The factors affecting air traffic controller workload - CiteSeerX

The factors affecting air traffic controller workload: a multivariate analysis based upon simulation modelling of controller workload.

Arnab Majumdar Dr. Washington Y. Ochieng

Centre for Transport Studies Department of Civil and Environmental Engineering Imperial College of Science, Technology and Medicine London SW7 2BU United Kingdom Phone: +44-20-7594-6105 Fax: +44-20-7594-6102

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1. INTRODUCTION The vital role air traffic controllers play in ensuring the safety of aircraft in airspace is becoming increasingly critical, especially in Europe where air traffic has increased rapidly, e.g. in the period between 1985-1990, air traffic in Europe increased by 7.1% annually (EUROCONTROL 1991). Furthermore, this air traffic is unevenly distributed throughout Europe, with the existence of a “core area”, consisting essentially of London-Brussels-Frankfurt-Milan (including Paris) area, where air traffic density is greatest. Forecast indicates a growth in air traffic for Europe between in 1990 and 2010 of 110%, leading to over 11 million flights per year over Western Europe in 2010 (ATAG 1996). Demand in the “core area” is forecast to increase by 2010. Hence, already very busy controllers in this “core area” will have to control more aircraft in the future. The major problem is the lack of a single, integrated ATC system throughout Europe. Each nation controls and manages its ATC infrastructure and air traffic within their airspace, leading to technology incompatibilities and the duplication of tasks and information. Amongst the major implications of these two factors are: •

the rise in flight delays in Europe;

•

non-optimal flight profiles;

•

extra route lengths;

•

possible safety implications.

The economic impact of delays, as well as other inefficiencies in the ATC system (e.g. nonoptimal flight profiles), was calculated to cost Europe in 1989, US $5 billion (in 1988 $) (Lange 1989). The European Commission also confirmed this figure in December 1999 (The Financial Times 1999).

In the late 1980s, the European Organisation for the Safety of Air Navigation (EUROCONTROL) developed the European Air Traffic Control Harmonisation and Integration Programme (EATCHIP) (EUROCONTROL 1991) to tackle the airspace capacity problems (ECAC 1990). EATCHIP aims to progressively harmonize and integrate the diverse ATC systems throughout Europe by using: •

new technology both in the control room and in the air, e.g. e.g. mandatory area navigation (RNAV) equipment on aircraft to enable greater position precision than at present;

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•

innovative control procedures, e.g. flexible use of airspace between civil and military ATC.

Many of the objectives of EATCHIP have been achieved and it has had some success in reducing the capacity problem, e.g. a 40% increase in capacity since 1990 to cope with a 35% increase in air traffic during the same period (ECAC 1998). However, the delays have not disappeared.

With predicted air traffic growth, EATCHIP has been replaced by the EATMP (European Air Traffic Management Programme) (EUROCONTROL 1998) whose main characteristic is the "gate-to-gate" concept, in which flights are treated as a continuum, from the first interaction with ATM until post-flight activities. To do this, a broad range of measures and technology are considered which has the potential to change the way in which controllers will work in the future ATC system of Europe.

The success of the any initiatives to improve current and future airspace capacity relies upon a reliable definition and measure of airspace capacity. This is complicated by the fact that airspace capacity in Europe depends not only upon spatial separation criteria, but also on the workload of air traffic controllers. Hence a first step is to understand the factors that affect controller workload and hence airspace capacity. These factors can be thought of as airspace capacity drivers. A subsequent step is to attempt derive a functional relationship between these factors and controller workload.

This paper aims to provide an investigation of the factors that affect the air traffic controller’s workload. The paper is in two parts and is organised as follows. Part one of the paper covers Sections 2 to 6. Section 2 provides a brief explanation of the factors underlying airspace capacity and emphasises the critical role of the air traffic controllers’ workload. Section 3 examines the literature on the factors that affect controller workload. In particular, this concerns the literature on air traffic and ATC sector factors affecting controller workload. Section 4 discusses the factors that affect the occurrence of operational errors (OEs). In the USA, an operational error (OE) is described as the occurrence of an error arising from the infringement in the applicable minimum separation criteria between aircraft. This can occur when there is a rise in controller workload, and hence an examination of the factors leading to OE occurrence provides valuable insight into the factors affecting controller workload. Section 5 briefly outlines recent research investigating the quantitative relationship between controller workload and air traffic and ATC sector features.

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Section 6 provides a list of factors which effect controller workload based upon the findings of the reviews of literature in Sections 3 to 5.

Part Two of this paper covers Sections 7 to 11 and is based upon the results of a multivariate analysis of simulation modelling of controller workload to provide further insights into the factors affecting controller workload. Section 7 outlines the model used, the European Airspace Model (EAM) (EUROCONTROL 1996), together with the data for the analysis. This data is based on five Area Control Centres (ACCs) in Europe. Section 8 outlines the results of regression analysis for this data, based in particular on techniques of the selection of regressors. Section 9 outlines the results of principal components analysis on the data, whilst Sections 10 involves factor analysis of the data. Section 11 concludes with suggestions for further analysis of the data.

2.

AIRSPACE CAPACITY

Unlike the situation in surface transport, airspace capacity is not defined simply by internationally specified spatial separation criteria between aircraft, based upon their performance. The experience in high air traffic density area such as Europe suggests that a safer measure of capacity is based on air traffic controller workload i.e. the mental and physical work done by the controller to control traffic. Thus airspace capacity is related to controller workload given that the controller’s workload limits determine the capacity of a sector.

With this in mind, the capacity of an ATC sector can be defined as the maximum number of aircraft that are controlled in a particular ATC sector in a specified period, while still permitting an acceptable level of controller workload. Note that one is dealing with the number of aircraft controlled, i.e. aircraft whose control generates work for the controllers, rather than the number of aircraft entering, exiting or passing through the sector, in a given period of time.

Controller workload is a confusing term and with a multitude of definitions and models in the literature, its measurement is not uniform (Jorna 1991). Workload is a construct, i.e. a process or experience that cannot be seen directly, but must be inferred from what can be seen or measured. Research, theory, models and definitions of workload are inter-related and there are numerous reviews of workload and its measurement (e.g. Jorna 1991).

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Irrespective of the definition and measure, research indicates that the workload experienced by air traffic controllers is affected by the complex interaction of (Mogford et al. 1995): 1. the situation in the airspace - i.e. by features of both the air traffic and the sector, which interact to produce air traffic control (ATC) complexity; 2. the state of the equipment - i.e. by the design, reliability accuracy of equipment in the control room and in the aircraft; and 3. the state of the controller, e.g. the controller’s age, experience, decision making strategies.

These parameters can be thought of as the drivers of controller workload, and consequently of airspace capacity, i.e. airspace capacity drivers. The effect of these parameters on airspace capacity must be understood if realistic and successful strategies for increasing capacity are to be implemented.

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AIRSPACE CAPACITY DRIVERS

Figure 1, based on Mogford et al. (1995), outlines how these capacity drivers affect controller workload. Mogford states that the primary factor affecting workload is the situation in the airspace. This situation is determined by (Mogford, Murphy, Yastrop, Guttman and RoskeHofstrand 1993): •

physical aspects of the sector, e.g. size or airway configuration; and

•

factors relating to the movement of air traffic through the airspace, e.g. the number of climbing and descending flights; and

•

a combination of the above factors which cover both sector and traffic issues, e.g. required procedures and functions.

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SOURCE FACTORS

MEDIATING FACTORS

RESULT

QUALITY OF EQUIPMENT

ATC COMPLEXITY: AIR TRAFFIC PATTERN AND SECTOR CHARACTERISTICS

INDIVIDUAL DIFFERENCE

CONTROLLER W ORKLOAD

CONTROLLER COGNITIVE STRATEGIES

FIGURE 1: FACTORS AFFECTING CONTROLLER WORKLOAD Source : Mogford et al. (1995), page 5

In theory, one could separate the structure of a sector from the characteristics of the air traffic that flows through it. In practice though, whilst a certain constellation of sector features might be easy to handle with low traffic volume or certain types of flight plans, more or different traffic might completely change this picture. Similarly, a given level of traffic density and aircraft characteristics may create more or less workload depending on the structure of the sector. Thus the impact of traffic variables on the controllers’ workload partially depends upon the features of the sector through which the traffic flows. This interaction between sector and traffic features is a complex process, and hence can be thought of as ATC complexity (Mogford et al. 1995). One can also think that this ATC complexity generates controller workload, i.e. the sector and traffic features interact to generate workload for the controller.

In addition, secondary mediating factors include: •

the cognitive strategies the controller uses to process air traffic information, i.e. the state of the controller;

•

the quality of the equipment (including the computer-human interface), i.e. the state of the equipment;

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•

individual differences (such as age and amount of experience), i.e. the state of the controller.

