EUROPEAN COMMUNITY COMPETITIVE AND SUSTAINABLE GROWTH PROGRAMME INTENT WP2.1. Capacity D2-1

Transcription

1 EUROPEAN COMMUNITY COMPETITIVE AND SUSTAINABLE GROWTH PROGRAMME INTENT WP2.1 Project acronym: INTENT Project full title: INTENT, The Transition towards Global Air and Ground Collaboration In Traffic Separation Assurance Project number: GRD Contract number: G4RD-CT Start date: 1 December 2000 Duration: 24 months INTENT Consortium: NLR Stichting Nationaal Lucht- en Ruimtevaartlaboratorium NL ONERA Office National d Etudes et de Recherches Aerospatiales F Eurocontrol European Organisation for the Safety of Air Navigation INT DUT Delft University of Technology, Faculty of Aerospace Engineering NL QinetiQ QinetiQ (formerly DERA) UK RC-F Rockwell-Collins France F SIA Smiths Industries Aerospace and Defence Systems UK AIRBUS FRANCE AIRBUS FRANCE F ECA European Cockpit Association INT AEA Association of European Airlines INT Date: Classification: Public

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4 Document Information Document title INTENT WP2-1 Version V08 Date Classification Public Workpackage WP2 Document identification INTENT_ D2-1_v08_ _P Contributing Partners Authors NLR R.C.J. Ruigrok, ONERA N. Imbert, J.L Farges, Florian Dentraygues, Sylvain Joly, Eurocontrol A. Nuic, C. Shaw, M. Bouassida, E. Hoffman, DUT R.A.A. Wijnen, H.G. Visser, Contact Information National Aerospace Laboratory, NLR Attn. Mr. R.C.J. Ruigrok Anthony Fokkerweg CM Amsterdam The Netherlands Tel.: Fax: ruigrok@nlr.nl Classification: Public Page 2/131

5 Partner Distribution list EC J. Forsman, NLR R.C.J. Ruigrok, M.S.V. Valenti Clari, L.J.J. de Nijs, ONERA J.L. Farges, N. Imbert, Eurocontrol E. Hoffman, A. Nuic, C. Shaw, M. Bouassida, DUT H.G. Visser, R. Wijnen, QinetiQ P. Platt, B. Booth, A. Magill, RC-F A. N'Diaye, C. Alber, S. Koczo, O.F. Bleeker, SIA J. Lomas, AIRBUS FRANCE D. Ferro, ECA / VNV R.C. Brons, rcbrons@wxs.nl R. Hoogeboom, vtz@vnv-dalpa.nl AEA / BA A. Fisher, Alex.B.Ficher@British-Airways.com A. Shand, Andy.N.Shand@BritishAirways.com A. Ellis, andrew.d.ellis@britishairways.com AEA / SAS B. Nilsson, bengt.nilsson@sas.se AEA / KLM B. Berends, benb@klm.nl Classification: Public Page 3/131

6 Version Date Partner Document change log V DUT First version of the document, all pages modified V DUT Merged text of tasks and 2.1.2, Inserted comments on chapter 1 Reasons for capacity limitations V ONERA Insertion of section Other possible metrics and Evaluation and comparison, accepted changes chapter 1. V DUT Rewrote chapter 2 entirely. Included views on capacity metrics as discussed at 2 nd technical meeting. V All task partners Final draft document V ONERA Final document V NLR Editorial and formatting changes, preparation for delivery V DUT Preparation for public delivery Classification: Public Page 4/131

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8 Summary This document is the final version of the D2-1 deliverable, which is a result of part 1 of WP2 Definition. The first part of this workpackage is dedicated to capacity and the work done should result in the definition of capacity and especially the metrics that can be used to assess capacity. The results of this workpackage are essential to the whole project since one of the primary goals is to quantify how intent information can be used to increase capacity of Western-European airspace. The results from this workpackage are directly needed for the next part of this workpackage that is dedicated to the design of the experiment. This deliverable contains three chapters. The first chapter gives the definition of capacity that will be used in the INTENT project and describes reasons for capacity limitations for both the present ATM system as well as future systems. Limitations for future systems are however only discussed briefly since a future system can have many different forms. Chapter 2 is dedicated to capacity metrics. First the basic idea of how capacity is assessed in this project will be explained. Then, an extensive overview of literature on capacity assessment and metrics will be given. There is also a section that describes some experiments and the associated tools that have been used to get acquainted with some of metrics. At the end of this chapter the metrics that are proposed to be used for capacity assessment in the various INTENT experiments are presented. The final chapter of this document contains the results of a study that investigates theoretical maximum capacity. This study looks at capacity from a geometrical point of view but also contains a number of experiments in order to simulate the dynamics of aircraft in airspace and their effect on airspace capacity. Classification: Public Page 6/131

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10 Table of Contents Summary...6 Table of Contents...8 List of figures...10 List of Tables Reasons for capacity limitations Introduction Present-day system Controller workload analysis Flow characteristics Sector geometry Technical means ATC rules Weather Airport limitations Effects of intent information Effects on conflict solving workload Effects on monitoring workload Effects on co-ordination workload Conclusion Future systems Ground-controlled Airborne Analysis through a qualitative model Capacity Metrics General Literature Review CARE-INTEGRA Flight Efficiency Dynamic Density Delahaye & Puechmorel Andrews and Welch study Clusters Computational Geometry Wyndemere study Conclusion Other possible metrics Delays Metric to evaluate pilot workload Evaluation and Comparison of the metrics DUT Simulator and results DUT Workload Evaluation Tool ONERA simulations Development of a program for metrics tests Simulations results...78 Classification: Public Page 8/131

11 Conclusion Proposed Metrics General Flight Efficiency Number of Intrusions Conflict Rate Subjective workload measurement Ground Dynamic Density Traffic disorder metrics Workload parameters Airborne Background information Proposed metrics Conclusion Theoretical Maximum Capacity Geometrical Basics Conflict probability for two airways Conflict prediction, avoidance Maximum density: geometrical considerations Upper static limit Upper dynamic limit: physical considerations Complexity metrics: theoretical study The Simulator Maximum experimental 2-D capacity Experiment General results Central density Complexity: experimental study References Classification: Public Page 9/131

12 List of figures FIGURE 1: PROPORTION OF FALSELY PREDICTED CONFLICTS (FROM MAGILL)...20 FIGURE 2: EXPERIMENT MATRIX...22 FIGURE 3: PHYSICAL LIMITATION HORIZONTAL AXIS: NUMBER OF AIRCRAFT N - VERTICAL AXIS: THROUGHPUT F AND FLIGHT TIME T...27 FIGURE 4: HYPOTHETICAL RESULTS...29 FIGURE 5: EXPERIMENT MATRIX...35 FIGURE 6: INFORMATION PROCESSED...36 FIGURE 7: BEHAVIOUR OF AN AIRCRAFT DURING ITS FLIGHT INSIDE THE A3S (AUTONOMOUS AIRCRAFT AIRSPACE)...55 FIGURE 8: CONFLICT MANAGEMENT IN A3S...56 FIGURE 9: DEFINITION OF TIME INTERVALS...58 FIGURE 10: VOLUME DEFINED FOR DETECTION PURPOSES...58 FIGURE 11: OVERVIEW OF THE SIMULATOR STRUCTURE...68 FIGURE 12: DYNAMIC DENSITY COMPUTATIONS WITH REGRESSION ANALYSIS WEIGHTS...69 FIGURE 13: CONVERGENCE COMPUTATIONS...70 FIGURE 14: DELAHAYE/PUECHMOREL AIRCRAFT DENSITY COMPUTATIONS...71 FIGURE 16: DYNAMIC DENSITY CALCULATION FOR HIGH-DENSITY SCENARIO (A) REGRESSION ANALYSIS WEIGHTS; (B) SUBJECTIVE WEIGHTS...72 FIGURE 17: CONVERGENCE AND AIRCRAFT DENSITY CALCULATION FOR REALISTIC HIGH-DENSITY SCENARIO...73 FIGURE 18: FREE FLOW OF TRAFFIC INTO A SECTOR RESULTS IN GREATER PROBABILITY OF TRANSIENT PEAKS IN SECTOR LOADING...74 FIGURE 19: DUT WORKLOAD EVALUATION TOOL...76 FIGURE 20: FIRST AIRWAYS CONFIGURATION...78 FIGURE 21: CONFLICT RATE FOR TWO AIRWAYS...79 FIGURE 22: DELAYS FOR TWO AIRWAYS CONFIGURATION...80 FIGURE 23: NUMBER OF INTRUSIONS...81 FIGURE 24: DELAYS FOR VARIOUS LEVELS OF INTENT...82 FIGURE 25: DELAHAYE DENSITY INDEX...83 FIGURE 26: DYNAMIC DENSITY...84 FIGURE 27: CONFLICT RATE...84 FIGURE 28: DELAYS...85 FIGURE 29: PERCENTAGE OF MANOEUVRING...86 Classification: Public Page 10/131

13 List of Tables TABLE 1: QUALITATIVE RELATIONS BETWEEN VARIABLES. LINE : CAUSE, COLUMN: EFFECT, 0 : NO EFFECT, + : INCREASE, - : DECREASE, N.A. : NOT APPLICABLE...25 TABLE 2: GENERAL METRICS...99 TABLE 3: METRICS FOR GROUND LOCATED SEPARATION ASSURANCE...99 Classification: Public Page 11/131

14 1. Reasons for capacity limitations 1.1. Introduction This section will identify all possible factors that limit capacity. Next to that, the anticipated effect of the use of intent information on these limiting factors will be described. Section 1.2 gives a description of the factors that limit capacity in the present-day system. The next section will identify factors that might limit capacity in future systems. In order to do this, that section will also include a qualitative model that shows the relationships between different variables, which are important within the experiment. However, before these factors are discussed, first the definition of capacity that will be used in the INTENT project will be given. In the INTENT project the following definition for capacity, taken from INTEGRA [1], which is adapted from the ATM Strategy Document [2], is used. 'The maximum number of flights that can be handled in unit time without violating constraints on safety, economy or environmental impact'. This definition already indicates that the determination of the capacity of an ATM system is directly related to assessing its handling capabilities. In general this involves evaluating some sort of workload, whether it is human workload or workload of a machine. Also incorporated into this definition are constraints with regard to safety, economy and environmental impact. These constraints can be monitored as follows: safety: evaluate number of intrusions and losses of separation; economy: evaluate flight efficiency in terms of fuel burn and flying time; environmental impact: this aspect will not be specifically addressed in INTENT but in fact part of the environmental impact (CO 2 part) is incorporated by considering fuel burn. A detailed description of how workload and the above mentioned constraints are monitored in the INTENT experiments will be described in the chapter 2. The reasons for capacity limitation are different depending on the ATM subsystem that is considered, e.g.: airport or terminal airspace : capacity limitation is mainly due to physical reasons (separation minima due to aerodynamic interactions); en-route airspace: for present-day situation capacity is mainly limited due to controller workload. Classification: Public Page 12/131

15 In the scope of the INTENT project, it has been decided to investigate the effect of intent information for en-route traffic only. Therefore the effects on airport capacity and/or terminal airspace capacity will not be explicitly considered. This section will mainly describe the limitations of capacity for en-route sectors. Capacity limitations for arrival and departure management will be studied as a possible limiting factor of sector capacity Present-day system Nowadays the separation process is ground located; a sector is assigned to two controllers (a planning controller and a radar controller) who have a global view of the current traffic distribution in the sector airspace and who can give orders to pilots in order to ensure that separation minima are not violated. If the number of aircraft increases, controller workload will eventually rise above a certain level, which disables a controller from performing his tasks under optimal conditions. Thus, controller workload is the main limitation of sector capacity for present day operations. Therefore this section will first give a general analysis of controller workload. This analysis also includes a list of all the physical parameters that affect workload. After that, section will analyse the effect of intent information on this workload Controller workload analysis An air traffic controller has to provide the following services: Preventing collisions between aircraft in the air; Assisting aircraft for conflict resolutions; Expediting and maintaining an orderly flow of traffic. Controller workload is very difficult to evaluate because it depends on physical factors like sector geometry or flow characteristics (section to ), but it also depends on human factors like the stress of controller. According to Delahaye [3], controller workload can be divided in three items: Monitoring workload: workload caused by monitoring aircraft in the sector; Conflict solving workload: workload caused by the resolution of conflicts; Co-ordination workload: workload caused by co-ordination (co-ordination is the task that controller must perform when an aircraft enters or leaves the sector; it is a negotiation with the controller of another sector). To precisely evaluate the effects of aircraft intent information on controller workload, a distinction must be made between a planning controller and a radar controller. These two controllers both have a global view of the current traffic distribution on a radar screen but have different tasks. Classification: Public Page 13/131

16 Planning controller The main task of planning controller is to do co-ordination. Ten minutes before a new aircraft enters in the sector, the planning controller receives a strip that contains information on this aircraft (the strip is a piece of paper where the aircraft identification, the speed and the flight plan are printed). Then the planning controller checks on his radar screen if the new aircraft will cause new conflicts. Three minutes before the aircraft enters the sector, the planning has a negotiation with the planning controller of the other sector and tells him how the new aircraft must enter in the sector. After he gives the strip to the radar controller (he can help the radar controller by indicating on the strip if the aircraft will cause future conflict). Therefore the planning controller workload is mainly co-ordination workload, but he can help the radar controller in the conflict detection (his look-ahead time for conflict detection is bigger than the radar controller look-ahead time). Radar controller The main task of the radar controller is to monitor aircraft, to detect and to solve conflicts between aircraft that are already in the sector. The radar controller workload is mainly related to monitoring and conflict solving. Controller workload depends on a lot of physical parameters, which are discussed in the following paragraphs. Each of these parameters might be affected by intent information, which therefore indirectly will have an effect on controller workload. If appropriate, these effects will also be described in the following sections Flow characteristics Flow characteristics determine the traffic distribution in the sector, the traffic heterogeneousness and therefore all the difficulties of en-route control. Those characteristics (sector throughput, number of aircraft) are regulated by the CFMU (Control Flow Management Unit) in order to avoid sector overloading. If the use of intent information reduce control workload, it will enable controller to handle more traffic and more complex flow characteristics. Sector throughput Sector throughput (aircraft/hour entering the sector) is directly linked with sector loading and controller workload. Control centers use sector throughput metrics to evaluate sector capacity. In a study of Tofukuji [4], a regression analysis between the number N of aircraft handled by hour and the frequency P of controller s intervention per hour was made for six major sectors in Japan. This analysis showed that N and P could be approximated by a linear equation. The frequency of controller s intervention is representative of controller workload, so sector throughput and controller workload might be approximated by a linear equation. Classification: Public Page 14/131

17 In his Ph.D. [3], D. Delahaye gives mathematical model to compute controller workload (monitoring, conflict solving and co-ordination). Each workload is proportional to aircraft flows. Thus, sector throughput is a major factor of controller workload and airspace capacity. Instantaneous aircraft count Instantaneous aircraft count is the number of aircraft in the sector at a precise time. Sector throughput is inadequate to represent all the controller workload because it does not take traffic peak into account: a throughput of 35 aircraft per hour which is concentrated on a very small period will give more stress to the controller than a throughput of 45 aircraft per hour which is well distributed. Some studies about queuing theory estimate the probability of having a certain aircraft count depending on traffic throughput (see [5], [6], [7] and [8]) and calculates the maximum of aircraft that controller can handle at a precise instant in order to compute sector throughput and sector capacity. Traffic diversity Differences between aircraft types cause more controller workload because aircraft can have different speed and different separation rules according to aircraft type, so controller have to do more mental efforts to know the characteristics of each aircraft. Traffic can also be heterogeneous in terms of trajectories: controller must handle evolutive (aircraft that climbs or descends) and non-evolutive aircraft. Traffic heterogeneousness causes an increase of controller workload and therefore limits airspace capacity. In a paper of Janic and Tosic (see [9]), the influence on capacity of the mix of aircraft types and the fraction of aircraft requesting a level change is studied. In this study, sector capacity is calculated by using the time between aircraft operations at a reference location in the sector; capacity represents the maximum of aircraft operations per hour at a reference location. Traffic flows are simulated for 10 NM horizontal separation: capacity decreases from 56 operations per hour (when there are only slow aircraft flying horizontally in the sector) to 34 operations per hour (when there are only slow aircraft from one entry point and all the rest are fast aircraft which can request a level change). Thus, traffic mix and the proportion of evolutive aircraft have an important impact on airspace capacity. Classification: Public Page 15/131

18 Sector geometry Sector geometry determines air traffic pattern and has consequences on flow characteristics and airspace capacity. The use of aircraft intent information might not have any effects on sector geometry that depends on physical characteristics (military zone, location of the nearest airports ). Sector size Sector size depends on the previous traffic density. To face traffic increase, sector is degrouped in smaller sectors in order to reduce controller workload in each of them. So the average time of transit in each sector is reduced. However the number of co-ordinations increases when the size of the sector becomes smaller, and the co-ordination workload can become the main factor of stress. The effects of intent information on controller workload will depend on the sector size. If the average transit time in the sector is 7 mn, a time horizon of 5 minutes will be enough for conflict resolution made by the radar controller. But if the sector is bigger, the radar controller will need a longer time horizon. Air routes configuration The number, length and angles of routes determine merge points and severity of potential conflicts. In a study of Tofukuji (see [4]), a new route structure of a sector was designed in order to reduce the controller s workload for arriving traffic; a new route has been designed in parallel with a route of the sector and a merge point has been divided in two merge points. The capacity of the new sector increased about 40 %. Therefore, air routes configuration has a major impact on capacity. When traffic density is very low (especially during the night), all the sectors are grouped and controller can accept that aircraft use direct routes. But for the current ATC system, controllers cannot handle high traffic density with direct routing. A study about direct routing (see [15]) compares fixed routes and direct routing use. It shows that flight times on direct routes is generally shorter than those for traffic airways and this leads to a lower traffic density (traffic density is reduced by almost 15%). Moreover, direct routing cause a reduction of interaction frequency by almost 40% for a separation threshold of 5 MN (the simulation counts aircraft interactions without separating the aircraft). However the use of direct routes tends to spread the traffic in the entire sector whereas the use of airways tends to concentrate the traffic in some parts of the sector. Therefore direct routing reduces certainly conflict-solving workload but increases monitoring workload. For the INTENT project, the case of direct routing will be studied for the different separation locations, namely ground and airborne. Special use airspace Special use airspace is airspace of defined dimensions wherein activities must be confined. Special use airspace like Military Operations Area (MOA) decreases the space available in sector to manage aircraft. It causes less flexibility for the choice of routes configuration and less space for conflict avoiding manoeuvres. Classification: Public Page 16/131

