An Assessment of General Aviation Pilot Performance During

ATSB RESEARCH AND ANALYSIS REPORT Aviation Safety Research Grant – B2004/0242

Final

An Assessment of General Aviation Pilot Performance During Simulated Flight

Dr Mark Wiggins

MARCS Auditory Laboratories, University of Western Sydney

April 2006

ATSB RESEARCH AND ANALYSIS REPORT

AVIATION SAFETY RESEARCH GRANT B2004/0242

An Assessment of General Aviation Pilot Performance During Simulated Flight

Dr Mark Wiggins MARCS Auditory Laboratories, University of Western Sydney

April 2006

Published by: Australian Transport Safety Bureau

Postal address: PO Box 967, Civic Square ACT 2608

Office location: 15 Mort Street, Canberra City, Australian Capital Territory

Telephone: 1800 621 372; from overseas + 61 2 6274 6590

Facsimile: 02 6274 6474; from overseas + 61 2 6274 6474

E-mail: [email protected]

Internet: www.atsb.gov.au

Aviation Safety Research Grants Program This report arose from work funded through a grant under the Australian Transport Safety Bureau’s Aviation Safety Research Grants Program. The ATSB is an operationally independent bureau within the Australian Government Department of Transport and Regional Services. The program funds a number of one-off research projects selected on a competitive basis. The program aims to encourage researchers from a broad range of related disciplines to consider or to progress their own ideas in aviation safety research.

The work reported and the views expressed herein are those of the author(s) and do not necessarily represent those of the Australian Government or the ATSB. However, the ATSB publishes and disseminates the grant reports in the interests of information exchange and as part of the overall safety aim of the grants program.

© University of Western Sydney

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CONTENTS

Acknowledgements ................................................................................................. v

Executive summary ............................................................................................... vi

Introduction ............................................................................................................ 1 1.1 General aviation accidents .................................................................... 1

1.1.1 Normative data.................................................................... 2 1.2 Assessing general aviation pilot performance ...................................... 3

2 Method ............................................................................................................. 7 2.1 Participants ........................................................................................... 7 2.2 The simulation ...................................................................................... 7 2.3 The simulator ........................................................................................ 8 2.4 Procedure ............................................................................................ 10

3 Results and discussion .................................................................................. 11 3.1 Data acquisition and reduction ........................................................... 11 3.2 Normative data ................................................................................... 12 3.3 Comparative analyses ......................................................................... 19

4 Conclusions.................................................................................................... 33

5 References...................................................................................................... 36

Appendix A: Map showing the route of the simulated flight............................ 38

Appendix B: Flight plan....................................................................................... 39

Appendix C: Area briefing .................................................................................. 41

Appendix D: Area forecast .................................................................................. 54

Appendix E: Demographic questionnaire .......................................................... 60

Appendix F: Instrument familiarisation slide.................................................... 66

Appendix G: Flight path diagrams – representative sample............................ 67

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DOCUMENT RETRIEVAL INFORMATION

Report No. B2005/0242

Publication date April 2006

No. of pages 96

ISBN 1 921092 49 1

Publication title An Assessment of General Aviation Pilot Performance During Simulated Flight

Author Wiggins, Mark

Organisation that prepared this document MARCS Auditory Laboratories, University of Western Sydney (ABN 53 014 069 881) Locked Bag 1797, Penrith South DC 1797

Funded by Australian Transport Safety Bureau PO Box 967, Civic Square ACT 2608 Australia www.atsb.gov.au

Acknowledgements Mark Wiggins and the MARCS Auditory Laboratories, University of Western Sydney

Abstract This study was designed to create a dataset that captured the performance of general aviation pilots during a simulated flight from Wagga Wagga to Bankstown, NSW. A total of 34 pilots were tested, ranging in experience from 25 hours of total flying experience to 8500 hours of total flying experience. Each pilot was provided with a meteorological briefing, maps, and all the equipment necessary to conduct the flight as it would occur within the operational environment. The performance of pilots was assessed at three levels of analysis, the broadest of which involved pilots’ self-reports of their performance in general, and their performance during the flight. At a more detailed level, the performance of pilots was rated by an observer across each of the five legs of the flight. A number of dimensions of performance were assessed, including the accuracy with which the aircraft was controlled, the accuracy of track-keeping, the accuracy in maintaining the prescribed altitude, and the level of fatigue management. The final level of analysis involved objective data that were recorded throughout the flight by the flight simulator.

Although the primary aim of the study was the collection of a dataset that captured performance, some comparative analyses were conducted, primarily to establish the basis for the differences in performance that were evident amongst pilots. Overall, the data indicated that performance during the flight was due less to pilots’ qualifications and recent experience and more to the stage of flight during which the assessment took place. Specifically, the final leg of the flight was associated with the greatest variability in performance and was associated with relatively poorer performance than the preceding stages of the flight. The results are discussed in terms of the impact of the nature of the task, and the impact of fatigue.

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ACKNOWLEDGEMENTS

The author acknowledges the funding support provided by the Australian Government, through the Australian Transport Safety Bureau’s Aviation Safety Research Program.

This research was made possible also through the assistance of Sandra Bollwerk, Adam Shehata, Jemma Harris and Colin Schoknecht.

The author would also like to acknowledge all of those pilots who generously gave up their time to participate in the research process.

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EXECUTIVE SUMMARY

The primary aim of this study was the development of a set of normative data that captured the performance of a sample of general aviation pilots during a simulated flight from Wagga Wagga to Bankstown via Canberra, Goulburn and Mittagong. A secondary aim was to consider the impact of pilot qualification on the performance of pilots during the five legs of the flight.

Pilots were issued a completed flight plan and all the relevant documents necessary to complete the flight, including weather information, maps, and an aircraft checklist. A total of 34 pilots were recruited to undertake the flight and the exercise was conducted as it would be expected to occur within the operational environment. The experimenter acted as the Flightwatch operator and air traffic controller where necessary, and recorded the details of the flight.

Data pertaining to in-flight performance were recorded at a number of different levels of analysis, the first of which was pilots’ own self-reports of their performance. Pilots’ performance was also rated by an observer, and assessments were made on a number of different dimensions including the accuracy with which the aircraft was controlled, the accuracy of the track flown, the accuracy in maintaining the prescribed altitude, the level of fatigue management, and the appropriateness of the communication. The final level of analysis involved objective data that were recorded each second that the simulator was in operation. For each of the five legs of the flight, a set of geographic boundaries were identified and representative data that occurred with these boundaries were summarised using measures of central tendency1.

In relation to the self-report data, pilots considered their performance in the flight simulator poorer than their performance in general. This may be explained by the difficulties that some pilots perceived in exercising control over the simulated aircraft. Indeed, of the eight dimensions assessed, aircraft control was associated with the lowest rating during the simulation. However, it should also be noted that relatively lower ratings were recorded for other variables including fuel management, fatigue management, scanning, and decision-making.

The observations of pilot performance revealed differences between perceived behaviour during the five legs of the flight. Specifically, performance during leg 5, the last leg, tended to be rated at a level consistently lower than performance during the preceding legs. Comparative analyses using pilot qualification as a between-groups factor failed to explain the basis for this difference in perceived performance.

The differences between the perceived performance of pilots in leg 5 and perceived performance during the preceding legs of the flight were further examined using the data recorded by the flight simulator. While differences were anticipated for variables such as altitude, it appeared that performance deteriorated on a range of variables, including the mean range of the heading and the mean range of the pitch angle of the aircraft. The variability in performance during the final leg of the flight

1 Measures of central tendency are statistical summaries of a set of data. The most common measure

of central tendency is the mean (average), followed by the median (the middle score in a series of rank-ordered data), and the mode (the most frequently occurring result).

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could not be explained on the basis of pilot qualification, and suggests that other factors may be impacting on performance. It was considered that these factors might include the impact of fatigue and/or the impact of the demands in conducting a stepped descent to avoid violations of controlled airspace during the approach to Bankstown airport.

Overall, the data acquired in the present study represent a useful normative dataset against which the performance of pilots can be assessed in the future. As expected, there is a significant level of variability in the performance of pilots who conducted the simulated approach. This variability was most evident during the final stage of the flight when the demands on pilots were most acute and when the impact of fatigue was most likely to occur. This represents an avenue for future research and development.

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INTRODUCTION

This report summarises the outcomes of a project to record general aviation pilot performance during a simulated flight from Wagga Wagga to Bankstown via Canberra. The specific aims of the project were to establish a baseline level of the performance of a random sample of general aviation pilots in Australia. This information is expected to represent a standard, against which the performance of future pilots could be compared.

1.1 General aviation accidents Over the past twenty years, aircraft accident and incident rates have indicated that general aviation pilots are consistently over-represented in accidents and incidents in comparison with their counterparts operating within airline operations (ATSB 2004b and 2006, Li & Baker 1999). There are a number of explanations for this disparity including the fact that general aviation pilots tend to be less experienced, in general, than airline pilots; they operate less advanced aircraft; they have access to fewer resources; and they operate within an operational environment in which there is greater exposure to convective and mechanical weather activity (Wiggins 1999).

Despite the differences between the general aviation and airline operating environments, the characteristics of those incidents and accidents that occur are relatively similar, particularly when the aetiology of these occurrences is considered from an information processing perspective. For general aviation and airline incidents, occurrences are most likely to be a function of information errors such as lapses (Sarter & Alexander 2000). Less frequent are incidents in which the errors are classified as decision-related or mistakes. However, in both general aviation and the airlines, those occurrences that are associated with mistakes tend to involve consequences, the outcomes of which are described as serious (O’Hare, Wiggins, Batt, & Morrison 1994).

The similarity between the information processing characteristics of occurrences in both general aviation and airline operations supports the contention that there is a common cognitive basis for human error, irrespective of the context in which the error occurs. The common cognitive basis of error is an important assumption associated with investigations of human performance, since it enables broad comparisons across groups (Wiegmann & Shappell 2003). However, the contextual nature of error can be lost with this type of approach, particularly where analyses of performance are based on data from post-hoc investigations of occurrences (Dekker 2003). As a result, inferences may be drawn about the performance of the broader population of operators that do not necessarily reflect the reality of the behaviour under consideration.

In addition to the potential loss of contextual data associated with post-hoc investigations of pilot performance, the data itself is often subjective in nature and, by necessity, is derived in the absence of experimental control. Consequently, the reliability and the validity of the data remain untested, although they may have been acquired using the most rigorous of post-event tests.

One of the aims of the present study was to overcome a reliance on post-hoc accident and incident data as the basis for assessing the contemporary performance

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of operators within general aviation. By using a non-experimental (descriptive) design, combined with a random sample of general aviation pilots, the study was intended to provide a set of normative data, against which the performance of general aviation pilots in the future can be assessed.

1.1.1 Normative data

By their very nature, normative data are presumed to reflect a general population and, more specifically, the range that exists within a population (Trunkey & Botney 2001). For example, in the case of personality, raw scores on a particular instrument are, in themselves, uninterpretable. The raw scores need to be compared against a set of norms to establish whether the scores obtained are within or outside the ‘normal range’ of scores for these individuals. Therefore, high raw scores on a particular instrument do not necessarily indicate that the individuals are ‘high’ on this dimension. It is only after the raw scores are compared against the normative data, that this conclusion can be drawn.

The usefulness of normative data lies partly in the ability to set a standard, against which subsequent performance can be assessed. In the case of a domain such as aviation, normative data pertaining to the performance of pilots is especially useful when changes to the nature of the environment are contemplated (Sherman, Helmreich, & Merritt 1997). Rather than a discussion based on the subjective perceptions of the potential impact on performance, it becomes possible to make valid comparisons between the performance of operators following the change and the normative data that was collected prior to the change. These changes can include the addition of equipment such as weather radar and global positioning systems or changes to procedures such as might occur following a change in the classification of airspace.

The ability to compare the performance of operators against a pre-existing standard obviates discussions based on hypothetical scenarios. It also clearly establishes the standard of performance of pilots so that interventions can be developed that might maximise improvements to the safety of the aviation system in general. Finally, it provides a useful means of establishing the validity of competencies that might be developed as a tool for the assessment of performance.

In aviation, like other high reliability – high consequence environments, changes to a system are often the subject of considerable debate and conjecture as to the appropriateness of the strategy. Much of the evidence that is drawn to support one side or another is based on evidence from situations that have occurred within a different context or, in some cases, are simply supposition. Neither approach is acceptable when it is conceded that changes will impact the safety and security of operators and their passengers. One strategy to test various suppositions and conjecture is to conduct a comparison of operator performance prior to, and following the proposed change as part of a human factors test.

In practice, human factors tests have not been widely adopted in environments such as aviation, primarily due to the costs and the time involved in conducting such comparisons. However, by establishing a set of normative data, the financial and temporal demands are reduced somewhat, to the extent that human factors tests can be conducted prior to the implementation of changes to an existing system.

It is important to note that, in the present research, the data do not provide normative data in the form of percentiles as is normally the case for personality or

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intelligence tests. To develop this type of dataset requires many hundreds of participants to ensure a distribution of responses that captures the extremes within a population. Rather, the dataset acquired in the present study is based on measures of central tendency that are designed to summarise a distribution of responses. This type of dataset enables comparisons to be conducted relatively efficiently at a later date.

1.2 Assessing general aviation pilot performance There are a number of different methods available to test pilot performance including observation, self reports, performance data, and/or psychophysiological measures. Each of the approaches has both advantages and disadvantages that, potentially, may impact the validity of the dataset. For example, in the case of observation, the data acquired are subject to the interpretation of the observer, the level of vigilance over the course of a flight, and the capacity of the observer to infer information on the basis of the occurrence or non-occurrence of a particular behaviour (Wiggins & Stevens 1999). Moreover, observations may be subject to the halo effect where the observed performance of the operator2 is influenced by the observer’s pre-existing expectation as to how the operator is likely to perform. This is also referred to as the self-fulfilling prophecy where the responses recorded by an observer appear to confirm a pre-existing perception (Wiggins & Stevens 1999).

In addition to the halo effect, the data acquired during observation trials are also influenced by performance bias, in which an operator performs beyond his/her normal range of behaviour. There are a number of explanations for this effect, including a desire on the part of the operator to meet the perceived demands of the observer, even though no demands have been explicitly articulated. Alternatively, operators may be seeking to instil amongst observers an impression in which they are perceived as capable operators. In either case, the performance of operators during observation may not reflect their performance in reality, and particularly over the longer term.

Like observations, self reports of performance are likely to be subject to a number of biases, the most significant of which relates to base rates. For example, when operators are asked to rate their performance relative to other operators of a similar level of experience, most tend to rate their own performance as above average (O’Hare 1990). This occurs despite the fact that, by definition, the performance of the majority of operators must be average.

Overestimations of self-reports of performance are presumed to occur due to the lack of knowledge, on the part of the operator, of the average level of operator performance (Goh & Wiegmann 2001). This type of information is referred to as a base rate and, in the absence of this knowledge, over or underestimations of performance are more likely to occur (Gigerenzer, Todd, and the ABC Research Group 1999). The result is possibly an inaccurate reflection of performance in comparison to the broader population of operators.

Although psychophysiological recordings can overcome the subjectivity associated with observations and self-reports, individual responses on various physiological

2 The term ‘operator’ is used here in a generic sense to describe an operator of equipment rather

than an airline or aircraft operator.

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measures tend to vary considerably and may not be simply due to the performance of a task. For example, measures such as skin conductance, heart rate, and blood pressure will yield very different results, depending on a range of factors, including the physical health of operators and their psychological reaction to assessments of performance. For some operators, assessments of performance can be associated with increased anxiety, the outcome of which is artificially elevated heart rate and skin conductance measures.

The individual variability associated with physiological measures is such that comparisons between individuals can be very difficult to establish in the absence of other forms of comparative data. As a consequence, investigations involving physiological recordings will normally impose an experimental control in which the responses of individuals will be compared across one or more sessions. This strategy overcomes the potential error associated with comparisons where the base levels (normally referred to as resting states) are quite different.

Like psychophysiological recordings, objective, numeric analyses of performance have the potential to overcome the subjectivity associated with observation and self-report measures. However, the judicious selection of objective data can also overcome problems with individual variability that may be evident in psychophysiological recordings. Unlike physiology, features of performance, such as the ability to maintain an aircraft heading, are acquired, both through initial training and through subsequent experience. Therefore, it might be argued that valid comparisons can be made across groups.