3.1.

The primary factors

Numerous studies have examined the effects of air traffic and sector characteristics on controller workload and performance, e.g. Grossberg et al. (1989), Schmidt (1978). There have also been various studies to investigate the relationship between the air traffic and sector characteristics and the occurrence of infringements in the applicable minimum separation criteria between aircraft attributed to ATC, a situation known in the USA as an operational error, e.g. Kinney et al. (1977), Schroeder (1982).

Table 1 summarises the results from those studies which attempt to define a relationship between air traffic and sector characteristics, i.e. ATC complexity factors, on controller workload, and are most directly relevant to this research. Details can be found in Mogford et al. (1995) and Rodgers et al. (1998).

These studies were carried out to define a relationship between ATC complexity and some measure of controller workload, e.g. controller performance or controller judgments. It is worth noting that whilst nearly all the studies mentioned found statistically significant relationships between the complexity factors and workload, a wide variety of different factors were found to be significant. Nevertheless, Table 1 provides an indication of those air traffic and sector features that affect controller workload and hence airspace capacity.

Table 1 Summary of literature on sector and air traffic features affecting controller workload Author Davis (1963)

Conclusions Statistically significant relationship between traffic density, traffic mixture and controller activities. Arad (1964) Placement of sector boundaries with respect to traffic flow greatly affects the controller’s routine load Arad et al. (1964) Routine load on controller affected by placement of sector boundaries with respect to traffic flow. Jolitz (1965) Mathematical model not a good predictor of workload; density better. Siddique (1973) Conflicts in en route airspace occur due to a loss of horizontal separation between aircraft at the same altitudes. Buckley et. al (1983) Sector geometry and traffic density interact in their effect on ten measures of controller performance. Couluris and Schmidt Cost of sectorization is additional workload (coordination) imposed by

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(1973) Soede et al. (1976)

placement of sector boundaries. Duration of ATC task components were correlated with communications with aircraft, presence of conflicts and the number of path changes. Schmidt (1976) Developed Control Difficulty Index (CDI) - the sum of the expected number of ATC events per hour multiplied by a weighting factor. Events in order of difficulty: 1. Preventing a crossing conflict. 2. Preventing an overtaking conflict. 3. Hand-offs. 4. Pointouts. 5. Coordination with other controllers. 6. Handling pilot requests. 7. Traffic structuring. Stein (1985) Controller workload is related to clustering of aircraft in a small amount of airspace, number of handoffs outbound, total number of flights handled and number of handoffs inbound. Mogford et al. (1993) Factors that may affect controller workload: 1. The amount of climbing or descending traffic. 2. The degree of aircraft mix (VFR, IFR, props, turboprops, jets, etc.). 3. The number of intersecting flight paths. 4. The number of multiple functions a controller must perform (e.g. approach control, terminal feeder, en route, in-trail spacing). 5. The number of required procedures that must be performed. 6. The number of military flights. 7. Amount of co-ordination or interfacing with other entities (e.g. adjacent sectors, approach controls, centre, military units, etc.). 8. The extent to which the controller is affected by airline hubbing or major terminal/airport traffic. 9. The extent to which weather-related factors affects ATC operations. 10. Number of complex aircraft routings. 11. The extent to which controller’s work is affected by restricted areas, warning areas, and MOAs and their associated activities. 12. The size of sector airspace. 13. The requirement for longitudinal sequencing and spacing. 14. Adequacy and reliability of radio and radar coverage. 15. Amount of radio frequency congestion. Magill (1997) Vast majority of conflicts in simulated European airspace involves climb or descent aircraft. The combination of a climbing flight with a descending flight accounts for over a third of all conflicts. NATS ATS Output Average Density of European airspace sectors is determined by: Approach, • Traffic count; Eurocontrol (2000) • Volume of airspace; • % of level traffic; • % of climbing and descending traffic. Delahaye and Intrinsic Complexity metrics of simulated air traffic is determined by the Puechmorel (2000) relative speed and positions of aircraft in a sector. Source: Mogford et al. (1995), Rodgers et al. (1998), Magill (1997).

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Table 2 summarises the findings of these studies. One can see from this table that certain common complexity factors emerge in the various studies reviewed. Table 2. Summary of the factors in the literature that affect controller workload. Variable Author AIRCRAFT Traffic density/ Number of flights Davis (1963); Arad (1963, 1964); Buckley et al. (1983); Stein (1985); Eurocontrol (2000). Traffic mix Davis (1963); Mogford et al. (1993) Separation standards/ Longitudinal, Arad (1963, 1964); Schmidt (1976); Mogford et al. (1993) sequencing, spacing Aircraft speeds Arad (1963, 1964); Schmidt (1976); Delahaye et al. (2000) Traffic flow rate Schmidt (1976) Cruising traffic Schmidt (1976); Eurocontrol (2000) Confliction Buckley et al. (1983) Occupancy Buckley et al. (1983) Delay Buckley et al. (1983) Fuel Consumption Buckley et al. (1983) Aircraft clustering Stein (1985); Angle of intersection between routes Schmidt (1976) Hourly traffic Hurst & Rose (1978); Peak Traffic Hurst & Rose (1978); Climbing/ Descending traffic Mogford et al. (1993); Eurocontrol (2000) Horizontal conflicts Siddique (1973) Ascending conflicts Magill (1997) Military flights Mogford et al. (1993) Airline hubbing Mogford et al. (1993) Aircraft position Delahaye et. al (2000) SECTOR Sector Size Sector/ flow design; Number of flight levels Coordinations Number of intersections Boundary location Airway configuration Number of intersecting flight paths Complex routing Restricted areas

Arad (1963, 1964); Mogford et al. (1993); Eurocontrol (2000) Arad (1963, 1964); Stein (1985) Schmidt (1976) Mogford et al. (1993) Courilis & Schmidt (1973) Courilis & Schmidt (1973) Courilis & Schmidt (1973) Mogford et al. (1993) Mogford et al. (1993) Mogford et al. (1993)

COMBINATION Radio and radar coverage Frequency congestion Communications

Mogford et al. (1993) Mogford et al. (1993) Buckley et al. (1983)

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Multiple control functions Required procedures Weather

Mogford et al. (1993) Mogford et al. (1993) Mogford et al. (1993)

3.2. Secondary mediating factors This paper is concerned with investigating the impact of the situation in the airspace, i.e. primary source factors in the airspace in Mogford’s (1995) terminology. However, the following points should be noted about the secondary factors.

The amount of workload experienced by the controller may be modulated by the information processing strategies adopted to accomplish the required tasks. Such techniques are learnt in training or evolve on-the-job and may vary in effectiveness. Their appropriate use can ameliorate the influence of a complex ATC environment on the workload, e.g. by simpler or more precise actions.

The effect of control equipment is another mediating factor on controller workload. The controller’s task may be made easier if a good user interface and useful automation tools are available to ensure that adequate and accurate information is presented to the controller. Conversely, a poor medium will increase the workload.

Controller workload can also be influenced by personal variables, such as age, proneness to anxiety and the level of experience. Variations in skill between controllers can be quite pronounced. These factors can have a strong effect on the workload experienced by a given controller in response to a specific array of ATC complexity factors.

This section has indicated that controller workload is generated by the ATC complexity factors. The effect of these factors on the workload can be mitigated by the three factors of quality of equipment, individual differences and controller cognitive strategies. This research will investigate only the effects of ATC complexity on the controller workload, whilst keeping the mediating factors constant.

Mogord et al. (1995) summarises the details of the research in these areas.

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4.

THE RELATIONSHIP BETWEEN ATC COMPLEXITY AND OPERATIONAL

ERRORS It is reasonable to assume that as the ATC complexity increases, the workload of the controllers increases. This rise in workload can have the consequence that controllers make errors in their task of keeping aircraft separated. In the USA, an operational error (OE) is described as the occurrence of such an error arising from the infringement in the applicable minimum separation criteria. OEs are described in the FAA’s ATC Handbook (FAA Order 7110.65) and their number is a primary index of the safety of the US National Airspace System (NAS).

Rodgers et al. (1998) state that “although the evidence is weak, controller workload is probably associated with OE commission. As task information processing requirements reach and exceed controller sensory and cognitive capabilities, aircraft may not receive sufficient attention and control to maintain required separation. The controller’s workload may be increased through the presence and interaction of several complexity factors that create competition for similar cognitive resources” (page 2).

Alternately, isolated ATC complexity factors may lead to unsafe condition by placing focussed demands on the controller. Such factors may be transitory or sustained and may pose undue strain on specific information processing channels or capabilities (e.g. memory). For example, the management of a sector may require the application of many required procedures. Forgetting to apply these at the correct time could lead to traffic problems, and subsequently errors.