19 Technical means Technical means include all the en-route center equipment to manage aircraft. Those technical means have to be adapted to the intent information sharing. Center capacity According to the density of the previous traffic, the en-route center will degroup a sector in a lot of smaller sectors and will open new working positions for each new sector. Therefore sector capacity depends on the number of working positions which can be simultaneously opened. Controller tools Controller workload depends on the systems that are available at the working position. Controller environment has effects on planning controller/radar controller collaboration and on controller ability to detect and solve conflicts. The presentation of intent information to the controller will induce a change on his working position. Radio-frequencies limitation Demand for new frequencies in the radio VHF band increases with the growth of air traffic and a shortage of VHF radio frequencies available can limit airspace capacity. In order to solve this problem in Europe, the 8.33 khz channel spacing program was introduced on October For the INTENT project, it is assumed that there will not be any problems for the transmission of intent information ATC rules ATC rules have consequences that are obvious on flow characteristics and controller workload. Semi-circular rule The semi-circular rule is a rule that attributes a flight level to aircraft depending on magnetic track in order to have no conflicts with two aircraft facing each other. For example, below FL 285, the attribution of flight levels is: If the magnetic track is between 0 and 179, aircraft must have a flight level that is an odd number multiplied by 10 (FL 150, 170,) If the magnetic track is between 180 and 359, aircraft must have a flight level that is an even number multiplied by 10 (FL 160, 180 ) Classification: Public Page 17/131

20 In the study of Alliot, Bosc, Durand and Maugis (see [16]), some simulations have been made without taking into account the semi-circular rule. It appears that suppressing semi-circular rule increases the number of conflicts by 15 to 20%. Separation rules Two aircraft are in conflict when they are closer than a distance defined by separation rules. Aircraft separation depends on the equipment available to control aircraft and on the aircraft equipment. It is usually 5 or 8 Nautical Miles for horizontal separation and 1000 or 2000 feet for vertical separation. The number of conflict depends directly on separation standard, therefore aircraft separation is an important factor that determines airspace capacity. Lots of studies have researched the relation between aircraft separation and airspace capacity, particularly for the application of RVSM (Reduced Vertical Separation Minima). In order to increase the number of available levels, the vertical separation, the RVSM application will reduce the vertical separation minimum from 2000 feet to 1000 feet. In the study of Alliot, Bosc, Durand and Maugis (see [16]), it is indicated that, with RVSM application, conflict rate is reduced on average by 50 %. In a paper of Janic and Tosic (see [9]), they simulate traffic flow characteristics in a sector with two different horizontal separation rules: 10 NM and 30 NM. In this study, sector capacity is computed by using the time between aircraft operations at a reference location in the sector; capacity represents the maximum of aircraft operations per hours at a reference location. It is showed that separation has a major impact on capacity; for the 30 NM separation rule, capacity varies between 14 and 21 operations per hours and for the 10 NM separation rule, capacity varies between 20 and 58 operations per hours Weather Weather conditions are random factors of capacity limitation for en-route airspace, which will not affected by the use of aircraft intent information. For reasons of low visibility (fog, low clouds) in airports, aircraft cannot land and are obliged to wait in the terminal airspace or in the sector. Thus, a radar controller has to monitor a stack of those aircraft that turn around a specific beacon and he can not handle the same sector throughput than for normal conditions. Therefore sector capacity has to be reduced; For reasons of turbulence or cumulonimbus in the sector, controller workload can be highly increased. Radar controller has often to face pilot requests for flight level changes (usually to avoid a flight level with high winds) or for heading changes (usually to avoid a cumulonimbus area) and thus, the space available to manage aircraft in the sector is reduced. Therefore bad weather conditions can highly reduce sector capacity and the use of aircraft intent information will not have effects on this limitation. Maybe if INTENT information contains some met Classification: Public Page 18/131

21 data, controllers would be earlier aware of the sector areas with met problems and would anticipate the pilot requests Airport limitations Airport capacity can be a limiting factor for sector capacity because it can oblige a radar controller to hold aircraft in his sector and it can so increase his workload. Airport capacity is mainly due to physical reasons like the separation minima due to possible aerodynamic interactions (wake vortex). Usually, airport capacity is defined as the maximum number of operations (arrivals and departures) that can be performed during a fixed time interval under given conditions such as runway configuration and weather conditions. In a study of Donohue and Laska (see [17]), the runway arrival rate is calculated as a function of intrail separation requirements. For a speed of 130 kts and a variation of separation from 2 NM to 6 NM, the runway arrival rate decreases from 70 arrivals per hour to 20 arrivals per hour. Thus, separation requirements are the major factor of airport capacity, but other limitation factors are weather (low visibility, high winds) or system failures Effects of intent information This section will describe the effect of intent information on workload of a controller, since this has been identified as the limiting factor for capacity. Like already discussed at the beginning of the previous section, controller workload can be divided into three parts. Each of these parts will be evaluated here separately Effects on conflict solving workload In a paper about the new ATC system ERATO [10], a cognitive model of the controller is described. It is said that controller anticipates the traffic configuration on the basis of the routine behaviour of the aircraft. For example, if a controller has to monitor an aircraft with Orly destination, he does not know the instant when the aircraft will begin its descent, but he knows that usually an aircraft for Orly destination begins its descent 30 MN before CHW (beacon of Chartres). Therefore controller considers that aircraft will have a routine behaviour and will ignore potential conflicts which can occur if the aircraft descend earlier. With the aircraft intent information, the controller will not make assumptions on the routine behaviour of the aircraft, but he will have precise information on the future behaviour of the aircraft (instant of descent, time of the next waypoint crossing ). Workload gains due to better accuracy are listed below. Reduction of falsely predicted conflicts With more information on future aircraft trajectories, a controller will have less doubt on the possibilities of conflicts and he can better distinguish between true conflicts and false conflicts. Therefore intent information will reduce time needed to deal with false conflicts. Classification: Public Page 19/131

22 In a paper from DERA [11], the rate of falsely predicted conflicts is computed in order to evaluate the effects of uncertainties on aircraft trajectories. The errors simulated are air speed errors (standard deviation of 8 kts) and climb and descent rate errors (standard deviation of 5%). For a look-ahead time of 5 minutes and a separation minimum of 5 Nautical Miles (NM), the frequency of falsely predicted conflicts relative to true conflicts is about 70%. For a look-ahead time from 10 to 20 minutes and a separation minimum of 5 NM, the frequency of falsely predicted conflicts relative to true conflicts ranges from 130% to 200% (see Figure 1). A look-ahead time of 5 minutes for the conflict detection is useful for the radar controller, but the planning controller will need a look-ahead time from 10 to 20 minutes. On the face of those numbers, uncertainties about aircraft trajectories cause an important rate of false conflicts. It is also indicated in the paper that approximately half of controller workload is conflict related, so the reduction of false conflict predictions with intent information might give rise to an important capacity benefit. Falsely predicted conflicts as % of true conflicts 200% 150% 100% 50% 0% < 5 miles < 10 miles < 15 miles Look-ahead Time (mins) Figure 1: Proportion of falsely predicted conflicts (From [11]) Increase of time available for conflict resolution A second benefit of conflict solving workload is the time available for conflict resolution. With intent information, a controller will detect conflicts earlier and will have more flexibility for scheduling conflict resolution tasks. Increase of available space for conflict avoiding manoeuvres A third benefit can be the space increase for conflict avoiding manoeuvres. Without intent, controller makes assumptions on aircraft future positions according to the flight plan and the knowledge of the routine behaviour of aircraft, so he must consider a large separation area around aircraft for conflict resolution. With more precision on aircraft trajectories, a controller will take fewer margins on separation minima and he will have more space for conflict avoiding manoeuvres. Classification: Public Page 20/131

23 Effects on monitoring workload If control avoiding actions are not made at the right moment, the difficulty of the resolution can increase very rapidly, so a controller must observe attentively the evolution of each possible conflict, which is a big part of the workload. A controller associates pertinent parameters to each potential conflict and he must check often those parameters in order to determine if a conflict will be certain or not. This workload can be reduced by the use of aircraft intent information: a controller will have more precision on the future position of aircraft in potential conflicts and he will not have to memorise the evolution of some parameters in order to know the future aircraft behaviour Effects on co-ordination workload Co-ordination workload corresponds essentially to the planning controller workload. When a new strip (paper which indicates a new aircraft arrival) is printed, the planning controller must check if the new aircraft will cause conflicts in the sector, taking into account the strip information (aircraft type, flight plan ) and the data from his radar screen. Then, he speaks with the planning controller of the other sector in order to say if he accepts the new aircraft and how he wants that the new aircraft enters in the sector. The use of aircraft intent information will not reduce the planning controller workload due to phone communications, but it can reduce the workload due to conflict prediction. This prediction must be longer than the conflict prediction needed by the radar controller. The planning controller receives the strip ten minutes before the new aircraft enters in the sector. Therefore, depending on the sector size, the planning controller will need a look-ahead time of 15 or 20 minutes Conclusion The use of intent information will give to controller more precision on the future position and behaviour of aircraft, thus controller workload is expected to be reduced. Given that aircraft are obliged to take specific airways and that controller has already lots of information with the strip in the present-day situation, the information gain with INTENT will not necessary cause an important capacity gain for the current air traffic procedures. However the effects of INTENT may be more important and maybe essential in the case of direct routing and airborne separation location. The effect of INTENT in these kind of future systems will be addressed in the following section. Classification: Public Page 21/131

24 1.3. Future systems In the INTENT project future ATM systems can be distinguished by location of responsibility for separation assurance. This distinction is important here since a discussion on possible limiting factors for capacity of these future systems will be given in this section. 1. Ground responsibility In this case responsibility is located on the ground, i.e. ground controllers are responsible for separation assurance. If level of intent is considered to be just position information then this gives the current ATC system as can be seen from figure 2. The limiting factor for this type of system is (radar) controller workload, which has been discussed extensively in the previous section. However, the future ATM systems that will be considered in INTENT will give a controller the ability to use a higher level of intent in one way or another, which is indicated on the horizontal axis in figure 2. From this figure it can also be seen that two different route structures for a ground-controlled environment will be considered. On the vertical axis, there is a structured and unstructured routes scenario can be identified. Section will describe the factors that could limit capacity for these kinds of systems. Airborne / Unstructured Routes (pilots) Ground / Unstructured Routes (ATCo) Separation Assurance Responsibility Metrics: capacity safety efficiency Ground / Structured Routes (ATCo) medium current ATC position low state intent (10 min) intent (15 min) intent (20 min) Level of Intent Traffic Load high ATC = Air Traffic Control ATCo = Air Traffic Controller Figure 2: Experiment Matrix Classification: Public Page 22/131

25 2. Airborne responsibility In this case responsibility for separation assurance is airborne situated, which means that controller workload does not have to be considered anymore. According to Eurocontrol s Operational Concept Document (OCD) [12] an ATCo is not performing tasks related to separation assurance anymore. The role of the ATCo in FFAS will be controlling troubled aircraft, where a conflict is not defined as trouble. Trouble means that an aircraft declares an emergency, its CD&R doesn't work, etc. The other role for ATC in FFAS is FIS and Meteo information provision. Such a concept will show a change in parameters that are limiting capacity. This will be discussed in section Ground-controlled Air traffic controllers are responsible for separation assurance in these kinds of systems. So, their workload will still be the limiting factor for capacity as is the case in the present-day system. However, the level of intent might have an influence on the particular controller that is critical. There could occur a shift from radar to planning controller and vice versa depending on the level of intent. With respect to a ground-controlled concept with unstructured routes it is expected that intent information might be crucial in order to maintain an acceptable controller workload. Due to the randomness of the flow it is foreseen that average traffic load have to be reduced in order to avoid high peaks in traffic load and therefore controller workload Airborne Since the task for separation assurance is now shifted to the flight deck, workload of a pilot might become the main limiting factor. For the airborne separation process, the human involved in separation problems will be: The first pilot; The co-pilot. The share of workload between pilot and co-pilot will depend on the aircrew procedures for conflict separation and it will be very difficult to make assumptions on those procedures in order to evaluate crew workload for airborne separation location. In a study for AM AIRBUS (see [14]), considerations are given on the future role of the pilot in airborne separation assurance concept. Those considerations entail that: Conflicts are detected and solved by conflict detection and resolution tools The best solution for a conflict resolution is displayed to the pilot For the change of his trajectory, a pilot has to validate the resolution flight plan after having checked that: - It clears the conflict without triggering any new one - It is compatible with the rest of the flight Classification: Public Page 23/131

26 Moreover it is assumed for INTENT simulations that conflict detection and resolution tools will be included to the FMS of each aircraft. The FMS will receive all data about other aircraft (like positions and intentions), detect, resolve conflicts and propose a new flight plan to the pilot. Thus the pilot workload for separations problems consists in checking and validating the new trajectory proposed by the FMS. For airborne separation location, the intent information sharing will be essential and the level of INTENT information will determine the precision of the conflict detection and resolution tools Analysis through a qualitative model The following qualitative model is used for reasoning about intent capacity relationship and analysing limitations in a future context. It is based on the capacity definition given above. For clarity, this definition is repeated here: The capacity of an ATM system or any component of an ATM system may be defined as: 'the maximum number of flights that can be handled in unit time without violating constraints on safety, economy or environmental impact. The model considers economy and environmental impact aspects only through the flight time and focuses on maximum number of flight handled and constraints on safety. The variables taken into account are: N: number of aircraft present in the airspace this is an input I: level of intent information this is an input R: location of separation assurance measured by the ratio of responsibility transferred to the air input O: level of airspace organisation this is an input H: cautiousness of the CD&R algorithm (inclusive look ahead horizon) this is an input A: level of automation this is an input T: flight time W c : controller workload W p: pilot workload V: versatility of intent M: separation violations F: throughput flow - it is the number of flights that can be handled in unit time Classification: Public Page 24/131

27 Taken into account the capacity definition, capacity (C) may be expressed by: C = Max (F) with respect to N with M < M max and T < T max for I, R, O, H and A given (1) where M max is a requirement on safety and T max a requirement on economy and environmental impact. The M < M max translates to the constraint on safety in the capacity definition. The input variables that are given (I, R, O, H and A) represent a context for the assessment of the capacity. This context may be positioned to the present value (small I, small R, large O, average H and small A) or to any value that represents a future situation. Qualitative causal relationships between variables are presented on Table 1. For instance, the - on line O, column W c indicates that an increase of airspace organisation reduces the controller workload. T W c W p V M F N I R O H A T N.A W c 0 N.A W p 0 0 N.A V N.A. + 0 Table 1: Qualitative relations between variables. Line : cause, column: effect, 0 : no effect, + : increase, - : decrease, N.A. : not applicable A limitation of the model is that it does not stipulate the structure of the mathematical relations between variables. The reason for this is that the knowledge on the relations is not homogenous. For instance on one hand the description of F corresponds to the well-known relation: F = N/T (2) On the other hand other relations present less mathematical or experimental basis. That is the case of the positive relation depicted between the number of aircraft N and pilot workload W p. This relationship is what is expected when responsibility for separation assurance is located in the air but there is probably no effect when this responsibility is on the ground. So, this relationship depends on the value of R, i.e. airborne location of responsibility. A model of the type: W p = k 1 (I,H,A,V).R.N+ k 2 (I,H,A,V) (3) Classification: Public Page 25/131

28 where k 1 () and k 2 () are positive unknown functions, may be stipulated but is not validated. It is also the case for the controller workload, where a model of the type: W c = (1-R)(k 3 (I,O,H,A).N 2 +k 4 (I,O,H,A)N) (4) where k 3 () and k 4 () are positive unknown functions, can be stipulated from the experimental evidence of linear and quadratic effects in controller workload. The maximum throughput of a particular system (defined by I, R, O, H, A) may be determined by increasing the number of aircraft N, just until M can no longer be satisfied (M=M max ). That is the case of the present situation where M reaches M max because of W c contribution but it could reaches the critical value because of other contributions: W p That could be the case when a large amount of responsibility is delegated to the pilot. V That could be the case when the intended trajectories oscillate because of parallel decision making. Thus, the model highlight two possible future limitations to the capacity in future scenarios where there is more airborne separation assurance: The workload of the pilot is too high; The parallel airborne decision making is unstable. If the constraint on M is no more active, because of a higher level of automation and a good tuning of the CD&R cautiousness, the constraint on T will become active first as N increases. Thus a further limitation of capacity may appears: The large amount of conflict solving generates too long aircraft trajectories. Even if no constraint becomes active an additional possible future limitation is presented on figure 3. Classification: Public Page 26/131

29 T max C T F = N/T N Figure 3: Physical limitation horizontal axis: number of aircraft N - vertical axis: throughput F and flight time T In that case the flight time increases more quickly than the number of aircraft and F presents a maximum because of the physical nature of the air traffic process. Thus a final reason is that the physical limit of the process is reached. However, as shown on the figure, that would imply flight times increases of at least 100%. This is possible only if T max is higher than two times T of today. That it is acceptable only either if aircraft engines present low consumption and emission rates or if commercial flights generate more income to compensate the price of fuel and less attention is given to environmental impacts. This future situation is not likely to occur. Classification: Public Page 27/131

30 2. Capacity Metrics This chapter describes the metrics, needed to characterise the various scenarios of the INTENT experiment matrix. It is important to quantify the output of the experiments in terms of different metrics in order to be able to assess capacity. The choice for such metrics is not directly evident as will become clear from a literature review, which is described in section 2.2. Another complication is the fact that metrics have to be used that are able to characterise very different concepts of an ATM system. Section 2.3 will discuss some other possible metrics that have been suggested by ONERA. In section 2.4 a number of tools are described that have been used to investigate the behaviour of some of the metrics. Section 2.5 will describe the metrics that are proposed to assess capacity for the various ATM concepts of the INTENT experiment matrix. Finally, section 2.6 concludes General The experiments that will be carried out in the INTENT project have to be quantified in terms of capacity, since it is investigated how the use of INTENT information can increase airspace capacity. Therefore, it is important to define capacity. As already stated in the first chapter of this document, the capacity definition from INTEGRA [1], which is adapted from the ATM Strategy Document [2], is used. For convenience, this definition is repeated here: The capacity of an ATM system or any component of an ATM system may be defined as: 'the maximum number of flights that can be handled in unit time without violating constraints on safety, economy or environmental impact'. So, capacity of an ATM (sub) system could be determined by progressively increasing the traffic through the system until the point is reached where one or more constraints is reached. However, in this study it has been decided to test a fixed set of traffic densities (see also scope document D1-1 [13]). The particular choice for traffic densities is present-day density and 2 and 4 times that value. Together with the other variables of the experiment matrix, i.e. location of responsibility for separation assurance, set-up of routes and level of intent, this gives a number of ATM concepts that have to be evaluated in terms of capacity. Here, an ATM concept is a combination of location of responsibility for separation assurance, structure of the routes and the level of intent. These concepts will not be tested by monitoring system constraints, while increasing the traffic density progressively. Instead, these concepts will be tested in terms of feasibility of the particular concept. In other words, which discrete value of traffic density (present-day, 2 or 4 times that value) can still be handled by the system? In order to give an example Figure 4 shows some hypothetical results. This figure presents the achievable traffic load for each ATM concept of the experiment matrix. Traffic load is depicted by means of bars that can only have three different values. These values are 1, which equals presentday traffic density, 2, which is double this value and 4, which is another doubling. From this example Classification: Public Page 28/131

