 Données statiques et dynamiques recueillies et traitées pour la présente étudeChapitre 6 Méthode employée pour évaluer la charge de travail des contrôleursChapitre 7 Analyse statistique par groupe de complexité au niveau des secteursChapitre 8 Résultats obtenus avec les données tant statiques que dynamiquesChapitre 9 Synthèse générale et conclusions2. OBJECTIFS DE L’ETUDE DE COMPLEXITE DU CENTRE DE MAASTRICHTLes principaux objectifs de létude 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.58 Projet COCA - Rapport CEE n° 403
  • 69. Étude de Complexité du Centre de Maastricht EUROCONTROL • 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 METHODELévaluation de la complexité opérationnelle inhérente aux flux de trafic du Centre de Maastricht etaux caractéristiques environnementales de lespace aérien sest faite selon une approchequantitative qui a consisté, dans un premier temps, à définir les mesures de complexité de natureà 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 dessecteurs et aspects figés, propres à l’environnement de l’espace aérien) et des donnéesdynamiques (comportement opérationnel, variabilité du trafic).L’ensemble de mesures obtenu a été systématiquement évalué pour tous les secteurs du Centrede Maastricht, dans chaque configuration (de secteur) rencontrée au cours des deux phases decollecte de données. Cest ainsi quont été obtenues des mesures quantitatives pour lesindicateurs sélectionnés, qui servent de base aux cartes didentité de secteurs.Toutes les cartes d’identité peuvent être consultées surhttp://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 résultatspour 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 auvendredi) et deux cartes distinctes pour le samedi et le dimanche. L’analyse a été réalisée sur lesimulateur 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 léchantillon ; • paramètres requis pour les indicateurs de complexité sélectionnés.Projet COCA – Rapport CEE n° 403 59
  • 70. EUROCONTROL Étude de Complexité du Centre de MaastrichtÀ l’issue de plusieurs réunions entre le Centre de Maastricht et l’Équipe COCA, les indicateurs decomplexité 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 desrésultats 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 CONCLUSIONSLétude a permis dévaluer la complexité opérationnelle de tous les secteurs situés dans lespaceaérien de Maastricht et détablir des cartes d’identité pour chaque secteur dans toutes lesconfigurations qui se sont présentées au cours des deux phases de l’étude. Une classification dessecteurs selon leur complexité (élevée, moyenne ou faible) a en outre été réalisée. Les résultatsde l’étude montrent que le secteur dans lequel est situé l’aide à la navigation REMBA était, etdemeure, un secteur de complexité élevée, mais les valeurs de complexité ont été sensiblementréduites à la suite du réaménagement de lespace aérien.La comparaison entre le secteur West Low et le nouveau secteur NICKY Low, où l’aide à lanavigation REMBA est située, fait apparaître une diminution d’environ 30% du nombre d’aéronefsen montée et d’environ 25% des vols en descente, ainsi qu’une baisse d’environ 30% du nombrede niveaux de vol franchis. Ces réductions, conjuguées à une charge de travail par vol moinsélevée, donnent clairement à penser que la réorganisation de l’espace aérien a permis de réduirela complexité dans la zone REMBA.Les cartes d’identité par secteur, qui fournissent des mesures objectives des facteurs decomplexité, serviront à établir une base de référence de la complexité pour l’évaluation desmodifications futures de lespace aérien. Elles donnent des mesures objectives des facteurs ayantune incidence sur la charge de travail des contrôleurs en rapport avec la complexité. L’indicateurde charge de travail par vol montre comment la charge de travail des contrôleurs est influencéenon seulement par le nombre de vols mais aussi par l’interaction entre les vols et les tâchesassignées au contrôleur dans le contexte de la complexité du secteur considéré.60 Projet COCA - Rapport CEE n° 403