Since operators must achieve a minimum level of performance prior to being awarded a licence, the range of responses amongst licensed operators is likely to be relatively restricted about the mean. If this is the case, the mean constitutes a valid representation of the performance of the sample of operators. Moreover, it can be referred to as a standard against which the performance of a subsequent sample can be assessed.

Although it can be tempting to assess human performance objectively across a number of different variables, a distinction needs to be made between those features of performance that represent ‘good practice’ and those features that are a legislative requirement. For example, conducting a check of system features periodically during the cruise stage of a flight might be regarded as good practice. However, it is not a requirement that is explicitly mandated by legislation. By contrast, maintaining a precise track and altitude while transiting controlled airspace is a requirement as it forms part of the regulations.

The distinction between ‘good practice’ and legislative requirements is important, since it is the latter that is designed to provide the minimum safeguard for the system. While it might be expected that pilots will adopt ‘good practices’, there is an implicit assumption embodied within the legislation to the effect that, even in the absence of good practices, adherence to the legislated requirements will minimise, to an appropriate level, the risks to the system.

By their very nature, good practices are difficult to represent as a standard, since their application will be valued differently by different operators. This difference may be due to a number of factors, including the extent to which good practices were reinforced during instruction, the motivation of the individual practitioner, and the extent to which an operator associates the application of good practices with good performance.

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Given the likely variability in the application of good practices amongst pilots, and given that these practices are not prescribed by legislation, it is not possible to consider good practices as a basis for a standard amongst pilots, despite the significance of good practices in maintaining the safety and security of the aviation industry. However, it is possible to make comparisons across groups to determine the extent to which the application of ‘good practices’ impacts the performance of pilots.

In the context of the present study, ‘good practices’ were limited to:

• leaning the mixture at cruise

• reducing the power setting at top-of-climb

• using the trim wheel to assist the control of the aircraft.

While there are, undoubtedly, a number of other aspects of pilot performance that might be regarded as characteristic of ‘good practice’, it can be difficult to observe some of these activities given the nature of the environment. More importantly, the fact that a particular behaviour occurs does not necessarily ensure the application of a particular skill. For example, the fact that a pilot’s eyes are fixated outside the cockpit does not necessarily indicate the application of a particular visual scanning technique. Similarly, the execution of a diversion to avoid inclement weather does not necessarily reflect the application of an appropriate decision-making strategy.

The potential dissociation between behavioural responses and the application of cognitive skills, such as decision-making, is an important issue in the context of aviation, since the ineffective and/or inaccurate application of cognitive skills features prominently as a significant factor in general aviation aircraft accidents and incidents (Besnard & Greathead 2003). The difficulty associated with cognitive skills is that their application cannot be observed directly, but must be inferred either through a behavioural response or through some other means, such as a cognitive interview (Wiggins in press). In the case of both of these approaches, some degree of judgement is necessary to establish whether or not a response is a reflection of the application of a particular cognitive skill and, if so, whether the cognitive skill was applied appropriately, given the context.

The reliance on subjective judgement as the basis for the assessment of the application of cognitive skills has been criticised for a number of reasons, not least of which is the potential for bias on the part of the assessor. In addition, the nature of the assessment often assumes a singular approach to a problem, to the extent that any variations in the application of cognitive skills on the part of the operator are interpreted as the failure to apply a particular cognitive skill and/or the misapplication of a cognitive skill. In either case, the interpretation is problematic, since there is a great deal of evidence to suggest that experienced and proficient practitioners adopt a range of strategies in response to a domain-specific problem, each of which may be equally appropriate (Wiggins & O’Hare 2003).

Considering the potential difficulties associated with the assessment of the application of cognitive skills in applied environments such as aviation, it was considered prudent to restrict the assessment of performance to behavioural responses, consistent with the competency-based framework for pilot assessment that has been introduced by the Australian Civil Aviation Safety Authority. The nature of these data limits, to the greatest extent possible, minimises sole reliance on subjective interpretations of pilot performance.

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Those features of pilot performance that are associated with the competency-based framework of pilot assessment issued by the Civil Aviation Safety Authority include the:

• maintenance of an assigned altitude

• maintenance of an assigned heading

• maintenance of an optimal airspeed

• appropriate control of the pitch attitude of the aircraft

• appropriate control of the bank angle of the aircraft.

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2 METHOD

2.1 Participants The participants in the study consisted of 34 pilots of Australian nationality who were licensed by the Civil Aviation Safety Authority to operate aircraft in Australia. The majority of participants held private (15 participants) or commercial (14 participants) pilot licences, while one pilot had completed the general flying progress test and four held airline transport licences. Seven pilots held instrument ratings and 11 held an instructor’s rating.

Of the participants, 27 were male and 7 were female and they ranged in age from 19 to 64 ( X = 36.91, SD = 13.68) 3. They had accumulated between 25 and 8,500 total flying hours, of which between 15 and 7,500 were conducted as pilot-in-command. A summary of the experience accumulated by participants is provided in Table 1

Table 1: Summary of flying experience (in hours) across participants

Mean Standard Deviation Range

Total Hours 1338.41 2454.37 25–8500

Pilot in Command 1023.44 2040.33 15–7500

Instrument Hours 392.23 1410.35 0–6500

Cross-Country Hours 1038.97 2322.32 0–8300

90 Days (Hours) 25.07 50.57 0–210

2.2 The simulation The simulation consisted of a flight in a Cessna 172 from Wagga Wagga to Bankstown Airport in New South Wales, a distance of 207 nautical miles. The flight was designed by a qualified flight instructor and was saved as a ‘situation’ in Microsoft Flight Simulator™. The weather conditions en-route were deliberately designed as relatively benign with clear visibility.

The participants were provided with all of the resources that they would normally have available during a flight, including a flight plan, an Area Briefing (see Appendix C), an Area Forecast (see Appendix D), a World Aeronautical Chart (see Appendix A), a Visual Terminal Chart, a Visual Navigation Chart, a Cessna 172 operating checklist, and a chart that detailed the features of both the simulator controls and the location of various instruments on the computer screen (see Appendix F).

On the basis of the prevailing weather conditions, the duration of the flight was planned as 96 minutes and required pilots to depart Wagga Wagga on runway 23, make a left turn after departure and intercept a visual track to Tumut. Having located Tumut, pilots tracked towards Canberra, then Goulburn, Mittagong, and

3 X = the mean or average; SD = standard deviation, which is the average deviation of the data

about the mean.

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finally, landed on runway 29 at Bankstown Airport. There were no restrictions on pilots’ use of radio navigation aids, and the flight was to be conducted under Visual Flight Rules (VFR).

2.3 The simulator The simulator was a Cirrus II flight console manufactured by Precision Flight Controls. The control system was augmented with rudder pedals with differential braking, and a digital avionics unit (see Figures 1 and 2).

The visual display comprised a Liquid Crystal Display (LCD) projector that was fixed above the simulator and projected a three metre by three metre image 2.5 metres ahead of the participant. The pilot was seated in an enclosed fibreglass cockpit (see Figure 3) and communicated with the experimenter through a headset. The experimenter acted as both the Flightwatch operator and as the air traffic controller (ATC) as required, and sat in a control room adjacent to the flight simulator.

Figure 1: An illustration of the main components of the Cirrus II flight control system

Magnetos (behind control wheel) Views (switches to select view & button to return)

Rudder trim

Parking brake (behind control wheel)Lights

Fuel selector

Alternate air (carburettor ice)

Flap selectorElectric trimThrottle

Mixture

Propeller/pitch (not used these simulations)

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Figure 2: An illustration of the main components of the digital avionics unit

Figure 3: External view of the flight simulator

Communications 1 radio Navigation 1 radio

Automatic direction finder (ADF)

Transponder

Heading Bug

Communications 2 radio Navigation 2 radio

Altimeter

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The control room was equipped with an instructor station that was connected via an Ethernet cable to the flight simulator. It also contained video recording equipment and two monitors with which to observe the behaviour of the pilot. The two cameras to which the monitors were connected were located in the flight simulator and were positioned so that one captured the behaviour of the pilot from the front, while the other was positioned over the pilot’s right shoulder. This ensured that the behaviour of pilots was captured from two angles.

2.4 Procedure On arrival at the laboratory, participants were advised that they would be asked to conduct a simulated flight between Wagga Wagga and Sydney, and that they should conduct the flight as they would within the operational environment. They read the information sheet, completed the consent form, and completed the first stage of the demographic questionnaire (see Appendix E). They were subsequently given a 20 minute self-managed opportunity for briefing in which they were encouraged to examine a previously developed flight plan, the weather information current for the flight, Notices to Airmen (NOTAMS), and maps.

Following the completion of the briefing session, pilots were seated in the simulator and the experimenter indicated the features of the simulator, including the visual displays, the various instruments available, the system controls, and the process for communication. Once participants indicated that they were comfortable with the features of the simulator, the experimenter loaded a trial flight that positioned the aircraft at 3,000 feet above Wagga Wagga in the cruise. The participants were encouraged to become familiar with the ‘feel’ of the simulator by moving the controls and placing the aircraft in a variety of orientations. The duration of the trial flight was 5 minutes, after which time, the experimenter positioned the aircraft at the threshold of Runway 23 at Wagga Wagga in preparation for departure.

Participants were encouraged to depart on the flight only when they felt comfortable with the situation. Having departed Wagga Wagga, they flew the flight based on the flight plan that they were issued at the outset. This required a left turn to track to Bankstown via Tumut, Canberra, Goulburn, and Mittagong. Having completed the flight, participants were thanked for their participation and paid $40.00 to reimburse them for their time.

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3 RESULTS AND DISCUSSION The results section is divided into three broad areas that reflect the research questions that were proposed at the outset. The first of these areas represents a summary of the normative data in relation to performance during the flight. No comparisons were intended, other than as a benchmark against which future data sets could be assessed. The second section involves a series of comparisons based on the licence held by the participant at the time of testing. The aim was to consider the extent to which different licence categories, controlling for recent experience, were associated with different levels of performance during the simulated flight. The final section involves comparisons of performance at different stages throughout the flight. In this case, the aim was to establish the extent to which performance deteriorates as the flight progresses.

3.1 Data acquisition and reduction Data were acquired from three sources, including participants’ subjective ratings, subjective observations made by the experimenter, and the flight simulator. In the case of the subjective ratings, data were tabulated and were summarised using appropriate measures of central tendency. In the case of the data from the flight simulator, data relating to 200 variables were recorded every second for the period of the flight. For the purposes of the present study, the flight was divided into five legs, consistent with observations of performance that were made by the experimenters. While this strategy resulted in some loss of variability in the data, it enabled comparisons across groups.

The variables recorded by the flight simulator that were considered relevant to the present study included:

• airspeed

• altitude

• communications radio frequency

• navigation radio 1 frequency

• latitude

• longitude

• attitude indicator pitch

• bank angle

• heading

• throttle lever position

• mixture lever position.

Data were extracted for each of the five legs of the flight by establishing geographic boundaries within which the data would be summarised. These geographic boundaries were defined on the basis of the latitude and longitude so that for Leg 1, data were recorded when the aircraft was located between 35 and 36 degrees south latitude and between 147 degrees, 45 minutes and 148 degrees east. The five legs and their associated geographic boundaries are listed in Table 2.

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Table 2: Summary of geographic boundaries for each of the legs of the flight

Latitude Boundaries Longitude Boundaries

Leg 1 35 00’S to 36 00’S 147 45’E to 148 00’E

Leg 2 35 00’S to 36 00’S 148 45’E to 149 00’E

Leg 3 35 00’S to 36 00’S 149 15’E to 149 30’E

Leg 4 34 00’S to 35 00’S 150 00’E to 150 15’E

Leg 5 34 00’S to 35 00’S 150 36’E to 150 51’E

The aim in establishing geographic boundaries was to capture a representative sample of the behaviour of the participant across a particular leg of the flight. In the case of each leg, the size of the geographic area was identical and the advantage of this approach was that it captured performance, even if participants were not necessarily maintaining the required track.

The disadvantage associated with use of geographic boundaries was that, potentially, some participants could generate a far greater number of data points than others, depending upon their behaviour within the region. However, the geographic boundaries generated a minimum of 352 data points for each variable across each leg of the flight. Moreover, the boundaries were selected so that the data acquisition occurred once flight along the leg had been established. In combination with the number of data points that were generated, it can be concluded that the summary data accurately reflected the performance of pilots and that valid comparisons can be made across individuals.

3.2 Normative data At various stages throughout the study, three types of data were acquired: (1) Subjective perceptions; (2) subjective observations; and (3) objective recordings of inputs to the flight simulator. This approach was designed to both capture different types of information and provide a level of triangulation for those data that were acquired.

In relation to subjective perceptions of performance, participants were asked to rate, prior to the flight, their perceptions of their capacity to: • plan a flight • maintain control of an aircraft • make aeronautical decisions • manage fatigue • manage fuel • navigate • communicate • scan the environment visually.

Having completed the flight, participants were asked again to rate these dimensions. However, in this case, they were asked to consider the dimensions in terms of their performance during the simulation. For each of the dimensions, performance was rated on a seven-point Likert scale, ranging from ‘extremely poorly’ (1) to ‘extremely well’ (7). A summary of the responses is provided in Tables 3 and 4.

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Table 3: Summary of subjective perceptions of performance in general


Flight Planning 5.79 0.08 3

Aircraft Control 5.59 0.96 4

Decision-Making 5.32 0.95 4

Fatigue Management 5.26 1.14 4

Fuel Management 6.09 0.97 4

Navigation 5.65 0.73 3

Communication 5.50 1.08 5

Scanning 5.53 0.86 3

Table 4: Summary of subjective perceptions of performance during the simulated flight


Flight Planning 4.06 1.34 5

Aircraft Control 3.41 1.35 5

Decision-Making 4.76 1.05 4

Fatigue Management 4.97 1.06 4

Fuel Management 4.68 1.67 5

Navigation 4.12 1.56 5

Communication 3.76 1.08 6

Scanning 4.18 0.86 5

In comparing Tables 3 and 4, what is immediately clear is that subjective perceptions of performance during the simulation were lower than the subjective perceptions of performance overall. This difference may be explained, in part, by the nature of the simulation, since pilots considered the flight as only marginally ‘realistic’ (scale of 1-7) ( X = 4.21, SD = 1.27), and perceived the simulator as only marginally consistent with the ‘operational environment’ (scale of 1-7) (X = 4.24, SD = 1.84). This is useful information to take into account when making judgements about the capacity of pilots to function in the simulated environment and their capacity to function within the operational environment.

In addition to subjective perceptions, pilot performance was rated by the experimenter at various stages throughout the flight. These stages were defined on the basis of the legs of the flight, which were:

1 Wagga Wagga to Tumut

2 Tumut to Canberra

3 Canberra to Goulburn

4 Goulburn to Mittagong

5 Mittagong to Bankstown.

13

A number of dimensions of performance were rated on the basis of the observed behavioural responses of pilots, including:

• the accuracy of the control exercised over the aircraft

• the accuracy of track keeping

• the accuracy in maintaining altitude

• the management of fatigue

• the appropriateness of the communication.

To ensure that the data represented information that was interpretable, a series of reliability coefficients were calculated for each of the variables across the five legs at which observations were made. An inspection of the alpha coefficients in Table 5 indicates that, for the accuracy of aircraft control, altitude, fatigue management, and communication, the level of reliability across the different legs of the flight was very high. The results associated with the accuracy of the track suggested that there was some variability in the observations.

Table 5: Reliability analysis of the subjective observations for each of the five legs of the flight

Cronbach’s Alpha 4 Cases

Accuracy of Aircraft Control 0.91 33

Accuracy of Track 0.76 33

Accuracy of Altitude 0.94 33

Accuracy of Fatigue Management 0.93 33

Accuracy of Communication 0.95 33

It is important to note that, although performance in relation to some of the variables could be assessed against an objective criterion (eg, altitude, aircraft control, and communication), it was more difficult to assess features such as fatigue management. Nevertheless, indicators of fatigue were available, and included rubbing of the eyes, movement in the seat, excessive shuffling of documents, and microsleeps. The identification of these behaviours occurred through observations of the video recordings.

Having established the reliability of the observation data, the results can be interpreted with some confidence. However, it is important to note that the results are based on subjective perceptions and should be interpreted in terms of the relative value across the flight as a whole, and not necessarily as absolute indicators of performance. Table 6 lists the mean subjective observations for each of the dimensions of performance, distributed across of the five legs of the flight.