31 figure it can be concluded that a concept which combines a ground-controlled environment and unstructured routes only becomes feasible when a level of intent of 10 minutes is used. Achievable Traffic Load 4 Traffic Load Airborne / Unstructured Ground / Unstructured 0 Position State Intent 10 Intent 15 Intent Level Intent 20 Ground / Structured Separation Assurance Ground / Structured Ground / Unstructured Airborne / Unstructured Figure 4: Hypothetical results Such a figure can be obtained by testing all the cells of the experiment matrix. This will be done by carrying out compressed-time simulations. The important question here is how the feasibility of the particular ATM concept can be evaluated. In order to determine the feasibility of a system a number of aspects are important. These aspects are: Safety - workload: this could be human workload or workload of a computer (e.g. processing time to come up with a valid resolution advisory). The first type of workload is a more subjective quantity; the latter is an objective one. With respect to human workload there also has to be made a distinction between controller and pilot workload; - loss of separation: this is an objective measure and is directly related to the separation standards that are used. Classification: Public Page 29/131

32 Flight Efficiency Direct Operating Cost (DOC) is an indicator for flight efficiency. DOC is a function of both flight time and fuel used. If both of these quantities are recorded during simulations, an objective measure for flight efficiency can be obtained. Note that the aspects discussed above reveal already some of the factors that limit capacity. Recall from the definition that capacity is the maximum number of flights that can be handled without violating constraints on safety, environmental impact and economy. So in order to determine the feasibility of the ATM concept under consideration, the capacity limiting factors and their constraints have to be determined. In order to be able to identify these factors and their constraints, an extensive literature study has been carried out. This literature survey will be presented in the next section Literature Review In order to assess capacity in this study, it has been decided to evaluate the feasibility of the particular ATM concept under consideration. This approach is taken because only a number of three fixed traffic loads will be considered, like has been explained in the previous section. In order to determine the feasibility of a system, the factors that might constrain the traffic handling ability of that system, and therefore capacity, have to be monitored. Recalling the definition of capacity, these factors can be related to safety, economy and environmental aspects. Before an overview of literature is given (section to 2.2.8), first some additional remarks on safety, economy and the environment will be given. Section will draw conclusions with respect to the applicability of the metrics found in literature for assessing those factors that are important for capacity. Safety Maintaining separation between aircraft according to the separation standards is needed in order to establish aircraft operations in a safe manner. Therefore, safety is related to workload of the humans or machines that are responsible for separation assurance. In case of humans, workload may not exceed a certain acceptable level. When machines are considered, safety depends on the computation times that are needed to obtain a valid solution for a conflict situation or the ability of the algorithm to solve the conflict situation. A direct indicator for safety is loss of separation. Economy The economy of an aircraft operation is usually expressed in terms of direct operating cost, which can therefore be used as quantification for the efficiency of a flight. Such a quantification is very important to airlines (one of the identified stakeholders, see also document D1-3 [21]) in order to judge the success of their (future) business. Classification: Public Page 30/131

33 Environment In order to reduce or at least not to increase the impact of aircraft operations on the environment more and more constraints might be imposed in the future. This can already be seen at a number of airports today where due to noise considerations, aircraft movements are constrained in one way or another. Since the scope of this project is limited to en-route traffic, environmental constraints are of less importance. However, in a way CO 2 emissions are taken into account because fuel used is considered as a measure for flight efficiency (see [20]). This section will continue with an overview of the literature that has been surveyed. It became clear that metrics, related to airspace capacity, are discussed from very different perspectives. In the review of literature, this perspective will be stated explicitly in order to avoid confusion in discussions about this topic. The literature review will be presented by category, as given in the following list. 1. CARE-INTEGRA This project is an initiative from Eurocontrol in order to determine which of the many proposed tools and procedures for capacity increase should be integrated into operational ATM systems and when. In order to investigate this, it is necessary to have a widely agreed set of metrics for ATM system safety, capacity, efficiency and environmental impact, with supporting methodologies for their measurement. The part of INTEGRA that is discussed here, concerns capacity metrics; 2. Flight Efficiency At NASA-AMES researchers have carried out a study that addresses performance evaluation of airborne separation for free flight. Their idea to evaluate flight efficiency will be presented here. Next to that, the cost-benefit analysis from the NASA/NLR Free Flight study is also discussed; 3. Dynamic density The concept of dynamic density has been proposed by the RTCA. They state that a dynamic density metric should be comprised of essential factors affecting conflict rate i.e. traffic density, complexity of flow and separation standards. A number of organisations are working on the development of such a dynamic density metric, including NASA-AMES and the University of Minnesota; 4. Delahaye & Puechmorel The research of these two people from ENAC (France) extends the previous study on dynamic density with a metric that accounts for the intrinsic traffic disorder; 5. Andrews and Welch study These two researchers from MIT show the effects on workload and evaluate the rate of multiaircraft conflicts that result from relaxing flow and routing restrictions; Classification: Public Page 31/131

34 6. Cluster metrics Several studies use the notion of clusters. A cluster is considered as being the transitive closure of all the aircraft in conflict with an aircraft already part of the cluster with a time/ distance around the entry of this aircraft in the cluster; 7. Computational Geometry This is a mathematical technique, which is suggested to be used to design future airspace; 8. Wyndemere study This study looks at the development and evaluation of a model of the perceived complexity of an air traffic situation, with specific regard to the traffic characteristics that impact the cognitive abilities of the controller. The outcome of their controller-in-the-loop experiments can be quite useful to INTENT, since some interesting issues concerning intent information are discussed. Therefore this study and a paper on DAG-TM which uses Wyndemere results, are briefly discussed at the end of this section. Note: The following paragraphs discuss the eight categories mentioned above. These paragraphs contain abstracts and/or parts from the original documents. This has been done to facilitate the exchange of information with the other partners of the INTENT consortium. This is indicated by using a italic font and highlighting the text CARE-INTEGRA This work has been taken as a starting point since it gives some very useful comments on airspace capacity and its assessment. Perspective This CARE action studies a very broad range of metrics to be used to characterise future ATM systems. Metrics related to capacity are reviewed from literature and a general methodology to assess the capacity of an ATM (sub) system is proposed. General The background of the CARE-INTEGRA work is the same as that of the INTENT project, namely: in order to satisfy an ever-increasing demand to fly, EUROCONTROL has identified the need to consider new airspace structures, new automated support tools, new control methods and procedures and new airspace user methods and procedures. The INTEGRA project has the task of determining which of the many proposed tools and procedures should be integrated into operational ATM systems and when. This determination will be based on quantitative assessment and comparison. In order to quantify the benefits to be expected from the proposed tools and procedures in a credible manner it is necessary to have a widely agreed set of metrics for ATM system safety, capacity, efficiency and Classification: Public Page 32/131

35 environmental impact, with supporting methodologies for their measurement. (from executive summary, [22])." The INTENT project addresses the specific question of how, where and when INTENT information should be used in order to give a technology roadmap for airborne and ground based equipment to increase airspace capacity. This section will address some of the issues and conclusions of the INTEGRA report, Capacity Metrics Study: WP3 report [2], because these are also of prime importance to the development of metrics for the INTENT project. These are summed up in the following list, accompanied by some comments. The chief conclusion from the study is that there is no single measurement methodology that can be applied to all problems of capacity assessment and comparison in ATM systems. The consequence of this conclusion is that, for each comparison or assessment to be made, the appropriate measurement methodology must be chosen on a case-by-case basis. INTENT considers a number of different ATM concepts so this conclusion should be considered a warning. Therefore, it has been decided to assess each ATM concept of the INTENT experiment matrix in terms of feasibility, as already explained in the previous section. In order make this assessment, a number of metrics will be recorded and monitored during the various experiments. After running the experiments, the results from the simulations will have to be interpreted in order to determine the feasibility of the particular concept under consideration; The INTEGRA Metrics Requirements document [23] specifies a number of general properties required of INTEGRA metrics. These may be summarised as follows: 1. Metrics shall be usable and measurable during modelling, fast-time simulation, real-time simulation and operational trials; 2. Metrics shall be independent of airspace definitions, of controller procedures, and of user procedures; 3. Metrics shall be applicable to all stages of flight in a gate-to-gate system; 4. Methods of measurement shall wherever possible be objective, and where a subjective methods are used for measuring a metric, these shall not be the only methods of measurement for that metric. These requirements have also to be imposed when metrics for INTENT are developed. The third requirement can be loosened, since INTENT only considers en-route traffic. It might be wise however, to develop metrics that can be applied or are easily extended to different stages of flight. An additional requirement, i.e. predictability, might be added to this list. Predictability of metrics might be necessary for controller tools that are used to provide some sort of planning functions; Chapter 5 of the INTEGRA report focuses on ATM system components that are related to airborne stages of flight. Unfortunately there is no direct way of measuring the capacity of air-based components so two indirect approaches are described below. The first approach is Human Workload Methods. These methods work well for estimating the capacity of components of today's systems and systems not Classification: Public Page 33/131

36 too far removed from today's systems, but they have some serious limitations for INTEGRA applications: They do not work well for systems far removed from today's systems because the measurements of times for controllers to perform elemental tasks are made in a particular context and cannot be carried across to significantly different contexts; the same tasks might not even exist in future systems; These methods require measurements from either operational systems or real-time simulations, which are then turned into capacity values by fast-time simulations. They therefore fall far short of the INTEGRA requirement that methods shall be useable in modelling, fast-time simulation, real-time simulation and operational trials. Nevertheless human workload methods are widely used, and are likely to continue to be used for the foreseeable future for capacity estimating purposes in systems intended for near-term operation. Up to now these methods have been used to analyse controller workload, but they might also be used for analysing pilot workload in those proposed future systems where more responsibility for separation is transferred to the cockpit. It is stated here that human workload methods are likely to be used for capacity estimating purposes in systems intended for near-term operation. For INTENT, these methods could therefore only be used with high confidence for a concept that is close to today s system. This is probably only the baseline system, which is today s system. The use of INTENT information by ATCo is something quite new, which needs additional procedures and or tools. However, human workload methods could be used to determine certain characteristics needed to model the ATCo in the compressed-time experiments. Pilot workload has already been investigated by NLR during the NLR/NASA Free Flight program. It is proposed to incorporate that approach into the INTENT part-task experiments, together with additional, more objective, monitoring of workload. This might be e.g. the following method that is described in the INTEGRA report. The second approach is the Interaction Frequency Method. When two aircraft become close enough to one another, avoiding action must be considered as a trajectory interaction or simply an interaction. The notion of interaction may be thought of as a generalisation of the notion of ATC conflict. The number of interactions per unit time (interaction frequency) is a measure of the amount of work per unit time which must be done by an ATM system component to maintain aircraft separations in a safe state, and forms the basis for a comparative measure of capacity. The main INTEGRA report, INTEGRA, Metrics & Methodologies, Detailed Specification of Capacity Metric, Input Processing Outputs [22], gives the algorithmic specifications of the capacity metrics developed for EUROCONTROL by DERA as part of the INTEGRA project. Some of the important parts of this report are listed below: The measurement of controller workload requires operating procedures to be known in some detail, and having controllers well versed in those procedures. This is not generally available for systems involving new tools and/or procedures that are not in use today. The situation is even more complicated than this. The introduction of new tools are not merely going to be limited to automating part or assisting the controller with part of his task but will, in some cases, change the tasks the controllers have to execute. For example, the introduction of 4Dtrajectory prediction, together with a planning function (either sector or multi-sector based) could remove the conflict detection to a strategic rather than a tactical task. Further, use of Classification: Public Page 34/131

37 datalink to pass resolution manoeuvre instructions to the aircraft FMS for future execution could move the conflict resolution activity to a strategic task. This could radically change the role of the tactical controller to that of monitoring and exception handling. This is not to say that his workload will be reduced or increased; it merely illustrates the fact that his task will be different and therefore will not be so easily comparable to a current operation for calibration of workload. Furthermore, ATM systems are already foreseen that would entail no controller involvement; so there would be no controller workload to measure. The remarks made above with respect to the role of a controller are also relevant to the INTENT project. In order to illustrate this, the experiment matrix is shown in. First, ground based separation assurance is considered. When in this case, intent information is increased, the controller will e.g. be able to predict conflicts at an earlier stage. In order to deal with this information, new procedures have to be developed, e.g. in order to prioritise solving two simultaneous predicted conflicts. When airborne separation assurance is considered, the ATCo will not be involved at all. Airborne / Unstructured Routes (pilots) Ground / Unstructured Routes (ATCo) Separation Assurance Responsibility Metrics: capacity safety efficiency Ground / Structured Routes (ATCo) current ATC position low state intent (10 min) intent (15 min) intent (20 min) Level of Intent medium Traffic Load high ATC = Air Traffic Control ATCo = Air Traffic Controller Figure 5: Experiment Matrix - The main report presents a broader perspective for looking at capacity of an ATM system. The main observation of the report is the following: Information processing is considered the basic task that has to be done to control traffic. A number of different parts of the ATM system have to process information. For example, there may be two or more controllers and computer tools (e.g. trajectory predictor, conformance Classification: Public Page 35/131

38 monitor, and conflict probe). In a free-flight system, the information processing would be distributed among many pilots and their in-cockpit separation tools, ground-based tools, the controllers. In addition, information is handled by communications systems, and by the controller s HMI. The information processed by all these actors needs to be considered, as any one may be the limiting factor The proposed method gives an indication of how much information processing was done by each actor, as a function of time. A graph like the one below is obtained for each actor. Figure 6: Information processed The graphs for a given actor from different simulation scenarios can be compared, to indicate the relative loading of that actor in the two scenarios [ ] Comparing the graphs for the relevant actors may show all that is necessary for a particular system. However, these graphs can also be used to assess which actors were modelled as near capacity in the simulation. This indicates which is/are the critical actor(s), that limit capacity in the modelled system [ ] Sustainable throughput is determined by how much information there is to process, compared with how much capacity there is to process information. In order to assess this, the experiment analyst will need to assess the cut-off point for the graphs of information processed vs. time. Finally, the amount of traffic in the simulation traffic samples can be increased until the critical actor(s) are modelled as being at their maximum sustainable processing level. The throughput then being achieved is the sustainable throughput. Classification: Public Page 36/131

39 A trajectory interaction, as defined earlier, is used as a trigger for actions to be taken. Such an action could be notification of an ATCo by a conflict probe in order to plan resolution or increasing monitoring in order to ensure that a predicted interaction is not worsening. The notion of information processing is an interesting one and it seems that already some work has been done in order to implement this method into simulation software. However, DERA remarked that the full-blown method as described here, needs a lot of tuning in order to model all the actors. Besides that, experience from previous studies has shown that there is not much confidence in those values Flight Efficiency This section first discusses a paper by researchers from NASA-AMES, titled Performance Evaluation of Airborne Separation Assurance for Free Flight [24]. After that a short description of the cost & benefit analysis of the NASA/NLR Free Flight program is given. Perspective The primary objective of these studies is to look at the feasibility of airborne separation assurance for free flight. In order to do that a number of performance metrics have been evaluated and a costbenefit analysis has been carried out. Abstract Airborne separation assurance is a key requirement for Free Flight operations. This paper investigates the feasibility of airborne separation assurance for free flight by evaluating the performance of Conflict Detection and Resolution (CD&R) schemes in a simulated air traffic environment. Two qualitatively different CD&R methods were utilised; one based on a geometric optimisation approach, and the other based on a modified potential-field approach. Both CD&R methods were evaluated in an air traffic simulation environment provided by the Future ATM Concepts Evaluation Tool (FACET). The evaluation was based on a realistic free flight traffic scenario constructed with initial conditions from actual air traffic data; approximately 1,000 aircraft were represented in this 6-hour air traffic scenario. Three metrics were utilised for the performance evaluation: safety, efficiency, and stability. The results of the performance evaluation data indicate that airborne separation assurance performed quite well in the Free Flight evaluations: (1) all of the conflicts were resolved; (2) the impact on flight efficiency, as measured by path-length and flight-time changes, was small; and, (3) the impact on system stability, as measured by additional trajectory deviations due to the 'domino effect', was low to moderate (depending on the CD&R method used). This study uses safety, efficiency and stability as performance metrics. Safety is thereby measured by recording the extent to which separation was maintained during operations with a CD&R scheme engaged. This has been done by monitoring losses of separation. Efficiency of conflict resolution has been measured as the incremental Direct Operating Cost (DOC) relative to the DOC of a reference trajectory. The direct operating cost is a function of time and fuel, given by: Classification: Public Page 37/131

40 DOC = C T + C W (5) i f f where C t ($/hr) and C f ($/lb) represent the costs of time and fuel respectively, T is the flight time and W f is the weight of the fuel consumed. So, the increment in direct operating cost, which is used to measure flight efficiency, can be expressed by: DOC = C T + C W i f f (6) Generally, the incremental fuel consumption is a function of both the incremental flight-time T and the incremental path-length. Hence, DOC = f ( T, l) (7) where f is a complex function that depends on the aero-propulsive characteristics of an aircraft. For the sake of simplicity, the study used the incremental flight time T and the incremental path-length. These are defined as: T = T CDR T ref (8) l = l CDR l ref (9) where the subscript CDR denotes the values for the scenario where CD&R is engaged and subscript ref denotes the reference scenario. A similar approach has been taken in the NASA/NLR Free Flight study [25], where a cost & benefit analysis of airborne conflict has been carried out. This analysis used more detailed aircraft models based on BADA aircraft performance data. Only one type of aircraft, i.e. a medium range twin-engine aircraft, has been used. Conflict resolution could also be performed in a vertical manner by performing a climb or descent. The costs of the resolution manoeuvres were expressed in the relative percentages of fuel consumed and flight time with respect to a reference flight. One of the promising results is that fuel consumption could even be improved by performing a vertical climb manoeuvre to solve a conflict. Classification: Public Page 38/131