  • 71. Étude de Complexité du Centre de Maastricht EUROCONTROLLes cartes d’identité de complexité et le calcul de la charge de travail pourraient servir à décelerles variations de complexité au cours dune journée, entre les différents jours de la semaine etentre les jours de semaine et de week-end, et entre les diverses configurations de secteur. Laconnaissance de ces variations aiderait les gestionnaires à optimiser les ressources encontrôleurs, qui sont directement liées à la variabilité de la demande de trafic et aux fluctuations dela complexité, et à concevoir les futures modifications des secteurs. Les experts en sécuritépourraient mettre à profit les données objectives et subjectives recueillies pour définir et mesurerles limites de sécurité des secteurs et évaluer les changements à apporter à l’espace aérien envue du renforcement de la sécurité. Compte tenu de l’essor prévu du trafic aérien, il pourrait êtrepossible de concevoir des processus de bout en bout, qui permettraient d’évaluer l’organisation del’espace aérien et les meilleures pratiques à adopter sur la base dune évaluation de lacomplexité ; solutions à mettre en œuvre pour s’assurer que les futurs aménagements de lespaceaérien sont apportés en toute sécurité.Toutefois, le travail effectué pourrait être complété par une validation de l’étude du Centre deMaastricht par rapport aux résultats d’autres prestataires européens de service de navigationaérienne ainsi que par la prise en compte de données de référence européennes sur la complexitéde lespace aérien.L’étude a montré que la méthode de calcul de la charge de travail est prometteuse et présentesouvent une bonne corrélation avec la charge de travail telle qu’elle est perçue par lescontrôleurs. Un complément d’étude est cependant nécessaire pour mettre en évidence lesindicateurs et les situations où le lien est le plus faible, voire absent, et les intégrer dans uneméthode améliorée de calcul de la charge de travail. Il faudrait en outre rechercher le point à partirduquel la charge de travail des contrôleurs n’est plus liée de façon linéaire au nombre de vols maissubit l’influence de facteurs jugés plus complexes, qui font accroître ladite charge. Par ailleurs, lesrésultats subjectifs ont mis en évidence le rôle potentiellement critique des combinaisons defacteurs, notamment les interactions associant une série denviron huit "facteurs précurseurs".Dans le cadre de recherches futures, il pourrait être intéressant d’étudier ces interactionspotentielles, 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, enparallèle, à lélaboration dune méthode et dune panoplie doutils destinés à prévoir la complexité àcourt et moyen termes dans un environnement en temps réel.Projet COCA – Rapport CEE n° 403 61
  • 72. EUROCONTROL Étude de Complexité du Centre de Maastricht Page laissée intentionellement blanche62 Projet COCA - Rapport CEE n° 403
  • 73. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL ANNEXESProject COCA - EEC Report No. 403 63
  • 74. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre Page Intentionally left blank64 Project COCA – EEC Report No. 403
  • 75. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLANNEX A - CENTRE CONFIGURATIONSThe configurations that have been used during the two phases are highlighted (configurationnumber highlighted). The percentage of use represents the percentage the configuration has beenused (separated according to the three period weekdays (We) /Saturday (Sa) /Sunday (Su)) duringthe time we were there.Phase 1BRUSSELS Percentage of useConfig No Grouping together of sectors Configuration composition We Sa Su WST H LNO H LUX H 1 EBMAASU 1% 0% 0% WST L LNO L LUX L WST H LNO H LUX H 2 EBMAWST+EBMAUCE E 4% 4% 29% WST L LNO L LUX L WST H LNO H LUX H 3 EBMAWST+EBMALNT+EBMALUX E E 10% 37% 3% WST L LNO L LUX L WST H LNO H LUX H EBMABHN+EBMALUX E 4 62% 24% 43% WST L LNO L LUX L EBMAWSL+EBMALNL E WST H LNO H LUX H EBMAWST+EBMABEH E 4.1 12% 17% 5% WST L LNO L LUX L EBMALNL+EBMALXL E WST H LNO H LUX H EBMABWH+EBMALNT+EBMALUX E E 4.2 3% 0% 15% WST L LNO L LUX L EBMAWSL WST H LNO H LUX H EBMABWH+EBMABEH E 5 9% 17% 0% WST L LNO L LUX L EBMAWSL+EBMALNL+EBMALXL E E WST H LNO H LUX H EBMABWH+EBMALNH+EBMALUX E E 5.1 0% 0% 5% WST L LNO L LUX L EBMAWSL+EBMALNL EProject COCA - EEC Report No. 403 65