4 Cronbach’s alpha is a statistical technique that is designed to establish how well a single set of

data relate to a single dimension. Where the value is closer to 1.0, it is assumed that the data are all measuring the same feature.

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Table 6: Mean subjective observation ratings for the various dimensions of performance, distributed across the five legs of the flight

Leg 1 Leg 2 Leg 3 Leg 4 Leg 5

Aircraft Control 4.00 4.30 4.42 4.67 3.70

Track 4.48 4.27 5.03 4.67 3.76

Altitude 4.29 4.15 4.45 4.06 3.64

Fatigue Management 4.67 4.42 4.52 4.27 3.73

Communication 4.30 4.52 4.67 4.70 4.00

The results associated with Table 6 reveal a relatively consistent pattern in which experimenter ratings of performance were lower for Leg 5 than for any of the preceding legs. The extent to which these observations reflect actual performance can be established through the flight simulation data.

From a normative perspective, the flight simulation data represent ‘snapshots’ taken at various stages throughout the flight. These snapshots enable inferences to be made about pilots’ behaviour. In the case of the present study, data were available that enabled assessments relating to a number of behaviours, from the capacity to maintain altitude and track, to the leaning of the mixture to optimise fuel consumption.

In the case of pilots’ capacity to maintain physical control of the aircraft, two elements were considered, the first of which related to their capacity to maintain a prescribed track and altitude. Data were summarised for each of the five legs of the flight and related to the mean altitude that was maintained during the leg. In the case of the first four legs of the flight, the geographic boundaries that established the area within which data were acquired were designed so that the required altitude would remain 5,000ft above mean sea level 5. The final leg of the flight captured the stepped-descent that is necessary to remain outside controlled airspace on approach to Bankstown airport. Clearly, the required heading changed as the course of the flight progressed. A summary of the mean altitude and the mean heading for each leg of the flight is provided in Table 7, together with the required altitude and heading.

Although the absolute means for variables such as altitude and heading have some value in terms of indicating levels of performance, normative information is better derived by considering a second element in maintaining physical control over a dynamic system: the range of responses. Consequently, both the minimum and maximum responses for altitude were examined across the five legs of the flight (see Table 8). The distribution reveals a considerable degree of variability in responses suggesting that some pilots experienced difficulty in maintaining the required altitude.

5 The QNH was identical for all participants.

15

Table 7: Mean altitude and mean heading and optimal altitude and heading, distributed across the five legs of the flight 6

Leg Mean Actual (SD) Required

Altitude 1 5350.58 (759.89) 5,000 ft

2 5446.07 (464.55) 5,000 ft

3 5582.87 (277.01) 5,000 ft

4 5481.66 (442.91) 5,000 ft

5 3491.11 (958.57) Variable Descent

Heading 1 90.40 (14.31) 098

2 87.00 (13.03) 081

3 33.72 (19.73) 026

4 52.65 (12.42) 050

5 41.69 (41.56) 015

Table 8: Minimum and maximum responses for altitude, distributed across the five legs of the flight

Leg Minimum Maximum Leg Minimum Maximum

1 2359.36 6484.41 4 3443.56 6365.70

2 3308.01 6181.46 5 966.63 5922.43

3 4258.97 6519.88

To further investigate the variability in pilot performance, the mean range of performance was considered, for altitude and headings, across the five legs of the flight. The results for altitude and heading are summarised in Table 9 and illustrate that there is a considerable range of performance amongst individual pilots. This is likely to be a reflection of the length of the flight and the inherent difficulties in maintaining control over a dynamic system. However, it also suggests it is unrealistic to assume that pilots operating general aviation aircraft, which are not equipped with an autopilot, can maintain, with a high level of accuracy, a prescribed altitude or heading over an extended period of time.

Table 9: Mean range for altitude and heading, distributed across the five legs of the flight

Altitude Leg Range (SD) Heading Leg Range (SD)

1 510.65 (336.18) 1 34.61 (25.07)

2 421.74 (301.06) 2 35.43 (26.59)

3 447.82 (266.69) 3 62.62 (101.75)

4 513.85 (369.43) 4 60.25 (102.10)

5 2259.25 (703.46) 5 142.85 (156.08)

6 See Appendix G for representative figures of the altitude and track flown by participants.

16

In addition to the maintenance of a prescribed altitude and headings, pilots are expected to manage other aspects associated with the performance of the aircraft, including the rate at which fuel is consumed, and the appropriate use of the elevator trim. In the case of fuel efficiency, pilots would normally be expected to optimise the ratio of fuel and air that resides in the engine cylinder prior to combustion and this requires the pilot to ‘lean the mixture’ once a cruising altitude has been established. Across of the five legs of the flight, 38.7% of pilots undertook a process of leaning the mixture. Amongst the remaining pilots, the rate of fuel burnt was higher than was necessary.

The use of the elevator trim differed considerably across different individuals and across different legs of the flight, despite the fact that the physical demands on the aircraft were identical. Table 10 lists, for each setting for the trim tab, the frequency of pilots who selected the position as their mean response, distributed across the five legs of the flight.

Table 10: Each position of the trim tab (degrees rounded to two decimal points) and the number of pilots who selected this position as their mean response, distributed across the five legs of the flight 7

Trim tab Leg 1 Leg 2 Leg 3 Leg 4 Leg 5

0.00 4 3 2 2 3

0.01 2 2 3 3 1

0.02 4 3 3 2 5

0.03 1 5 6 5 6

0.04 5 5 5 5 4

0.05 7 4 2 4 3

0.06 0 2 2 1 1

0.07 1 2 3 1 1

0.08 1 1 0 2 2

0.09 2 0 0 0 0

0.10 2 1 1 1 0

0.11 1 1 0 1 2

0.12 0 0 1 0 0

0.13 0 0 0 1 1

0.14 0 1 1 0 0

0.15 1 0 0 0 0

Consistent with the variability associated with the control of the aircraft, considerable variability was also evident amongst pilots in terms of their use of the radio and their use of radio navigation aids. Although the flight was simulated, pilots were asked to use the radio as they would in actual flight. This was expected to yield a relatively consistent approach to the use of the radio amongst pilots. Table 11 lists, for each leg of the flight, the number of pilots who changed the frequency of the communications radio and the number of pilots who did not

7 Extreme outliers were excluded from this distribution.

17

change the frequency during each leg of the flight. It is important to note that no judgements are being made as to the appropriateness or otherwise of particular behaviour. What is of interest is the level of variability in what might otherwise be referred to as a relatively standardised procedure.

Table 11: The number of pilots who changed (‘yes’) the frequency of the communications radio and the number of pilots who did not change (‘no’) the frequency, distributed across the five stages of the flight

Leg Yes No Leg Yes No

1 9 22 4 3 27

2 4 26 5 10 20

3 11 19

Although the flight was conducted under Visual Flight Rules, this does not preclude the use of radio navigation aids to assist the maintenance of a track. However, the results indicated that 13% of participants changed the frequency of the radio navigation aid in leg 1, 6.5% changed the frequency in leg 2, 2.2% changed the frequency in leg 3, 13% changed the frequency in leg 4, and 8.7% changed the frequency in leg 5. In the majority of cases, the failure to change the frequency of the navigation aid is a reflection of the disuse of these aids as a basis for assisting navigation.

Consistent with the results for altitude and heading, there was considerable variability amongst participants in terms of the airspeed that was maintained throughout the five stages of the flight. The mean airspeed ranged from a minimum of 85.65 kts in leg 3 to 125.21 kts in leg 5. The mean airspeed and the associated mean ranges for each of the five legs of the flight is listed in Table 12.

Table 12: The mean airspeed and mean range of airspeeds, distributed across the five legs of the flight

Leg Mean Airspeed (kts)

Mean Range (kts)

Leg Mean Airspeed (kts)

Mean Range (kts)

1 102.38 22.73 4 104.38 20.78

2 103.71 22.40 5 109.82 33.09

3 102.69 21.92

Overall, the subjective and objective normative data provide a picture of pilot performance in which there is a considerable degree of variability, despite the fact that all of the pilots experienced the same conditions and had all met the minimum requirements for the award of a licence. The variability in performance is not, altogether surprising, given the range of individuals who apply for and ultimately, are awarded a pilots licence. However, the variability does point towards a potential lack of standardisation in conducting various activities associated with an aircraft flight. The question is whether the variability in performance can be explained by other factors such as the level of licence that has been attained and/or the recent experience of the pilot.

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3.3 Comparative analyses The comparative analyses in the present study were limited to comparisons between participants in different licence categories and performance across the five legs of the flight. Since there were two licence categories that comprised relatively small numbers of participants, the licence categories were collapsed so that one group (non-commercial) contained pilots who had attained either a private licence or who had completed the general flying progress test, while the other (commercial) contained pilots who had attained either a commercial or airline transport pilots licence. This resulted in 16 participants in the non-commercial licence group and 18 participants in the commercial licence group.

To reduce the likelihood of a Type I error8, analyses were undertaken using a series of mixed-methods analyses of covariance. In the case of the pilots’ subjective perceptions of their performance, the manipulated variables included two levels of pilot group based on their qualifications (private licence and commercial licence) as a between-groups factor and two levels of rating (‘general’ and ‘during the flight’) as a within-groups factor. The measured variables included flight planning, aircraft control, decision-making, fatigue management, fuel management, navigation, communication, and scanning.

In the case of flight planning, the results of the analysis of covariance (ANCOVA9) failed to reveal a main effect for pilot qualification (F (1, 31) = .315, p = .58)10 or an interaction between pilot qualification and the subjective ratings (F (1, 31) = .971, p = .33). However, a statistically significant main effect was evident for subjective ratings (F (1, 31) = 49.45, p = .00) in which flight planning ratings for the simulated flight were significantly lower than the ratings for general performance. A similar pattern of results was evident for subjective ratings for aircraft control (F (1, 31) = 87.76, p = .00), fuel management (F (1, 31) = 28.99, p = .00), navigation (F (1, 31) = 42.44, p = .00), and communication (F (1, 31) = 25.58, p = .00). In each case, the mean subjective rating in general was significantly greater than the subjective rating for the simulated flight. No other main effects or interactions were evident.

Of some interest in the present study was the fact that no main effects or interactions were evident for either pilot qualification or subjective ratings for perceptions of fatigue management and decision-making. These results suggest that,

8 Inferential statistical techniques such as Analysis of Variance are based on probabilities. Where a

statistically significant difference between two means is established, it is argued that the probability of the difference occurring given that there is no difference between the means in reality, falls below a level that is set prior to the analysis (typically 1 in 20 or .05). However, since this is a probabilistic reference (.05), there remains the possibility that the difference evident is due to chance and not due to actual difference. This possibility is referred to as a Type I error and normally the intention is to reduce this likelihood by further reducing the probabilistic reference (also referred to as critical alpha), or by undertaking multiple comparisons at once, such as occurs with the analysis of covariance.

9 The Analysis of Covariance (ANCOVA) is a statistical technique that is designed to test differences between the means of two or more groups while taking into account the influence of another variable. In effect, it ensures that any difference identified as part of the analysis is not due to the influence of some other factor.

10 F = Degrees of Freedom, which is the extent to which the data can vary; p = the probability that the effect would have occurred had there been no difference between the means.

19

for these variables, participants did not perceive differences between their performance in general and their performance during the simulated flight. This contrasts with the results for those variables which tended to relate directly to the management of the aircraft (e.g. aircraft control).

Consistent with the analysis of the results associated with pilots’ subjective ratings of performance, the observations of pilots’ performance was also examined using a series of mixed-methods analyses of covariance. In each case, the manipulated variables comprised two levels of pilot qualification (non-commercial and commercial) as a between-groups factor, and the five legs of the flight as a within-groups factor. The number of flight hours accumulated in the 90 days preceding participation in the study was used as a covariate to control for the recent experience of pilots. The measured variables included observations of aircraft control, aircraft track, aircraft altitude, fatigue management, and communication.

The results associated with the level of control exercised over the aircraft revealed a statistically significant main effect (sphericity assumed) for the leg of the flight (F (4, 120) = 5.32, p = .001) and a statistically significant interaction between the leg of the flight and pilot qualification (F (4, 120) = 4.27, p = .003). There was no main effect for pilot qualification (F (1, 30) = 0.26, p = .613). Post-hoc tests, using the Bonferroni Correction11, indicated that the main effect for the leg of the flight was most likely due to a difference between the observation ratings for legs 3 and 5 (see Figure 4).

Figure 4: Mean observation ratings for aircraft control, distributed across the five legs of the flight

Leg 1 Leg 2 Leg 3 Leg 4 Leg 50.00

1.00

2.00

3.00

4.00

5.00

Mea

n

11 The Bonferroni Correction is a statistical technique that is usually applied following an Analysis

of Variance or Analysis of Covariance to identify where differences exist between means when there are more than two groups being compared.

20

The interaction evident between observation ratings of aircraft control and the leg of flight revealed a pattern in which non-commercial pilots tended to be associated with a relatively higher level of aircraft control during the initial stages of flight, but were associated with relatively lower levels of aircraft control during the latter stages of the flight, in comparison to commercial pilots (See Figure 5). It is also useful to note that the pattern of deterioration in the observed accuracy of aircraft control during the latter three stages of flight was relatively consistent for both groups, suggesting that there may have been factors other than pilot qualification that were influencing the relative level of control over the aircraft. However, it is also the case that higher levels of qualification appeared to mediate, to some extent, the reduction in observed performance.

Consistent with the results associated with level of control exercised over the aircraft, a main effect for the leg of flight (F (4, 120) = 4.61, p = .002) and an interaction between flight leg and pilot qualification (F (4, 120) = 2.65, p = .037) were evident for observed performance in maintaining the optimal altitude. Post-hoc tests using the Bonferroni correction indicated that the main effect lay between the mean observed performance during leg 3 and the mean observed performance during leg 5 (see Figure 6).

Figure 5: Mean observation ratings for aircraft control for non-commercial licence and commercial licence participants, distributed across the five legs of the flight

1.00 2.00 3.00 4.00 5.00

Leg of the Flight

0.00

1.00

2.00

3.00

4.00

5.00

Mea

n

Non-CommercialCommercial

21

Figure 6: Mean observation ratings for accuracy of aircraft altitude, distributed across the five legs of the flight


1.00

2.00

3.00

4.00

5.00

Mea

n

The analysis of the interaction between the leg of the flight and pilot qualification revealed a pattern of performance in which pilots who held non-commercial licences were associated with a relatively lower level of performance during the final leg of the flight (see Figure 7). By contrast, the relative deterioration in observed performance amongst commercial pilots was less pronounced for the final leg of the flight but more pronounced than non-commercial pilots between legs 3 and 4.

22

Figure 7: Mean observation ratings for aircraft altitude for pilots with non-commercial and commercial licences, distributed across the five legs of the flight.

1.00 2.00 3.00 4.00 5.00

Leg of the Flight

0.00

1.00

2.00

3.00

4.00

5.00

Mea

n

Non-CommercialCommercial

Although interactions were evident between the leg of the flight and pilot qualification for both observed aircraft control and observed accuracy in maintaining altitude, no interaction was evident for observed performance in maintaining the track (F (4, 120) = 1.97, p = .103). Neither was there a main effect for pilot qualification (F (1, 30) = .632, p = .433). The only main effect evident was for the leg of the flight and post-hoc tests using a Bonferroni correction indicated that the differences lay between leg 3 and leg 5 and between leg 4 and leg 5 (see Figure 8). This suggests that leg 5 is associated with a relatively marked deterioration in perceived performance that was not observed during the preceding legs of the flight.

23

Figure 8: Mean observation ratings for accuracy of aircraft track, distributed across the five legs of the flight.


1.00

2.00

3.00

4.00

5.00

6.00

Mea

n

The results associated with observations of pilot communication were consistent with the results involving observed track in which there was no main effect for pilot qualification (F (1, 29) = .499, p = .486) and there was no interaction evident between pilot qualification and the leg of the flight (F (4, 116) = 1.31, p = .270). However, a main effect was evident for the leg of the flight (F (4, 120) = 5.29, p = .001). Post-hoc tests using a Bonferroni correction indicated that the difference lay between leg 4 and leg 5 (see Figure 9). This outcome is consistent with previous comparative analyses in which an apparent deterioration in performance is observed during leg 5.