41 Dynamic Density Perspective The RTCA proposed that a dynamic density metric should be comprised of essential factors affecting conflict rate i.e. traffic density, complexity of flow and separation standards. Therefore, a number of organisations the development of such a dynamic density metric. An overview of the current studies will be given in the following. 1. NASA-AMES Perspective This study proposes a dynamic density metric that, next to traffic density, also incorporates traffic complexity terms. The goal of the study is to find out whether controller workload correlates to the dynamic density equation that has been defined. a) Initial work The initial work by NASA on the development of dynamic density is described in the report Dynamic Density: An Air Traffic Management Metric [19]. An abstract of the important parts of this report is given here. Abstract The definition of a metric of air traffic controller workload based on air traffic characteristics is essential to the development of both air traffic management automation and air traffic procedures. Dynamic density is a proposed concept for a metric that includes both traffics density (a count of aircraft in a volume of airspace) and traffic complexity (a measure of the complexity of the air traffic in a volume of airspace). It was hypothesised that a metric that includes terms that capture air traffic complexity will be a better measure of air traffic controller workload than current measures based only on traffic density. A weighted linear dynamic density function was developed and validated operationally. The proposed dynamic density function includes a traffic density term and eight traffic complexity terms. A unit-weighted dynamic density function was able to account for an average of 22% of the variance in observed controller activity not accounted for by traffic density alone. A comparative analysis of unit weights, subjective weights, and regression weights for the terms in the dynamic density equation was conducted. The best predictor of controller activity was the dynamic density equation with regression-weighted complexity terms. Classification: Public Page 39/131

42 Introduction The function that describes dynamic density is given by: n DD = ( W TC ) + TD CI (10) i i i + The first term in the equation is a weighted sum of eight complexity terms, which are categorised as follows: heading, speed and altitude changes; minimum distance for 0-5 and 5-10 nm range; conflict predicted for 0-25, and nm range. The second term is traffic density and the third term refers to controller intent. This last term is not considered in the study, because it seems impossible to determine controller intent in an operational trial. This implicates that workload is only measured by looking at observable behaviour. Traffic Factor Selection The traffic factors included in the dynamic density equation have been selected based on an informal interview process with qualified air traffic controllers from an ARTCC. Only factors that can be computed in real time from radar track data could be chosen. This resulted in the three categories listed above, which are discussed in more detail in the following list: 1. Heading Change (HC) The number of aircraft that made a heading change of greater than 15 degrees during a sample interval of two minutes; 2. Speed Change (SC) The number of aircraft that had a computed airspeed change of greater than 10 knots or 0.02 Mach during a sample interval of two minutes; 3. Altitude Change (AC) The number of aircraft that made an altitude change of greater than 750 feet during a sample interval of two minutes; 4. Minimum Distance 0 5 n. mi. (MD 5) The number of aircraft that had a Euclidean distance of 0 5 n. mi. to the closest other aircraft at the end of each two minute sample interval. This measure does not include converging aircraft that are predicted to be in conflict. Predicted conflicts are accounted for in other traffic factors. The Euclidean distance was computed as the shortest distance between two aircraft whose positions were defined by values in the x, y, and z-dimensions; 5. Minimum Distance 5 10 n. mi. (MD 10) The number of aircraft that had a Euclidean distance of 5 10 n. mi. to the closest other aircraft at the end of each two minute sample interval, excluding conflict aircraft; 6. Conflict Predicted 0 25 n. mi. (CP 25) The number of aircraft predicted to be in conflict with another aircraft whose lateral distance at the end of each two-minute sample interval was 0 Classification: Public Page 40/131

43 25 n. mi. The lateral distance was computed as the shortest distance between two aircraft whose positions were defined by values in the x and y dimensions; 7. Conflict Predicted n. mi. (CP 40) The number of aircraft predicted to be in conflict with another aircraft whose lateral distance at the end of each two minute sample interval is n. mi.; 8. Conflict Predicted n. mi. (CP 70) The number of aircraft predicted to be in conflict with another aircraft whose lateral distance at the end of each two-minute sample interval is n. mi. Dynamic Density Function An a priori decision was made to evaluate a linear combination of traffic density and traffic complexity factors. The dynamic density equation was as follows: DD = W ( HC) + W ( SC) + W ( AC) + W ( MD5) + W ( MD10) + W ( CP25) + W ( CP40) + W ( CP70) + TD (11) The dynamic density equation was programmed into the Center TRACON Automation System as a selectable function with adjustable factor weighting capability. Dynamic density was computed using a two-minute sample interval for selected air traffic control sectors. The dynamic density function output file included a Zulu time stamp, sector number, dynamic density values, and separate values for each of the traffic complexity factors. While the difference between the dynamic density and traffic density functions was promising, it was not an adequate demonstration of the usefulness of the added complexity terms in the dynamic density equation. A validation of the equation with operational data was required. An independent measure of observed controller workload was needed to evaluate the relative merits of the dynamic density and air traffic complexity terms and to compute differential weights for the air traffic complexity terms. What was required was an independent measure of controller workload that could be collected concurrently with dynamic density data. The limitations on the choice of the independent workload measure were (1) that it not interfere in any way with the activity at an operational sector and (2) that it measure only observed actions as opposed to the mental state of the controller. The noninterference requirement was due to the safety concerns related to collecting data in an operational air traffic control facility. As noted earlier, the CI term of the dynamic density equation was not considered in this study. One implication of the elimination of the CI term is the limitation of the workload measure to observable behaviour. Operational Validation From the variables identified, eight air traffic controller activities were selected for use in the dynamic density metric validation study. The selected activities included radio communications activities and radar scope related activities. 1. Zoom In/Out Controller changes the radar scope field of view. Typically the controller will zoom out and then return the field of view to its normal setting. This constitutes a single event; Classification: Public Page 41/131

44 2. Trend Line Manipulation of a single trend line, or a global change to all displayed trend lines. Typically, the controller will extend a single aircraft s trend line, assess the aircraft trajectory, and then remove the trend line. This constitutes a single event; 3. Conflict Flight Data Blocks (FDB) of two aircraft flash, indicating a potential conflict. This is a timed event. The first key press starts the timer. When the conflict stops flashing, the second key press stops the timer; 4. Route Line Controller displays an aircraft filed route of flight. This constitutes a single event; 5. Minimum Separation Ring Activation of a mini-mum separation ring. This is a timed event. The first key press starts the timer. When the minimum separation ring is deactivated by the controller, the second key press stops the timer; 6. ATC Communication Any controller-initiated communication with an aircraft, together with the aircraft response. This constitutes a single event; 7. Say Again A controller who misses a communication will issue a sectorwide communication, aircraft calling center, say again. This constitutes single event and is not also recorded as an ATC Communication; 8. Pilot Communication Any pilot-initiated communication with the controller together with the controller response. This constitutes a single event; 9. Incorrect Readback Any aircrew error in reading back ATC instructions. This constitutes a single event. Dynamic Density Validation Study The validation study was carried out in which the participants were air traffic controllers who were observed while performing their normally assigned duties at the R-side position of Sectors 9, 16, 17, and 28 of the Denver ARTCC. Activity observations were recorded on laptop computers by single observers sitting immediately behind and to the side of the R-side controller at the sector. A total of four observers were used in the study. Observers collected activity observations with the Activity Catalog Tool The participants were air traffic controllers who were observed while performing their normally assigned duties at the R-side position of Sectors 9, 16, 17, and 28 of the Denver ARTCC. Conclusions The correlation of dynamic density with observed controller activity was consistently higher than correlations of traffic density with observed controller activity across the various data collection periods. These data support the conclusion that the traffic complexity terms of the dynamic density equation are robust in their ability to capture more of the variance in controller activity than the traffic density term alone. When the data were collapsed across sector and collection period, the overall unit-weighted dynamic density factors accounted for 22% of the variance in observed controller activity that was not accounted for by traffic density alone. These data support the hypothesis that the eight proposed traffic factors (or some subset of these factors) can better account for increased controller workload due to complexity in sector traffic. The multiple regression dynamic density equation was a better predictor of controller activity than the subjectively weighted dynamic density equation. The multiple regression equation was able to Classification: Public Page 42/131

45 account for 50% of the total variance in air traffic controller activity in the set of test cases, suggesting that the dynamic density equation has some generalizability across operational conditions. The multiple regression analysis identified statistically significant weights for four of the eight proposed traffic factors, with two additional factors approaching significance. It would be premature, however, to remove any of the proposed traffic complexity terms from consideration until the proposed dynamic density equation has been tested in a variety of operational settings. b) Further research Based on the conclusion of the previous study, i.e. that dynamic density correlates well to controller workload, a paper by Sridhar [26] investigates how well dynamic density can be predicted in an operational setting. This research was presented at the 2 nd USA/Europe ATC R&D Seminar and is described in the paper Airspace Complexity and its Application in Air Traffic Management [26]. Results have shown that dynamic density can be predicted up to 20 minutes in advance and that errors in predictions can be further reduced by accounting for departure traffic. Abstract Predicted growth in air traffic and the desire for more user preferred routes in the National Airspace System (NAS) will impose additional demand on air traffic control and management systems. This demand can be met by alternate airspace configurations, modified traffic patterns, and staff reassignment. There is a need to understand the effect of changing airspace configurations and traffic patterns on the workload of air traffic controllers. This complex relation is referred to as Airspace Complexity. Research on dynamic density indicates that it is a good measure of airspace complexity. Dynamic density is a function of the number of aircraft and their changing geometry in a given airspace. In order to use dynamic density as a planning tool, it is necessary to project its behaviour over the planning horizon. The objective of this work is to study how well dynamic density can be predicted into the future using the trajectory generation feature of the Center-TRACON Automation System (CTAS). This paper describes the application of trajectory prediction to computation of actual and predicted dynamic density using traffic data from Dallas/Fort Worth airspace. Results show that dynamic density can be predicted up to 20 minutes in advance and errors in predictions can be further reduced by accounting for departure traffic. Introduction [...] The growth in air traffic and developments in the ATM, such as, free flight, requires a new understanding of the complex relationship between traffic pattern, Sector/Center geometry, procedures and controller workload. It has been suggested by the Radio Technical Commission on Aeronautics (RTCA) that the monitor/alert function should be extended to include measures of Sector complexity and controller workload. These measures should be based not only on the number of aircraft, but their relation to each other, airspace geometry and varying traffic flow conditions. This concept has come to be known as Dynamic Density. For dynamic density and other airspace complexity measures to be useful as traffic management tools, it is necessary to predict their future behaviour. The approach of this paper is to adopt a measure of complexity of the Sector and Center airspace that can be related to controller workload, and to examine how well it can be used with the Classification: Public Page 43/131

46 predicted traffic estimates to forecast future workload levels. This assessment can then be used for TFM decisions. A measure of airspace complexity has been developed at the NASA Ames Research Center (ARC). This paper assumes it to be a good measure of controller workload, and studies how well dynamic density can be predicted up to a specified period in advance. This analysis was applied to predict dynamic density at the Dallas/Fort Worth (ZFW) ARTCC using the Center-TRACON Automation System (CTAS). CTAS predicts future aircraft locations using radar tracks, flight plans, aircraft dynamic models, and weather data from National Centers for Environmental Prediction (NCEP). These predicted aircraft positions and speeds are used for computing dynamic density in the future. [ ] Conclusions The ability to predict trends in controller workload is necessary for the management of air traffic. Earlier research has shown that controller workload is related to dynamic density. Results have been presented with predictions of aircraft counts and dynamic density for the Dallas/Fort Worth ARTCC airspace. This prediction capability can be used by the Area Supervisors for resource allocation and by TFM staff for airspace/traffic planning. Currently the predictions have been made up to 20 minutes into the future. Availability of inter-center data (specifically, aircraft intent), can help extend this analysis for larger prediction intervals. With improved wind estimates, reduced radar tracker errors, and better aircraft models, the parameters can be estimated more accurately. This paper has assumed a specified definition of dynamic density as a good measure of controller workload. The current measure represents only the traffic flow conditions and could be improved by incorporating effects of structural characteristics like airway intersections, as well as other dynamic flow events such as weather. There is also a need for developing measures of airspace complexity that can be used for addressing not only the physical aspect but also the cognitive aspect of controller workload. The cognitive workload aspects are important because past research indicates that infrequent but critical events such as loss of separation, altitude deviations, VFR pop-ups and incorrect pilot read backs impose considerable mental workload on the controllers. Classification: Public Page 44/131

47 2. University of Minnesota Perspective This research focuses on an index of dynamic density that describes collision risk. The point of view in this study is the flightdeck. a) Initial work At the university of Minnesota, Smith et al have proposed and evaluated an mathematical index of dynamic density, D, that described collision risk. A paper titled An Index of Dynamic Density [27] describes a series of sensitivity analysis that illustrate how D responds to frequently encountered air traffic conflict situations. It is also shown how D can be used in order to characterise pilot performance and efficiency in experimental free flight simulations. A model of dynamic density is presented that provides an initial approach to the assessment of the complexity of spatial dynamics that focuses on separation, which is the most important factor in the estimation of collision risk. An index of dynamic density is defined (D) that is a componentbased function of time and of the number and distribution of aircraft in a sector of airspace. It is proposed to use this metric to solve human factors problems. Patterns of dynamic density values especially those associated with loss of separation provide unique insight into relationships between performance (e.g. pilot avoidance manoeuvres) and changing airspace configurations. The function that is used to describe dynamic density is based on the reciproke value of the ration between relative distance between aircraft and current separation standards, i.e.: f = N 1 N 1 i= 1 j= i+ 1 ( d ij c ) a (12) where: d ij : distance between aircraft pair; c: (horizontal) separation standard of 5 nm; a: empirical weighting factor. This metric has been used to assess pilot performance in self-separation traffic scenarios. In their simulation facility, commercial airline pilots were asked to navigate an aircraft in a manner consistent with a pre-programmed flight plan and to progress to a destination airport. The pilots had full authority to execute decisions for routing and separation with none of the current FAAmandated routing and altitude restrictions in effect. The only requirement was to maintain a minimum separation of 5 nm laterally and 1000 ft vertically. Cockpit instrumentation included a primary flight display, a cockpit display for traffic information and a flight management system. The scenarios are characterised by complexity factors such as Classification: Public Page 45/131

48 number of aircraft, traffic type, configuration (crossing or converging traffic) and angle of approach. Success or failure of maintaining a rule of separation is used as the traditional criterion for pilot performance. Aircraft pairs that violate this rule make an operational error. This gives an indication of overall performance but it does not indicate how separation was lost. Therefore, graphs of the index of dynamic density against time are also used. b) Extended research In a second paper by Smith et al [28], their previous work on dynamic density is extended to include sector capacity. Dynamic density is a measure of the complexity and severity of pairwise conflict from the viewpoint of the flightdeck. Sector capacity is a similar measure from the viewpoint of ATC. A number of four alternative metrics were investigated and it was found that no one metric can be expected to capture conflict severity while maintaining a low false alarm rate. Introduction The paper addresses the design of metrics to be used to assist in the identification and discrimination of critical events in a complex dynamic environment. In this case, the United States National Airspace System is under consideration for which an alerting system to be used by air traffic controllers for the short-term projection of airspace complexity has to be improved. It is stated that, the ideal description of airspace complexity would reflect all factors that influence controller workload and collision risk. Among these factors are the number of aircraft, their separation, distribution and relative velocities. Instead of enumerating and quantifying all these factors an alternative approach is taken, i.e. it is assumed that these factors combine to modulate the likelihood of conflict. Likelihood of conflict is the signal embedded in traffic complexity. A controller sees complexity and his job is to detect and predict the signal and resolve the impending conflict. Their approach began by investigating the sources of complexity that influence pilots' ability to avoid collisions [insert ref]. That study employed an index of dynamic density that was used to assess their performance. This paper continues that line of work but shifts the focus from flightdeck to air traffic control. Here a modelling study of alternative algorithms for assessing traffic separation and complexity is discussed. Smith et al. [27] introduced an alternative metric that was called the index of dynamic density. This index is an unbounded reciprocal exponential function: URE = N 1 N 1 ( d c) i= 1 j= i+ 1 3 (13) where d = distance, separation in the horizontal (xy) plane and c = critical distance, the FAA's criterion for horizontal separation, five nautical miles. The double summation is the URE for all pairwise interactions and would be the preferred form of the equation for sector capacity. A single summation, which would consider only the pairwise interactions with a given aircraft, would be the preferred form of the equation for dynamic density. Based on this function a bounded reciprocal exponential function is defined, called the Truncated Classification: Public Page 46/131

49 Reciprocal Exponential (TRE). A double summation of this function is the TRE for sector capacity; a single summation is the TRE for dynamic density. A second metric that is introduced is based on a sigmoid function. This metric is again the sum of the product of two components of pairwise separation. Both the xy and z components are continuous functions that asymptote to value of two at zero separation and to a value of zero at infinite separation. The double summation is S for sector capacity; a single summation would be S for dynamic density. The sigmoid emulates a psychometric function and is designed to capture the dynamics of human performance evident in many perceptual tasks. We expect the sigmoid to represent the dynamics of workload and risk as a function of separation. The half value of each component is designed to be one at the boundary of the protected zone. In order to test the utility of the metrics, experiments were carried out in the flight simulator at the University of Minnesota's Human Factors Research Laboratory. Subjects in the experiment were 32 commercial airline pilots. Their task was to fly an 'ownship' in a series of simulated air traffic conflict situations without the assistance of air traffic control. During these experiments the time histories of the different metrics were recorded. Conclusion It was found that there is a trade off between sensitivity to conflict severity and a low false alarm rate. A single metric cannot capture information about severity while avoiding false alarms. Both types of information are useful and desirable. It is concluded that both types of metrics will be necessary to capture and represent conflict information, dynamic density, and sector capacity. Classification: Public Page 47/131