  • 76. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentreDECO Configuration Grouping together of Configuration Percentage of use number sectors composition We Sa Su DEL H COA H 1 EDYYDCO 2% 4% 0% DEL L COA L DEL H COA H 2 EHDELTA+EDYYCST E 24% 42% 63% DEL L COA L DEL H COA H EHDELHI+EDYYCST E 3 60% 54% 38% DEL L COA L EHDELMD DEL H COA H EHDELHI+EDYCOHI E 4 13% 0% 0% DEL L COA L EHDELMD+EDYCOLO EHANNOVER Percentage of use Configuration Config No Grouping together of sectors composition We Sa Su RHR H MNS H SOL H HAM H 1 EDYMRHS 0% 0% 1% RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H 2 EDYMURH+EDYYEST E 1% 3% 0% RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H 3 EDYMURH+EDYYSOL+EDYYHAM E E 27% 35% 27% RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H 3.1 EDYYRHR+EDYYMNS+EDYYEST E EDYYEST EDYYEST 0% 0% 0% RHR L MNS L SOL L HAM L66 Project COCA – EEC Report No. 403
  • 77. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL RHR H MNS H SOL H HAM H EDYYRHR+EDYYMNS+ E 4 EDYYSOL+EDYYHAM E 56% 38% 45% RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYMURH+EDYESHI E 4.2 EDYSOLO+EDYHALO E 0% 6% 0% RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYYRHR+EDYYMNS+EDYESHI E E 5 13% 0% 26% EDYSOLO+EDYHALO E RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYYHHW+EDYYSOL+EDYYHAM EDYYHHW E EDYYHHW E 5West 0% 19% 0% EDYRHLO+EDYMNLO E RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYYHHW +EDYESHI E 6 EDYRHLO+EDYMNLO+ 3% 0% 0% EDYSOLO+EDYHALO RHR L MNS L SOL L HAM LPhase 2BRUSSELSThe KOKSY and NICKY sectors have been introduced. This operational change was called forfollowing safety concerns in the REMBA area, known to be particularly complex. In order to providemore space to resolve conflicts in this area, the new sector boundaries were extended eastwards. Percentage ofConfig No Grouping together of sectors Configuration composition use We Sa Su KOK H NIK H LNO H LUX H 1 EBMAASU 0% 0% 0% KOK L NIK L LNO L LUX L KOK H NIK H LNO H LUX H 2 EBMAWST+EBMAUCE E 5% 3% 1% KOK L NIK L LNO L LUX L KOK H NIK H LNO H LUX H 3.1 EBMAWST+EBMALNT+EBMALUX E E 4% 14% 24% KOK L NIK L LNO L LUX LProject COCA - EEC Report No. 403 67
  • 78. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre KOK H NIK H LNO H LUX H 3.2 EBMAKOK+EBMANIK+EBMAUCE E E 1% 0% 0% KOK L NIK L LNO L LUX L KOK H NIK H LNO H LUX H EBMABWH + EBMAUCE 3.3 0% 0% 0% KOK L NIK L LNO L LUX L EBMAWSL KOK H NIK H LNO H LUX H 4.1 EBMAKOK+EBMANIK+EBMALNT+EBMALUX 0% 0% 0% KOK L NIK L LNO L LUX L KOK H NIK H LNO H LUX H EBMAWST + EBMABEH 4.2 0% 0% 0% KOK L NIK L LNO L LUX L EBMALNL + EBMALXL KOK H NIK H LNO H LUX H EBMAWST+EBMALNH+EBMALUX E E 4.3 0% 0% 0% KOK L NIK L LNO L LUX L EBMALNL KOK H NIK H LNO H LUX H EBMABHN + EBMALUX 4.4 55% 47% 40% KOK L NIK L LNO L LUX L EBMAWSL + EBMALNL KOK H NIK H LNO H LUX H EBMABWH+EBMALNT+EBMALUX E E 4.5 2% 0% 0% KOK L NIK L LNO L LUX L EBMAWSL KOK H NIK H LNO H LUX H EBMABWH+EBMALNT+EBMALUX E E 5.1 0% 0% 0% KOK L NIK L LNO L LUX L EBMAKOL+ EBMANIL KOK H NIK H LNO H LUX H EBMAKOK+EBMABHM+EBMALUX E E 5.2 0% 0% 0% KOK L NIK L LNO L LUX L EBMANIL+EBMALNL E KOK H NIK H LNO H LUX H EBMABHN + EBMALUX 5.3 22% 10% 0% KOK L NIK L LNO L LUX L EBMAKOL+EBMANIL+EBMALNL E E68 Project COCA – EEC Report No. 403