A stream of research in the USA has investigated the factors that cause operational errors in controlled airspace. Immediately after the detection of an OE, a detailed investigation is conducted to try and fully describe the events associated with its occurrence. This includes statements from the controllers involved, together with relevant data, e.g. voice and computer tapes, and a detailed review of the events leading to the error’s occurrence. In addition, the Systematic Air Traffic Operations Research Initiative (SATORI) system has been developed by the FAA to re-create the error situation in a format much like the one originally displayed to the air traffic controllers (Rodgers and Duke, 1993). Once the error has been thoroughly investigated, an OE Final Report is filed which contains detailed information about each error obtained during the investigation process. Table 3 provides a summary of the major studies investigating OE occurrence.

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Table 3. Summary of the literature on the relationships between OEs and workload. Author Schroeder (1982)

Kinney et al. (1977)

Empson(1987); LanganFox and Empson (1985) Redding (1992)

Stager and Hameluck (1990); Stager et al. (1989) Schroeder and Nye (1993) Rodgers and Nye (1993)

Fowler (1980) Grossberg (1989)

Rodgers and Manning (1995)

Conclusions Study on FAA’s operational errors database revealed that most errors occurred under low or moderate workload. Other aspects of the situation apart from traffic volume affect workload (sector factors?). Coordination is a is a direct or contributing factor in many errors. OEs occur under low to moderate workload and moderate complexity. In en route centers, 95% of errors are attributed to attention, judgement or communications. Most errors occur in level flight. Controller workload is related to airspace structure, procedural demands, traffic type and control over task presentation rate. Failure to maintain SA causes most errors in moderate traffic load. Communication, coordination, and misuse of radar data account for errors. Definitions of direct and contributing causes. OE s occur under low to moderate workload conditions. Causes are attention, judgement, and communications problems. OEs occur under average or lower traffic complexity. Most frequent causes of OEs are problems with radar display; communication; coordination and data posting. Most OEs occur with one aircraft in level flight and another descending or ascending. Most moderate errors are between aircraft in level flight. Horizontal, not vertical, separation varies with severity. Higher horizontal separation for SA OEs. Sector complexity affected by problems with coordination, procedures, LOAs and weather. Sector complexity factors include control adjustments to merge and space aircraft, climbing and descending aircraft flight paths, mixture of aircraft types, frequent coordination and heavy traffic. OE time period shows increases in sector transit time, handoff acceptance latency, vertical separation, and aircraft density and decrease in the number of handoffs accepted.

When one considers the evidence from OE occurrence studies, despite the inconsistencies and inadequate categories in the database reports, two points become apparent: •

most OEs seem to occur at moderate traffic and reported workload conditions;

•

co-ordination between controllers is cited as a major contributory cause time and again.

This leads one to conclude that OE occurrence, and hence the actual workload associated with it, must depend on factors other than the sheer traffic volume. The suspicion that prime amongst these factors could be inappropriate sector design, is confirmed by the presence of the co-

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ordination problems noted in many OE reports. Table 4 summarises the findings of these studies by isolating the ATC complexity factors which affect the occurrence of OEs.

Table 4. Summary of the relationships between traffic and sector variables and OE occurrence. Variable Author Traffic load Empson (1987); Grossberg (1989); Redding (1992); Rodgers and Nye (1995). Traffic complexity Redding (1992); Schroder and Nye (1993). Time pressure Empson (1987); Climbing/ descending traffic Grossberg (1989) Traffic mix Grossberg (1989) Frequent coordination Rodgers & Manning (1995); Grossberg (1989); Schroder (1982); Redding (1992); Schroder and Nye (1993); Sector transit time Rodgers and Manning (1995) Horizontal conflicts Kinney et al. (1977) Light/ moderate workloads Schroder (1982); Stager et al. (1990) Rodgers and Nye (1995). Conflicts when one aircraft in ascend/descend and other aircraft in cruise Separation control Grossberg (1989) Vertical separation Rodgers and Manning (1995) Airspace structure Empson (1987); Procedures Empson (1987); 5.

RECENT

RESEARCH

TOWARDS

DEVELOPING

A

FUNCTIONAL

RELATIONSHIP The previous sections considered the evidence of research into the factors that affect controller workload. These studies were useful in obtaining a list of factors that affect controller workload. In particular these studies attempted to identify simple relationships between the measure of controller workload chosen and the relevant factors e.g. workload increases with an increase in traffic. However, barring a few exceptions, e.g. Arad (1963), few of these studies have attempted to either formulate a functional relationship between workload and airspace capacity drivers, or even to investigate in-depth the quantitative aspects of the complexity factors, e.g. possible differential weighting of the factors. Recently, a series of studies in Europe and the USA have attempted to investigate these issues. They are divided into three categories according to the method by which the data was obtained, Table 5.

Table 5. Classification of quantitative studies in ATC complexity

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Real-time

Analysis

Existing data

Analysis

Simulation

Simulation

Analysis

Modelling

Study

Author

Study

Author

Study

Author

NASA

Laudeman

Dalitchampt

Estimating

Majumdar and

Dynamic

et al.

EUROCONTROL Maastricht study

et al. (1995)

European

Polak (2001)

Density

(1998)

Airspace

Metric

Capacity

NATS Aircraft Proximity

Lamoreux (1999)

EUROCONTROL ATS Production metric

EUROCONTROL (2000)

Wyndemere Controller Capacity

Pawlak et FAA Sector Complexity Study al. (1996)

EmbryRiddle Study

Corker et

Rodgers et al. (1998)

al. (1999)

Each of these studies has its own objectives in research, with a variety of different measures of controller workload and validation studies, measuring controller workload and validation. It is worth noting that all the studies state that simply the number of aircraft in a sector is cannot appropriately account for the workload experienced by a controller. Instead they account in oneway or another for greater detail in aircraft movements, e.g. by promixity measures. In addition, it also seems sector features are not explicitly accounted for in many of these analyses.

However, most of these studies use simple linear regression models, with no attempt to model interactions between the variables or any possible quadratic terms. Often, the final regression can only account for 50% of the variance in the observed/ recorded workload, suggesting more care in correct model specification is needed. There seems to be a case for a more rigorous approach to the functional analysis, especially with regard to multiple regression, a fact Majumdar and Polak (2001) indicate and build upon in their modeling strategy.

6.

LITERATURE-REVIEW

BASED

LIST

OF

FACTORS

AFFECTING

CONTROLLER WORKLOAD

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The previous sections have shown that air traffic controller workload, and hence airspace capacity, is primarily determined by the construct of ATC complexity (Mogford, Guttman, Morrow and Kopardekar, 1995). This construct incorporates the: •

physical aspects of a sector, e.g. size or airway configuration, and

•

factors relating to the movement of air traffic through airspace, e.g. the number of climbing or descending flights.

Of course, the quality of the system transmitting the information about the sector and the aircraft within it also affects the adequacy of the information reaching the controller’s senses. Once this information has been perceived and classified, the cognitive tactics used to identify problems and make decisions will also influence controller workload. Thus these mediating factors - the information display and cognitive mediators - must be also considered in order to assess the ultimate effect sector and traffic features have on the controller’s workload.

There are two sources of research that attempt to identify specific airspace and traffic factors that contribute to ATC complexity (Mogford et al., 1993). One source considers the effects of airspace and traffic features as they impact upon the controller’s performance and workload. Another source considers the underlying causes behind operational errors, since it is probable that operational errors occur when the controller workload is high.

In addition, there have been several recent attempts to derive a functional relationship between controller workload and the ATC complexity factors. From these various sources, one can derive a list of factors that impact upon controller workload, see in Table 6.

Table 6. List of air traffic and sector factors that can affect ATC complexity and controller workload. Air Traffic Factors Total number of aircraft Peak hourly count Traffic mix Climbing/ descending aircraft Aircraft speeds Horizontal separation standards Vertical separation standards Minimum distance between aircraft Aircraft flight direction

Sector Factors Sector size Sector shape Boundary location Number of flight levels Number of facilities Number of entry and exit points Airway configuration Proportion of unidirectional routes Number of facilities.

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Predicted closest conflict distance 7.

Winds

MULTIVARIATE RESEARCH USING THE EUROPEAN AIRSPACE MODEL

(EAM) The previous sections outlined a set of possible factors that affect controller workload. This section considers the results of a variety of multivariate analytical techniques to further ascertain which factors affect controller workload in a sector based upon simulation modelling of air traffic controller workload. For this study, the European Airspace Model (EAM) simulation model of controller workload was used. The EAM model has been used in European airspace planning and management for over 25 years, and well verified by controllers (EUROCONTROL 1996). In the EAM model, two controllers – a Planning and Tactical Controller – are assigned to each sector, which is a three-dimensional volume of airspace. These areas maintain information regarding the flights that wish to penetrate them, and have associated separation minima and conflict resolution rules that need to be applied for each controller. This reflects the teamwork aspect of control seen in practice. The simulation engine permits the input of rules for the controllers that mimic reality. The tasks for the controllers are based on a total of 109 tasks being undertaken by controllers, together with their timings and position, grouped into five major areas. The use of the EAM means that the controller workload is measured on a task-time basis, i.e. total execution time required for undertaking control tasks. This total execution time allows for the incorporation of time required for a cognitive planning element for each task physically observed.