The preceding comparative results may be explained, in part, by observations in relation to the management of fatigue. In particular, a main effect was evident for both pilot qualification (F (1, 30) = 4.66, p = .039) and the leg of the flight (F (4, 120) = 10.92, p = .00), although there was no interaction evident between the two variables (F (4, 120) = 1.05, p = .38). An inspection of the means for non-commercial and commercial pilots indicated that the mean level of fatigue management observed amongst commercial participants ( X = 4.72, SE = .255) was greater than the mean level of fatigue management amongst participants with non-commercial licences ( X = 3.90, SE = .263)12. This outcome suggests that, in comparison to pilots who were classified as non-commercial, pilots who were classified as commercial exhibited relatively fewer behaviours that were associated

12 In this case, the Standard Error (SE) is used instead of the Standard Deviation (SD), since it

represents the standard deviation of multiple sample means. The SD is used only when there is one sample mean under consideration.

24

with the onset of fatigue and that this relative effect remained consistent across the flight

Figure 9: Mean observation ratings for appropriateness of pilot communication, distributed across the five legs of the fligh


1.00

2.00

3.00

4.00

5.00

Mea

n

Despite the differences between pilots on the basis of their qualifications, the results pertaining to the management of fatigue also revealed differences according to the various legs of the flight. In particular, a post-hoc test using a Bonferroni correction indicated that the main effect associated with the legs of the flight was due to differences between the performance observed during leg 5 and the performance observed during the preceding legs of the flight.

25

Figure 10: Mean observation ratings for the level of fatigue management, distributed across the five legs of the flight


1.00

2.00

3.00

4.00

5.00

Mea

n

Figure 10 indicates that observation ratings associated with fatigue management were lowest in leg 5 compared to the preceding legs. These results may explain the deterioration in observed performance that was evident for aircraft control, aircraft altitude, aircraft track, and communication during leg 5. Specifically, fatigue may have resulted in an overall deterioration in performance that may have been manifest in specific behaviours such as the capacity to control the aircraft and the capacity to maintain the accuracy of the aircraft altitude and track.

In interpreting the observational data, it is important to note that the observed ratings were subjective and that the experimenters were not blind to the qualifications of the participant. Therefore, some of the results may have been due to a perceived halo effect, in which observations of pilots’ performance were influenced by expectations of their capabilities as a result of their qualifications (Wiggins & Stevens 1999). More conclusive interpretations require some augmentation of the observation data with more objective information such as might be derived from the flight simulator.

Consistent with the analyses of the subjective data, comparisons between the performance of pilots on the objective data involved a mixed-methods analysis of covariance incorporating two levels of pilot qualification (commercial and non-commercial) as a between-groups factor and the five legs of the flight as a within-groups factor. The number of flight hours accumulated in the 90 days preceding involvement in the study was included as a covariate to control for the impact of recent experience.

In relation to the mean altitude that was maintained by pilots across the five legs of the flight, a mixed-methods ANCOVA failed to reveal a main effect for pilot qualification (F (1, 27) = 1.48, p = .235) or an interaction between pilot

26

qualification and the leg of the flight (F (4, 108) = 1.57, p = .189). However, a statistically significant main effect was evident for the leg of the flight (F (4, 108) = 49.69, p = .000). Post-hoc tests using the Bonferroni correction indicated that the differences lay between leg 5 and the preceding legs of the flight (see Figure 11). This effect may have been due to a number of factors, not least of which was a descent to avoid transitioning into controlled airspace.

To investigate the performance of pilots in greater detail, a mixed-methods ANCOVA was employed to examine the impact of pilot qualification and the leg of the flight on the mean range of altitudes that were maintained during the flight. Where the mean range of altitudes was relatively high, it was assumed that this reflected an active decision to conduct a descent, rather than a loss of control of the aircraft per se. As might be expected, the results revealed a main effect for the leg of the flight (F (4, 108) = 66.84, p = .000) and post-hoc tests indicated that the difference lay between leg 5 and the preceding legs of the flight (see Figure 12).13

Figure 11: Mean altitude (in feet), distributed across the five stages of the flight


1,000.00

2,000.00

3,000.00

4,000.00

5,000.00

6,000.00

Mea

n

13 Under normal circumstances, some of the responses of participants in Leg 5 might be considered

outliers. However, given the nature of questions being asked as part of this study, it was decided to retain the outliers, even though this will inevitably skew the distribution. The use of parametric statistical analysis, although tenuous with a skewed distribution, nevertheless, enables the inclusion of covariates in this case.

27

Figure 12: Mean altitude range (in feet), distributed across the five stages of the flight


500.00

1,000.00

1,500.00

2,000.00

2,500.00

Mea

n

In combination with the results pertaining to the mean altitude, the results arising from an analysis of the mean altitude range suggests that pilots were exercising a decision to descend, probably as a means of avoiding controlled airspace. However, the apparent variability in performance was not explained by the qualifications of the pilots as there was no main effect for pilot qualification (F (1, 27) = .038, p = .847) or interaction for pilot qualification and the leg of the flight (F (4, 108) = .46, p = .765).

While it might be argued that the effects in relation to the mean altitude range were a product of differences amongst pilots in the location at which the descent was initiated, the rate at which the descent was initiated, and/or the minimum altitude at which the aircraft was flown, the decision to initiate a descent, in and of itself, would not necessarily be expected to impact other variables such as mean range of heading. However, a mixed-methods ANCOVA with the mean heading range as the dependent revealed a pattern of results similar to those observed for the mean altitude. In particular, a main effect was evident for the leg of the flight (F (4, 108) = 4.85, p = .001) but there was no main effect for pilot qualification (F (1, 27) = 1.27, p = .270) and there was no interaction between pilot qualification and the leg of the flight (F (4, 108) = .629, p = .643). A post-hoc test using a Bonferroni correction indicated that the main effect for the leg of the flight was due to differences between legs 1 and 2, and leg 5 (see Figure 13).

28

Figure 13: Mean heading range (in degrees), distributed across the five stages of the flight


30.00

60.00

90.00

120.00

150.00

Mea

n R

ange

In addition to the marked increase in the mean range of heading during leg 5, it is also important to note the apparent increase in range for legs 3 and 4. This suggests that factors, in addition to a controlled descent may have been impacting pilots’ performance during the latter legs of the flight. This assertion can be tested using data that relates directly to the control of the aircraft, including the range in airspeed, range of bank angle, and range of pitch angle.

A mixed-methods ANCOVA with the mean range in airspeed as the dependent variable revealed a main effect for the leg of the flight (F (4, 108) = 4.74, p = .001). There was no main effect for pilot qualification (F (1, 27) = 2.00, p = .168) and there was no interaction evident between pilot qualification and the leg of the flight (F (4, 108) = 1.22, p = .304). Post-hoc tests using the Bonferroni correction indicated that the main effect for the leg of the flight was due primarily to a difference between the mean range in airspeeds for legs 1, 3, and 4, and the mean range in airspeed for leg 5 (see Figure 14). This pattern of results is consistent with the assertion that the performance of pilots in the final stage of the flight involved factors other than the decision to descend to avoid controlled airspace.

29

Figure 14: Mean airspeed range (in knots), distributed across the five stages of the flight


10.00

20.00

30.00

40.00

Mea

n R

ange

To establish whether the effects observed in relation to performance during leg 5 of the flight related to fundamental features associated with the control of the aircraft, the performance of pilots was examined in terms of the mean range of bank angle and the mean range of pitch angle that occurred throughout the five legs of the flight. The results of a mixed-methods ANCOVA with the mean range of bank angle as the dependent variable failed to reveal a main effect for the leg of the flight (F (4, 108) = 2.06, p = .091). Similarly, there was no interaction evident between pilot qualification and the leg of the flight (F (4, 108) = .820, p = .515). However, a main effect was evident for pilot qualification (F (1, 27) = 12.19, p = .002) with non-commercial pilots recording a significantly greater mean bank angle range ( X = 40.23, SE = 2.99) than pilots who were classified as commercial ( X = 24.99, SE = 2.99). Although these results are not necessarily consistent with the preceding outcomes in relation to leg 5 of the flight, it should be noted that, of the mean ranges in bank angle, the greatest was associated with leg 5.

The differences evident between non-commercial and commercial pilots in the mean range of bank angle suggests that pilots with relatively higher qualifications are better able to anticipate and redress deviations from stable flight should they occur. However, this capacity was not evident in other aspects of aircraft control such as the mean range of pitch angle. Indeed, a mixed-methods ANCOVA failed to reveal a main effect for pilot qualification (F (1, 27) = .620, p = .438) or an interaction between pilot qualification and the leg of the flight (F (4, 108) = .777, p = .543). Nevertheless, there was a main effect evident for the leg of the flight (F (4, 108) = 2.62, p = .039) and post-hoc tests using the Bonferroni correction indicated that this difference lay primarily between leg 2 and leg 5 (see Figure 15).

30

Figure 15: Mean pitch angle range (in degrees), distributed across the five stages of the flight


5.00

10.00

15.00

20.00

Mea

n R

ange

In addition to the physical control of the aircraft, pilots are expected to manage other features associated with the aircraft including engine performance, communication, and navigation. Managing the performance of the engine requires pilots, once they have reached the top of climb, to: (a) lean the mixture to ensure the efficient consumption of fuel; and (b) retard the throttle to reduce demands on the engine.

A mixed-methods ANCOVA with the mean mixture setting (percentage) as the dependent variable failed to reveal a main effect for pilot qualification (F (1, 26) = .698, p = .411) or an interaction between pilot qualification and the leg of the flight (F (4, 104) = .606, p = .659). However, consistent with the previous results, a main effect was evident for the leg of the flight (F (4, 104) = 3.79, p = .006). Despite the statistically significant main effect, a Bonferroni correction failed to establish significant differences in pairwise comparisons. This is probably due to the fact that the Bonferroni correction represents a relatively conservative post-hoc test and raises the level of critical alpha for multiple tests. Nevertheless, an inspection of the means indicated that legs 1 and 5 were associated with the highest percentages – 100% being full rich – for the mean mixture setting.

Like previous results, there was a considerable degree of variability amongst pilots, with 63.3% operating the mixture at 100% in leg 1, 55.2% operating the mixture at 100% in leg 2, 48.35 operating the mixture at 100% in legs 3 and 4, and 51.7% operating the mixture at 100% in leg 5. A less marked effect was associated with the use of the throttle with 37.9% of pilots operating at a setting of 100% in leg 1, 39.3% operating at 100% in legs 2 and 3, 35.7% operating at a setting of 100% in leg 4, and 17.9% of pilots operating at a setting of 100% in leg 5.

Consistent with the results associated with the mean setting for the mixture, an ANCOVA for the mean setting of the throttle revealed a main effect for the leg of

31

the flight (F (4, 100) = 9.22, p = .000). No main effect was evident for pilot qualification (F (1, 5) = .007, p = .934) and there was no statistically significant interaction between pilot qualification and the leg of the flight (F (4, 100) = .460, p = .765). Post-hoc tests using the Bonferroni Correction indicated that the difference lay between the mean throttle setting in leg 5 and the mean throttle settings in the preceding legs (see Figure 16). This result is not surprising, since the aircraft was involved in a descent during leg 5.

Figure 16: Mean throttle setting (percentage), distributed across the five legs of the flight


20.00

40.00

60.00

80.00

100.00

Mea

n

The final analyses involved a series of chi-square comparisons that were designed to assess the relationship between pilot qualification and whether or not the communications radio frequency was changed across the five legs of the flight. The results failed to reveal any statistically significant relationships between the variables, suggesting that any differences in behaviour were not explained by the level of the qualifications of pilots who were involved (see Table 13).

Table 13: The number of pilots who changed (‘yes’) the frequency of the communications radio and the number of pilots who did not change (‘no’) the frequency, for the five stages of the flight, distributed across pilot qualification

More qualified Less qualified Leg

Yes No Yes No

1 5 10 4 12

2 2 13 2 13

3 5 10 6 9

4 2 13 1 14

5 5 10 5 10

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4 CONCLUSIONS The primary aim of this study was to develop a set of normative data that reflect general aviation pilot performance during a simulated cross-country flight. The collection of these data was expected to provide a baseline, against which the performance of pilots could be assessed in the future. It is important to note that it was not the aim of this study to make judgements about the appropriateness or otherwise of the performance that was observed. Rather, it was designed to capture the range of behaviours that pilots exhibit at various stages throughout a flight, even though some of these behaviours might be regarded as ‘poor practice’.

The data on which the assessments of performance were based comprised both subjective data in the form of pilot self-reports and experimenter observations and objective data derived from the flight simulator. A number of variables were examined that ranged in their level of analysis of the performance of pilots. For example, in the case of pilots’ self-reports, participants were asked to rate, using a Likert Scale, their performance overall, and during the simulated flight, in flight planning, aircraft control, decision-making, fatigue management, fuel management, navigation, communication, and scanning.

In general, the outcomes of the self-reports of performance indicated that pilots considered that they performed during the simulated flight at a level relatively lower than they would perform in general. The most significant deterioration in perceived performance related to aircraft control and communication. The perceived difference in the accuracy of aircraft control may have been due to the fact that the flight was simulated and that pilots may have perceived some difficulty in gaining the ‘feel’ of the simulator, despite the opportunity to gain experience in using the flight simulator during the practice flight.

At a more detailed level of analysis, experimenters rated the performance of pilots on a number of dimensions across the five legs that comprised the flight. These dimensions included the accuracy of aircraft control, the accuracy of the track flown, the accuracy in maintaining the prescribed altitude, the level of fatigue management that pilots engaged, and the appropriateness of the communication. Each of the dimensions was assessed with the benefit of an instructors’ station that mapped the progress of the flight, and two cameras that provided both a front view and an ‘over the shoulder’ view of the pilots’ behaviour.

The analysis of both the experimenter observations and the subsequent flight simulation data involved two approaches. The first of these approaches was simply to record mean responses, consistent with the primary aim of the study in establishing a set of data that described the performance of pilots. However, the second approach was designed to provide a comparative analysis and explain some of the differences that were observed during the acquisition of descriptive data. The variable on which pilot performance was compared was the level of qualification, since it was assumed that this variable would capture both differences in the level of competence of pilots and differences in the nature and extent of the experiences that pilots had accumulated. The number of flight hours accumulated in the 90 days prior to pilots’ participation in the study was used as a covariate in the subsequent series of analyses of covariance.

While it might have been opportune to examine the impact of a number of different variables on performance during the five legs of the flight, it is important to note

33

that multiple tests increase the likelihood of a Type I statistical error. Therefore, comparative analyses were limited to those variables that have been established previously as differentiating levels of pilot performance.

In the case of the experimenter observations, a relatively consistent pattern of responses emerged in which relatively poorer performance was associated with the accuracy of aircraft control, track, and altitude for leg 5 of the flight. Furthermore, the level of fatigue management and the appropriateness of the communication were also rated as relatively poorer during leg 5, in comparison to the other legs of the flight. In the case of the accuracy of the aircraft altitude, a statistically significant interaction was evident between pilot qualification and the leg of the flight. An inspection of the results indicated that the performance of non-commercial pilots exceeded the performance of commercial pilots during leg 1 but this pattern of responses was reversed in leg 5.

Despite the results associated with the accuracy of the altitude, there were no similar interactions for any of the other observational data. Therefore, it might be concluded that pilot qualification and/or experience only partly explains the apparent reduction in perceived performance that was associated with leg 5. Indeed, the flight simulation data clearly suggests that performance during leg 5 was markedly different from performance during the preceding legs of the flight. To some extent, this difference in performance might be explained by the fact that the requirements during leg 5 differed from the demands that occurred during the other legs of the flight. Unlike legs 1 to 4, leg 5 involved a descent to ensure that the aircraft remained outside controlled airspace. Around, Sydney, controlled airspace is represented by a series of steps and it is important for pilots to ensure that the descent is made prior to the edge of the step that defines controlled airspace. It is conceivable that the demands associated with this stepwise descent resulted in the variability that was evident in leg 5.