  • 79. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL KOK H NIK H LNO H LUX H EBMAKOK+EBMANIK+EBMABEH E E 5.4 0% 0% 0% KOK L NIK L LNO L LUX L EBMALNL + EBMALXL + + KOK H NIK H LNO H LUX H EBMABWH+EBMALNH+EBMALUX E E 5.5 0% 11% 3% KOK L NIK L LNO L LUX L EBMAWSL + EBMALNL KOK H NIK H LNO H LUX H EBMAKOK + EBMABNE 5.6 0% 0% 0% KOK L NIK L LNO L LUX L EBMANIL+EBMALNL+EBMALXL E E KOK H NIK H LNO H LUX H EBMABWH+EBMABEH E 5.7 9% 15% 33% KOK L NIK L LNO L LUX L EBMAWSL+EBMALNL+EBMALXL E E KOK H NIK H LNO H LUX H EBMABWH+EBMABEH E 6 2% 0% 0% EBMAKOL+EBMANIL+EBMALNL +EBMALXL E E E KOK L NIK L LNO L LUX LDECO Grouping Percentage of use Configuration together of Configuration composition number We Sa Su sectors DEL H COA H 1 EDYYDCO 0% 0% 4% DEL L COA L DEL H COA H 2 EHDELTA + EDYYCST 6% 31% 50% DEL L COA L DEL H COA H EHDELHI + EDYYCST 3.1 76% 69% 38% DEL L COA L EHDELMD DEL H COA H EHDELTA + EDYCOHI 3.2 0% 0% 0% DEL L COA L EDYCOLO DEL H COA H EHDELHI + EDYCOHI 4 18% 0% 8% DEL L COA L EHDELMD + EDYCOLOProject COCA - EEC Report No. 403 69
  • 80. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentreHANNOVER Config Configuration Percentage of use Grouping together of sectors No composition We Sa Su RHR H MNS SOL H HAM H 1 EDYYMRHS EDYYMRHS EDYYMRHS 0% 0% 0% RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H 2 EDYMURH + EDYYEST EDYMURH EDYYEST EDYMURH EDYYEST 0% 3% 10% RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYMURH + EDYYSOL + EDYMURH EDYYSOL EDYMURH EDYYSOL 3.1 EDYYHAM 16% 23% 24% EDYYHAM EDYYHAM RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYYRHR + EDYYMNS + EDYYRHR EDYYMNS EDYYRHR EDYYMNS 3.2 EDYYEST 0% 18% 8% EDYYEST EDYYEST RHR L MNS L SOL L HAM L 3.3 RHR H MNS H SOL H HAM H EDYYHHW + EDYYEST EDYYHHW EDYYEST EDYYHHW EDYYEST 0% 0% 0% RHR L MNS L SOL L HAM L EDYYHLW EDYYHLW EDYYHLW RHR H MNS H SOL H HAM H EDYYMURH + EDYESHIII EDYYMURH EDYESH EDYYMURH EDYESH 3.4 0% 0% 0% RHR L MNS L SOL L HAM L EDYYHLE EDYYHLE EDYYHLE RHR H MNS H SOL H HAM H EDYYRHR+EDYYMNS+EDYYSOL+E EDYYRHR EDYYMNS EDYYSOL E EDYYRHR EDYYMNS EDYYSOL E 4.1 DYYHAM DYYHAM 59% 54% 42% DYYHAM RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYMURH + EDYESHIII EDYMURH EDYESH EDYMURH EDYESH 4.2 1% 0% 8% RHR L MNS L SOL L HAM L EDYSOLO + EDYHALO EDYSOLO + EDYHALO EDYSOLO + EDYHALO RHR H MNS H SOL H HAM H EDYMURH+EDYSOHIII EDYYHA EDYMURH EDYSOH + EDYYHA EDYMURH EDYSOH EDYYHA M M M 4.3 0% 0% 0% RHR L MNS L SOL L HAM L EDYSOLO EDYSOLO EDYSOLO70 Project COCA – EEC Report No. 403
  • 81. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL RHR H MNS H SOL H HAM H EDYYHHW + EDYYEST EDYYHHW EDYYEST EDYYHHW EDYYEST 4.4 0% 0% 0% RHR L MNS L SOL L HAM L EDYRHLO + EDYMNLO EDYRHLO EDYMNLO EDYRHLO EDYMNLO RHR H MNS H SOL H HAM H EDYYRHR+EDYMNHIII EDYYEST EDYYRHR+EDYMNH + EDYYEST EDYYRHR+EDYMNH EDYYEST 4.5 0% 0% 0% EDYMNLO EDYMNLO EDYMNLO RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYYHHW+EDYYSOL+EDYYHAM EDYYHHW EDYYSOL EDYYHAM EDYYHHW EDYYSOL EDYYHAM 5.1 0% 0% 0% EDYRHLO+EDYMNLO EDYRHLO EDYMNLO EDYRHLO EDYMNLO RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYYRHR+EDYMNHIII EDYESHIII EDYYRHR EDYMNH + EDYESH EDYYRHR EDYMNH EDYESH 5.2 0% 0% 0% EDYMNLO+EDYYHLE EDYMNLO EDYYHLE EDYMNLO EDYYHLE RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYYRHR+EDYYMNS+EDYESHIII EDYYRHR EDYYMNS EDYESH EDYYRHR EDYYMNS EDYESH 5.3 23% 0% 8% EDYSOLO+EDYHALO EDYSOLO EDYHALO EDYSOLO EDYHALO RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYYHHW+EDYSOHIII EDYYHAM EDYYHHW EDYSOH + EDYYHAM EDYYHHW EDYSOH EDYYHAM 5.4 0% 0% 0% EDYYHLW+EDYSOLO EDYYHLW EDYSOLO EDYYHLW EDYSOLO RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYYRHR+EDYMNHIII EDYYSOL+ EDYYRHR EDYMNH + EDYYSOL EDYYRHR EDYMNH EDYYSOL EDYYHAM EDYYHAM EDYYHAM 5.5 0% 1% 0% RHR L MNS L SOL L HAM L EDYMNLO EDYMNLO EDYMNLO RHR H MNS H SOL H HAM H EDYYHHW + EDYESHIII EDYYHHW EDYESH EDYYHHW EDYESH 6.1 0% 0% 0% EDYRHLO+EDYMNLO+EDYSOLO EDYRHLO EDYMNLO EDYSOLO EDYRHLO EDYMNLO EDYSOLO RHR L MNS L SOL L HAM L +EDYHALO EDYHALO EDYHALO RHR H MNS H SOL H HAM H EDYYRHR+EDYMNHIII+EDYESHIII EDYYRHR EDYMNH + EDYESH EDYYRHR EDYMNH +EDYESH 6.2 1% 0% 0% EDYMNLO+EDYSOLO+EDYHALO EDYMNLO EDYSOLO EDYHALO EDYMNLO EDYSOLO EDYHALO RHR L MNS L SOL L HAM L RHR H MNS H SOL H HAM H EDYYRHR+EDYMNHIII EDYYRHR EDYMNH + EDYYRHR EDYMNH EDYSOHIII EDYYHAM EDYSOH + EDYYHAM EDYSOH EDYYHAM 6.3 0% 0% 0% RHR L MNS L SOL L HAM L EDYMNLO + EDYSOLO EDYMNLO EDYSOLO EDYMNLO EDYSOLOProject COCA - EEC Report No. 403 71