The results of EAM simulation for sectors from five Area Control Centres (ACCs) were analysed. These five ACCs can be considered as high, medium and low in terms of the traffic through the sectors, Table 7. Each ACC had associated with it its own task base. This data was provided by Jean Michel Lenzi from DED4 EUROCONTROL.

Three main types of analysis were carried out on a variety of aircraft-profile related data obtained for each sector of each ACC in the peak hour controller workload: •

regression analysis;

•

principal components analysis; and

•

factor analysis.

The results and comments for each analysis are given in the following sections.

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Table 7. The classification and the number of sectors in each ACC simulated. ACC

Classification

Number of Sectors

Lisbon

Low

7

Copenhagen

Medium

11

Roma

Medium

16

Maastricht

High

9

Karlsruhe

High

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8.

REGRESSION ANALYSES

Simple ordinary least squares (OLS) regression analysis was undertaken using the results obtained from the simulation for the peak hour. The workload in each ACC was unique, and therefore each ACC required its own regression analysis. For all five ACC areas, no suitable linear regression relationships were obtained for workload with: •

total number of flights;

•

total number of flights and (total number of flights)2;

•

individual flight profiles;

•

total flight time;

•

flight times by cruise, ascend and descend.

Therefore, to detect possible regressors for a linear relationship with workload, two alternative techniques were attempted: Maximum R2 and Forward Selection methods (Gujerati 1995). The results of these techniques for the five areas are outlined in the Tables following, with the shaded left column indicating those variables common to both techniques.

The following can be noted: •

the one common variable for all five areas is the number of flights with climb-cruise-descend flight profile.

•

with the Maximum R2 technique, the variables relating to amount of time for the various flight attitudes, e.g. total climbing flight time, and variables relating to the exit and entry of flights into a sector, e.g. number of flights exiting sector in climb, are significant for all of the ACCs.

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•

the forward selection technique, the route based factors, e.g. bi-directional concentration, seem to be significant.

Table 8. The results of the Maximum R2 and Forward Selection techniques for each ACC. Copenhagen Maximum R2

Forward Selection

Climbing Flight Time Descending Flight Time

Descending Flight Time Number of flights in the busiest 30 minute period

Number of flights with Climb-Cruise-Descend Number of flights with Climb-Cruise-Descend Flight Profile

Flight Profile

Continuous Climb Flight Profile

Number of flights exiting sector in cruise

Number of flights entering sector in cruise

Number of flights entering sector in descend


Geographical concentration of flights

Number of flights exiting sector in climb

Difference in Upper and Lower flight levels

Number of flights exiting from the sector to Average speed of the aircraft in the sector another in the same ATC unit Number of flights exiting from the sector to a Climbing Flight Time sector in another ATC unit Difference in Upper and Lower flight levels Maastricht Maximum R2

Forward Selection

Average instantaneous number of flights

Number of flights with Cruise-Descend Flight Profile

Cruising Flight Time

Number of flights entering the sector from a sector in another ATC unit

Number of flights with Cruise-Descend Flight Profile

Number of flights in Continuous Descend Flight Profile

Number of flights with Climb-Cruise-Descend Number of flights with Climb-Cruise-Descend Flight Profile

Flight Profile

Number of flights in Continuous Descend Number of flights exiting sector in descend Flight Profile

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Number of flights entering sector in climb Geographical concentration of flights Average speed of the aircraft in the sector Lisbon Maximum R2 Mean distance flown Total Cruise flight time Total Descend flight time

Number of flights entering sector in climb Climbing Flight Time

Forward Selection Average instantaneous number of flights Number of flights with Climb-Cruise-Descend Flight Profile Number of flights exiting from the sector to a sector in another ATC unit

Number of flights with Climb-Cruise-Descend Flight Profile Bi-directional route concentration Average speed of the aircraft in the sector

Bi-directional route concentration Number of flights in the busiest 30 minutes

Karlsruhe Maximum R2

Forward Selection

Total distance flown

Number of flights exiting sector in descend

Average instantaneous aircraft Total Cruise flight time

Vertical concentration of flights Number of flights exiting sector in climb

Total Climb flight time


Number of flights with Climb-Cruise-Descend Flight Profile Number of flights entering sector in climb

Total Climb flight time Bi-directional route concentration


Number of flights with Climb-Cruise Flight Profile


Mean distance flown


Number of flights with Climb-Cruise-Descend Flight Profile

Number of flights entering the sector from a Average speed of the aircraft in the sector sector in another ATC unit Geographical concentration of flights

Number of flights entering the sector from a sector in the same ATC unit

Bi-directional route concentration Vertical concentration of flights

Difference in Flight Levels

19

Roma Maximum R2

Forward Selection

Mean flight time

Descending Flight Time

Total distance flown


Average instantaneous aircraft

Continuous descending flight profile

Descending Flight Time

Number of flights entering the sector from a sector in another ATC unit

Number of flights with Climb-Cruise Flight Profile

Number of flights with continuous climbing flight profile

Number of flights with Climb-Cruise-Descend Mean flight time Flight Profile Number of flights with continuous climbing Number of flights entering in climb flight profile Number of flights with continuous descending Total Cruise flight time flight profile Number of flights with continuous cruise flight Difference in flight levels profile Number of flights entering sector in cruise

Number of flights entering the sector from a sector in the same ATC unit

Number of flights entering sector in climb

Number of flights in climb-cruise flight profile


Number of flights exiting from the sector to a sector in another ATC unit



Number of flights entering from the sector to a sector in another ATC unit Average speed of the aircraft in the sector

Bi-directional route concentration

20

9.

PRINCIPAL COMPONENTS ANALYSIS.

A principal components analysis is concerned with explaining the variance-covariance structure of a set of variables through a few linear combinations of these variables. Its general objectives are (Johnson and Wichern, 1998): i)

data reduction, and

ii) interpretation.

Although p components are required to reproduce the total system variability, often much of this variability can be accounted for by a small number k of the principal components. If this is the case, then there is almost as much information in the k components as there is in the original p variables. The k principal components can then replace the initial p variables, and the original data set, consisting of n measurements on p variables, is reduced to a data set consisting of n measurements on k principal components. An analysis of principal components often reveals relationships that were not previously suspected and thereby allows interpretations that would not ordinarily result.

Analysis of principal components are more of a means to an end rather than an end in themselves, because they frequently serve as intermediate steps in much larger investigations, e.g. principal components may be inputs into a multiple linear regression. Therefore a principal components analysis of the variables obtained for the peak hours for the sectors in the five ACCs were undertaken. For this analysis, the total number of flights, the total time flown and the total distance flown were ignored and instead their discriminated elements used, e.g. total flight time disaggregated to total cruising time, total climbing time and total descending time.

The results of the principal components analysis are outlined below in Tables 9. and 10. From Table 9. it can be seen that for all the five ACCs, there is really one dominant principal component, accounting for 70% or more of the variance. In addition, with the inclusion of the second principal component at least 85% of the variance is accounted for.

On the basis of Table 9, the factor loadings need to be examined. In order to ascertain these principal components, one can obtain the eigenvectors associated with each variable for each of the principal factors. A summary of the eigenvectors obtained for the first principal component for each ACC is shown below in Table 10.The first principal component for all the ACCs has:

21

•

high positive loadings associated with the total cruise flight time.

In addition, for four ACCs: •

the difference in the Upper and Lower Flight Levels has high positive loadings;

For two ACCs positive loadings occur for, •

the bi-directional concentration;

•

the mean distance travelled.

For one ACC, there is a positive loading for: •

the average navigational speed in the sector.

Therefore the principal components for all the ACCs can be viewed as being associated with the nature of the aircraft in cruise through a sector’s flight levels. With this principal component accounting for so much of the total variance, it appears on the basis of this analysis that for any future optimisation technique relating the controller workload in a sector to a variety of air traffic and sector variables, the nature of the aircraft cruising through a sector must be accounted for. This component can be specific for each ACC, e.g. for both the Roma and Karlsruhe sectors, the high positive loadings of the bi-directional concentration means that one can think of the principal component as the nature of the aircraft cruising through the routes, especially bi-directional, of a sector.