Of some note is the fact that the variability in performance that was evident in leg 5 was not confined to altitude. Increases in the mean range of values were evident for the heading, airspeed and pitch angle of the aircraft. In each case, it suggests that during leg 5, control over the aircraft was more variable than had been the case during the preceding legs of the flight. The only variable in which performance during leg 5 did not differ from the preceding legs was the bank angle. In this case, a main effect was evident for pilot qualification, in which the mean range of bank angle for non-commercial pilots was significantly greater than the mean range of bank angle for commercial pilots. This suggests that pilots with commercial and airline transport licences are better able than pilots with private licences to restrict variations in the bank angle, irrespective of the leg of the flight.

In addition to features associated with the control of the aircraft, the performance of pilots was also examined in terms of the practices that they engaged to ensure the efficiency of the flight. Consistent with the previous results, considerable variations in performance were evident for the optimal use of the throttle, leaning of the fuel/air mixture, and the use of radio navigation aids to assist visual navigation. It should be noted that a significant proportion of pilots operated the aircraft at full throttle, did not lean the mixture, and did not use radio navigation aids throughout the flight.

At one level, the results of the present study represent a baseline against which the performance of pilots could be assessed in the future. At another level, however, the results also indicate a relative deterioration in performance during the latter stages

34

of the flight. Although the present study was naturalistic and did not necessarily control for extraneous variables, it might be concluded that the deterioration in performance during the latter stages of the flight was due to a combination of the increasing demands associated with flight adjacent to controlled airspace, and the level of fatigue that was being experienced by pilots having spent the preceding 90+ minutes operating the flight simulator.

It is conceivable that the combination of fatigue and increasing demands on pilot performance during leg 5 resulted in a reduction in the precision with which the aircraft was flown. This assertion is consistent with anecdotal evidence arising from an aircraft accident involving a Piper PA-28-161 and Socata TB-9 on May 5, 2002 on approach to Bankstown Airport. The Piper was being flown from Wagga Wagga to Bankstown and was cleared to land on Runway 29C. At the same time, the Socata was cleared to conduct a touch-and-go on Runway 29L. According to the Australian Transport Safety Bureau (ATSB 2004a), the collision between the two aircraft occurred when the pilot of the Piper crossed the extended centreline of Runway 29C.

In addition to a number of operational issues and defences that were identified by the ATSB as having been involved in the occurrence, the results of the present study suggest that the combination of fatigue and the demands associated with operating within a complex environment may have impacted the pilots’ capacity to exercise precise control over the aircraft.

The issue of pilot control is further illustrated by an analysis of the causes of fatal general aviation aircraft accidents in Australia from 1991 to 2000. According to the ATSB, half of the accidents investigated were due to a collision following a loss of control of the aircraft in-flight (ATSB 2004b). This suggests that there is a need to consider, in greater detail, the relative impact of fatigue on the performance of pilots operating general aviation aircraft. Moreover, there is a need to systematically consider the impact of pilot experience and competence on the management of fatigue, particularly during the latter stages of lengthy flights and where the demands on pilots are acute.

35

5 REFERENCES ATSB—see Australian Transport Safety Bureau.

Australian Transport Safety Bureau 2004a, Bankstown midair collision: Piper PA-28-161 VH-IBK and Socata TB-9 VH-JTV. Author, Canberra, Australia.

Australian Transport Safety Bureau 2004b, General aviation fatal accidents: How do they happen? A review of general aviation fatal accidents 1991 to 2000. Author, Canberra, Australia.

Australian Transport Safety Bureau 2006, Analysis of fatality trends involving civil aviation aircraft in Australian airspace between 1990 and 2005. Author, Canberra Australia.

Besnard, D, & Greathead, D 2003, ‘A cognitive approach to safe violations’, Cognition, Technology, and Work, Vol.5, pp. 272-282.

Dekker, S 2003, ‘Illusions of explanation: A critical essay on error classification’ International Journal of Aviation Psychology, Vol. 13, pp. 95-106.

Gigerenzer, G, Todd, PM, & the ABC Research Group 1999, Simple heuristics that make us smart. Oxford University Press, Oxford, UK

Goh, J, & Wiegmann, DA 2001 ‘Visual flight rules into instrument meteorological conditions: An empirical investigation of possible causes’, International Journal of Aviation Psychology, Vol.11, pp. 359-379.

Li, G & Baker, SP 1999, ‘Correlates of pilot fatality in general aviation crashes’, Aviation, Space & Environmental Medicine, vol. 70, pp. 305-309.

O'Hare, D, Wiggins, M, Batt, R, & Morrison, D 1994, ‘Cognitive failure analysis for aircraft accident investigation’, Ergonomics, vol.47, pp. 1855-1869.

O'Hare, D 1990, ‘Pilots' perception of risks and hazards in general aviation.’ Aviation, Space, and Environmental Medicine, vol.61, pp. 599-603.

Sarter, NB, & Alexander, HM 2000, ‘Error types and related error detection mechanisms in the aviation domain: An analysis of aviation safety reporting system incident reports’, International Journal of Aviation Psychology, Vol.10, pp. 189-206.

Sherman, PJ, Helmreich, RL, & Merritt, AC 1997, ‘National culture and flightdeck automation: Results of a multination survey, International Journal of Aviation Psychology, Vol.7, pp. 311-329.

Trunkey, DD & Botney R 2001, ‘Assessing competency: A tale of two professions’, Journal of the American College of Surgeons, Vol. 192, pp. 385-395.

Wiegmann, DA & Shappell, SA 2003, A human error approach to aviation accident analysis: The human factors analysis and classification system. Ashgate, Aldershot, UK.

Wiggins, MW 1999, ‘The development of computer-assisted learning systems for general aviation’, Human performance in general aviation, ed, D O’Hare, Avebury Aviation, Aldershot, UK, pp. 153-172.

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Wiggins, MW in press, ‘Cue-based processing and human performance’, Encyclopedia of ergonomics and human factors (2nd ed), ed, W. Karwowski, Taylor and Francis, London, UK.

Wiggins, MW & O’Hare, D 2003 ‘Expert and novice pilot perceptions of static in-flight images of weather’, International Journal of Aviation Psychology, Vol.13, pp. 173-187.

Wiggins, MW & Stevens, C 1999, Aviation social science: Research methods in practice, Ashgate, Aldershot, UK.

37

APPENDIX A: MAP SHOWING THE ROUTE OF THE SIMULATED FLIGHT

38

APPENDIX B: FLIGHT PLAN

39

40

APPENDIX C: AREA BRIEFING

1103 UTC today AIRSERVICES AUSTRALIA AREA BRIEFING Prepared for: MARCS Pilot Valid from 1103 UTC today to 1103 UTC tomorrow NOTAM INFORMATION ---------------------------------- BATHURST (YBTH) C3/05 REVIEW C2/05 AWIS 133.25 CMSD AMD ERSA FAC B-42 DATED 25 NOVEMBER 2004 FROM 01 112232 TO PERM C4/05 ALL GRASSED AREAS NOT AVBL DUE SOFT WET SFC FROM 02 020439 TO 02 040500 EST COOMA (YCOM) C5/05 HBN LOC COOLRINGDON HILL (4KM NNW ARP) NOT AVBL FROM 02 231009 TO 02 280100 EST HOXTON PARK (YHOX) C2/05 REVIEW C1/05 TEMPO OBST CRANE 415FT AMSL ERECTED BRG 150 MAG 3300M FM ARP FROM 02 010045 TO 03 080100 EST JERVIS BAY (YJBY) C18/04 REVIEW C17/04 AIP ENROUTE SUP AUSTRALIA EFFECT DATE 25 NOV 2004 AMD AS FOLLOWS JERVIS BAY ENTRY FAC J-200, ATS COM FAC AMD FLW LINES TO READ: APP(1) NOWRA APPROACH 118.35(2) 123.5(3) 352.15 CEN(1) NOWRA CENTRE 132.3 325.8 FROM 11 250609 TO PERM MALLACOOTA (YMCO) C11/05 REVIEW C21/04 INCREASED NUMBERS OF LARGE KANGAROOS MAY BE PRESENT ON AD FROM 02 170105 TO 05 170100 EST ORANGE (YORG) C14/04 RWY 04/22 AND RWY 11/29 GRADIENT AND STODA CHANGES: RWY TODA 04 843(2.86) 22 843(3.14) 11 1797(1.91) 29 1816(1.68)

41

STODA: RWY 11 1703(1.6) 1797(1.9) 29 1762(1.6) FROM 12 022108 TO PERM C24/05 REVIEW C23/05 TWY TO RWY 04/22 AND OTHER GENERAL TWY NOT AVBL DUE WIP TWY TO RWY 11/29 AVBL REFER MOWP 2005/1 FROM 02 180050 TO 03 180600 EST PARKES (YPKS) C83/04 RWY 04 AND RWY 11 GRADIENT/STODA CHANGES: RWY TODA 04 1744 (2.19) 11 1683 (5.65) STODA: RWY 04 1199(1.9) RWY 11 1347(3.3) 1653(5.0) FROM 08 242146 TO PERM C127/04 REVIEW C93/04 RWY 04/22 LIRL SDBY PWR AVBL PN RWY 11/29 NO PTBL LGT AVBL FROM 12 122235 TO 03 180500 EST C10/05 REVIEW C7/05 VOR 'PKS' NOT AVBL FROM 02 202138 TO 03 010900 EST BANKSTOWN (YSBK) C266/04 REVIEW C245/04 TEMPO OBST LGT TOWER 265FT AMSL ERECTED BRG 022 MAG 2225M FM ARP FROM 12 200230 TO 02 280300 EST C20/05 REVIEW C19/05 TEMPO OBST CRANE 165FT AMSL ERECTED BRG 160 MAG 2100M FM ARP FROM 02 062037 TO 02 280800 1900-0800 DAILY C39/05 TEMPO OBST CRANE ERECTED 148FT AMSL BRG 077DEG M 1250M FM ARP FROM 02 242000 TO 02 250100 CANBERRA (YSCB) C351/04 REVIEW C244/04 MIL ACFT HANDLING AND PARKING ARRANGEMENTS. VERY LTD PRKG AND HANDLING AVBL. PPR WITH MINIMUM 24HR NOTICE FOR ALL MIL MOVEMENTS FROM DEFENCE OPS MANAGER PH 612 6127 6301 FROM 11 110614 TO PERM

42

C405/04 VOR 'CB' PILOT MNT FROM 12 130950 TO 03 130800 EST C414/04 FIRE AND RESCUE UPGRADED TO CAT 7 FROM 12 311845 TO PERM C29/05 REVIEW C24/05 FIRE AND RESCUE AMD HR OF OPS MON 1855-1210 TUE 1845-1210 WED 1845-1210 THU 1845-1110 FRI 1845-1110 SAT 1845-1140 SUN 1855-1115 FROM 01 310550 TO 04 300600 EST C46/05 REVIEW C45/05 AMD AIP ENROUTE SUPPLEMENT AUSTRALIA (ERSA) 25 NOVEMBER 2004 PAGE FAC C-86 ATS COMMUNICATIONS FACILITIES AMD NOTE 7 TWR/APP HR: MON-SAT 1930-1300, SUN 2000-1300 (1HR EARLIER HDS) FROM 02 131830 TO PERM NORFOLK IS (YSNF) C1/05 FIRE AND RESCUE AMD CAT 6 DURING HR OF SKED OPS - TUE, WED, SAT AND SUN. ON REQUEST AND OTHER TIMES CONTACT EMERGENCY SERVICE COORDINATOR: 0011 672 380 207 AMD ERSA PAGE FAC N-295 DATED 25 NOV 2004 FROM 01 252230 TO PERM NOWRA (YSNW) C283/04 REVIEW C230/04 ATS COM FAC AMD AS FOLLOWS: APP NOWRA APPROACH 123.5 352.15 243.0 ACC NOWRA CENTRE 132.3 325.8 SMCV NOWRA GROUND 122.05 AUTOMATIC RETRANSMIT FAC EXISTS BTN 123.5/352.15 REMAINDER NO CHANGE. AMD AIP ERSA DATED 25 NOV 2004 FROM 11 241850 TO PERM C307/04 REVIEW C306/04 MILITARY RADAR OPR AT PSN YSNW 056 MAG 2.1 NM POSSIBLE AVIONICS INTERRUPTIONS/ERRORS WI 150M OF RADAR BLW 500FT AGL FROM 12 192118 TO 03 160600 C9/05 REVIEW C6/05 ACFT WASH DOWN FACILITY NOT AVBL FROM 01 172158 TO 02 280600 EST

43

C19/05 REVIEW C265/04 ARST GEAR RWY 21 AND RWY 03 NOT AVBL FROM 01 280015 TO 02 280200 EST C40/05 WINCHING AND HOVER OPS NOT TO BE CONDUCTED IN VCY OF FLT DECK PROC TRAINER BACK OF HOUSE AREA OPS TO THE PROC TRAINER AS PER NORMAL FROM 02 212112 TO 05 200001 EST RICHMOND (NSW) (YSRI) C798/04 REVIEW C704/04 ATS REDUCED: MON - FRI 2000 TO 1100 SAT,SUN,PUBLIC HOLIDAYS AND OTHER TIMES 48HR PN FOR HEAVY ACFT. ATS MAY NOT BE AVBL DURING NOTAM HR. CTC ALG OPS 02 4587 2222 TO CONFIRM ATS AVBL. THIS SUPERCEDES ATS PUBLISHED IN AIP ENROUTE SUP AUSTRALIA FROM 10 312355 TO 03 310000 EST C860/04 REVIEW C629/04 FIRE AND RESCUE AMD HR CAT 6: MON - WED 2100 - 1300 CAT 4: OT AND JF FROM 11 300101 TO 02 280100 EST C82/05 REVIEW C903/04 PRKG POSITION 11 NOT AVBL DUE TO TEMPO HANGER FROM 01 280202 TO 04 280200 EST C83/05 REVIEW C905/04 TWY LGT TO ACFT WASH FAC NOT AVBL FROM 01 280204 TO 04 280200 EST C112/05 TACAN 'RIC' SUBJ TO INTRP PILOT MONITORED FROM 02 080303 TO 02 272300 C144/05 AMD AIP ENR SUP AUSTRALIA EFFECTIVE 25 NOV 04 AND 17 MAR 2005 RICHMOND ENTRY, FAC R - 346 RADIO NAVIGATION AND LANDING AIDS AMD NOTE 3A TO READ: RANGE 100 (HN 75) FROM 02 210434 TO PERM C154/05 START CNCE RQ ALL ACFT DUE MIL PJE ENGINE SHUTDOWN REPORT RQ FROM 02 232210 TO 02 261100 THURS AND FRI HJ 02260400-02261100 C155/05 MIL CTR DEACTIVATED SUBJ TO RECALL AT SHORT NOTICE MBZ PROC APPLY FREQ 135.5MHZ PILOT RESPONSIBILITY TO CK AND MNT STS

44

FROM 02 240850 TO 02 242000 SYDNEY (YSSY) C721/04 REVIEW C716/04 RTZL RWY 16R NOT AVBL FROM 05 280214 TO PERM C1805/04 REVIEW C1174/04 AMD AIP DAP E SYDNEY NSW ILS PRM USER INSTRUCTIONS PAGE 1 DATED 25 NOVEMBER 04 REPLACE SECTION 'TCAS ON TA ONLY' IN TOTO WITH NEW SECTION: TCAS SELECTION: PILOTS MAY SELECT TCAS IN THE TA MODE OR MAINTAIN RA MODE ON RECEIPT OF INSTRUCTIONS TO CONTACT THE TOWER. NEW PRM VIDEO REFLECTING THESE CHANGES IS AVAILABLE ONLINE AT HTTP:// WWW.AIRSERVICESAUSTRALIA.COM/PILOTCENTRE/PROJECTS/PRM/CHANGESPRM.ASP FROM 11 241623 TO PERM C1837/04 REVIEW C1834/04 AMD AIP DAP EAST RWY 16R ILS DATED 25 NOV 04 AMD NAVAID RQ: FM SY DME TO SY DME (LLZ ONLY) FROM 11 292239 TO PERM C1960/04 AMD AIP ENROUTE SUPPLEMENT AUSTRALIA FAC S - 378 DATED 25 NOVEMBER 2004 'HELICOPTER GROUND OPERATIONS' ADD NOTE 6 HELICOPTER BANNER TOWING OPS NOT PERMITTED TO OR FROM AERODROME FROM 12 160554 TO PERM C2018/04 AMD AIP ERSA DATED 25 NOVEMBER 2004 PAGE FAC S 372 TAXIWAY RESTRICTIONS DELETE NOTE 25. FROM 12 220117 TO PERM C2060/04 REVIEW C1558/04 TEMPO OBST TREES INFRINGING RWY 25 APCH SFC INFRINGED 7.3M (24FT) BY TREE LOC 516M FM RWY 25 THR 210M NORTH OF CL 74FT AMSL RWY 16L APCH SFC INFRINGED 4.2M (13.8FT) BY TREE LOC 520M FM RWY 16L THR 206M EAST OF CL 60FT AMSL FROM 12 282334 TO 03 310800 EST C16/05 REVIEW C2/05 TEMPO OBST MARKED CRANES ERECTED AT FLW LOCATIONS 361FT AMSL BRG 072 MAG 2.86NM FM VOR 263FT AMSL BRG 353 MAG 2.30NM FM VOR 230FT AMSL BRG 035 MAG 2.79NM FM VOR 201FT AMSL BRG 355 MAG 1.18NM FM VOR 294FT AMSL BRG 088 MAG 2.57NM FM VOR 240FT AMSL BRG 352 MAG 2.36NM FM VOR