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  • 83. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLANNEX B - CIVIL AND MILITARY CONFIGURATION SHEETSTable 16 is an example of the forms that were used in-situ to collect information on the civil sectorconfigurations. Table 16: Table used to capture the sector configuration changes for the DECO group DECO configuration Date : 07:00 10:00 13:00 … Local time CNF1 x x CNF2 x x x x x CNF3 x x x CNF4 x CNF5 x xTable 17 is an example of the forms that were used in-situ to collect information on the activationand deactivation of military areas. Table 17: Table used to capture the military area activation for the Brussels group BRUSSELS Military Zones Date: 07:00 10:00 13:00 16:00 … Local Time CBA1ABC EBTRANB EBTRASB EDR305B G1 LFTSA20 LIPPEN LIPPEN2 NL NL2 PINOTProject COCA - EEC Report No. 403 73
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  • 85. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLANNEX C - REPORTED WORKLOAD QUESTIONNAIRESPhase 1Figure 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 1Project COCA - EEC Report No. 403 75
  • 86. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentrePhase 2Figure 42 shows an example of the phase 2 questionnaires that were filled in by the controllers. Ident Date Sector 08:00 08:10 08:20 08:30 08:40 08:50 Start Time (LOCAL) 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 276 Project COCA – EEC Report No. 403
  • 87. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLANNEX D - MACROSCOPIC WORKLOAD MODELSIn 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 ModelThe 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 FormulaWhere:ωRoT , ωLC and ωCNF are respectively the times (expressed in seconds) needed to execute routinetasks, level change tasks, and conflict tasks andnAC, nLC and nCNF are respectively the number of aircraft, flight levels crossed and the conflictsearch/resolutions.These different parameters (ω and n) are estimated at sector level. Two steps are required toobtain 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 aredetermined with input from operational experts (the choice is described in reference [5]). In theMWM, the macro-task durations are identical for each sector of the study.Adapted Macroscopic Workload ModelIndicators other than number of flights, number of level changes and number of conflicts areevaluated at sector level. Clearly, the interactions and influence of these indicators on thecontroller workload varies amongst ATC sector types. As a consequence, there is a need to adaptthe macroscopic workload model according to the complexity characteristics of sector types.In classifying sectors, we can identify groups of sectors sharing similar complexity indicators. Fromthese groups of indicators, an adapted macroscopic workload model (AMWM) was built (seereference [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 withstatistical analysis to compile a list of relevant complexity indicators.Project COCA - EEC Report No. 403 77
  • 88. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentreStep 2 Computation of the complexity indicators identified (including the occurrences (n) ofthe 3 macro-tasks) for the sector for the day.Step 3 Classification of the sectors into an appropriate number of homogenous groups, orcomplexity clusters to arrive at sector types, see Annex E.Step 4 Determination of macro-task durations (ω) for each group: the set of macro-taskdurations is determined via an optimisation process described in reference [2]. In the AMWM, themacro-task durations are identical for each of the sectors within a complexity cluster.78 Project COCA – EEC Report No. 403