Table 9. The loadings for the first principal component for each of the ACCs. ACC

Principal Component Number One

Lisbon

0.538( TotalCruiseFlightTime ) + 0.778( DifferenceinFLs ) + 0.227( MeanDist . Travelled )

Copenhagen

0.752(TotalCruiseFlightTime) + 0.549( DifferenceinFLs) + 0.233( AverageSpeed )

Roma

0.666(TotalCruiseFlightTime ) + 0.501( DifferenceinFLs ) + 0.499( Bidirectionalconc.)

Maastricht

0.988(TotalCruiseFlightTime) + 0119 . ( MeanDist .Travelled )

Karlsruhe

0.546(TotalCruiseFlightTime) + 0.745( DifferenceinFLs ) + 0.255( Bidirectionalconc.)

Table 10.a. Lisbon Results of a principle components analysis - I. Eigenvalue

Proportion

Cumulative

22

Principal Component 1

41244.3

0.70

0.70


10545.3

0.18

0.88


4455.6

0.08

0.96

Table 10.b. Copenhagen Results of a principle components analysis - I. Eigenvalue

Proportion

Cumulative


46367.9

0.77

0.77


8857.5

0.14

0.91


3346.7

0.06

0.97

Table 10.c. Roma Results of a principle components analysis—I Eigenvalue

Proportion

Cumulative


54967.1

0.71

0.71


11262.9

0.14

0.85


4606.6

0.06

0.91

Table 10.d. Maastricht Results of a principle components analysis - I. Eigenvalue

Proportion

Cumulative


56458.9

0.79

0.79


13694.2

0.19

0.98


663.4

0.01

0.99

Table 10.e. Karlsruhe Results of a principle components analysis - I. Eigenvalue

Proportion

Cumulative


30056.12

0.76

0.76


5479.0

0.14

0.90


2367.7

0.06

0.96

10.

ROTATED FACTOR ANALYSIS

23

The scaled principal components obtained in the previous section are “one “factoring” of the covariance matrix for factor analysis. Factor analysis is the name given to a class of multivariate statistical methods, whose primary purpose is data reduction and summarization. It addresses the problem of analyzing the interrelationships among a large number of variables and then explaining these variables in terms of their common underlying dimensions (factors).

Factor analysis is an interdependence technique in which all variables are simultaneously considered. In a sense: •

each of the observed (original) variables is considered as a dependent variable that is the function of some underlying, latent hypothetical set of factors (dimensions).

•

each factor is a dependent variable that is a function of the originally observed variables.

The general purpose of factor analysis is to find a way of condensing (summarising) the information contained in a number of original variables into a smaller set of new composite dimensions (factors) with a minimum loss of information: i.e. to search for fundamental constructs or dimensions assumed to underlie the original variables. More specifically, four functions of factor analysis are: 1. Identify a set of dimensions that are latent (not easily) observed in a large set of variables: R factor analysis. Take the identification of the underlying dimensions or factors as ends in themselves: the estimates of the factor loadings are all that is required for the analysis.

2. Devise a method of combining or condensing large numbers of the observations into distinctly different groups within a larger population: Q factor analysis. Take the identification of the underlying dimensions or factors as ends in themselves: the estimates of the factor loadings are all that is required for the analysis.

3. Identify appropriate variables for subsequent regression, correlation or discriminant analysis from a much larger set of variables. Relies on factor loadings and uses them as a basis for identifying variables for subsequent analysis.

4. Create an entirely new set of a smaller number of variables to partially or completely replace the original set of variables for inclusion in subsequent regression, correlation or discriminant

24

analysis. Estimates of the factors themselves (factor scores) need to be obtained, then the factor scores are used as independent variables in e.g. regression analysis.

10.1.

Rotation of Factors.

An important concept in factor analysis is the rotation of factors. Specifically, the reference axes of the factors are turned about the origin until some other position has been reached. The rotations are either: 1. Orthogonal - in which axes are maintained at 90 degrees. 2. Oblique - the axes do not retain the 90 degree angle of rotation.

Two stages are involved in the derivation of a final factor solution.

a) Compute unrotated factor matrix. The initial unrotated factor matrix is computed to assist in obtaining a preliminary indication of the number of factors to extract.

If the analyst is simply interested in the best linear combination of variables - best in the sense that the particular combination of original variables would account for more of the variance in the data as a whole than any other linear combination of variables. Therefore: •

the first factor may be viewed as the best single summary of linear relationships exhibited in the data;

•

the second factor is defined as the second best linear combination of the variables subject to the constraint that it is orthogonal to the first factor.

To be orthogonal to the first factor, the second one must be derived from the proportion of the variance of the variance remaining after the first factor has been extracted. Thus the second factor is defined as the linear combination of variables that accounts for most of the residual variance after the effect of the first factor is removed from the data. Subsequent factors are defined similarly until all the variance in the data is removed.

b) Rotation of the factor matrix. Unrotated factor solutions achieve the objective of data reduction, but the question is if the unrotated factor will provide the information that offers the most adequate interpretation of the

25

variables under examination. In most instances, this is not the case. Thus the basic reason for employing a rotational method is to achieve: •

simpler and

•

theoretically more meaningful factor solutions.

Rotation of the factors in most cases improves the interpretation by reducing some of the ambiguities that often accompany initial unrotated factor solutions. Thus: i)

the unrotated factor solution may or may not provide a meaningful patterning of variables;

ii) if the unrotated factors are expected to be meaningful, the user may specify that no rotation be performed; iii) generally rotation will be desirable because it simplifies the factor structure and because it is usually not difficult to determine whether unrotated factors will meaningful or not.

Unrotated factor solutions extract factors in the order of importance. The first factor tends to be a general factor with almost every variable loading significantly, and it accounts for the largest amount of variance. The second and subsequent factors are then based upon the residual amount of variance. Each accounts for successively smaller portions of variance. The ultimate effect of rotating the factor matrix is to redistribute the variance from earlier factors to later ones to achieve a simpler, theoretically more meaningful, factor pattern.

The same general principles pertain to oblique as to orthogonal rotations. The oblique rotation method is more flexible because the factor axes need not be orthogonal. It is also more realistic because the theoretically important underlying dimensions are not assumed to be uncorrelated with each other. Hence the oblique solution provides information about the extent to which factors are actually correlated with each other.

Often, many direct unrotated solutions are not sufficient. That is, in most cases rotation will improve the interpretation by reducing some of the ambiguities that often accompany the preliminary analysis. The major option available to an analyst in rotation, is to choose an orthogonal or an oblique method. The ultimate goal of any rotation is to obtain some theoretically meaningful factors and, if possible, the simplest factor structure. Orthogonal rotations are used more frequently because the analytical procedures for performing oblique rotations are not as well developed and are still the subject of considerable controversy. The usual practice is to use

26

orthogonal rotation procedures when the objective is to use the factor results in a subsequent statistical analysis. This is because the factors are orthogonal and eliminate collinearity. However, if an analyst is simply interested in obtaining theoretically meaningful constructs or dimensions, the oblique factor rotation is more desirable because it is theoretically and empirically more realistic.

Finally, one should note that there are several different approaches for performing either orthogonal or oblique rotations. Considers only the orthogonal approaches to factor rotation. In practice, the objective of all methods of rotation is to simplify the rows and/or columns of the factor matrix to facilitate interpretation, i.e. making as many values in each row as close to zero as possible. A major orthogonal approach is the VARIMAX criterion, which aims to simplify the columns of the factor matrix, and with this approach, the maximum possible simplification is reached if there are only 1’s and 0’s in a single column. That is, the VARIMAX method maximizes the sum of variances of required loadings of the factor matrix. With the VARIMAX rotational approach, there tends to be some high loadings (i.e. close to -1 or +1) and some loadings near 0 in each column of the matrix. The logic that interpretation is easiest when the variable-factor correlations are either close to +1 or -1, thus indicating a clear association between the variable and factor, or close to 0, indicating a clear lack of association. This indicates the fundamental aspect of a simple structure. The VARIMAX method has proved very successful as an analytic approach to obtaining an orthogonal rotation of factors.

Rotating a set of factors does not change the statistical explanatory power of the factors. It cannot be said that any rotation is better than any other rotation from a statistical point of view; all rotations are equally good statistically. Thus, the choice amongst different rotations must be based on non-statistical grounds, with the preferred rotation being that which most easily interpretable.

If two rotations give rise to different interpretation, those two interpretations must not be regarded as conflicting. Rather, they are two different ways of looking at the same thing, two different points of view in the common-factor space. Any conclusion that depends upon one and only one rotation being correct is invalid. Figure 2. shows the general steps usually followed in any application of factor analysis techniques. Note that component analysis in this figure refers to the principal components analysis.

27

RESEARCH PROBLEM Which variables to include? How many variables? How are variables measured? Sample Size?