45

350FT AMSL BRG 022 MAG 2.66NM FM VOR 230FT AMSL BRG 035 MAG 2.85NM FM VOR 291FT AMSL BRG 126 MAG 2.11NM FM VOR 291FT AMSL BRG 114 MAG 2.38NM FM VOR FROM 01 040657 TO 03 300800 EST C35/05 REVIEW C1399/04 TWY INT DECLARED DIST MOV AREA GUIDANCE SIGNS WITH TORA NOT AVBL FROM 01 070200 TO 02 280200 EST C92/05 AMD AIP EN ROUTE SUPPLEMENT DATED 25 NOVEMBER 2004 PAGE FAC S-373 AD AND APPROACH LGT "RWY 16R HIGH INTENSITY APPROACH LGT-CAT1-RCLL SPACED AT 30M- HIGH INTENSITY RWY LGT-MEDIUM INTENSITY RWY LGT-T-VASIS 3 DEG 41FT" FROM 01 140117 TO PERM C91/05 SW SECTOR APRON WORKS DETAILS SUP H5/05, AVBL FM AVFAX CODE 81522 AND AIRSERVICES WEBSITE WWW.AIRSERVICESAUSTRALIA.COM/PUBLICATIONS/AIP.ASP FROM 01 140130 TO 04 140100 EST C188/05 REVIEW C186/05 TWY BRAVO-5 AND TWY BRAVO-6 NOT AVBL TO ACFT ABV 22000KG MAX TKOF WT. MAX TYRE PRESSURE 1400KPA. AMD AIP ENROUTE SUP AUSTRALIA (ERSA) DATED 25 NOV 2004 PAGE FAC S-372 - NOTE 26 - INSERT REPLACEMENT TEXT FROM 01 260653 TO 03 161600 C211/05 OBST LGT ON LIGHTING MAST ACFT PARKING BAY 11 N APRON NOT AVBL FROM 01 281750 TO 02 270600 EST C228/05 REVIEW C2067/04 RWY THR IDENT LGT (RTIL) RWY 07 NOT AVBL FROM 01 310550 TO 03 020600 EST C232/05 REVIEW C2063/04 INSET HOLDING POINT LIGHTS DECOMMISSIONED AT HOLDING POINTS WHERE ELEVATED RWY GUARD LIGHTS ARE OPR FROM 01 311337 TO 02 280300 EST C270/05 MANDATORY MOV AREA GUIDANCE SIGN EASTERN SIDE OF TWY BRAVO, S SIDE OF RWY 07/25 NOT AVBL DUE UNSERVICEABILITY USE PAVEMENT MARKINGS AND HLDG POINT LGT FROM 02 051734 TO 02 280900 EST C328/05 MANDATORY MOVEMENT AREA GUIDANCE SIGN SOUTHERN SIDE OF TWY GOLF EAST OF RWY 16R/34L NOT AVBL USE PAVEMENT MARKINGS AND HOLDING POINT LGT

46

FROM 02 130936 TO 02 271900 EST C354/05 OBST CRANE (ON RAILS) 345FT AMSL OPR BTN THE FLW PSN: BRG 114 MAG 4718M (2.54NM) FM DVOR BRG 125 MAG 4305M (2.32NM) FM DVOR INFRINGES HORIZONTAL SFC BY 54M (177FT) FROM 02 160151 TO 05 160100 C391/05 TWY JULIET BTN TWY ALPHA AND TWY YANKEE NOT AVBL DUE WIP REFER MOWP 05/002 FROM 02 200800 TO 06 040600 C405/05 AMD TFC HLDG ADVISORY FOR ALL TRAFFIC ARRIVING: BTN 0000 AND 2000 - 20 MIN BTN 2000 AND 0000 - 15 MIN EXC SKED ACFT ARR FM AFRICA OR W COT OF NORTH AMERICA - 10 MIN THIS SUPERCEDES TFC HLDG AS SPECIFIED IN AIP ENRT SUP AUSTRALIA FROM 02 202330 TO 02 280600 EST C408/05 REVIEW C233/05 RUNWAY STRIPS RWY 16R/34L AND RWY 07/25 REDUCED TO 150M DUE WIP FROM 02 210307 TO 03 210300 EST C409/05 REVIEW C407/05 AMD AIP ENROUTE SUP AUSTRALIA (ERSA) DATED 25 NOVEMBER 2004 PAGE FAC S - 374 SPECIAL PROCEDURES DELETE IN TOTO NOTE 2 AND REPLACE WITH: INT DEP RWY 16L FM TWY T1 NOT AVBL HN FROM 02 210403 TO PERM C415/05 REVIEW C229/05 TWY RESTR DUE LEADOUT LIGHTING FAILURE RWY 16R EXIT BRAVO-6 NOT AVBL RWY 16R EXIT BRAVO-4 NOT AVBL RWY 34L EXIT BRAVO-4 NOT AVBL RWY 34L EXIT BRAVO-3 NOT AVBL RWY 34L EXIT FOXTROT NOT AVBL FROM 02 210644 TO 03 210700 EST HN C416/05 REVIEW C230/05 TWY BRAVO-5 NOT AVBL DUE LGT U/S FROM 02 210646 TO 03 210700 EST HN C413/05 RWY 16R/34L 1493M S END NOT AVBL

47

DUE WIP RWY 34L TEMP DISP THR LOCATED 1493M FM RWY 34L SOT MARKED BY STROBE LGT HJ AND HN WITH GREEN LGT HN AND "V" BAR MARKERS EACH SIDE OF RWY HJ RWY 34L GP AND T-VASIS NOT AVBL. TEMP PAPI LH SIDE LOCATED 1879M FM 34L SOT. PAPI 3DEG TCH 67FT OBST 10 FT AGL ON RWY 2675 FM RWY 16R SOT. DECLARED DIST RWY TORA TODA ASDA LDA 16R 2470 2530(2%) 2470 2470 34L 2470 2560(10.57%) 2470 2470 SUPP TAKEOFF DIST RWY 16R 2498(1.6%) 2525(1.9%) RWY 34L 2084(1.6%) 2317(1.9%) 2450(2.2%) 2540(2.5%) TKOF RWY 16R ENTER VIA TWY BRAVO-1, BRAVO-2 OR ALPHA-1 LDG RWY 34L LAST AVBL EXIT TWY ALPHA-1 OR BRAVO-1 INTERSECTION DEP FM TWY FOXTROT REDUCE ALL DIST BY 536M AND DEP FM TWY GOLF REDUCE ALL DIST BY 1113M REF METHOD OF WORKS PLAN 04/004 FROM 02 211200 TO 02 241900 DAILY 1200/1900 C425/05 REVIEW C402/05 TWY GOLF-5 WEST MANDATORY MAG SIGN NOT AVBL DUE WIP REF METHOD OF WORKING PLAN 04/007 FROM 02 220149 TO 03 010000 EST C428/05 RWY 07/25 LGT UPGRADE BEGINS MAR 05 DETAILS AIC H4/05, AVBL FM AVFAX CODE 81527 AND AIRSERVICES WEBSITE WWW.AIRSERVICESAUSTRALIA.COM/PUBLICATIONS/AIP.ASP FROM 02 220409 TO 05 120100 C439/05 REVIEW C367/05 RCL LGT RWY 16R/34L NOT AVBL FROM 02 240114 TO 03 240200 EST C443/05 RWY 25 EXIT TWY YANKEE NORTH NOT AVBL RWY 07 EXIT TWY GOLF-3 NOT AVBL DUE LEADOUT LGT FAILURE FROM 02 241035 TO 02 281900 EST HN C441/05 TWY CHARLIE BTN BRAVO-8 AND BRAVO-10 NOT AVBL TWY BRAVO-10 AND LIMA BTN RWY 16R/34L AND RWY 16L/34R NOT AVBL DUE WIP FROM 02 241200 TO 02 241900 C442/05 RWY 16L/34R NOT AVBL DUE WIP REF METHOD OF WORKS PLAN 04/007 FROM 02 241200 TO 02 271900 0502241200 TO 0502241900 0502271200 TO 0502271900

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WAGGA WAGGA (YSWG) C71/04 RWY 05/23 AND RWY 30 GRADIENT/STODA CHANGES RWY TODA 05 1828 (2.00) 23 1828 (1.58) 30 1586 (1.53) STODA RWY 05 1198 (1.6) 1811 (1.9) 23 DELETE STODA FROM 11 150000 TO PERM C7/05 FIRING RANGE POSITION 300M LEFT RWY 30 THR RADIUS 100M SFC TO 500FT AGL ACT BY NOTAM AIRCRAFT SHOULD REMAIN CLEAR AMD ERSA SPECIAL PROCEDURES 25 NOV 2004 FROM 01 210325 TO PERM C10/05 23M RANGE LOC S35 10 28.7 E147 28 05.8 (300M LEFT OF THR RWY 30) ACT BY DISPLAY OF R FLAGS HJ AND R FLASHING LGT HN. ACFT SHOULD OBSERVE A SAFETY DIST OF 100M LATERALLY AND 500FT VERTICALLY WHEN RANGE ACT FROM 02 170420 TO PERM TUMUT (YTMU) C7/04 RWY 17/35 GRADIENT/STODA CHANGES RWY TODA 17 1120 (6.87) 35 1120 (2.99) STODA RWY 17 980 (3.3) 1094 (5.0) 35 856 (2.5) FROM 11 150000 TO PERM YOUNG (YYNG) C11/04 RWY 01/19 DECLARED DISTANCE AND GRADIENT CHANGES: RWY TODA 19 1280(8.49) SUPPLEMENTARY TKOF DIST: RWY 01 985(1.6) 1276(1.9) RWY 19 1061(2.2) 1119(2.5) 1183(3.3) 1239(5.0) FROM 08 232255 TO PERM C2/05 AD NOT AVBL DUE WIP (RESEALING RWY) FROM 02 212319 TO 02 281300 D451 MARULAN (D451)

49

C2/05 D451 MARULAN ACT SFC TO 4500FT AMSL FROM 02 202000 TO 02 250800 DAILY 2000/0800 R537 RICHMOND (R537) C85/05 R537 DEACTIVATED SUBJ RECALL AT SHORT NOTICE MBZ PROC APPLY FREQ 135.5MHZ PILOT RESPONSIBILITY TO CK AND MNT STS FROM 02 240850 TO 02 242000 NOWRA AIRSPACE (NWX) C287/04 REVIEW C213/04 R422 DEACTIVATED AND D423 ALSO DEACTIVATED. AREA SUBJ TO RECALL FROM 12 150041 TO 03 150100 EST C32/05 MIL CTR AND R420ABCDE DEACTIVATED AREA SUBJ TO RECALL MBZ PROC APPLY FREQ 118.85 MHZ FROM 02 240630 TO 02 242200 TASMAN SEA AIRSPACE (TSX) C50/05 R453CDEFGHJKLMNP ACT SFC TO 10000FT AMSL FROM 02 222130 TO 02 250630 DAILY 2130/0630 C51/05 R495ABC ACT SFC TO 10000FT AMSL FROM 02 222130 TO 02 250630 DAILY 2130/0630 WILLIAMTOWN EAST AIRSPACE (WEX) C34/05 R595 DEACTIVATED AIRSPACE SUBJ TO RECALL AT SHORT NOTICE PILOT RESPONSIBILITY TO CK AND MNT STS FROM 02 240239 TO 02 242200 WILLIAMTOWN AIRSPACE (WMX) C779/04 WEF 0501161300 R587B WILLIAMTOWN MIL FLYING TRAINING AMD HR OF ACT TO READ 'NOTAM' AMD AIP-ERSA DAH ERC-H1 ERC-H3 ERC-H5 ERC-L4 FROM 12 162349 TO PERM C73/05 R587A R580 AND R583A DEACTIVATED AIRSPACE SUBJ TO RECALL AT SHORT NOTICE PILOT RESPONSIBILITY TO CK AND MNT STS FROM 02 240240 TO 02 242200

50

C75/05 R578ACDE R596 DEACTIVATED AIRSPACE SUBJ TO RECALL AT SHORT NOTICE PILOT RESPONSIBILITY TO CK AND MNT STS FROM 02 240600 TO 02 242200 BRISBANE FIR (YBBB) C2847/04 REVIEW C2582/04 AMEND AIP ENROUTE SUPPLEMENT (ERSA) GEN REVISED ROUTE FLIGHT PLANNING REQUIREMENTS BRISBANE AREA DETAILS SUP H49/04 DATED 23 DEC 2004 AVBL FM AVFAX CODE 81568 AND AIRSERVICES WEBSITE. WWW.AIRSERVICESAUSTRALIA/COM/PUBLICATIONS/AIP SUP H46/04 CNL BY SUP H49/04 FROM 12 280325 TO 03 161600 MELBOURNE FIR/BRISBANE FIR (YMMM/YBBB) C2233/04 REVIEW C2232/04 UNNOTIFIED INTENSE AVIATION ACTIVITY ASSW FIREFIGHTING OPS MAY OCCUR WI 5NM RAD AND BLW 3000FT AGL OF OBSERVED FIRES. ACFT NOT COORDINATED THROUGH THE RELEVANT STATE EMERG SERVICE ARE REQ TO REMAIN CLEAR FROM 10 060117 TO 02 281400 C2385/04 EASTERN DAYLIGHT SAVING TIME IN THE STATES OF NEW SOUTH WALES, VICTORIA AND THE AUSTRALIAN CAPTIAL TERRITORY. UTC TIMES PROMULGATED IN DOCUMENTS FOR THE PROVISION OF SERVICES AND ACTIVATION OF AIRSPACE WILL BE EFFECTIVE ONE HOUR EARLIER UNLESS SPECIFIED AS HDS OR AMD BY NOTAM FROM 10 301600 TO 03 261600 C2384/04 CENTRAL DAYLIGHT SAVING TIME EFFECTIVE IN THE STATE OF SOUTH AUSTRALIA. UTC TIMES PROMULGATED IN DOCUMENTS FOR THE PROVISION OF SERVICES AND ACTIVATION OF AIRSPACE WILL BE EFFECTIVE ONE HOUR EARLIER UNLESS SPECIFIED AS HDS OR AMD BY NOTAM FROM 10 301630 TO 03 261630 C112/05 AT AUSTRALIAN AD WHERE LAND AND HOLD SHORT OPS (LAHSO) ARE BEING CONDUCTED QANTAS B767-300 SERIES ACFT ARE APPROVED TO PARTICIPATE IN BOTH ACTIVE AND PASSIVE OPS FROM 01 191600 TO 03 191600 EST MELBOURNE FIR (YMMM) C1055/04 LIGHT WEATHER BALLOON RELEASES WILL OPR FM JINDABINE (PSN COOMA 238 MAG 028NM) AND KHANCOBAN (PSN CORRYONG