  • 89. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLANNEX E - CLASSIFICATION PROCESSGeneral methodClassification methods (see reference [6]) are used to build groups of objects which share thesame properties. The groups are characterised on the basis of discriminating variables selected tohighlight relevant properties of the objects. In this study the goal was to classify the sectors intocomplexity 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 thesectors. This step is particularly important for two reasons: first, the chosen classification methodwill pull together the sectors sharing the same properties. Then, the workload evaluation will becompleted for each different complexity cluster and therefore, the workload coefficients will beadapted to the types of the sectors.To classify the sectors into complexity clusters we used the DIVAF technique. DIVAF is ahierarchical method which gives, at the end, a decision tree. The method has already been usedby the COCA team for several studies and is documented in reference [7]. The method can bebriefly 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 correspondingquestion (binary type) is associated to the selected indicator which makes it possible to distributethe elements of the cluster into the two sub-clusters. By repeating this process until getting asatisfactory final number of clusters (leaves), a decision tree is built. Advantages of this method arethat it is easy to interpret as well as allowing for operational input on the selection of thediscriminating 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 sectorsThe four steps necessary to carry out the classification have been carefully followed using, asinput, the set of complexity indicators (I/D cards) evaluated for each daily sector. The successiveresults 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 sectorsfor which an I/D card has been computed. When using the term “daily sector”, we understand thecomplexity information of one sector according to a specific day. The sample chosen to build thetree corresponds to the set of “daily sectors” which have been opened for minimum 4 hours perday. It contains 136 elements.Project COCA - EEC Report No. 403 79
  • 90. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentreStep 2:What we want at the end is to be able to classify the sectors into complexity clusters independentlyof 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. Thelonger 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 averagedthe complexity data at daily level.The normalised and aggregated sample is made of 20 elements10 (9 sectors from Brussels, 6sectors 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 beclassified).It was agreed that three complexity clusters are sufficient to draw a fair analysis. In effect, we werein search of one group of high complexity sectors, one group of medium complexity sectors and alast 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 sameindicator 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 datais represented in Figure 43. Sample of data (20 sectors) DIF < 0.12 DIF ≥ 0.12 Rest of the sample of data (13 sectors) DIF < 0.07 DIF ≥ 0.07 Cluster 3 Cluster 2 Cluster 1 (8 sectors) (5 sectors) (7 sectors) Low complexity sector Medium complexity sector High complexity sector Figure 43: MUAC sectors classification: Building of the binary tree from the data sample10 These elements are highlighted in blue in the classification results table (see Table 18).80 Project COCA – EEC Report No. 403