50 Delahaye & Puechmorel Perspective This study tries to define an intrinsic measure of the traffic complexity in order to better quantify the congestion in air sector. The goal is to extend previous work, like dynamic density (NASA, see previous section) or perceived complexity (Wyndemere, see section 2.2.8), with a new metric of disorder. The paper, Air Traffic Complexity: Towards Intrinsic Metrics [18], presented at the 3 rd USA/Europe ATM R&D Seminar addresses the problem of measuring the air traffic complexity, given the observed positions and speeds of aircraft present in air space. Abstract [ ] Different studies have been conducted following various approaches like dynamic density and cognitive models. However, an intrinsic measure of the traffic complexity is still to be introduced in order to complete those previous works. Two classes of indicators are investigated. The first one uses geometrical properties in order to build a new complexity co-ordinate system in which sector complexity evolution through time is represented. The second one uses a representation of air traffic as a dynamical system, yielding, through the topological Kolmogorov entropy, an intrinsic measure of complexity. The paper describes two indicators, which can be used as an intrinsic measure of traffic disorder. The first indicator is based on a geometrical approach and uses the properties of the relative positions and the relative speeds of aircraft in a sector. Geometrical approach When a set of aircraft is considered in a sector, it is possible to identify different areas for which the structure of traffic is different. For example, it is possible to identify some high-density zones and clusters of traffic with strong disorder. This identification is done by our brain, which investigates the different structure and is able to recognise structure symmetries. The current approach, propose some metrics in order to quantify this feeling of disorder and produces a new representation for which each aircraft may be assigned to a point in a complexity co-ordinate system.[ ] When two aircraft are considered, it is possible to define their relative distance and their relative speed. The relative distance is given by: d ij = P j P i (14) and the relative speed is given by: v ij = v j v i (15) Classification: Public Page 48/131

51 The following equation gives the definition of the Delahaye/Puechmorel aircraft density measure. Dens( i) = 1+ N j = 1 j i e r d ij α R (16) where: i = the aircraft for which the local density is computed [-] Dens = local density [-] N = total number of aircraft [-] α = a weighted coefficient [-] R = a neighbourhood distance [m] The values of α and R in equation (16) are not given in [18], but should be determined by a calibration process in which air traffic controller are used 1. With equation (16), an indication can be given of the local density that is experienced by each aircraft separately. Besides this aircraft density measure, also the level of disorder should be used to determine the complexity of an air traffic situation. This level of disorder of a traffic flow is computed with equation (17) and (18), which represent the divergence and convergence of a traffic flow respectively. These equations are obtained by differentiating equation (16). Div( i) r d d N ij = 1 + R j = 1 dt j i r d d dt ij e r dij α R (17) r N d d ij Conv( i ) = 1 R dt j = 1 j i d r d dt ij e r dij α R (18) where Div Conv = local divergence [m/s] = local convergence [m/s] 1 The authors suggested the following values received in an from them: α = 0.5 and R = 15 nm. Classification: Public Page 49/131

52 1 + = 1 if R r d d dt r d d ij 0 if dt r d d ij 1 = 1 if R dt r d d ij 0 if dt ij > 0 <= 0 (19) < 0 >= 0 (20) The complexity of a traffic situation in a certain volume of airspace is defined by a combination of the density and disorder of the aircraft in the airspace volume. According to [18], with this approach the dynamic density definition can be extended in order to better determine or predict the controller workload. [ ] Finally a third axis (z) will support the sensitivity of the relative distance to the classical manoeuvre such as speed and heading changes in case of convergence. Two different metrics may be used to describe this indicator depending of the user objective. The first one is related to the gradient of the relative distance. This indicator measure the change in term of relative distance when small modification is applied to the speeds and the headings of the aircraft involved. This indicator is very sensitive to the angle of crossing and is maximum for a face to face convergence. The second indicator is related to the sensitivity of the conflict duration with the speed and heading modifications. Due to the fact that a convergent situation with a high sensitivity is better than a convergent situation with a low sensitivity, the induced complexity will be higher in the later than in the former. [ ] A detailed description of this sensitivity indicator is available in the paper. [ ] Our complexity co-ordinate system has now three co-ordinates: density, divergence/convergence and insensitivity. The complexity of a given traffic situation will be represented by a path in this new co-ordinate system. Computation of this indicator on some representative traffic situation is given at the end of the paper and is compared with the dynamical system approach that is now presented. Dynamical systems and complexity Through the geometric approach, a dynamic representation of the traffic in terms of potential conflicts and aircraft density was defined. However, the evolution of the set of aircraft as a whole is not taken into account. In many cases, most of the perceived complexity arises from the observation of the history of the traffic. It appears thus that the dynamical aspect of air traffic is of utmost importance in the definition of a complexity metric. Several smoothing in time procedures can be designed using a geometric complexity measure sampled periodically, but will lack both intrinsic character and theoretical foundation. A more promising approach is to model the history of air traffic as the evolution of a hidden dynamical system such that aircraft correspond to pointwise observations. Of course, Classification: Public Page 50/131

53 there are an infinite number of dynamical systems that may fit the requirement that observed aircraft trajectories be system trajectories as well. Practically, additional assumptions will be made on the representing dynamical system so that the model can be uniquely chosen. Having the underlying dynamical system at hand, it exists a measure of its intrinsic complexity: the topological entropy, which is the root of the mathematical ergodic theory [ ]. For more information about the topological entropy and its adaptation to the problem described above, refer to [18]. Calibration process The two previous approaches have several degree of freedoms, which have to be fixed with operational data sets. To reach this goal, as for the dynamic density adjustment, a set of traffic situations will be presented to several controller teams who will compare them in term of complexity by answering if situation A is more complex than situation B for all pair of samples. From those answers, it will be possible to determine the most discriminant parameters of the models. Finally, the value of those parameters will be adjusted by a statistical procedure. Results Four characteristic simulated traffic situations have been investigated: a random flow (41 aircraft with random initial position and speed), a parallel flow with no conflicts, a right angle crossing and a low angle crossing. Geometric complexity representation is drawn for reach of the previous situation. The curves Density-Convergence and insensitivity-time are drawn on the same graph. We observe that the fully organised situation (parallel flow) does not generate complexity at all, either from the geometrical or dynamical system point of view (0 entropy). Next one in complexity is right angle, then random flow, then the most complex of the four, namely the low angle crossing. It may be noted that both indicators give the same order in complexity. Conclusion As previously pointed out, the present definition of sector capacity is not sufficient to describe the real complexity of the airspace. The number of aircraft in an air sector does not encompass the strong complexity of the mental workload involved in the control process, even if those two entities are very dependent. From the mathematical point of view, it seems impossible to build an exact model of this control workload since it depends of many parameters, which are both qualitative and quantitative. Between those two extremes, different levels of detail may be find to model the control workload. Several efforts are underway, but all of them do not take into account the intrinsic disorder of the traffic, which is critical in the definition of the airspace complexity. Two new approaches have been proposed to refine those previous works. The first one describes an air traffic complexity indicator based on the structure and the geometry of the traffic. This indicator produces a point representing the sector complexity in a new co-ordinate system. The second approach is based on the dynamic system theory and uses the Kolmogorov- Entropy to measure the global disorder of the aircraft system when it evolves with time. Those two new indicators may be used to improve and upgrade the concept of dynamic density. Classification: Public Page 51/131

54 Andrews and Welch study Andrews and Welch [7] have looked at workload implications of free flight concepts. An abstract of their paper is given here. Abstract The removal of constraints upon traffic flow in order to allow more efficient user-preferred routing is a major thrust of current ATM research. Many of the constraints that currently exist arise from the requirement that workload be matched to the capability of a human controller who is responsible for a fixed volume (sector) of airspace. This paper evaluates the average workload, workload peaks, and rates of multi-aircraft conflicts that result from relaxing flow and routing restrictions. The increased variability in sector workload that results from unstructured traffic flow can have a negative effect upon sector productivity even when average traffic levels are unchanged. As traffic density increases, the conflict workload experienced by individual aircraft grows more slowly than the conflict rate experienced by sector controllers. A practical implication is that the accommodation of increasing traffic densities without increased flow restrictions will eventually require the introduction of innovative traffic management architectures in which the conflict resolution workload currently assigned entirely to controllers in static sectors will be distributed among automation, aircrews, and controller teams. Introduction This paper will present a set of simple mathematical models that provide approximate answers to some fundamental ATM performance issues associated with free flight. The effects of increased randomness in traffic flow upon sector loading are analysed. Conflict rates experienced by both aircrews and sector controllers are estimated and the resulting sector workload intensity is estimated using a simple workload model. The probability of multiple simultaneous conflicts is computed and shown to be a potential constraint upon traffic densities. Conclusions The analysis presented provides quantitative models that suggest that: The variability in sector loading (number of aircraft in a sector) increases as traffic flow structure is relaxed; Variability in sector loading can reduce sector productivity by forcing the average loading to be kept further below the maximum tolerable loading; The conflict rate experienced by the sector controller is often five or six times greater than the conflict rate experienced by individual aircraft; The sector controller workload intensity grows approximately as the square of traffic density whereas the traffic-related workload of the aircrew grows linearly; Simultaneous multiple conflicts will be a routine occurrence when traffic flow is not highly conditioned, and may impose a limitation on the tolerable traffic loading. These conclusions confirm that there is a need for productivity increases. This conclusion from the paper will repeated here. Classification: Public Page 52/131

55 Traffic growth in a sectored ATM system can be accommodated by a combination of increasing sector productivity and making sectors smaller (thus increasing the number of sectors in the system). Making sectors smaller has the drawback that it produces an increase in the controller work force, thus increasing the costs of ATM. A second and more fundamental difficulty arises from the fact that there are practical limits to the minimum size of air traffic control sectors. If sectors become too small, the sector controller does not have enough time (or airspace) to effect control actions, inter-sector coordination becomes more complex, and the transients associated with hand-off begin to degrade the reliability of the system. Thus, sector productivity must eventually increase in order to prevent the limitations of the sectored system from becoming a barrier to traffic growth. The traditional response to increasing traffic density is to institute more restrictive flow control and flow structure. Because of the costs and inefficiencies that result, the aviation community in the United States has made a commitment to avoid this response. A preferable solution is to increase sector productivity through automation support and decision-support tools. This can be important to the progress of free flight in the near term. However, as the analysis has shown, reasonable assumptions regarding the amount of workload relief provided by decision- support tools leads to the conclusion that only limited growth can be accommodated by this means. A transition to new ways of providing air traffic control services will be required in the foreseeable future. Classification: Public Page 53/131

56 Clusters Perspective A number of papers and projects proposed clusters as an indicator for complexity. An overview of all these studies and all the proposed definitions of clusters are given hereafter: 1. An Experimental Study of ATM capacity J.M. Alliot; J.F. Bosc; N. Durand; L. Maugis 1997 This paper [16] introduced the notion of Cluster. It defines a cluster as a set of interfering aircraft. A simulator has been used to study various global traffic parameters (vertical separations, horizontal separations, time compression ) and to evaluate the influence of these parameters on the number of conflicts and clusters. Some figures, showing the evolution of the number of clusters, are available: number of clusters versus density, vs horizontal separation and vs vertical separation. The indicators used were: the number of clusters, average number of conflicts per cluster, maximum number of conflicts per cluster, average number of aircraft per cluster, maximum number of aircraft per cluster. The paper proposes the average number of conflicts per cluster as a criterion for traffic complexity. More detailed results are available in the document. 2. MAICA - Modelling and Analysis of the Impact of the Changes in ATM Perspective The MAICA project aimed at evaluating, by using ATM simulation tools, the consequences on global ATM performance (capacity, efficiency and safety) of various changes envisaged for Airspace Management, Air Traffic Control, airport and aircraft operations. It mainly addressed the medium and long-term horizon (i.e and beyond). This project did not cover all ATM aspects, but rather focused on specific topics which provide sufficient insights and recommendations for future activities. MAICA objectives were: to identify and describe some future significant changes which might affect the ATM, to evaluate the impacts of a subset of these changes using simulations, to draw conclusions from the simulation results, to make a set of recommendations for future investigations and developments. Cluster Definition In the MAICA project, a cluster is considered as being the transitive closure of all the aircraft in conflict with an aircraft already part of the cluster with a time/ distance around the entry of this aircraft in the cluster. The following figure summarise this definition: Classification: Public Page 54/131

57 A Conflict2 d Conflict1 B c D time Conflict 1 Conflict 2 τ Figure 7: Behaviour of an aircraft during its flight inside the A3S (Autonomous Aircraft AirSpace) When in the A3S, an aircraft is responsible of its separation with other aircraft. The values for the lateral, longitudinal and vertical separation minima were parameters of the simulation. Conflict can be detected as soon as the involved aircraft are in ADS-B range. When a conflict is detected, the corresponding cluster is computed. The resolution of a conflict starts as soon as the involved aircraft are at less than a certain time from the conflict (TCRA = Time before Conflict before Resolution Activation) which is randomly selected between two values provided as parameters of the simulation. This random choice is re-done for every conflict The first step is then the definition of the aircraft, which should perform the collision resolution manoeuvre using the EFR, defined in FREER. In regards to these rules all the aircraft of the simulation are in normal operation. The aircraft in charge of the collision resolution manoeuvre defines its new trajectory using either the rules of the air or GEARS for horizontal resolution and the additional vertical resolution feature. This trajectory should not generate any conflict within a defined time after the initial conflict (MTBC = Minimum Time Between Conflict). The number of aircraft involved in a conflict resolution (two initial aircraft and other aircraft potentially involved in induced conflicts) were recorded (conflict resolution complexity). When a conflict resolution manoeuvre is achieved, the aircraft, which has performed it, must return to its previous status (i.e. either heading to the virtual waypoint or to the exit-point). In some situation the waypoint could be given up in order to avoid a turn back of the aircraft (direct heading to the exit point). During the flight in the A3S, an aircraft can change of flight level. Periodically since its entry in the A3S, the simulated pilot of an aircraft can decide to change its FL. The frequency and the probability to decide such changes are parameters of the simulation. When a change is decided, its amplitude is randomly chosen between parameterised limits around the current flight level. A change of flight level is possible only if the new trajectory is conflict free within MTBC range. Classification: Public Page 55/131

58 Aeronef A (cruise, normal) Conflict predicted ("convergence") t1 t2 Conflict resolution trajectory (proposed) Aeronef B (cruise, normal) Aeronef C (cruise, normal) Conflict resolution trajectory (selected) t1 : distance of A to the new conflict point t2 : distance of C to the new conflict point t1 < MTBC or t2 < MTBC => A, B and C are part of the same cluster Figure 8: Conflict management in A3S Simulations During all the simulations performed, a set of values were measured in order to establish the complexity of the situations encountered by the «pilots» and the results of the procedure used (dynamic trajectory computation). The values related to clusters were: number of clusters per second versus traffic density. % clusters size versus traffic density: the frequencies of occurrence are computed. They are expressed in percentage of cluster of size X per second. 3. Traffic complexity analysis to evaluate the potential for limited delegation of separation assurance to the cockpit Anne Cloerec, Karim Zegal, Eric Hoffman A study [29] using fast-time simulations has been carried out to analyse traffic complexity and thus to evaluate the potential for delegation. The notion of cluster (as defined by Alliot et al) that models complexity of conflicts has been reused. New metrics integrating constraining and interfering environmental aircraft have been defined, extending the notion of cluster. Classification: Public Page 56/131

59 Cluster definition In this paper, a cluster is defined as a group of aircraft, which are in conflict directly, or indirectly, using transitivity of time and distance closure. For example, if A and B are in conflict, and A and C are also in conflict, A, B and C are in the same cluster if both resolution processes (A vs. B) and (A vs. C) overlap. Environmental aircraft around these conflicts are not considered but their identification is critical since they may impact on the resolution process. Therefore, a new notion of Environmental Aircraft for a subject aircraft in a conflict is introduced. Environmental Aircraft An environmental aircraft is defined with respect to a given aircraft in conflict referred to as subject aircraft. An environmental aircraft is not part of the conflict with the subject aircraft but it is in some vicinity of the subject aircraft, and may therefore constrain or interfere in the resolution of the conflict. Three kinds of environmental aircraft are introduced depending on the impact on resolution: Surrounding aircraft: an aircraft, which has no impact on the subject aircraft resolution process Constraining aircraft: an aircraft which constrains the subject aircraft resolution process, but not involved in any conflict during that process: it has to be considered in the elaboration of the new subject aircraft trajectory. Interfering aircraft: an aircraft which is itself in conflict and whose own resolution process may interfere with the subject aircraft resolution process, i.e. there is an overlap of both resolution processes. The following figures summarise each case. For each figure, aircraft A and B are in conflict. E is the environmental aircraft. The dotted lines correspond to possible resolution trajectories for A. Surrounding Aircraft Constraining Aircraft Interfering Aircraft Classification: Public Page 57/131

60 Identification of Different Environmental Aircraft For the purpose of environmental Aircraft identification, the following time intervals were defined: R: time span of the resolution process. S: time interval between the last opportunity to manoeuvre to solve the conflict and the loss of separation. L: look-ahead time, i.e. the time interval during which constraining aircraft have to be considered. L always starts at the beginning of the resolution process but may extend beyond the end of the loss of separation. Figure 9: Definition of time intervals In the context of the study, constraining aircraft for the subject aircraft is defined as an aircraft intersecting a specified volume around the subject aircraft during the look-ahead time ( L). Surrounding and constraining aircraft were not differentiated. An interfering aircraft for the subject aircraft is identified as a constraining aircraft in conflict with an other aircraft and whose resolution process overlaps that of the subject aircraft. As shown in Figure 10, the volume defined for detection of environmental aircraft is a cylinder centred on the subject aircraft with a radius of twice the lateral separation (10Nm) and a height of four times the vertical separation. D = 2 * lateral separation H = 4 * vertical separation Figure 10: Volume defined for detection purposes Simple Clusters The number of simple problems occurring in traffic is an indicator for complexity. This notion is modelled by the definition of simple cluster. A simple cluster is a cluster involving only 2 conflicting aircraft with a maximum of 3 constraining environmental aircraft (an aircraft is constraining for a cluster if it is constraining for at least one aircraft of the cluster). An upper bound of 3 constraining aircraft is a rough estimate of the characteristics of simple problems as inferred from discussions with a few ATC controllers. Classification: Public Page 58/131