CORRELATION MATRIX R verus Q

Component Analysis

FACTOR MODEL

Common Factor Analysis

EXTRACTION METHOD Orthogonal? Oblique?

UNROTATED FACTOR METHOD Number of factors

ROTATED FACTOR METHOD Factor interpretation

FACTOR SCORES for subsequent analysis: Regression Discriminant analysis Correlation

Figure 2. Factor analysis decision diagram.

An initial unrotated factor analysis was computed for each of the ACCs. Unrotated factor solutions extract factors in the order of importance. The first factor tends to be a general factor with almost every variable loading significantly, and it accounts for the largest amount of

28

variance. The second and subsequent factors are then based upon the residual amount of variance. Each accounts for successively smaller portions of variance. However, due to the difficulty in interpreting the unrotated factors, rotation of factors is usually conducted. The ultimate effect of rotating the factor matrix is to redistribute the variance from earlier factors to later ones to achieve a simpler, theoretically more meaningful, factor pattern. In addition: •

rotation of the factors in most cases improves the interpretation by reducing some of the ambiguities that often accompany initial unrotated factor solutions.

•

generally rotation will be desirable because it simplifies the factor structure and because it is usually not difficult to determine whether unrotated factors will meaningful or not.

Consequently, a rotation of the factors, using a VARIMAX method - as well as other methods - is required for further analysis.

The results of a preliminary VARIMAX rotation are outlined in Table 11.a for the sectors in the Copenhagen ACC. The loading values obtained have been multiplied by 100 and rounded to the nearest integer. With certain variables having high loadings, those values greater than 60 are shaded. The main item of interest is to now interpret these factors. The first factor has high positive loadings for the cruise elements of flight and vertical concentration of flights, but high negative loadings for the climbing aspects of flight. Thus this first factor, related to flight profiles, can be thought of as the trade-off between cruising and climbing aircraft.

Similarly, the second factor has high positive loadings for variables concerned with the average distance and flight time in the sector. This can be thought of as a spatial-temporal measure, which does not which is not concerned with flight profiles as such. The third factor has high positive loadings on variables related to descending aircraft, and can thus be thought of as the descending aircraft measure.

This analysis points to the interesting observation that the three factors outlined above need to be included in any formulation linking workload to the sector and air traffic variables. Similar analysis for the other ACCs are outlined in Table 11.b-11.e.

Table 11.a. The loadings of the first three components after a VARIMAX rotation Copenhagen ACC

29

Variable

Principal Factor 1

Principal Factor 2

Principal Factor 3

Flights Entering in Cruise Vertical concentration Continuous Cruise Profile Flights Exiting in Descend Flights Exiting in Cruise Average Navigational Speed Flights in busiest 30 minutes Geographical concentration of flights Difference in Upper & Lower FLs Flights Entering in Climb Flights Exiting in Climb Continuous Climb Profile Mean Flight Time Bi-directional concentration Mean Distance Travelled Average Instantaneous Count Total Cruise Flight Time Flights Exit to another ATC unit Flights Exit to same ATC unit Continuous Descend Profile Total Descend Flight Time Cruise-Descend Flight Profile Total Climb Flight Time Flights Entering in Descend Flights Enter from same ATC unit Climb-Cruise-Descend Profile Climb-Cruise-Flight Profile Flights Enter from another ATC unit

92 91 86 84 81 76 61 57

25 17 27 -12 33 16 -30 -24

-12 -1 -39 5 23 -47 59 -34

51

46

-48

-80 -80 -84 33 7 44 53 62 42 33 10 -17 10 -48 -27 60

-46 -51 -48 90 89 83 78 72 54 -58 -60 1 9 3 -6 -4

31 7 6 7 -19 -7 19 -18 43 -1 5 94 91 77 75 72

-12 27 -8

-12 -3 1

62 30 -55

Table 11.b. The loadings of the first three components after a VARIMAX rotation. Rome ACC

Variable

Principal Factor 1

Principal Factor 2

Principal Factor 3

Flights in busiest 30 minutes Flights Enter from same ATC

94 83

-11 1

-15 -36

30

unit Geographical concentration of flights Flights Exiting in Descend Flights Exit to same ATC unit Continuous Descend Profile Vertical concentration Mean Distance Travelled Mean Flight Time Average Navigational Speed Continuous Cruise Profile Difference in Upper & Lower FLs Flights Entering in Cruise Total Descend Flight Time Flights Exit to another ATC unit Flights Entering in Descend Cruise-Descend Flight Profile Flights Exiting in Climb Flights Entering in Climb Total Climb Flight Time Continuous Climb Profile Flights Enter from another ATC unit Flights Exiting in Cruise Climb-Cruise-Descend Profile Climb-Cruise-Flight Profile Average Instantaneous Count Total Cruise Flight Time Bi-directional concentration The three factors are: •

81

3

10

79 69 61 50 -71 -73 -17 47 21

3 66 -29 -5 32 25 93 69 69

-9 20 29 -17 -23 -13 -1 -50 -39

48 44 -10 25 29 11 5 -3 5 -29

64 -69 -82 -84 -86 -4 6 3 7 16

-50 3 -22 -9 -27 97 97 96 94 55

50 11 -16 -22 -26 -2

21 -17 -1 40 46 50

-73 10 16 -7 -45 -48

the first factor can be thought of as a flight measure which measures trade-off between the concentration of flights in the sector, e.g. high positive loadings for flights in busiest 30 min, geographical concentration, and the average statistics of flights in the sector, e.g. high negative loadings for mean distance travelled and mean flight time.

•

the second factor can be thought of as a trade-off between the cruising and descending traffic in the sector;

•

the third factor can be thought of as associated with the climbing aircraft in the sector.

Table 11c. The loadings of the first three components after a VARIMAX rotation Karlsruhe ACC Variable

Principal Factor 1

Principal Factor 2

Principal Factor 3

31

Flights Exiting in Climb Continuous Climb Profile Flights Entering in Climb Total Climb Flight Time Flights Entering in Descend Continuous Descend Profile Total Descend Flight Time Mean Distance Travelled Mean Flight Time Bi-directional concentration Difference in Upper & Lower FLs Flights Exit from same ATC unit Flights in busiest 30 minutes Continuous Cruise Profile Flights Entering in Cruise Vertical concentration Flights Exit to same ATC unit Flights Exiting in Cruise Total Cruise Flight Time Climb-Cruise-Flight Profile Average Navigational Speed Flights Exiting in Descend Climb-Cruise-Descend Profile Cruise-Descend Flight Profile Average Instantaneous Count Flights Exit to another ATC unit Flights Enter from another ATC unit Geographical concentration of flights •

95 95 90 83 80 80 71 -55 -55 -70 -73 -21 19 -49 -49 -27 -5 -48 -51 -5 -33 59 16 13 -6 59 29

-21 -20 -28 -30 -22 -24 -20 -48 -47 -8 42 92 90 83 83 82 82 80 64 -21 36 -8 -23 18 63 -10 -7

-12 -14 -6 -20 -37 -44 -51 32 19 -9 41 9 -5 20 16 27 -20 34 34 80 68 -60 -78 -15 18 -12 27

-32

23

40

The first factor can be thought of as a measure associated with the climbing and descending traffic in the sector, with high factors loadings for variables associated with climb and descend.

•

The second factor can be thought of as a flight measure associated with both the flight concentration and the cruising aircraft in the sector.

•

The third factor is difficult to interpret.

Table 11d. The loadings of the first three components after a VARIMAX rotation Maastricht ACC Variable

Principal Factor 1

Principal Factor 2

Principal Factor 3

Flights Entering in Descend Flights Exiting in Descend

95 93

10 -7

23 -8

32

Flights Enter from same ATC unit Flights Exit to another ATC unit Flights Exiting in Climb Continuous Climb Profile Cruise-Descend Flight Profile Continuous Descend Profile Total Descend Flight Time Flights in busiest 30 minutes Flights Entering in Cruise Geographical concentration of flights Mean Distance Travelled Mean Flight Time Total Cruise Flight Time Average Instantaneous Count Climb-Cruise-Descend Profile Flights Enter from another ATC unit Flights Exit to same ATC unit Flights Exiting in Cruise Vertical concentration Total Climb Flight Time Flights Entering in Climb Climb-Cruise-Flight Profile Average Navigational Speed Bi-directional concentration Continuous Cruise Profile Difference in Upper & Lower FLs •

92

24

15

90 88 88 85 85 84 77 75 48

16 -23 -23 9 -8 10 16 19 -33

4 10 10 35 32 15 60 45 44

-8 -8 21 29 -19 31

98 97 96 94 81 -14

11 17 12 16 14 88

9 49 -3 1 41 13 25 -55 34 0

-6 22 -4 -6 -13 -17 29 -10 19 0

87 82 70 33 52 61 1 30 11 0

The first factor can be thought of as a measure associated with the co-ordination and climbing and descending traffic in the sector. There are high factor loadings for variables relating to the entry and exit into a sector, as well as for aircraft in climb and descend.