  • 91. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLStep 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 DIFvalues. The other four cannot be classified because of the “lack” of information (e.g. some of themwere only opened for one day and for less than 30 minutes which is not sufficient for a systematicclassification process). Two of them belong to Brussels: KOKSY total (EBMAKOK) and NICKYtotal (EBMANIK), one belongs to Hannover: Munster High (EDYMNHI) and the last one belongs toDECO: Delta and Coastal collapsed (EDYYDCO).The classification process of the whole MUAC sectors lead to the distribution between the threeComplexity Clusters shown in Table 18. Table 18: Classification results tableCOMPLEXITY CLUSTER 1 COMPLEXITY CLUSTER 2 COMPLEXITY CLUSTER 3 BRUSSELS OLNO AND LUXBRUSSELS WEST HIGH BRUSSELS OLNO AND WEST HIGH COLLAPSEDBRUSSELS KOKSY LOW BRUSSELS ONLO AND LUX HIGH DECO COASTAL HIGHBRUSSELS OLNO LOW BRUSSELS OLNO HIGH DECO COASTAL LOWBRUSSELS LUX TOTAL BRUSSELS OLNO TOTAL DECO COASTAL TOTAL BRUSSELS KOKSY AND NICKYBRUSSELS LUX LOW DECO DELTA HIGH COLLAPSEDBRUSSELS NICKY LOW HANNOVER MUNSTER LOW DECO DELTA LOWBRUSSELS WEST LOW HANNOVER RUHR LOW DECO DELTA TOTAL HANNOVER HAMBURG ANDHANNOVER HAMBURG LOW HANNOVER MUNSTER TOTAL SOLLING HIGH HANNOVER MUNSTER AND RUHRHANNOVER SOLLING LOW HANNOVER RUHR TOTAL COLLAPSED HANNOVER HAMBURG AND HANNOVER SOLLING TOTAL SOLLING COLLAPSED HANNOVER HAMBURG TOTALProject COCA - EEC Report No. 403 81
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  • 93. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLANNEX F - COMPLEXITY INDICATORSAbout the MeshAs mentioned in chapter 8.1, some of the indicators were evaluated using a mesh. This involvesdividing the MUAC airspace into identical 4D cells, collecting data in each cell and then using thedata 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 ifits centre is included within the sector’s boundaries. Figure 44: Horizontal view of a sector tiled by the meshTo prevent boundary effects, a spatial displacement of the mesh is applied. In this study, for eachcomputation phase, the mesh is shifted four times both in latitude and longitude. When computingan 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 minThe 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 timeconsumed in computations and the level of accuracy of the results.Project COCA - EEC Report No. 403 83
  • 94. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentreThe ‘spatial mesh’ refers to the dimensions of the cells, so in Figure 44, if we consider the mesh tobe 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 tenminute time steps, and therefore 6 x 19 = 114 cells.Detailed Descriptions of the IndicatorsThis section provides detailed descriptions of the indicators used in the I/D cards shown inchapters 8.1 to 8.3.Flight InteractionsDIF 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”. y N NW NE x W E SW SE S Figure 45: Possible track values z cruising y x climbing descending Figure 46: Possible phase values84 Project COCA – EEC Report No. 403
  • 95. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLLet n_types be the total number of possible behaviours of a flight. For each cell, the phase andtrack of each aircraft is determined when it enters the cell. Then the number of flights Ni belongingto each behaviour is counted (i.e.: for i=1 to n_types).In each cell, the DIF indicator is given by the following formula: n _ types n _ types DIF (Ck ,t ) = ∑ ∑ N (C j =1 i =1, i > j i k ,t ) N j (Ck ,t )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 ofdata 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. Toaggregate DIF(Ck,t) at time level, we compute the sum over the temporal mesh. The correspondingDIF value per day for a sector made of Ncells is given by: T N cell DIF = ∑ ∑ DIF (Ck ,t ) t =0 k =1where T denotes the number of time steps.At sector level, the result is the normalised value of the DIF per minute flown: DIF DIFperMin = T N cells ∑ ∑d t =0 k =1 k ,twhere dk,t is the total time flown by the aircraft present in cell k for the time period t expressed inminutes.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: 200 MIX (cl , de) = × (cl (16cl 3 − 32cl 2 + 11cl + 5) + de(16de 3 − 32de 2 + 11de + 5)) 9 This indicator ranges from 0 to 100.Project COCA - EEC Report No. 403 85