61 It was mentioned that additional studies are required to validate this upper bound. Simulations The RAMS tool developed by Eurocontrol was used to simulate the different traffic samples. A postprocessing tool was developed to compute the complexity metrics (cluster and environmental aircraft). The following numerical values for the three time intervals were used: S is null. The resolution can occur up to the beginning of the loss of separation; R is 5 minutes. This is considered to be the typical time before the loss of separation for a controller to start a manoeuvre to solve a crossing conflict; L is 7 minutes. In the traffic samples used the mean conflict duration is about 1min 30s. Therefore, for the majority of the conflicts, all constraining aircraft up to the end of the loss of separation are identified with a look-ahead of 7 minutes. Some figures with results concerning cluster characteristics were presented in this study: Cluster composition in number of aircraft; Number of environmental aircraft around cluster of 2 aircraft; Percentage of simple clusters for different numbers of constraining aircraft; Influence of resolution and look-ahead periods on the percentage of simple clusters. Classification: Public Page 59/131

62 Computational Geometry This mathematical technique is supposed to be used to design airspace more efficiently. Fulton describes its application in the paper Airspace design: towards a rigorous specification of conflict complexity based on computational geometry [30]. Abstract The degree of complexity and randomness of aircraft tracking which can be safely managed is a fundamental question for a given airspace design. To answer this question and to provide optimum conflict resolution strategies, the nature of conflict needs to be thoroughly understood. This paper reviews traditional conflict models, showing how these models may be integrated into a more unified model based on computational geometry. This mathematical approach is being investigated for the specification and management of future airspace, achieving a more solid architectural basis for airspace design. This approach has the potential to significantly reduce the subjectivity when attempting to model controller and pilot workload, an important requirement in future designs. Remarks This research seems interesting since it is stated that there is the potential to significantly reduce the subjectivity when attempting to model controller and pilot workload. Objectivity of the metrics to be developed is one of the requirements in the INTENT project. It is has however not yet become clear how this research could contribute to the development of metrics in INTENT. Classification: Public Page 60/131

63 Wyndemere study This section first describes a study by Wyndemere Inc. [31][32]. After that a DAG-TM study is discussed that uses the results from this study. Perspective Development and evaluation of a model of the perceived complexity of an air traffic situation, with specific regard to the traffic characteristics that impact the cognitive abilities of the controller. Abstract [ ] Previous studies of ATC complexity have based their measures on the amount of physical workload experienced by an Air Traffic Specialist (ATS). Unfortunately, many of these studies typically discount the importance of cognitive activities of the controller, simply because this information is not easily measured. It is our position, however, that the complexity of ATC is better revealed through the analysis of controller strategies (cognitive tasks), and that this type of complexity may not be accurately reflected through measures of physical workload alone. In this paper we describe a framework for developing and evaluating a model of the perceived complexity of an air traffic situation, with specific regard to the traffic characteristics that impact the cognitive abilities of the controller. The framework does not depend on any specific type of procedures for ATC, so it can be used to evaluate complexity in both the current and future ATC environments. Remarks It is remarked in the paper that, since perceived complexity of an air traffic situation is being evaluated, communication with as many controllers as possible is necessary in order to get a proper sampling of their perceptions and a better understanding of the complexity associated with their jobs. This implies that real-time, controller-in-the-loop simulations are required. These controllers have to be current ATC controllers working at an air traffic control center. These simulations probably have to be executed more than one time in order to obtain the necessary (valid) results (e.g. because controllers have to get used to providing verbal data). One of the issues that will be addressed in future study will be INTENT. It became clear that controllers found the knowledge of intent or lack thereof to be quite significant. The importance of a controller having knowledge of the intent of other aircraft also came forward from a critical decision interview that was organised. It was also said that sector density and the presence of weather might be considered to contribute to the perceived complexity of an air traffic situation. From their controller-in-the-loop experiments some useful information with respect to the use of intent information can be obtained. One of the conclusions is that the communication of intent information is a requirement for controllers. The methodology and findings of this study might e.g. be used when part-task simulations for a ground-based situation have to be set up in the INTENT project. Wyndemere also determined a linear combination of the factors that affect the complexity of an air traffic situation in order to establish a single measure for perceived complexity. The paper that is described in the following section used a number of these factors to characterise the traffic scenarios. Classification: Public Page 61/131

64 DAG-TM research Perspective A limited number of traffic complexity factors, as defined by Wyndemere, are used to characterise traffic scenarios. Limited application of the dynamic density equation is described in a paper titled A Decentralized Control Strategy for Distributed Air/Ground Traffic Separation [31], that presents research in the field of DAG-TS (Distributed Air Ground Traffic Separation). Abstract This research investigates a decentralised control strategy for distributed air/ground traffic separation. First, we introduce the concept of a distributed air/ground traffic separation system. We then briefly review the literature to identify key technologies that address decentralised control techniques for distributed systems, and are applicable to air traffic management. From these techniques, we adapt an approach and build models to investigate the characteristics of a trade-off between system performance and stability. Based on Monte Carlo simulation studies, we present results that identify trade-offs parameterised by traffic complexity, as measured by a dynamic density measure. Remarks This study shows how the dynamic density metric can be used to characterise an ATM system. Another interesting point is the fact that system performance (efficiency) and system stability (safety) are being evaluated in this study. Performance is characterised by Direct Operating Cost and stability is related to the potential of a domino effect. It is also nice to read that system stability is used as an indicator/equivalent for safety just as this was proposed during the INTENT KoM. One of the conclusions is that poor stability performance is much more a function of the initial orientation of the aircraft in conflict than that of overall system dynamic density. This indicates that the definition of dynamic density may need to encapsulate such orientations. Classification: Public Page 62/131

65 Conclusion By means of a survey of literature, discussed in the previous sections, a rather complete picture has been provided with respect to the assessment of the capacity of ATM systems. This can also be confirmed by referring to an earlier report (WP1) of INTEGRA [34], which identified the following general metrics: Expert Opinion; NASA Key Metrics; Mathematical Index of Dynamic Density; Interactions and Encounters; Capacity and Safety. Reflecting to this report, it can be concluded that by means of the literature survey all these fields are covered. First of all, using the involvement of certified Air Traffic Controllers (from Maastricht ACC) is foreseen, which covers the use of expert opinion. Second, the determination of feasibility of an ATM concept involves a quantification of flight efficiency, which resembles the Key Metrics of NASA (in terms of block fuel and time). Dynamic density is also a primary candidate to be used as a metric. However, this will probably not be the mathematical index of dynamic density but more likely the one defined by NASA. Interactions and encounters are similar to conflict rate which has also been covered. Finally, it has been observed that safety is monitored by counting number of intrusions. Before the behaviour of some of the metrics is being investigated (section 2.4), two more metrics will first be presented in the next section. Classification: Public Page 63/131

66 2.3. Other possible metrics This section discusses some other metrics that were defined by ONERA Delays In the present situation, the delays encountered by aircraft are the most obvious sign of the saturation of airspace. So that it may be interesting to keep this metric to evaluate the results of fast time simulations. For this study the en-route delay is defined as the difference between the actual flight time for the part of trajectory considered in the simulation and the predicted time of flight. In case of ground controlled traffic it may be evaluated in differentiating the sectors: In that case the delay for a sector is the difference between actual time spent by the aircraft in the sector and the predicted sector crossing-time according to the flight plan. Those delays are mainly due to conflict-avoiding manoeuvres. During a flight, the delays due to the conflict-avoiding manoeuvres will increase with the number of conflicts detected and the number of conflict-avoiding manoeuvres. This index can be an interesting measure of capacity. The computation of those delays enables to evaluate the average number of conflicts in the sector and the severity of manoeuvres to avoid those conflicts. Depending on the level of intention, controller will be allowed a certain flexibility to solve conflicts and delay due to a manoeuvre will be different according to the severity of this manoeuvre. Advantages of this capacity index are that delays do not take into account relative distances between aircraft and that delays are independent of control procedures. Note: (by DUT) very similar to flight efficiency metrics discussed in section except that as a reference the original flight plan is used. So, here a local comparison is made instead of a global comparison Metric to evaluate pilot workload In the case of airborne separation assurance, the share of workload between pilot and co-pilot will depend on the procedures for conflict separation and it is difficult right now to make assumptions on what those procedures will be. Therefore the workload of first pilot and co-pilot will not be differentiated in the following. The pilot workload depends on the role of the pilot in the aircraft separation problems. So assumptions have to be made on how pilot will detect and resolve conflicts. Classification: Public Page 64/131

67 In a study for AM AIRBUS (see [14]), considerations are given on the future role of the pilot in airborne separation assurance concept. Those considerations entail that: conflicts are detected and solved by conflict detection and resolution tools the best solution for a conflict resolution is displayed to the pilot for the change of his trajectory, pilot has to validate the resolution flight plan after having checked that it: - clears the conflict without triggering any new one - is compatible with the rest of the flight So it is assumed for this metrics study that a conflict detection and resolution tools is included to the FMS of each aircraft. The FMS will receive all data about other aircraft (like positions and intentions), detect, resolve conflicts and propose a new flight plan to the pilot. Thus the pilot workload for separations problems consists in checking and validating the new trajectory proposed by the FMS. In order to quantify pilot workload, it is useful to define the possible behaviours of the pilot during a scenario and to associate a workload to each behaviour. We can consider that, during its flight, aircraft can be in three states: state P1 : no conflicts are detected. So the pilot has just a monitoring and a piloting workload. If we consider that conflicts are detected automatically by a detection tool, the workload is only piloting workload, which is constant. state P2 : some conflicts are detected but pilot has not started any avoiding manoeuvre. If we consider that the possible trajectories, which solve the conflicts, are found automatically by a resolution tool and that new trajectories are displayed, the pilot has to check the new trajectories and to monitor the evolution of the conflicts. This workload depends on the number of conflicts detected and on the severity of the conflicts. There is also a piloting workload, which is constant. state P3 : pilot does conflict avoiding manoeuvres The piloting workload is bigger for this state and pilot has to monitor the evolution of conflicts during the manoeuvre. Intuitively, the more important workload is for the state P3 and the less important workload is for the state P1. Classification: Public Page 65/131

68 Having defined the 3 possible states, some workload metrics may be introduced: count of the transition number between the different states The number of transition corresponds to the number of the state changes during the scenario, so it can characterise the pilot disruption. In fact, each transition corresponds to the fact that a conflict appears or disappears. So, the count of the transition number will be similar to the count of conflicts. Therefore, a possible capacity index can be the number of conflicts. flight time metrics The time of flight of each aircraft is : t = t + t + t (21) flight P 1 P 2 P 3 where t is the time spent in the state P i. P i We can consider that if aircraft do manoeuvres more than x % of the total flight, workload limit is exceeded. t t P 3 So if > x %, capacity is exceeded. flight Therefore, a possible capacity index can be the mean of the value scenario. t t P 3 flight for each aircraft of the We can consider that a certain pilot workload corresponds to each state P i, and that the workloads corresponding to the states P2 et P3 are proportional to the number of conflicts. So a workload index could compute the time that aircraft spend in each state and multiply this time by the amount of workload which is associated to each state and the number of conflicts. For an aircraft: t t t Workload = n n P1 P2 P3 α P1 + α P2 cd + α P3 cd (22) t flight t flight t flight where α P i is a weighting factor corresponding to the workload associated to the state P i, t P i is time spent in the state P i and t flight the number of conflicts which are detected, time of the flight. n cd is is the total Classification: Public Page 66/131

69 2.4. Evaluation and Comparison of the metrics As demonstrated in the previous paragraphs, the possible metrics are numerous and before deciding which ones have to be used in the simulations, it seemed useful to try to evaluate and compare them having in mind the objectives of the project. The approach retained for that was to develop simple simulators: - A first simulator has been developed by DUT. The main results useful for the INTENT project are relative to the comparison of the metrics for different CD&R; - In order to verify the results from the paper from Andrews and Welch a simple MATLAB GUI tool has been put together. This tool could also be used to verify and/or predict results from the INTENT experiments; - Another simulator has been developed at ONERA. The main results are relative to the evolution of the metrics when the intent horizon varies DUT Simulator and results A simulator has been developed by DUT with the goal of comparing CD&R algorithms. In the frame of INTENT project, it is used to evaluate the reaction of the different possible metrics when different CD&R are used. This simulator is programmed in MATLAB and an overview of the structure is given in figure 11. It includes a random traffic generator. The maximum number of aircraft that can be simulated simultaneously with this simulator is set at 200. Several CD&R systems are implemented and therefore can be compared. For the simulations, two types of random scenarios have been created. First, random scenarios that are meant to stress the algorithm have been simulated to see how the algorithm will perform in complex, random situations. Secondly, more realistic random scenarios have been simulated in which aircraft are flying through a sector from airport to airport. Two controller workload metrics have been compared for the results of the simulations for different CD&R. - Dynamic density; - Delahaye complexity metric. The CD&R algorithms are the following: - Field potential method; - NLR algorithm; - Geometric optimisation algorithm; - Faces algorithm; - DUT developed algorithm developed by H.H.Versteegt. Classification: Public Page 67/131

70 Simulation setup Sim. visualisation Simulation Read input file Heading waypoint If selected: CDR calc. If selected: CDR controls If selected: Capacity metr. If selected: new flight Control settings Equations of motion Plot plot data Save data if t < tend if t = tend End Figure 11: Overview of the simulator structure Results for ten aircraft random scenarios In these scenarios ten aircraft are flying at the same altitude, with different speeds and in different directions through a circle with a radius of 92.6 km. The aircraft are all at a given time at a random position within this circle, resulting in a number of conflicts. In this scenario, five two aircraft conflicts will occur if no CD&R is used. In Figure 12a to c, the results for the dynamic density are displayed. Classification: Public Page 68/131

71 (a) (b) (c) Figure 12: Dynamic density computations with regression analysis weights The feeling of disorder of a controller, represented by the convergence, has also been calculated for the four CDR algorithms that have been used. In figure 13 the results of these convergence computations are given. Classification: Public Page 69/131

72 (a) (b) (c) Figure 13: Convergence computations In figure 14 the Delahaye/Puechmorel aircraft density measures for the four CDR algorithms are given. Classification: Public Page 70/131

73 Figure 14: Delahaye/Puechmorel aircraft density computations Classification: Public Page 71/131

74 Realistic scenarios A number of realistic scenarios have been created and simulated. For these scenarios the dynamic density, the convergence and the aircraft density measure have been computed to investigate the effect of the developed algorithm on the controller workload measured through different metrics. The results of the developed algorithm will be compared with the results obtained with the NLR algorithm. Scenarios with varying traffic densities have been used. Figures 15 and 16 show the results for a high traffic density scenario. First, in figure 15, the traffic built up during this simulation is shown. The average number of aircraft within the defined circular sector during the second half of the simulations is approximate 52, which is almost 2.5 times the standard density of a sector. In figure 16 the results for the dynamic density calculations are presented for this scenario. Regression analysis weights are used in figure 16a and subjective weights in figure 16b. In both cases, the developed algorithm results in a lower controller workload, but the greatest reduction is obtained when the subjective weights are used. Figure 15: Traffic built up in high-density scenario (a) (b) Figure 16: Dynamic density calculation for high-density scenario (a) regression analysis weights; (b) subjective weights. Classification: Public Page 72/131

75 The results for the convergence and aircraft density computations are shown in figure 17. For both the convergence (figure 17a) and the aircraft density (figure 17b), there is no significant difference between the developed algorithm and the NLR algorithm. (a) (b) Figure 17: Convergence and aircraft density calculation for realistic high-density scenario The results for the standard density scenarios are in line with these results for a high traffic density scenario. Classification: Public Page 73/131

76 DUT Workload Evaluation Tool In the paper by Andrews and Welch [7] an interesting view on sector loading for the free flight concept versus the structured flight concept. Figure 18 taken from the above reference shows sector loading results are given for a sector with a fixed arrival rate of 45 AC/hr (Poisson process) for a structured (conditioned) flight situation as well as a free flight situation. In both scenarios the average sector loading is 11.5 aircraft. The transit time for all aircraft is 900 seconds in both cases. In the case of structured flight, aircraft enter on a single route with a 60 second in-trail requirement. In the free flight case, aircraft enter on separate routes with no in-trail requirement. Figure 18: Free flow of traffic into a sector results in greater probability of transient peaks in sector loading (from [7]) A significant difference can be seen in the tails of the distribution. In the in-trail case, there are never more than 15 aircraft in the sector (15x60 seconds = 900 seconds). For the unstructured free flight case, there is a significant probability of having 16 or more aircraft in the sector. In fact, as many as 22 aircraft can be observed in the free flight scenario. Sector loading efficiency, ε, can be defined as the average number of aircraft in a sector E[N] divided by the maximum accepted traffic loading N max, i.e., Note that α represents the probability of overload (Number of aircraft in sector > N max, with N max here assumed to be 15). Clearly, for the structured case the probability of overload is zero, in case it is assumed that N max = 15. With an average sector loading E [N] = 11.5 the sector loading efficiency ε is 76% again assuming that N max =15. For the corresponding free flight case the probability of overload α is clearly nonzero. As a matter of fact, the average number of aircraft in the sector E[N] has to be reduced to a value significantly below 11.5, in order to achieve practical small values of α, e.g., for Classification: Public Page 74/131

77 α = 0.01, E[N] = 8.25 and for α = , E[N] = Thus in order to avoid transient peak loads exceeding the load limit, the number of aircraft accepted must be kept relatively small in a (ground controlled) free flight sector. The conclusion is that there appears to be a significant penalty in efficiency when the sector loading is conditioned to accommodate the randomness of traffic flow associated with free flight. Additional remarks It is stated in the paper that allowing unstructured traffic flow has potentially significant implications for sector controller workload for which a number of reasons are given. Therefore, these potential stresses upon the sector controller must be understood and an adequate set of compensating procedures and automation must be provided in a timely manner. One of the compensating procedures, could be the use of INTENT information by an ATCo. In fact this will be one of the results of the experiments, since direct routes/ground responsibility is part of the experiments. Another observation that is being made is the fact that workload intensity for a controller grows quadratically with the increase of traffic density while aircrew workload grows linearly. This observation indicates that, at increasing traffic densities, it is necessary to relieve the sector controller of routine conflict resolution tasks. This can e.g. be done by introducing automated systems, which analyse trajectories and suggest resolution actions while the involved aircrews serve as human monitors of the result. In fact, this concept is just what will be considered in INTENT when the airborne cases are considered. In INTENT no controller involvement will be considered at all in this case. Andrews and Welch also make some observations with respect to conflict rate. Since an ATCo has to deal with the global conflict rate, the maximum allowable workload will be exceeded much earlier when traffic density increases; a pilot only sees its own (local) conflict rate so higher traffic densities can be handled much easier. These observations are in line with the work of Hoekstra [35], which in fact justifies the transition from ground based to airborne based responsibility for separation assurance. In order to be able to check and or predict the INTENT results with these theoretical results, a small MATLAB tool has been developed. The looks of the tool are illustrated by Figure 19. Numerical values of the following parameters can be selected by using the input boxes: a/c density; sector volume; sector loading efficiency; transit time; separation standards; average relative speed; controller workload parameters τ C, τ R and τ S. The parameters related to controller workload are used to determine workload intensity according to: G = τ λ + τ λ S S C C (23) Classification: Public Page 75/131