51

NDB 095 MAG 14NM) PRIOR AND DRG FRONTAL STORM ACTIVITY SFC TO 69000FT AMSL FROM 05 310000 TO PERM C338/05 OBST LGT ON TELSTRA TWR 289FT AGL BONDI JUNCTION PSN S33 53 46 E 151 15 15 FROM 02 150040 TO 03 140300 EST C310/05 BRIGHT PARAGLIDING OPEN COMPETITION UP TO 100 HANG GLIDERS OPR IN CLASS G AIRSPACE WI AREA BOUNDED BY HARRIETVILLE, MOUNT BUFFALO PLATEAU, CHESHUNT, GLENROWAN, PEECHELBA, BEECHWORTH, DEDERANG SUBSTATION, BULLIOH, MOUNT BOGONG,HARRIETVILLE. HANG GLIDERS WILL REMAIN CLEAR OF MT HOTHAM MBZ AND WILL OPERATE BELOW 4500FT AMSL BELOW THE ALBURY CLASS C AIRSPACE STEP. COMPETITION DIRECTOR BRIAN WEBB PH 0417 530 972. SFC TO 8500FT AMSL FROM 02 190100 TO 02 260923 DAILY HJ C404/05 OBST LGT 2RN MAST (260 MAG 5NM FM BANKSTOWN ARP) NOT AVBL FROM 02 211008 TO 02 280500 EST C379/05 TETHERED BALLOON 3M LONG 1.5M DIAMETER VIVID ORANGE IN COLOUR MOORED AT PSN S33 44.0 E150 42.5 (PENRITH- BRG 286 MAG 26.5NM FM SYDNEY VOR) MOORING LINE MARKED WITH 1M VIVID ORANGE STREAMERS AT INTERVALS OF 15M BALLOON WILL BE RAISED TO MAX HGT THEN LOWERED TO GND LVL IN 1HR CYCLES BALLOON FITTED WITH RAPID DEFLATION DEVICE AND WILL REMAIN 500FT VERTICALLY BLW CLOUD SFC TO 2500FT AMSL FROM 02 221900 TO 03 040600 0502221900 TO 0502230600 0502231900 TO 0502240600 0502241900 TO 0502250600 0503011900 TO 0503020600 0503021900 TO 0503030600 0503031900 TO 0503040600 C333/05 EAST SALE SSR DATA NOT AVBL RADAR BASED TFC INFO SER REDUCED BLW F200 S AND SE OF COOMA, S OF MORUYA AND OUTSIDE OF 50NM E OF MELBOURNE FROM 02 240630 TO 03 010930 0502240630 TO 0502240930 0503010630 TO 0503010930 GLOBAL POSITIONING SYSTEM (GPS) C32/05 REVIEW C31/05 GPS OPERATIONAL ADVISORY

52

NUMBER 54 WEF 23 FEBRUARY 2005 AVBL FM AVFAX PRODUCT CODE 81392 EXPLANATORY INFO FOR GPS STATUS REPORT AVBL FM AVFAX PRODUCT 81575 FROM 02 231627 TO 03 020400 EST For restricted areas with no current NOTAM, check ERSA for the vertical limits and hours of activation as not all restricted areas are activated/deactivated by NOTAM. The following requested locations have no current NOTAM: BINDOOK (BIK) SHELLYS (SLS) WEE JASPER (WJS) WILLIAMSDALE (WLE) YASS (YAS) RUGBY (RUG) COOTAMUNDRA (YCTM) CUDAL (YCUA) COWRA (YCWR) GOULBURN (YGLB) KATOOMBA (YKAT) LORD HOWE IS (YLHI) MERIMBULA (YMER) MORUYA (YMRY) CAMDEN (YSCN) HOLSWORTHY (YSHW) WOLLONGONG (YWOL) D423 NOWRA (D423) D456 BASS PT (D456) D527 CADIA GOLD MINE (D527) D538A RICHMOND (D538A) D538B RICHMOND (D538B) D539A BANKSTOWN (D539A) D539B BANKSTOWN (D539B) D552 CAMDEN (D552) D556A BANKSTOWN (D556A) D556B BANKSTOWN (D556B) D593A WILTON (D593A) D593B WILTON (D593B) R405A SYDNEY (R405A) R405B SYDNEY (R405B) R418 KAPOOKA (R418) R424 CANBERRA (R424) R425 TIDBINBILLA (R425) R426 TIDBINBILLA (R426) R455 CANBERRA (R455) R473 NORTH HEAD (R473) R517 MARRANGAROO (R517) R519 BOGAN GATE (R519) R521 LUCAS HEIGHTS (R521) R525 PARKES (R525) R536A KINGSWOOD (R536A) R536B KINGSWOOD (R536B) R555A HOLSWORTHY (R555A) R555B HOLSWORTHY (R555B) R555C HOLSWORTHY (R555C) R555D HOLSWORTHY (R555D) RICHMOND AIRSPACE (RIX)

53

APPENDIX D: AREA FORECAST

BANKSTOWN YSBK 00:22 UTC, today TAF YSBK 050022Z 0214 22015KT CAVOK FM04 15010KT 9999 FEW040 T 29 28 23 20 Q 1001 1001 1003 1006 BANKSTOWN YSBK - Update 00:22 UTC, today TAF YSBK 051358Z 1402 28010KT CAVOK FM16 27020KT 9999 SCT035 T 20 19 19 16 Q 1006 1008 1008 1010 04:06 UTC, today METAR YSBK 050400Z AUTO 24013KT 9999 // SCT072 29/14 Q1001 RMK RF00.0/000.0 BATHURST YBTH 01:09 UTC, today SVC CORRECTION 050052 YSRFYMYX STOP TAF YBTH 050108Z 0214 25015G25KT 9999 SCT020 SCT030 FM10 24012KT 9999 DZ SCT015 BKN030 FM02 MOD TURB BLW 5000FT TILL 10 T 21 23 19 16 Q 1007 1007 1009 1012 04:04 UTC, today METAR YBTH 050400Z AUTO 25015KT 9999 // SCT048 SCT056 22/11 Q1006 RMK RF00.0/000.2 CAMDEN YSCN 00:24 UTC, today TAF YSCN 050024Z 0214 20015KT CAVOK FM05 13008KT 9999 FEW040 T 29 29 23 19 Q 1001 1001 1003 1006 04:01 UTC, today METAR YSCN 050400Z AUTO 25014KT 9999 // ////// 28/11 Q1001 RMK RF00.0/000.0 CANBERRA YSCB 02:46 UTC, today TAF AMD YSCB 050245Z 0320 26015G25KT 9999 SCT035 FM08 27012KT 9999 SCT030 FM03 MOD TURB BLW 5000FT TILL 10 T 23 23 18 16 Q 1004 1005 1007 1010 04:02 UTC, today TTF METAR YSCB 050400Z 28016KT CAVOK 24/13 Q1004 RMK RF00.0/000.0 USE TAF FOR ARRIVALS AFTER 0700Z FM0400 MOD TURB BLW 5000FT

54

COOMA YCOM 01:05 UTC, today TAF YCOM 050105Z 0214 21020G35KT 9999-SHRA SCT030 BKN040 FM10 21015KT 9999 -SHRA SCT020 BKN035 FM02 MOD TURB BLW 5000FT TILL 10 T 19 21 16 12 Q 1005 1005 1008 1011 04:06 UTC, today METAR YCOM 050400Z AUTO 27011KT 9000 // SCT024 BKN040 OVC048 15/12 Q1005 RMK RF00.4/000.4 COOTAMUNDRA YCTM 00:58 UTC, today TAF YCTM 050057Z 0214 23015G25KT 9999 SCT035 FM09 23012KT 9999 SCT030 FM02 MOD TURB BLW 5000FT TILL 09 T 25 29 21 17 Q 1007 1007 1008 1011 METAR not available COWRA YCWR 00:56 UTC, today TAF YCWR 050055Z 0214 25013G24KT 9999 BKN035 FM09 25012KT 9999 BKN030 FM02 MOD TURB BLW 5000FT TILL 09 T 26 30 25 19 Q 1007 1007 1008 1011 04:03 UTC, today METAR YCWR 050400Z AUTO 24016KT //// // ////// 26/10 Q1005 RMK RF00.0/000.2 GOULBURN YGLB 00:55 UTC, today TAF YGLB 050054Z 0214 28018G30KT 9999 SCT035 FM09 28015KT 9999 SCT030 FM02 MOD TURB BLW 5000FT TILL 09 T 22 23 18 14 Q 1005 1006 1008 1010 04:02 UTC, today METAR YGLB 050400Z AUTO 28020KT //// // ////// 23/11 Q1004 RMK RF00.0/000.0 LORD HOWE ISLAND YLHI 23:04 UTC, yesterday TAF AMD YLHI 042304Z 2308 03015KT 9999 -SHRA FEW015 SCT025 FM01 33015KT 9999 -SHRA SCT030 FM04 24018KT 9999 -SHRA FEW010 SCT025 TEMPO 2301 VRB25G40KT 3000 TSRA BKN010 SCT040CB PROB30 INTER 0108 VRB25G40KT 3000 TSRA BKN010 SCT040CB T 21 23 24 24 Q 1005 1005 1004 1002

55

04:08 UTC, today METAR YLHI 050400Z 05007KT 9999 FEW011 BKN019 23/20 Q1000 RMK RF00.0/004.2 MALLACOOTA YMCO 02:09 UTC, today TAF AMD YMCO 050209Z 0208 24015G28KT 9999 -SHRA -DZ FEW015 BKN025 TEMPO 0208 4000 SHRA DZ BKN008 T 17 17 15 Q 998 999 1001 04:02 UTC, today SPECI YMCO 050400Z AUTO 24018G29KT //// // ////// 19/12 Q1002 RMK RF00.0/000.0 MERIMBULA YMER 00:40 UTC, today TAF YMER 050040Z 0214 22015G25KT 9999 -SHRA FEW030 FM04 20015G25KT 9999 -SHRA SCT030 FM02 MOD TURB BLW 5000FT T 22 22 19 17 Q 1001 1003 1005 1008 04:04 UTC, today SPECI YMER 050400Z AUTO 22017G27KT //// // ////// 24/11 Q1001 RMK RF00.0/000.0 MORUYA YMRY 00:44 UTC, today TAF AMD YMRY 050044Z 0214 17013G23KT 9999 -SHRA BKN030 FM10 17012KT 9999 -SHRA BKN030 FM02 MOD TURB BLW 5000FT TILL 10 T 24 24 22 18 Q 1002 1003 1005 1008 04:06 UTC, today METAR YMRY 050400Z AUTO 06005KT //// // ////// 22/16 Q1000 RMK RF00.0/000.0 NORFOLK ISLAND YSNF 23:35 UTC, yesterday TAF AMD YSNF 042335Z 0014 31014KT 9000 FEW010 INTER 0314 -SHRA SCT010 SCT025 T 24 24 23 22 Q 1009 1009 1009 1009 04:05 UTC, today METAR YSNF 050400Z 30012KT 9999 FEW008 24/20 Q1007 RMK RF00.0/000.0 *NOWRA YSNW 01:17 UTC, today TAF YSNW 050116Z 0214 27014KT 9999 -SHRA SCT050 FM10 32010KT 9999 FEW040 T 26 21 20 18 Q 1001 1001 1002 1003

56

04:06 UTC, today TTF METAR YSNW 050400Z 24006KT 9999 SCT035 BKN085 26/13 Q1001 RMK RF00.0/000.0 DA +2300FT NOSIG ORANGE YORG 01:10 UTC, today TAF YORG 050109Z 0214 25015G25KT 9999 BKN025 FM09 25015KT 9999 DZ SCT015 BKN020 FM02 MOD TURB BLW 5000FT TILL 09 T 18 21 18 14 Q 1008 1008 1010 1013 04:06 UTC, today METAR YORG 050400Z AUTO 26013KT //// // ////// 19/11 Q1008 RMK RF00.0/000.0 PARKES YPKS 00:39 UTC, today TAF YPKS 050038Z 0214 23014KT 9999 SCT045 FM10 19010KT 9999 SCT025 T 23 28 23 19 Q 1007 1006 1008 1010 04:09 UTC, today METAR YPKS 050400Z AUTO 22015KT //// // ////// 25/11 Q1006 RMK RF00.0/000.0 *SYDNEY YSSY 03:46 UTC, today TAF AMD YSSY 050345Z 0424 24018G28KT CAVOK FM06 23018KT 9999 FEW035 FM09 19015KT 9999 SCT025 FM04 MOD TURB BLW 5000FT TILL 10 T 29 24 22 20 Q 1001 1002 1004 1006 04:03 UTC, today TTF METAR YSSY 050400Z 24017KT CAVOK 28/12 Q1001 RMK RF00.0/000.0 FM0400 MOD TURB BLW 5000FT TEMORA YTEM 00:42 UTC, today TAF YTEM 050042Z 0214 23016KT 9999 FEW040 SCT045 FM10 19010KT 9999 FEW025 SCT035 T 24 29 24 20 Q 1007 1006 1007 1009 METAR not available WAGGA WAGGA YSWG 01:01 UTC, today TAF YSWG 050101Z 0214 25015G25KT 9999 FEW035 FM08 25012KT 9999 FEW030 FM02 MOD TURB BLW 5000FT TILL 08 T 22 25 21 17 Q 1006 1006 1008 1011

57

04:09 UTC, today METAR YSWG 050400Z 25021KT CAVOK 26/11 Q1005 RMK RF00.0/000.0 WOLLONGONG YWOL 00:48 UTC, today TAF YWOL 050048Z 0214 23010KT 9999 SCT030 FM04 16015KT 9999 SCT030 FM12 29012KT 9999 -SHRA SCT030 T 27 27 22 19 Q 1002 1003 1004 1006 04:07 UTC, today METAR YWOL 050400Z AUTO 26012KT //// // ////// 27/13 Q1000 RMK RF00.0/000.0 YOUNG YYNG 00:58 UTC, today TAF YYNG 050057Z 0214 23015G25KT 9999 SCT035 FM09 23012KT 9999 SCT030 FM02 MOD TURB BLW 5000FT TILL 09 T 25 29 21 16 Q 1007 1007 1008 1011 04:02 UTC, today METAR YYNG 050400Z AUTO 23011G21KT //// // ////// 25/08 Q1005 RMK RF00.0/000.0 Area Forecast: AREA 21 02:39 UTC, today AMEND AREA FORECAST 050300 TO 051700 AREA 21 AMD OVERVIEW: ISOLATED SHOWERS W OF ORANGE/COOMA AND OVER COAST/SEA. LOW CLOUD IN AND NEAR PRECIPITATION. MODERATE TURBULENCE BELOW 7000FT E OF BATHURST/COOMA. AMD WIND: 2000 5000 7000 10000 14000 18500 230/30 230/25 250/25 230/35 PS01 240/35 MS06 240/45 MS15 REMARK: WINDS BELOW 7000FT 10KT STRONGER SEA/COAST. AMD CLOUD: SCT ST IN/NEAR PRECIPITATION 1500/2500 SEA/COAST, 3500/5000 RANGES, 2000/4000 W SLOPES. AREA SCT CU/SC 3000/9000 SEA/COAST, 5000/9000 RANGES/W SLOPES. WEATHER: -SHRA. VISIBILITY: 7000M -SHRA.

58

AMD FREEZING LEVEL: 10000 ICING: MOD IN CLOUD ABOVE FREEZING LEVEL. TURBULENCE: MOD IN CU. MOD LEE RANGES E OF YBTH/YCOM. AMD CRITICAL LOCATIONS: [CLOUD HEIGHTS ABOVE MEAN SEA LEVEL] MT VICTORIA: 9999 SCT SC 5500 BOWRAL: 9999 SCT SC 4000

59

APPENDIX E: DEMOGRAPHIC QUESTIONNAIRE

School of Psychology and MARCS

General Aviation Pilot Performance

Demographic Information

60

Demographic Information

Please indicate your age (in years):

Please indicate your gender: Male Female

Please indicate which of the following licences that you hold:

GFPT

Private

Commercial

ATPL

Please indicate which of the following ratings that you hold:

Instructor

Instrument

Each of the following questions is related to your flying experience. Please estimate these figures as accurately as possible.

Number of hours(total) experience:

Number of hours(total) as pilot in command:

Number of hours(total) actual IFR experience:

Number of cross-country hours experience (excluding training):

Number of hours(total) during the previous 90 days:

Number of cross-country hours during the previous 90 days

Number of times that you have experienced an in-flight decision that required a comparison between alternates:

61

Over the previous six months, how often have you used GPS as your primary source of navigation?

Never Rarely Sometimes Frequently Always

Over the previous six months, how often have you used weather radar (or stormscope) during flight?

Never Rarely Sometimes Frequently Always

Which of the following is most like the strategy that you employ when you make in-flight decisions (Place a cross in the box adjacent to the most appropriate statement – Please only select one alternative).

