EUROPEAN ORGANISATION                      FOR THE SAFETY OF AIR NAVIGATION                                  EUROCONTROL  ...
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A Complexity Study of the Maastricht Upper Airspace Centre                                                              EU...
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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
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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
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Complexity study maastricht_upper_airspace

  1. 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. 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. 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
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  5. 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. 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. 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. 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. 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. 10. EUROCONTROL A Complexity Study of the Maastricht Upper Airspace Centre Page Intentionally left blankxii Project COCA - EEC Report No. 403
  11. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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