78 where τ S is the seconds of routine work per aircraft handled by the sector and τ C is the seconds of work associated with each conflict resolved. Schmidt [insert ref] suggested to use τ S = 60 seconds and τ C = 50 seconds. τ R is the average time required by the controller (after detection) to devise and communicate a conflict resolution. Note that this parameter is not used in the workload equation (17). This parameter is used in an analysis that looks at controller workload while dealing with multiple conflicts. Figure 19: DUT Workload Evaluation Tool Classification: Public Page 76/131

79 ONERA simulations Development of a program for metrics tests In order to evaluate the best metrics to measure the benefits of intent information, an air traffic simulator has been made. The simulation program has been written in C language. This program simulates the aircraft movements and measures sector capacity with the metrics defined above. The region in which traffic is simulated is a two dimensional airspace. The program simulates only one flight level with only levelling en-route aircraft. All aircraft have the same speed 485 Kts and the same turn rate 1.0degree/sec. The wind and meteorological effects on aircraft trajectories are not simulated. The horizontal separation is 5 NM. Each aircraft flies from an origin airport to a destination airport. If there are no conflicts, the trajectory is the direct route from departure to arrival. If there are conflicts during the flight, aircraft avoid conflicts and, when conflicts are avoided, the aircraft uses the direct route from its position to its arrival airport. All aircraft flying in the area are independent and follow freely their way to their destination airport. Thus, there is no controller giving global solution to organise the flow. When two aircraft detect a potential conflict in a future time closer than the detection horizon, the one having no priority performs an avoidance manoeuvre. The priority is given to the oldest aircraft in the simulation. The avoidance consists in turning left or right until the conflict risk disappears. The turn direction is calculated to be optimal. All conflict predictions are made for straight-line trajectories and aircraft only take into account the soonest conflict. This surely introduces false predictions, pointless avoidance manoeuvre and nondirect conflicts. But such conflicts will exist in a real situation as long as no global solution is found and strictly followed by all the pilots. A set of airports is defined with the flow on each corresponding airway. During the simulation aircraft takeoff from each airport according to the separation of 5NM and to a Poisson law which is simulated as the limit of a binomial law. Of course the separation criteria prevent the Poisson law from being correctly reproduced as long as the flow is to high on the corresponding airway. In order to represent simply the level of intention, two parameters are introduced - the horizon of detection, which is the look-ahead time for conflict detection - the safety margin factor, which sets the effective separation distance used by the solver for the conflict prediction and avoidance. With a safety margin factor of 1, the separation distance is 5Nm and this distance is enlarged to 7,5 NM for a value of the factor of 1.5. It is then assumed than a low level of intention corresponds to poor accuracy on the trajectory prediction, implying a greater safety margin. So a high level of intention corresponds to a high detection horizon and a low safety margin factor. A low level of intention corresponds to a low detection horizon and a high safety margin factor. Note that the distance used to check and count the intrusions between aircraft remains the 5Nm separation rule whatever the margin is. The fact that no global solution is computed to organise the flow is realistic if we consider a very highdensity sector, where no controller would be able to find a simple logical solution. Algorithms do exist, but they work (in short time decision situation) for limited number of aircraft, and configurations where Classification: Public Page 77/131

80 the number of aircraft is fixed. Not for a continuous flow where aircraft can enter the sector at any time and interact with an already organised traffic. Moreover an organisation sometimes appears naturally. In the simulation aircraft move like independent mobiles, but they happen to organise their trajectories as if they were controlled by an intelligent agent. By the way, in the case of two airways forming a right angle, a natural solution is to divide each incoming flow in two (or more) separated and parallel ones. That way new crossings with lower conflict risks are created. And as the flow is lighten on each branch, aircraft can get trough the traffic whereas without this organisation they might not have been able to do so. When such a situation is simulated, it is funny to see that aircraft behave exactly this way: they separate naturally when necessary and form two incoming flows, passing on the left way or on the right one in turn. In the case of frontal collision (two opposed flows on the same airway) the same behaviour of logical and automatic organising can be observed: the two incoming flows separate, one aside the other, and it remains so as long as their are enough aircraft to maintain the situation stable Simulations results In order to study how capacity metrics vary with the intent level, a simple configuration has been considered. Two perpendicular airways of 200 NM are crossing and there are only two aircraft flows: one flow from airport 1 to airport 2 and one flow from airport 3 to airport 4 (see Figure 20). Figure 20: First airways configuration The rate of aircraft departure for airports 1 and 3 is 21 aircraft per hour. Classification: Public Page 78/131

81 In order not to take into account the complexity due to conflicts in arrival area, the capacity metrics will be computed in a specific area. This sector will be a square of dimensions 100 NM 100NM in the centre of the four airports. General results: first observations. a) Collision rate. For two airways with the same set flow, the collision probability at a crossing point is expected to behave like the square of the observed flow. The following graph shows the agreement of experimental results and theory. conflicts rate (without avoidance) for two airways owning the sam e flow log(conflict number) experimental interpolated (2*X+A) log(observed flow) Figure 21: Conflict rate for two airways Classification: Public Page 79/131

82 b) Delays Delay is proportional to the number of conflict warnings 25 number of predicted conflicts (for 1 aircraft) delay (second for 1 aircraft) Figure 22: Delays for two airways configuration The delay per aircraft (= difference between the direct route time and the observed one) is measured during the simulation in order to understand how traffic density can influence it. Figure 22 shows that delay is almost proportional to the number of avoidance manoeuvre (predicted conflicts), which might have been guess. The delay induced by a single avoidance manoeuvre is very short (less than 10 second), and corresponds to what can be calculated by geometrical considerations (speed 485Kts, Intent time 200Sec, 5Nm obstacle). c) Intent horizon influence on conflict avoidance. The influence of the Intent horizon parameter on the conflict avoidance ability is studied in order to find if an optimal value exists. Indeed it can be expected that a short value would make it too difficult for the aircraft to avoid others properly whereas a large value would introduce too many false detections and create new conflicts, degrading the traffic behaviour. The following graphs show that, whatever the causes, an optimal value of Intent does exist and in our simulation it is about 200Sec. Classification: Public Page 80/131

83 occured intrusions intrusions (for a 10h simlation time, perpendicular airways) Intent=50s Intent=100s Intent=150s Intent=200s Intent=250s Intent=300s traffic density: number of aircraft on a 600Nm long airway. Figure 23: Number of intrusions The number of non-solved conflicts decreases as Intent becomes larger, but the performances do not improve after 200Sec. Another interesting point is the study of the influence of Intent on delays. The fact that aircraft do not avoid the others with the same manoeuvre is expected to change delay s behaviour in two ways. As each simple avoidance manoeuvre is different, so must be the delay. More precisely, the sooner an aircraft begins to avoid a conflict, the smoother its trajectory is, and the smaller the delay. The second effect of Intent on delays is opposed. Due to the change in predicted conflicts number, the global change in traffic configuration (which will be studied latter) is bound to introduce more delays. If the aircraft make larger avoidance manoeuvres those manoeuvres make them leave their airway sooner, and as it influences the rest of the traffic by the mean of other aircraft conflict prediction systems, it tends to fill more space around the airways, which means more delays for all aircraft (more twists and turns). The following graph shows that kind of opposed effects: when Intent increases, delays increase and then tend to a lower value which is reached roughly for Intent=200Sec. Classification: Public Page 81/131

84 Delays / Intent Intent=50s delays (in sec for 1 aircraft) Intent=100s Intent=150s Intent=200s Intent=250s Intent=300s trafic density (number of aircraft on a 600Nm long airway) Figure 24: Delays for various levels of INTENT Determining the value of the mean delay is not as easy as could be expected after having viewed the graph on the previous page: delay is a non-linear function and depends on all parameters. This has to be kept in mind while some models of traffic behaviour will be discussed in a latter chapter. Sensitivity of capacity metrics In order to see how capacity metrics are sensitive to the level of intention, simulations are run with several values of detection horizon (hd) and safety margin (smf). a) Delahaye density index The Delahaye density index does not vary with the horizon of detection (see Figure 25a). In fact, the horizon of detection has only an effect on the time of conflict detection. In simulations, aircraft begin conflict-avoiding manoeuvre as soon as a conflict is detected. Therefore for a long horizon of detection, aircraft will begin manoeuvre earlier, but the relative distances between aircraft will not vary too much. Classification: Public Page 82/131

85 delahaye density index ( smf = 1.5) for different horizons of detection Delahaye density index ( hd=200s ) for different safety margin factors 300 hd=200s hd=400s 300 smf =1,5 smf = time (number of minutes) time (number of minutes) a) different horizons of detection b) different safety margin factors Figure 25: Delahaye density index During a conflict, the smallest distance between the two aircraft in conflict is determined by the safety margin factor. Therefore, this parameter has more influence on Delahaye density index (see Figure 25b) and the peaks of complexity are more important for low value of safety margin factor. For a safety margin factor smf = 1.5, aircraft can fly at a minimum relative distance of 7.5 NM and for a safety margin factor smf = 3, aircraft can fly at a minimum relative distance of 15 NM. Given that Delahaye density index takes only into account relative distances between aircraft, the results are quite coherent. b) dynamic density Considering that dynamic density metrics take also into account relative distances between aircraft, the results are nearly the same than for Delahaye density metrics (see figure Figure 26a and b). Dynamic density does not vary with the horizon of detection and is a little influenced by the safety margin factor. Actually dynamic density takes also into account the conflict rate, but the variation of dynamic density with intent parameters is not evident. Classification: Public Page 83/131

86 dynamic density (hd=200s) for different safety margin factors dynamic density ( smf = 1.5) for different horizons of detection 60 inc=1,5 inc=3 60 hd=200s hd=400s number of minutes time (number of minutes) a) different horizons of detection b) different safety margin factors Figure 26: Dynamic density c) conflict rate Conflict rate is more important for a long horizon of detection (see Figure 27a). If the detection horizon increases, aircraft will detect more conflicts. Conflict rate is also more important for a large safety margin factor (see Figure 27b). The conflict rate is the number of aircraft closer than a certain distance which depends on the safety margin factor. If the safety margin increases, this distance increases and more conflicts are detected. conflict rate ( hd=200s ) for different safety margin factors conflict rate (smf =1,5) for different detection horizons 6 smf =1,5 smf =3 6 hd=200s hd=400s time (number of minutes) time (number of minutes) a) different horizons of detection b) different safety margin factors d) en-route delays Figure 27: Conflict Rate Classification: Public Page 84/131

87 En-route delays are computed in terms of seconds per aircraft. The graphs representing delays for different intent parameters can be easily distinguished (see Figure 28a and b). Those delays correspond to the time lost in conflict-avoiding manoeuvre. Delays are equal to 0 during the 25 first minutes of the scenario, it is because delays are computed only when aircraft arrive at their destination airport and the time of flight between two airports is about 25 minutes. For long detection horizon, aircraft detect conflicts earlier and they begin earlier conflict-avoiding manoeuvres. So the time spent to solve the conflicts is smaller for long detection horizon than for short detection horizon. Therefore delays are more important for low values of detection horizon delays in seconds per aircraft (smf =1,5 ) for different detection horizons hd = 200s hd=400s number of minutes ( 4 hours of simulation ) delays in seconds per aircraft (hd =200s) for different safety margin factors (inc) inc=1, number of minutes inc=3 a) different horizons of detection b) different safety margin factors Figure 28: Delays For large safety margin factors, aircraft have to fly far from other aircraft during the conflict-avoiding manoeuvre, so manoeuvre takes more time than for small safety margin factor. e) manoeuvres Metrics about manoeuvres are computed as the percentage of manoeuvres per aircraft flight. The graphs representing the percentage of manoeuvres for different intent parameters can be easily distinguished (see Figure 29a and b). Manoeuvres percentage is equal to 0 during the 25 first minutes of the scenario, it is because this percentage is computed only when aircraft arrive at their destination airport and the time of flight between two airports is about 25 minutes. For long detection horizon, aircraft detect conflicts earlier and they begin earlier conflict-avoiding manoeuvres. So manoeuvres last more time for long detection horizon than for small detection horizon, but the manoeuvre are smoother. Therefore the percentage of manoeuvres is more important Classification: Public Page 85/131

88 for large values of detection horizon. In order to be more relevant this metric should consider for each manoeuvre a weighting factor corresponding to the severity of the manoeuvre percentage of manoeuver per flight ( smf =1,5 ) for different detection horizons hd = 200s hd=300s hd=400s number of minutes ( 4 hours of simulation ) percentage of manoeuver per flight (hd =200s) for different safety margin factors (inc) inc=1,5 inc=3 inc=4, number of minutes a) different horizons of detection b) different safety margin factors Figure 29: Percentage of manoeuvring Conclusion According to the simulations, capacity metrics have different sensitivity to the solver and to level of intention. The sensitivity of the metrics to the solver for a given level of intention has been studied for Delahaye density index and for Dynamic density. The simulation results tend to demonstrate that the Delahaye metrics is less sensitive to the choice of the solver than the dynamic density metric. From the simulations aiming at comparing the influence of the level of intention on the different metrics, the following conclusions have been obtained. - The variation of parameters that simulate the intent level has not really influence on Delahaye density metrics and dynamic density metrics. Whereas the conflict rate, en-route delays and the percentage of manoeuvre are more sensitive to those intent parameters. Classification: Public Page 86/131

89 - Actually the variations of capacity metrics with intent level are related to the algorithm implemented in the solver. During a conflict resolution, non priority aircraft begins its conflictavoiding manoeuvre when conflict is detected and aircraft fly at a relative distance from other aircraft equal to the separation standard multiplied by the safety margin factor. Therefore density metrics do not vary with the detection horizon, given that those metrics take mainly into account relative distances between aircraft and that relative distance does not vary so much with the detection horizon. - The metric associated with delays is rather sensitive to the level of intent, with a decrease of delays for long-term intention. - The metric used to evaluate pilot workload, that is the time spent in manoeuvre has to be weighted by the severity of the manoeuvre to be representative. Therefore future results of INTENT compressed-time simulations have to be analysed cautiously in order that the results on capacity increase will not be too much influenced by the behaviour of the solver with the level of intention. In order to be as objective as possible, several metrics have to be used in compressed-time simulations. Classification: Public Page 87/131

90 2.5. Proposed Metrics The INTENT project considers a number of different ATM concepts for which its capacity has to be assessed. It has been decided to test each ATM concept of the INTENT experiment matrix in terms of feasibility. In order make this assessment, it will be necessary to monitor and record multiple metrics during the various experiments. After running the experiments, the results from the simulations will have to be interpreted in order to determine the feasibility of the particular concept under consideration. This section discusses the metrics that are proposed to be used when carrying out the various experiments in INTENT. A number of concepts, as shown by the experiment matrix have to be evaluated. For the metrics, to be used in INTENT, the following requirements (partly taken from INTEGRA) will have to be met: 1. Metrics shall be usable and measurable during modelling, fast-time simulation, real-time simulation and operational trials; 2. Metrics shall be independent of airspace definitions, of controller procedures, and of user procedures; 3. Metrics should at least be applicable to the en-route stage of flight. Use of the metrics for all stages of flight in a gate-to-gate system would be convenient, since future experiments might focus on other stages than en-route; 4. Methods of measurement shall where ever possible be objective, and where a subjective methods are used for measuring a metric, these shall not be the only methods of measurement for that metric; 5. Metrics shall be predictable. In that way the predicted time-history can be used to regulate the traffic flow, which might be needed by tools or procedures used out by Air Traffic Controllers; or by pilots to avoid high-density areas. In discussing the metrics there will be a distinction between metrics that are generally applicable and metrics that are applicable to either concepts where responsibility for separation assurance is ground located or those where responsibility is airborne located. Classification: Public Page 88/131

91 General The metrics proposed in this section are applicable to all ATM concepts that will be considered in INTENT Flight Efficiency Flight efficiency can be used to express the economics of a flight. Therefore it is important to have a metric that evaluates this quantity. It has to be noted that flight efficiency comparisons can be made in two ways. This will be discussed in the following paragraphs. Comparisons between cells Flight efficiency can be evaluated in terms of the incremental path length and flying time with respect to a baseline scenario. This baseline scenario could be the traffic sample of today s system that is obtained from Eurocontrol s CFMU. The flying time and path lengths observed in this traffic scenario can be used as a reference. When for each concept that is being evaluated these quantities are also registered, the following expressions are to be evaluated: # (24) T = T cell T ref # (25) l = l cell l ref These increments represent the change in Direct Operating Cost for the particular concept that is under consideration. This is an important metric for airlines because this expresses the potential benefits for the ATM concept under consideration. Within one cell For each of the scenarios that will be considered according to the INTENT experiment matrix an evaluation can also be made between actual flight time/fuel used and the predicted flight time/fuel used. This gives an impression of how efficient a certain ATM concept handles a particular traffic scenario. In this case flight efficiency can be monitored in the following fashion. However, now the reference situation is the flying time and path lengths associated with the original flight plans of the particular cell under consideration. After the simulation run, these reference values are compared with the values in the observed flight plans. So, the following expressions are to be evaluated: T = T actual T ref (26) Classification: Public Page 89/131

92 l = l actual l ref (27) These increments represent the change in Direct Operating Cost for the planned and the actual flight that is under consideration Number of Intrusions A direct measure of safety is the number of intrusions that are observed. It has been discussed that intrusions might not even be observed unless an infinite number of simulation runs are carried out. It is however a good idea to monitor intrusions just to be sure. An intrusion is defined as a violation of the protected zone of an aircraft, which means that aircraft do not meet the required separation standards anymore i.e., either 5 nm horizontally or 1000 ft vertically Conflict Rate Conflict rate is the number of conflict alerts in a given time period, as experienced by a controller or pilot. An air traffic controller will have to deal with the global conflict rate but a pilot only has to deal with a local conflict rate. That is why previous free-flight studies showed that pilots in a free-flight environment easily handle increased traffic densities [25]. Namely, the local conflict rate increases linearly with the number of aircraft whereas the global conflict rate increases quadratically. Therefore this quantity can be used to assess capacity for both ground as well as airborne-based concepts. It is a metric, which can easily be monitored and possibly be correlated to workload. During part-task experiments it should be determined what values of the conflict rate workload becomes the limiting value. Another interesting aspect of this metric is that it has also been used in the analytical study by Andrews and Welch for which the results can be reproduced by the MATLAB tool described in section Subjective workload measurement In order to gain confidence in the observed values of the more objective metrics, evaluation of controller workload should be carried out when conducting real-time experiments. There hasn t been any literature scan on possible methods but it is proposed to use those methods that were used earlier during NASA/NLR Free Flight experiments. Classification: Public Page 90/131