•

The second factor can be thought of as a spatio-temporal measure associated with the movement of aircraft, particularly cruising aircraft, in the sector.

•

The third factor is difficult to interpret.

Table 11e. The loadings of the first three components after a VARIMAX rotation.

Lisbon ACC Variable

Principal Factor 1

Principal Factor 2

Principal Factor 3

Flights in busiest 30 minutes

91

13

20

33

Vertical concentration Flights Entering in Cruise Flights Enter from another ATC unit Flights Exit to another ATC unit Difference in Upper & Lower FLs Mean Distance Travelled Mean Flight Time Flights Exiting in Descend Cruise-Descend Flight Profile Flights Entering in Descend Continuous Descend Profile Total Descend Flight Time Total Climb Flight Time Flights Exiting in Cruise Continuous Cruise Profile Bi-directional concentration Average Instantaneous Count Climb-Cruise-Flight Profile Total Cruise Flight Time Climb-Cruise-Descend Profile Average Navigational Speed Flights Exiting in Climb Continuous Climb Profile Flights Entering in Climb Flights Enter from same ATC unit Flights Exit to same ATC unit Geographical concentration of flights •

86 78 63

-17 -35 -25

11 35 -16

57 -58

20 -39

50 45

-92 -93 -7 -15 15 15 -67 -6 63 67 -54 -16 26 -3 3 26 37 37 44 19

-14 -14 97 93 91 91 70 45 -68 -73 -77 -26 -25 -42 19 -50 50 50 1 20

32 31 -20 -24 -34 -34 -19 22 38 -9 -17 93 91 90 89 75 -64 -64 -11 35

26 50

-31 6

-40 9

The first factor can be thought of as a measure associated with the trade-off between concentrations of traffic in the sector, e.g. at sector boundaries, and the average movement of aircraft in the sector.

•

The second factor can be thought of as the trade-off between cruising and descending aircraft, in the sector.

•

The third factor can be thought of as the trade-off between the cruising and climbing air traffic in the sector.

In summary, factor analysis for the five ACC areas provides one with interesting details on the variables that may be of interest. One can see that certain factors occur in more than one ACC, e.g. those associated with the climbing, cruising and descending aircraft. But what is interesting is to note that the factors of interest are associated with the trade-offs between these aircraft, and the nature of the trade-off can vary between the ACCs.

34

11.

CONCLUSIONS OF THE EAM SIMULATIONS

The results of the multivariate analysis have provided some interesting insights into the factors affecting controller workload. All the analyses have shown that certain factors associated with aircraft profiles in the peak hour affect controller workload, much in line with the results from previous literature reviews mentioned in part one of this paper. However, this analysis indicates that each ACC tends to have unique factors affecting its workload. Furthermore, factor analysis indicates that there is a need to account for the trade-offs between the different aircraft profile factors, a fact that was not apparent in the literature reviews.

Based upon the results obtained thus far, there is a need to consider alternative analytical techniques for further analysis of both this data as well as data for other ACCs. Amongst the techniques to be utilised include the following: i)

weighted regression analysis to account for cross-sectional variation;

ii) spatially autocorrelated regression analysis; iii) maximum likelihood analysis; iv) panel data analysis for each sector, to account for the variables relating to the workload for each hour simulated; v) time-series analysis; vi) non-linear regression analysis.

References and Select Bibliography Andrews, J.W. and J. D. Welch (1997) Workload Implications of Free Flight Concepts, Paper presented at the First Europe/ USA Air Traffic Management R&D Seminar, Saclay, France 17-20 June, 1997. ATAG (1996) European traffic forecasts 1980-2010, ATAG: Geneva, Switzerland. Arad, B.A., Golden, B.T., Grambart, J.E., Mayfield, C.E. and H.R. Van Saun (1963) Control load, control capacity and optimal sector design, Report No. RD64-16, Atlantic City, NJ : Federal Aviation Administration. Arad, B.A., (1964a) The control load and sector design, Journal of Air Traffic Control, pp.13-31. Arad, B.A., (1964b) Notes on the measurement of control load and sector design in the Enroute environment, Report No. AD-659035, Washington DC: Federal Aviation Administration.

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Buckley, E.P., O’Connor,W.F., and T. Beebe (1969) A comparative analysis of individual and system performance indices for the air traffic control system, Report No. NA-69-40, Atlantic City, NJ : Federal Aviation Administration. Buckley, E.P., DeBaryshe, B.D., Hitchener, N. and P. Kohn (1983) Methods and measurements in real-time air traffic control system simulation, Report No. DOT/FAA/CT-83/26, Atlantic City, NJ : Federal Aviation Administration. Canadian Aviation Safety Board (1990) Report on a special investigation into air traffic control services in Canada, Report no.90-SP001, ISBN 0-662-17693-6. Chi, M.T., P.J. Feltovich and R. Glaser (1981) Categorization and representation of physics problems by experts and novices, Cognitive Science, 5, 121-152. Collins, W.E., Schroeder, D.J. and L.G. Nye (1991) relationships of anxiety scores to screening and training status of air traffic controllers, Aviation, Space and Environmental Medicine, 62, 236-240. Corker, K., Fleming, K. and J. Lane (1999) Measuring Controller Reactions to Free Flight in a Complex Transition Sector Corker K. M., Gore B. F., Fleming K. and J. Lane (2000) Free Flight and the Context of Control: Experiments and Modeling to Determine the Impact of Distributed Air-Ground Air Traffic Management on Safety and Procedures, Presented at 3 rd USA/Europe Air Traffic Management R & D Seminar, Napoli, Italy 13-16 June 2000 Couluris, G.J. and D.K. Schmidt (1973) Air traffic control jurisdictions of responsibility and th

airspace structure, in Conference on Decision and Control, 4 Annual Symposium on Adaptive Processes, pp 236-240, Institution of Electrical and Electronics Engineers, New York. The Daily Telegraph. (1999) Four planes a week in near-misses over Britain, September 16, pg 15. Dalichampt, M., Graham, R., Mahlich, S. and A. Tibichte (1995) European en-route capacity studies, EEC Task AM01, Internal Document, EUROCONTROL, Bretigny-sur-Orge, France. Davis, C.G., Danaher, J.W. and M.A. Fischl (1963) The influence of selected sector characteristics upon ARTCC controller activities, Contract No. FAA/BRD-301, Arlington, Virginia: The Matrix Corporation. Empson, J. (1987) Error auditing in air traffic control, in Information systems: Failure analysis; Proceedings of the NATO Advanced Workshop, pp. 191-198, Springer Verlag: New York.

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ECAC (1998) Integrating air traffic management - the European experience, Paper presented by 36 Contracting States of ECAC to the World-wide CNS/ATM Systems Implementation Conference, Rio de Janeiro, May 1998. EUROCONTROL EXPERIMENTAL CENTRE (1996) AMS (Air Traffic Control Model Simulation Studies) - an introduction for potential users, Internal EEC Note No. 2/AMS/1996, Model Based Simulations Sub-Division, EUROCONTROL, Bretigny-surOrge, France. EUROCONTROL (1998) Air Traffic Management Strategy for 2000+, Volumes 1&2, EUROCONTROL, Belgium. EUROCONTROL (2000)Investigating the air traffic complexity: potential impacts on workload and costs, EEC Note 11/00, Project GEN-4-E2, Performance and Economy Research, EUROCONTROL, Bretigny-sur-Orge, France. The Financial Times (1999) EU calls for unified air traffic control, Thursday 2 December. Gawron, V.J., Scheflett, S.G. and J.C. Miller (1989) Measures of in-flight workload, in R.S. Jensen (ed.) Aviation Psychology, 240-87, Gower: Brookfield, Virginia. Grossberg, M. (1989) Relation of sector complexity to operational errors. In Quarterly report of the FAA Office of Air Traffic Evaluation and Analysis, Washington, DC : Federal Aviation Administration. Gujerati, D.N. (1995) Basic Econometrics, McGraw-Hill Inc., Maidenhead, United Kingdom. Harper, R.H.R., Hughes, J.A. and D.Z. Shapiro (1991) Working in harmony: an examination of computer technology in air traffic control. In Bowers, J. and S.D. Benford (eds.), Studies in computer supported cooperative work: theory, practice and design, Amsterdam, Elsevier, North Holland. Harris, J.E. and A.J. Wilkins (1982) Remembering to do things: a theoretical framework and an illustrative experiment, Human Learning, 1, 123-136. Hopkin, V.D. (1995) Human factors in air traffic control , Taylor & Francis, London. Huey, B.M. and C.D. Wickens (Eds) (1993) Workload transition: Implications for individual and team performance, Committee on Human Factors, National Research Council, National Academy Press, Washington DC, USA. Hurst, M.W. and R.M. Rose (1978) Objective job difficulty, behavioral response, and sector characteristics in air route traffic control centres, Ergonomics, 21(9), 697-708. Jorna P.G.A.M (1991) Operator workload as a limiting factor in complex systems, Automation and Systems Issues, (eds.) J. Wise and D. Hopkin, NATO ASI series.