  • 96. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre Z 100 50 0 0.0 0.5 0.0 1.0 0.5 Y 1.0 X Figure 47: Graphical illustration of the mix of traffic attitudes indicatorIn Figure 47 the x-axis represents the percentage of climbing flights (cl) and the y-axis representsthe percentage of descending flights (de). This figure is a plot of the surface defined by the externalfunction MIX(cl,de).This function reaches its maximum value (100) when both cl and de equal 50%. The minimumvalue (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 thesum 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.86 Project COCA – EEC Report No. 403
  • 97. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROL • 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. α 45° Figure 48: Proximate pairs: along track -30° α Figure 49: Proximate pairs: opposite directionTraffic EvolutionThe extent of vertical movements of flights can be important to capture the traffic complexityrelating 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.Project COCA - EEC Report No. 403 87
  • 98. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace CentreDensity • 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 TypesEach aircraft is associated with an altitude, an attitude and a type. The altitude is determined atsector entry according to the flight plan information; the attitude is determined by the sign of thedifference between the altitude at sector entry and the altitude at sector exit. Finally, the aircrafttype 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, rateof 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 modeland BADA 3.6 performance coefficients.In our analysis, true airspeed and ground speed are considered to be equivalent because of thelack 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 correspondingparameters (airspeed) from a synonym table. From our statistics, about 98 % of the aircraft typeswere 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.11This database (current version is BADA 3.6) provides a set of ASCII files containing performance andoperating procedure data for 295 different aircraft types. BADA is being maintained and developed by theEEC.88 Project COCA – EEC Report No. 403
  • 99. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLSectors 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.Project COCA - EEC Report No. 403 89
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  • 101. A Complexity Study of the Maastricht Upper Airspace Centre EUROCONTROLANNEX G - COMPLEXITY FACTOR LIST Airspace complexity factors, self-reportedFor 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 5 Restricted flight 6 Mix of climbing and 18 Military or other 25 Controlling traffic in 1 R/T congestion profile descending traffic restricted area another sector 29 Staffing 26 Late transfer of 7 Traffic flows converging at 19 FLs not available communications to control 30 On-the-job 2 Blocked frequency same point for use sector training (OJT) 3 Pilots not listening / 20 Lack of holding 27 Interface with another 31 Nr required complying with R/T 8 Mix of OAT/GAT areas sector / centre procedures ++ 9 Multiple crossing points in 21 Change to non 28 Opening / closing of a 32 Equipment 4 Pilot requests sector RVSM sector status** / ++ 10 Crossing points close to 22 Turbulence / 33 Other sector boundaries Weather 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” factorsProject COCA - EEC Report No. 403 91

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