93 Ground Metrics proposed to be used when a ground-controlled environment is involved are described here Dynamic Density This metric can easily be implemented into simulation software. It describes the air traffic situation in terms of complexity and is therefore well correlated to controller workload. This has been found as one of the main conclusions from the NASA study. Correlation of the dynamic density metric with controller activity was most accurate when weighted terms were incorporated into the dynamic density equation. An issue that has to be addressed is the use of the weighting terms in the dynamic density equation. As stated before, it has been found that correlation was best when the terms in the dynamic density equation were weighted. However, the values of the weighting terms have to be determined by operational trials, which is not quite possible when a new concept (here: the use of INTENT by a controller) is considered. This also involves operational trials which is not possible within the timeframe of the INTENT project. So, in INTENT a unit weighted dynamic density function will have to be used. This metric also correlates nicely with controller workload, which is a usable characteristic for some of the cases in the INTENT experiment matrix. For ground based responsibility for separation, dynamic density could be employed in the following fashion: 1. Evaluate dynamic density for real-world traffic sample. This traffic sample will be obtained from a certain part of Western European Airspace that is showing high loads. The observed maximum levels for dynamic density can be considered as the maximum-allowed levels. These levels should not be exceeded in order to prevent controller overload; 2. Probe the ground plane of the experiment matrix. It is already clear that increasing traffic density is not a first option, since this would directly exceed the maximum allowed level of dynamic density, since traffic density is part of this metric. Therefore, increasing level of INTENT will first be considered. By increasing the level of INTENT, the time history for dynamic density probably changes. This will be repeated for each level of INTENT. After that, combinations of level of INTENT and traffic density can be evaluated. Remarks on predictability This predictability feature is quite useful and might be necessary since this makes it possible to control traffic flows by means of the predicted values of the metric. This information could be provided to a tool that is used to assist the controller. Classification: Public Page 91/131

94 Traffic disorder metrics In order to supplement dynamic density the metrics defined by Delahaye and Puechmorel need to be evaluated Workload parameters In order to compare the results from the INTENT project with those from Andrews and Welch s theoretical study it is necessary to obtain the numerical values of the following parameters during controller-in-the-loop experiments: a/c density [AC/10000 nm 3 ]; sector volume [nm 3 ]; transit time [s]; separation standards [nm]; average relative speed [kts]; controller workload parameters: τ C [s/ac] τ R [s] τ S [s]. Classification: Public Page 92/131

95 Airborne Background information Airborne location of the traffic separation assurance process coupled with the unstructured routes is one of the cases of the INTENT project experimental matrix. It is certain that airborne separation assurance will require aircraft intent information up to a certain time horizon (level of intent information). The INTENT project aims to investigate what the optimum level of intent information should be in relation to capacity and, in this case, airborne location of responsibility for the separation assurance process. During the experiments four levels of intent information will be considered (aircraft present state information, aircraft flight plan information 10, 15 and 20 minutes ahead) and different level of traffic density. The objective of this task is to define a set of metrics that would enable the capacity to be measured or the capacity between different configurations to be compared. Process of identifying various factors influencing capacity when separation is airborne situated and development of a set of measures that could be used is presented in this section. Information obtained from the TK1.2 Survey of past and ongoing activities and in particular findings from the past studies addressing capacity metrics have been used as a baseline and a starting point for development of the capacity metrics. Several studies have addressed airspace capacity and have shown that there are many ways of expressing capacity. Some of the examples: are traffic density, sector throughput, dynamic density, conflict rate, etc. Most of them tackled the airspace capacity by looking at the ground side and expressed capacity through the air traffic controller perspective. They have based their measures on the amount of the physical workload experienced by a controller. Some of the recent studies addressed recognised the importance of the air traffic complexity and focused on analysis of controller cognitive tasks (strategies, decision making) in addition to physical tasks. Airborne Separation Assurance System was addressed by following studies reviewed in the TK1.2: Freer Flight EACAC/ FAST, 3FMS, MAICA, CENA studies, NLR studies. During the projects experiments the metrics were related to the part of the ASAS sub-system that was a subject of the study and were used for: evaluation of CD&R algorithm validation of the operations or proposed procedures used in ASAS sub-systems (traffic complexity, conflict complexity, frequency occupation) measurement of the impact of ASAS sub-systems on human factors (pilot workload, acceptability of ASAS, confidence in ASAS, CDTI evaluation, pilot awareness) Classification: Public Page 93/131

96 The metrics obtained from flight crew s point of view were usually subjective metrics collected through questionnaires, observations or debriefings during the real time simulations. The objective metrics were usually collected during fast time experiments or by using specific tools during real time simulations, e.g. measure of controller workload by counting eye blinking or heart rate (MAICA and NLR). Following the work and results provided by CARE-INTEGRA project it is worth repeating a statement already given in the chapter of this document. Namely, CARE-INTEGRA states that there is no direct way of measuring the capacity. For capacity of air based components two indirect approaches are proposed: 1. Human Workload Methods used to analyse human (pilot or controller) workload 2. Interaction Frequency Method - the number of interactions per unit time is a measure of the amount of work per unit time which must be done by an ATM system component to maintain aircraft separations in a safe state and forms the basis for a comparative measure of capacity This approach of considering subjective and objective measures for capacity should be followed in INTENT study as well. In case of autonomous aircraft, responsibility for separation assurance is transferred from the ground to a cockpit. It is considered that autonomous aircraft are able to detect and solve conflicts without any intervention from the ground system. Pilots of autonomous aircraft are responsible for separation from other aircraft and they should be able to cope with every situation they could encounter. The situation complexity that a pilot of autonomous aircraft would have to handle is directly related to his/ her workload, stress, etc. Some of the past studies have been investigating how pilots workload is impacted by the autonomous aircraft concept (NLR, 3FMS, MAICA) where pilots workload has been assessed through the subjective and objective measures. Some of the objective measures used for assessment of pilots workload are: Eye-point-of-gaze; Pilots eye blink. In addition to these measures complexity of air situation has been studied in order to assess and evaluate the influence of air traffic complexity on pilots workload and/ or stress. Some of the objective metrics used in the previous studies in order to assess the complexity of air traffic situation are listed below: Conflict rate; Conflict geometry; Conflict attitude; Clusters; Analysis of resolution manoeuvres (heading, speed, altitude); Flight time without conflict; Number of simultaneously detected conflicts. Classification: Public Page 94/131

97 The link between the workload/ stress level of the pilot and air traffic complexity is then used to reflect the capacity. Identification of air traffic complexity factors that might capture traffic complexity and that would be possible to measure is found to be essential for the development of the metrics when autonomous aircraft concept is studied. Based on the results of the past studies, the air traffic complexity factors can be grouped as follows: conflict density; conflict complexity; conflict resolution; In the following text each of these groups will be described in more details. Some of the metrics from the previous studies will be considered and proposed for the use in INTENT project. Conflict density Number of detected conflicts is an important indicator of the air situation complexity. The distinction can be made between the conflicts: that were detected but never occurred due to the change of trajectory of one of the aircraft involved in the conflict before the beginning of the resolution process; conflicts that were experienced (presented to the pilot) and had to be solved. When the number of detected conflicts is simultaneously experienced and presented to the pilot his/ her workload and/ or level of stress increases. Therefore, this indicator should be taken into account as a good indicator of the complexity of the current traffic situation. Conflict rate defined as a number of detected conflicts experienced by a pilot over the period of time is another indicator of the traffic complexity during the flight of the aircraft through the simulated area. Flight time without conflict can also be measured for each aircraft and could be related to the workload of the pilot over the whole flight. Conflict complexity Cluster represents a group of interfering aircraft where conflicts are close in time and distance. The complexity of an air traffic situation can be modelled through the number of aircraft per cluster and the number of conflicts in the cluster. Size of cluster indicates the complexity of the air traffic situation and provides a direct link to a pilot workload and stress level. Environmental aircraft around these conflicts have been considered as well in the study "Traffic complexity analysis to evaluate the potential for limited delegation of separation assurance to the cockpit", Anne Cloerec, Karim Zeghal, Eric Hoffman [29]. Their identification is critical since they may impact on the resolution process (1). This new metric introduced by the study will be reused within Classification: Public Page 95/131

98 INTENT project. Identification of constraining and interfering aircraft will be adopted to the INTENT project (CD&R algorithm). Conflict attitude is another indicator of the conflict complexity and is represented by a flight phase of an aircraft at the loss of separation. Aircraft can be in cruise, climbing or descending when a conflict is detected. Based on the track angle between the two conflicting aircraft conflict geometry is calculated. There are three possibilities: aircraft in opposite direction, angle of convergence less than 15º head-on; aircraft in the same direction, angle of convergence less than 15º overtake; aircraft either in opposite or same direction, angle of convergence between 15º and 165º crossing. Another attempt to define the air traffic complexity has been done by Delahaye and Puechmorel. In the their work a metric of traffic have been introduced and air traffic complexity has been defined through the measure of disorder of the speed vector field in the three dimensional space. Conflict resolution The complexity of conflict resolution might give some indication on the complexity of traffic situation by counting the number of aircraft involved in a resolution process, number of manoeuvres required to solve each conflict, etc Proposed metrics The following metrics are proposed to be used in the experiments where an airborne location for separation assurance is considered. 1. Traffic density Instantaneous number of aircraft - per FL for the sector; - globally per sector; - per range around subject aircraft. 2. Traffic complexity metrics (a) Conflict density Total number of conflicts detected versus FL for whole flight; Classification: Public Page 96/131

99 For each aircraft the number of detected conflicts between this aircraft and all the other aircraft is computed every time a new trajectory is activated. These conflicts are not necessarily experienced by a pilot and occurred in future due to a change in the trajectory of one of the aircraft involved in the conflict before the beginning of the conflict; Total number of detected conflicts for an aircraft as experienced by a pilot versus FL during whole flight of the aircraft. A detected conflict can be considered as "experienced by a pilot" when the other a/c involved in the conflict is within the range for which aircraft INTENT information is available; Number of simultaneously detected conflicts experienced by a pilot for each aircraft versus FL; Number of detected conflicts experienced by a pilot over the period of time conflict rate; Flight time without conflicts. (b) Conflict complexity Cluster Instantaneous number of clusters Size of cluster: - number of conflicts per cluster - number of aircraft per cluster Number of environmental aircraft - constraining - interfering Number of simple clusters Conflict attitude flight phase for each aircraft at the loss of separation (in cruise, climbing or descending); Conflict geometry based on the track angle between the two conflicting aircraft head-on overtake crossing (c) Conflict resolution Number of aircraft taken into account for each resolution - number of aircraft that the aircraft in charge of the resolution has to take into account; Classification: Public Page 97/131

100 Number of non-solved conflict; Number of manoeuvres required to solve each conflict; (d) Metric of traffic disorder (Delahaye and Puechmorel). Classification: Public Page 98/131

101 2.6. Conclusion This section will give a shopping list as a conclusion on the metrics proposed in the previous section. Table 2 to 4 give all the metrics that are available together with the type of experiments in which they can be used. Id. Metric Correlated to Used to assess Type of experiment 1a path length DOC flight efficiency fast-time 1b flying time DOC flight efficiency fast-time 2 # of intrusions loss of separation safety fast-time, part-task 3 conflict rate workload (?) capacity, safety fast-time, part-task Table 2: General metrics Id. Metric Correlated to Used to assess Type of experiment 4 dynamic density workload capacity, safety fast-time, part-task 5 traffic disorder workload capacity, safety fast-time, part-task 6 subjective workload method workload capacity, safety part-task 7 workload parameters workload theoretical results Andrews & Welch part-task, full-scale Table 3: Metrics for ground located separation assurance Classification: Public Page 99/131

102 For a better understanding of this table is it probably necessary to read the previous section on airborne metrics at the same time. Id. Metric Correlated to Used to assess Type of experiment Traffic density 8 instantaneous nb of a/c: a) - per FL b) c) - per sector - per range around a/c Traffic complexity 9 a) b) c) conflict density: nb of conflicts: - detected during whole flight - experienced by a pilot - simultaneously experienced by a pilot conflict rate flight time without conflict workload capacity, safety part task, fast-time workload capacity, safety part task, fast-time 10 a) b) c) 11 a) b) c) conflict complexity: - cluster - conflict attitude - conflict geometry conflict resolution: nb of a/c taken into account for each resolution nb of non-solved conflicts nb of manoeuvres to solve each conflict workload capacity, safety part task, fast-time workload capacity, safety part task, fast-time 12 traffic disorder workload capacity, safety part task, fast-time Pilot workload 13 metric for pilot workload workload capacity, safety part task, fast-time 14 subjective workload capacity, safety part-task, full-scale Table 4: Metrics airborne located separation assurance Classification: Public Page 100/131

103 Classification: Public Page 101/131

104 3. Theoretical Maximum Capacity This part of the project is dedicated to the theoretical maximum capacity of the airspace. This problem is a very complex problem due to the multiplicity of possible approaches and hypotheses. A possible approach is described in this section, and some results are presented. This part tries to explain how the different parameters, like the Intent level (that is to say the time threshold for anticipations supplied to the pilots) act on the traffic organisation and maximum capacity. All the study is made in the 2-D traffic case, for simplicity reasons, and with a home made simulator. The traffic organisation is measured through complexity metrics, which are studied theoretically and experimentally. A maximum air traffic density value is found by geometrical considerations, and then compared to the one obtained experimentally. So the first part of this section [3.1, 3.2, 3.3] theoretical developments are proposed. The 2 nd part [3.4 and 3.5] presents experimental results obtained from the ONERA simulator presented in the section relative to metrics Geometrical Basics Before any detailed study, some simple and yet useful points can be recalled. In the following sections, the term conflict is used when an aircraft is closer than 5Nm from another one (5Nm=separation standard, imposed for safety reasons). The air traffic management system s aim is to lead the aircraft as fast as possible from their departure to their destination, respecting this separation standard of 5Nm between aircraft. A potential conflict is a conflict that will occur in the future if non-avoidance manoeuvre is done Conflict probability for two airways Presently, most of the aircraft use airways to fly from one point to another. This behaviour, opposed to the free flight concept, is imposed by the organisation of the air traffic management. It allows easy geometrical studies. In the case of a 2-D situation where two airways cross each other making an angle of θ, it is possible Classification: Public Page 102/131

105 to calculate the conflict rate as a function of the flows on the two airways and θ. There are different ways of calculating it but only the result is shown here (calculations are rather long): Conflict Rate = 2 * Ne * flow1* flow2 V *cos( teta / 2) where V is the speed of the aircraft (same on each airway) and Ne is the standard separation (5Nm). Simulations have been performed to check both the formula and the simulator, and a very good concordance has been found. The interest of this formula is to show that the conflict rate is maximum when θ = 90, and that it depends linearly on the flows. We can imagine that, in order to lighten the conflict rate, a split of the airways into new parallel ones can be made. Thus each flow will be divided (see above on the right). Of course it will introduce more intersections, and the total conflict rate won t change. But each aircraft will have to cross a zone with lower obstacles density and the avoidance will be easier Conflict prediction, avoidance Let us consider now the case of two aircraft whose initial positions are X1 and X2, and speed are V1 and V2. The square of the distance between the two planes is simply a second-degree polynomial with respect to time, and the conflict time is root of: (Ne=5Nm) V 2 V 1 ² * t² + 2 X 2 X 1 V 2 V 1 * t + X 2 X 1 ² Ne ² Classification: Public Page 103/131

106 The discriminant is = 4 *( X 2 X1 V 2 V1 V 2 V1 ² *( X 2 X1 ² Ne² ) The roots are R1/2 = X 2 X1 V 2 V1 V 2 V1 ² ± The condition for the conflict to happen in the future (and not in the past) is a convergent roads condition: <X2-X1.V2-V1> < 0 and in that case the conflict time is simply the smallest root (R2). It can be helpful to make an easier graphical representation of the situation, using the X2-X1 and V2- V1: Let us now deal with the avoidance. We will suppose that when a potential conflict is detected, only one of the two aircraft involved in the conflict changes its way and make an avoidance manoeuvre. To avoid a single and straight-line conflict, an aircraft has two possibilities: either to come behind the predicted position of the other aircraft, either before. Those two possibilities prevent the set of the admissible trajectories from being connex, which in turn prevents classical optimisation algorithms from being relevant. On the previous graph it corresponds to the two possibilities of rotation for the speed vector V2-V1 to avoid the circle. Clearly, one of the two is optimal, and corresponds to the smallest rotation of V2-V1. Note that it is not always possible for an aircraft to come in front of the other. On the previous graph, avoidance corresponds to a rotation of the speed vector V2-V1, but if only one of the two aircraft turns not every direction is admissible for V2-V1. Next graphs show what may happen. Concerning the previous point, note that a limitation mainly occurs if X2-X1 is too small when the avoiding manoeuvre starts. If this manoeuvre is well anticipated (large Intent) then the problem roughly disappears. All those considerations have been taken into account when the simulator has been made. In particular the avoidance algorithm is based on the previous results: when a conflict is detected, the aircraft having priority does not change its heading, whereas the other turns in the optimal direction until the conflict risk disappears. Classification: Public Page 104/131

107 3.2. Maximum density: geometrical considerations Today the airspace maximum capacity is defined by the main limiting factor, which is the controller s ability to deal with a complex situation. In the future others limiting factors will appear, such as computer s capacity, pilot s ability, or reliability of the global control system. But some geometrical boundaries to airspace capacity can already be found, and their order of magnitude can be compared with any present or expected capacity. Let s recall that today s upper limit to controller s capacity is said to be of 15 aircraft at the same time, in a sector which dimensions are usually 100Nm. It gives a maximum density of 0,0015 aircraft/nm² Upper static limit The easiest way to reach a maximum 2-D density is to imagine all aircraft flying in the same direction as closely as possible (the traffic is static because relative positions are steady). The distance between aircraft is at least of 5Nm, and if we introduce a safety margin S factor (larger than 1) this distance is D= S*5Nm. Classification: Public Page 105/131