37

Kahneman, D. (1973) Attention and effort, Prentice Hall, Englewood-Cliffs, New Jersey, USA. Kelly, C.J., and P.J. Goillau (1996) Cognitive aspects of ATC: Experience from the CAER and PHARE simulations, Paper presented at the Eight European Conference on Cognitive Ergonomics (ECCE’8), University of Granada, Spain 10-13 September. Kinney, G.C., Spahn, J. and R.A. Amato (1977) The human element in air traffic control: Observations and analyses of the performance of controllers and supervisors in providing ATC separation services. Report No. MTR-7655, McLean, Virginia : METREK Division of the MITRE Corporation. Klein G. and B.W. Crandall (1995) The role simulation in problem solving and decision making, in Hancock, P., Flach, J, Caird, J, and Vicente, K. (Eds.) Local applications of the ecological approach to human-machine systems, Lawrence Erlbaum, Hillsdale, New Jersey, USA. Lamoureux,, T. (1999) The influence of aircraft proximity data on the subjective mental workload of controllers in the air traffic control task, forthcoming, Ergonomics. Langan-Fox, C.P. and A.C. Empson (1985) “Actions not as planned” in military air traffic control, Ergonomics, 28(11), 1509-21. Lange, D G F (1989) The Crisis of European Air Traffic Control: Costs and Solutions Wilmer, Cutler and Pickering, London/ Brussels/ Washington DC Laudeman, I.V., Shelden S.G., Branstrom, R. and C.L. Brasil (1998) Dynamic Density: an air traffic management metric, NASA-TM-1998-112226, April 1998. Majumdar A. and J. Polak, (2001) Estimating The Capacity Of Europe’s Airspace Using a Simulation Model of Air Traffic Controller Workload, Transportation research Record, 1744, Paper No. 01-3250. Magill, S. A. N. (1997) Trajectory predictability and frequency of conflict-avoiding action, Paper presented at the CEAS 10th International Aerospace Conference, Amsterdam, the Netherlands, 1997. Magill, S. A. N. (1998) The effect of direct routing on ATC capacity, , Paper presented at the 2nd Europe/ USA Air Traffic Management R&D Seminar, Saclay, France 17-20 June, 1997. Marston, P. (1998) Air controllers file record total of 'overloads', The Electronic Telegraph, Issue 1424. Mogford, R.H., Murphy, E.D., and J.A. Guttman (1993) Using knowledge exploration tools to study airspace complexity in air traffic control, The International Journal of Aviation Psychology, 4(1), 29-45.

38

Mogford, R.H., Murphy, E.D., Roske-Hofstrand, R.J., Yastrop, G. and J.A. Guttman (1994) Research techniques for documenting cognitive processes in air traffic control: sector complexity and decision making, U.S. Department of Transportation FAA Report Number DOT/FAA/CTTN94/3, Federal Aviation Administration, Technical Center, Atlantic City International Airport, New Jersey, USA. Mogford, R.H., J.A. Guttman, S. L. Morrow and P. Kopardekar (1995) The complexity construct in air traffic control: a review and synthesis of the literature, DOT/FAA/CT-TN92/22,

Department

of

Transportation/

Federal

Aviation

Administration Technical Center, Atlantic City, NJ. Morrow, D., A. Lee and J.A. Purcell (1993) Analysis of problems in routine controller-pilot communications, International Journal of Aviation Psychology, 3(4), 285-302. Muckler F. A. and S.A. Seven (1992) Selecting performance measures: “Objective” verus “Subjective” measurement, Human Factors, 34(4), 441-455. Pawlak, W.W., Brinton, C.R., Couch, L. and K.M. Lancaster (1996) A framework for the evaluation of air traffic complexity, Proceedings of the 1996 Meeting of Aeronautics and Astronautics. Raby, M., and C.D. Wickens (1993) Strategic workload management and decision biases in aviation, International Journal of Aviation Psychology, 4(3), 211-240. Rasmussen, J. (1986) Information processing and human machine interaction: an approach to cognitive engineering, North Holland, New York. Redding, R.E., J.R. Cannon and T.L.. Seamster (1992) Expertise in air traffic control (ATC): what is it, and how can we train for it?, Proceedings of the 36th Annual Meeting of the Human Factors Society, pp1326-1330, Santa Monica, California, Human Factors Society, USA. Rodgers, M. (1993) An examination of the operational error database for en-route traffic control centers, Federal Aviation Administration Civil Aeromedical Institute report. Rodgers, M.D., Mogford, R.H. and L.S. Mogford (1998) The relationship of sector characteristics to operational errors, FAA Report DOT/FAA/AM-98/14. Sarter, N.B. and D.D. Woods (1995) How in the world did we ever get into that mode? Mode error and awareness in supervisory control, Human Factors, 37(1), 5-19. Schmidt, D.K. (1976) On modelling ATC work load and sector capacity, Journal of Aircraft, 13(7), 531-7. Schmidt, D.K. (1978) A queuing analysis of the air traffic controller’s work load, IEEE Transactions on Systems, Man and Cybernetics, SMC-8(6), 492-498.

39

Seamster, T.L., R.E. Reading, J.R. Cannon, J.M. Ryder and J.A. Purcell (1993) Cognitive task analysis of expertise in air traffic control, International Journal of Aviation Psychology, 3(4), 257-283. Siddiqee, W. (1973) A mathematical model for predicting the number of potential conflict situations at intersecting air routes, Transportation Science, 7(2), 571-577. Sperandio, J.C. (1971) Variation of operator’s strategies and regulating effects on workload, Ergonomics, 14, 571-577. Sperandio, J.C. (1976) From the plane space to the air mobile space: experimental comparison between two displays of spatio temporal information, Travail Humain, 30, 130-154. Sridhar, B., Sheth K.S. and S. Grabbe (1998) Airspace complexity and its application in air traffic management, Paper presented at the 2nd. USA/EUROPE Air Traffic Management R&D Seminar, Orlando, Florida, December 1998. Stager, P., Hameluck, D. and R. Jubis (1989) Underlying factors in air traffic control incidents, in Proceedings of the 33th Annual Meeting of the Human Factors Society, Santa Monica, California, Human Factors Society, USA. Stager, P., and D. Hameluck (1990) Ergonomics in air traffic control, Ergonomics, 33, 493-499. Stager, P.M. (1991) Error models for operating irregularities: implications for automation. In Wise, J.A., Hopkin, V.D. and M.L. Smith (eds.) Automation and systems issues in air traffic control, Springer Verlag: Berlin. Stamp, R.G. (1990) The Assessment of airspace capacity, Speaking notes to the Royal Aeronautical Society Conference, London. Stein E.S. and B. Rosenberg (1983) The measurement of pilot workload. DOT/FAA/CT-TN 84/24, Department of Transportation/ Federal Aviation Administration technical Center, Atlantic City, NJ. Stein E. S. (1984) The measurement of pilot performance - a master-journeyman approach, DOT/FAA/CT-83/15, Department of Transportation/ Federal Aviation Administration Technical Center, Atlantic City, NJ. Stein E. S. (1985) Air traffic controller workload: an examination of workload probe, DOT/FAA/CT-TN 84/24, Department of Transportation/ Federal Aviation Administration Technical Center, Atlantic City, NJ. Stein E. S. (1990) Evaluating air traffic controller workload using real-time person in the loop simulation, Journal of Air Traffic Control.

40

Stein E. S. (1993) Workload for operators in complex person-machine systems, in Pelegrin, M. and Hollister, W.M. (Eds.) Concise encyclopedia of aeronautics and space systems, pp.439-449, Pergamon Press, Oxford UK and New York USA. Wickens C.D., A.S. Mavor. and J.P. McGee (Eds) (1997) Flight to the Future: human factors in air traffic control, National Academy Press, Washington DC, USA. Wickens, C.D. (1992) Engineering psychology and human performance, Harper Collins, USA. Wickens, C.D. and J. Flach (1988) Human information processing, in Wiener, E. and Nagel, D. (Eds.) Human factors in aviation, pp.111-115, Academic Press, New York USA. Wierwille W. W. and F.T. Eggemeier (1993) Recommendations for mental workload measurement in a test and evaluation environment, Human Factors, 35(2), 263-281.

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