I recall previous examples of situations in which I have made similar decisions, and I try and adapt these experiences.

I tend to consider the pros and cons of each alternative and choose the strategy that is likely to lead to the most favourable outcome.

I tend to use a specific rule which specifies when I make a decision and how I make it.

I recall previous examples of situations that I have read/ heard about, and I use this information as the basis for making a decision.

I have a model of the way in which weather-related decisions should be made, and I use this as the basis for making my own decisions.

I know immediately that whether it is Ok or not.

62

To be Completed Following the Flight

To what extent is the process of information acquisition presented in the scenario consistent with the way in which you make comparisons in the operational environment?

Extremely Moderately Somewhat Equidistant Somewhat Moderately Extremely Dissimilar Dissimilar Dissimilar Similar Similar Similar

1 2 3 4 5 6 7

How realistic did you find the scenario?

Extremely Moderately Somewhat Equidistant Somewhat Moderately Extremely Unrealistic Unrealistic Unrealistic Realistic Realistic Realistic

1 2 3 4 5 6 7

How fatigued did you feel after the completion of the scenario?

Extremely Moderately Somewhat Equidistant Somewhat Moderately Extremely Energetic Energetic Energetic Fatigued Fatigued Fatigued

1 2 3 4 5 6 7

63

How well do you think that you perform the following tasks in your general flying? Flight Planning

Extremely Moderately Somewhat Equidistant Somewhat Moderately Extremely Poorly Poorly Poorly Well Well Well

1 2 3 4 5 6 7

Flight Control


1 2 3 4 5 6 7

In-Flight Decision-Making


1 2 3 4 5 6 7

Fatigue Management


1 2 3 4 5 6 7

Fuel Management


1 2 3 4 5 6 7

Navigation


1 2 3 4 5 6 7

Communication


1 2 3 4 5 6 7

Visual Scanning


1 2 3 4 5 6 7

64

How well do you think that you performed the following tasks during the simulated flight? Flight Planning


1 2 3 4 5 6 7

Flight Control


1 2 3 4 5 6 7

In-Flight Decision-Making


1 2 3 4 5 6 7

Fatigue Management


1 2 3 4 5 6 7

Fuel Management


1 2 3 4 5 6 7

Navigation


1 2 3 4 5 6 7

Communication


1 2 3 4 5 6 7

Visual Scanning


1 2 3 4 5 6 7

65

APPENDIX F: INSTRUMENT FAMILIARISATION SLIDE

Airspeed Indicator

Altitude

AttitudeIndicator

Non-Directional Beacon

VHF Omnidirectional

RangeFuel

Exhaust Gas Temperature

Oil Temp and Pressure

Turn and Balance

Directional Indicator

Vertical Speed

66

APPENDIX G: FLIGHT PATH DIAGRAMS – REPRESENTATIVE SAMPLE

Participant 4

147.48147.47147.59147.71147.84147.97148.11148.24148.38148.51148.64148.78148.90149.02149.15149.27149.37149.47149.56149.65149.73149.85149.97150.09150.21150.33150.44150.53150.63150.70150.77150.86150.97151.00150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Valu

e A

ltitu

de

147.48147.47147.58147.70147.83147.96148.09148.22148.35148.48148.62148.75148.87148.99149.11149.24149.34149.44149.53149.62149.70149.80149.92150.04150.15150.27150.38150.48150.57150.66150.72150.80150.90151.01151.03150.99

Longitude

-35.40

-35.10

-34.80

-34.50

-34.20

-33.90

Valu

e La

titud

e

67

Participant 5

147.48147.48147.47147.58147.73147.90148.07148.24148.42148.59148.77148.95149.12149.26149.37149.49149.61149.77149.92150.08150.24150.39150.52150.63150.73150.84150.98151.00150.99150.99150.99150.99150.99150.99150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Valu

e A

ltitu

de

147.48147.48147.48147.57147.72147.88148.05148.22148.39148.56148.73148.91149.08149.23149.34149.45149.57149.71149.87150.02150.17150.33150.48150.58150.68150.79150.90151.04150.99150.99150.99150.99150.99150.99150.99150.99

Longitude

-35.40

-35.10

-34.80

-34.50

-34.20

-33.90

Valu

e La

titud

e

68

Participant 6

147.48147.48147.48147.48147.45147.56147.72147.87148.03148.21148.40148.53148.69148.87149.05149.22149.34149.47149.60149.74149.91150.08150.24150.40150.49150.58150.70150.82150.91151.02150.99150.99150.99150.99150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

147.48147.48147.48147.48147.45147.55147.70147.86148.00148.18148.36148.54148.65148.83149.00149.18149.30149.43149.56149.69149.84150.01150.17150.33150.44150.53150.64150.75150.87150.99150.99150.99150.99150.99150.99150.99

Longitude

-35.40

-35.20

-35.00

-34.80

-34.60

-34.40

-34.20

-34.00

-33.80

Valu

e La

titud

e

69

Participant 7

Missing

147.48147.48147.48147.46147.56147.71147.86148.02148.18148.33148.47148.59148.71148.86149.02149.17149.27149.37149.47149.57149.72149.85149.99150.13150.27150.41150.54150.65150.75150.84150.89150.97151.02150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.45147.55147.69147.84148.00148.15148.30148.44148.56148.67148.82148.97149.13149.25149.34149.43149.53149.66149.80149.93150.07150.21150.34150.47150.61150.70150.80150.88150.91151.01150.99150.99

Longitude

-35.40

-35.20

-35.00

-34.80

-34.60

-34.40

-34.20

-34.00

-33.80

Valu

e La

titud

e

70

Participant 8

Missing

147.48147.48147.48147.48147.48147.47147.56147.70147.85148.01148.17148.33148.49148.64148.80148.96149.12149.26149.37149.48149.60149.74149.89150.05150.21150.34150.46150.51150.57150.67150.78150.92151.01150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.48147.48147.48147.54147.68147.83147.98148.13148.29148.45148.59148.75148.90149.06149.22149.33149.44149.55149.67149.83149.96150.13150.28150.40150.50150.52150.61150.72150.82150.96150.99150.99

Longitude

-35.40

-35.10

-34.80

-34.50

-34.20

-33.90

Valu

e La

titud

e

71

Participant 9

Missing

147.48147.48147.48147.48147.48147.48147.44147.51147.63147.77147.94148.07148.18148.29148.44148.58148.76148.95149.14149.27149.40149.51149.64149.82149.99150.15150.32150.49150.63150.75150.86151.02150.98150.98

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.48147.48147.48147.46147.49147.60147.74147.91148.05148.16148.26148.40148.52148.70148.88149.07149.22149.34149.46149.58149.74149.91150.07150.24150.40150.56150.68150.79150.91150.99150.98150.98

Longitude

-35.40

-35.10

-34.80

-34.50

-34.20

-33.90

Valu

e La

titud

e

72

Participant 10

Missing

147.48147.48147.48147.48147.46147.56147.71147.85147.99148.14148.29148.44148.59148.73148.87149.02149.16149.26149.36149.45149.56149.67149.79149.92150.04150.12150.21150.33150.46150.59150.73150.84150.98150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.48147.47147.55147.68147.83147.97148.11148.26148.40148.55148.69148.83148.97149.11149.23149.32149.41149.51149.61149.73149.85149.98150.08150.15150.26150.38150.50150.64150.77150.88150.98150.99

Longitude

-35.40

-35.10

-34.80

-34.50

-34.20

-33.90

Valu

e La

titud

e

73

Participant 11

Missing

147.48147.48147.48147.46147.55147.66147.80147.95148.09148.23148.38148.53148.68148.83148.98149.12149.25149.33149.43149.54149.65149.78149.92150.06150.19150.33150.46150.56150.67150.76150.86150.97151.01150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.46147.54147.65147.79147.93148.07148.21148.35148.50148.64148.79148.93149.08149.22149.29149.39149.50149.61149.72149.85150.00150.13150.26150.39150.51150.61150.71150.80150.91151.01150.99150.99

Longitude

-35.40

-35.10

-34.80

-34.50

-34.20

-33.90

Valu

e La

titud

e

74

Participant 12

Missing

147.48147.48147.48147.46147.59147.76147.91148.08148.19148.26148.43148.58148.68148.84149.01149.17149.31149.44149.59149.76149.92150.10150.22150.34150.48150.60150.70150.88151.01150.96150.92150.97151.04150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.47147.57147.73147.88148.05148.19148.22148.39148.54148.66148.79148.96149.11149.26149.39149.53149.69149.84150.02150.16150.27150.40150.53150.64150.77150.92150.98151.00150.91151.01151.01150.99

Longitude

-35.40

-35.20

-35.00

-34.80

-34.60

-34.40

-34.20

-34.00

-33.80

Valu

e La

titud

e

75

Participant 13

Missing

147.48147.48147.48147.48147.48147.48147.48147.61147.76147.92148.09148.25148.41148.58148.74148.90149.06149.22149.33149.44149.56149.68149.83149.99150.14150.29150.44150.56150.66150.75150.84150.97151.00150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.48147.48147.48147.46147.59147.73147.89148.05148.21148.37148.53148.69148.85149.00149.16149.28149.39149.50149.62149.76149.91150.06150.20150.35150.50150.61150.70150.78150.87151.00150.99150.99

Longitude

-35.40

-35.10

-34.80

-34.50

-34.20

-33.90

Valu

e La

titud

e

76

Participant 14

Missing

147.48147.48147.48147.48147.46147.59147.72147.86148.01148.16148.28148.43148.58148.73148.89149.04149.20149.30149.41149.52149.64149.76149.91150.05150.19150.33150.47150.57150.65150.73150.83150.95151.00150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.48147.45147.57147.70147.84147.98148.13148.26148.39148.54148.69148.85149.00149.15149.27149.37149.48149.59149.71149.85149.99150.13150.26150.41150.52150.61150.69150.77150.88151.00150.99150.99

Longitude

-35.40

-35.10

-34.80

-34.50

-34.20

-33.90

Valu

e La

titud

e

77

Participant 15

Missing

147.48147.48147.48147.48147.48147.48147.48147.52147.64147.81147.97148.13148.29148.46148.63148.79148.96149.13149.25149.36149.47149.58149.72149.87150.02150.17150.32150.47150.59150.68150.76150.86151.01150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.48147.48147.48147.48147.50147.62147.77147.93148.09148.24148.41148.57148.73148.89149.06149.20149.31149.42149.53149.64149.79149.94150.08150.23150.38150.52150.62150.71150.79150.90151.00150.99

Longitude

-35.40

-35.10

-34.80

-34.50

-34.20

-33.90

Valu

e La

titud

e

78

Participant 16

Missing

147.48147.48147.48147.48147.48147.48147.46147.55147.70147.88148.05148.22148.36148.54148.71148.89149.06149.22149.34149.44149.56149.68149.83150.00150.15150.30150.46150.60150.70150.82150.91151.03150.99150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.48147.48147.48147.47147.53147.67147.85148.02148.19148.33148.50148.67148.84149.01149.18149.31149.40149.51149.63149.77149.93150.09150.23150.39150.54150.65150.76150.87150.97151.00150.99150.99

Longitude

-35.40

-35.20

-35.00

-34.80

-34.60

-34.40

-34.20

-34.00

-33.80

Valu

e La

titud

e

79

Participant 17

Missing

147.48147.48147.48147.48147.48147.46147.58147.73147.87148.02148.19148.32148.47148.63148.80148.97149.13149.23149.35149.47149.57149.71149.84150.01150.18150.35150.51150.65150.80150.91150.96150.94151.00151.00

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.48147.48147.47147.56147.70147.84147.99148.16148.28148.44148.59148.75148.91149.08149.19149.31149.42149.53149.65149.77149.93150.10150.27150.42150.57150.71150.87150.93150.94151.00151.00151.00

Longitude

-35.50

-35.00

-34.50

-34.00

Valu

e La

titud

e

80

Participant 18

Missing

147.48147.48147.48147.48147.48147.53147.67147.84148.01148.17148.21148.39148.57148.75148.93149.11149.27149.39149.52149.63149.71149.78149.95150.13150.30150.45150.59150.73150.82150.93150.99151.00151.00150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.48147.48147.52147.65147.81147.98148.16148.17148.35148.52148.70148.87149.06149.22149.35149.47149.59149.71149.70149.88150.05150.22150.38150.52150.65150.77150.87150.97151.02151.00151.02150.99

Longitude

-35.40

-35.20

-35.00

-34.80

-34.60

-34.40

-34.20

-34.00

-33.80

Valu

e La

titud

e

81

Participant 19

Missing

147.48147.48147.48147.48147.50147.62147.77147.91148.07148.23148.38148.54148.70148.85149.01149.16149.28149.39149.50149.61149.73149.87150.02150.16150.30150.44150.56150.69150.80150.83150.90150.96151.02150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.48147.49147.61147.75147.89148.04148.20148.35148.51148.66148.81148.96149.12149.25149.36149.46149.57149.68149.81149.95150.10150.24150.37150.51150.61150.77150.81150.85150.93150.99150.99150.99

Longitude

-35.40

-35.10

-34.80

-34.50

-34.20

-33.90

Valu

e La

titud

e

82

Participant 20

Missing

147.48147.48147.48147.44147.57147.70147.84147.98148.15148.30148.44148.60148.75148.94149.12149.26149.34149.45149.58149.67149.81149.96150.11150.26150.42150.54150.66150.75150.86150.93150.98151.01151.02150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.44147.57147.70147.84147.98148.15148.30148.44148.60148.75148.94149.12149.26149.34149.45149.58149.67149.81149.96150.11150.26150.42150.54150.66150.75150.86150.93150.98151.01151.02150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

83

Participant 21

Missing

147.48147.48147.51147.62147.75147.90148.04148.18148.33148.47148.62148.75148.88149.03149.16149.26149.35149.46149.56149.68149.81149.93150.04150.17150.31150.44150.53150.62150.71150.80150.91151.02150.99150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.51147.61147.74147.88148.02148.16148.31148.45148.59148.72148.85148.99149.12149.23149.32149.42149.52149.63149.76149.88149.99150.11150.24150.37150.49150.57150.66150.75150.84150.95151.03150.99150.99

Longitude

-35.40

-35.20

-35.00

-34.80

-34.60

-34.40

-34.20

-34.00

-33.80

Valu

e La

titud

e

84

Participant 22

Missing

147.48147.48147.48147.48147.64147.81147.97148.13148.29148.45148.62148.78148.95149.11149.23149.35149.45149.55149.66149.74149.89150.05150.20150.36150.49150.57150.66150.77150.83150.96150.99150.99150.99150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.47147.62147.79147.94148.10148.26148.42148.58148.74148.91149.07149.20149.31149.42149.51149.62149.69149.83149.98150.13150.28150.44150.53150.61150.71150.77150.87150.99150.99150.99150.99150.99

Longitude

-35.40

-35.10

-34.80

-34.50

-34.20

-33.90

Valu

e La

titud

e

85

Participant 23

Missing

147.48147.47147.58147.70147.86148.01148.16148.32148.45148.50148.65148.80148.95149.09149.22149.32149.43149.53149.65149.79149.93150.07150.23150.38150.52150.57150.68150.71150.79150.87150.92150.99151.02150.99

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

7000.00

Valu

e A

ltitu

de

Missing

147.48147.46147.57147.69147.84147.99148.14148.29148.44148.47148.62148.77148.92149.06149.19149.29149.40149.49149.61149.74149.87150.02150.16150.31150.47150.55150.61150.70150.74150.83150.89150.97150.99151.02150.99

Longitude

-35.40

-35.20

-35.00

-34.80

-34.60

-34.40

-34.20

-34.00

-33.80

Valu

e La

titud

e

86

Participant 24

Missing

147.48147.48147.48147.48147.48147.48147.48147.53147.65147.79147.99148.17148.36148.55148.72148.89149.07149.24149.35149.42149.58149.77149.94150.12150.31150.46150.56150.70150.70150.83151.02151.03151.04151.04

Longitude

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Valu

e A

ltitu

de

Missing

147.48147.48147.48147.48147.48147.48147.48147.51147.63147.76147.95148.13148.32148.50148.68148.84149.01149.20149.31149.39149.51149.69149.87150.04150.22150.39150.52150.62150.73150.71150.91150.99150.97151.01151.04

Longitude

-35.50

-35.00

-34.50

-34.00

Valu

e La

titud

e

87

Documents

An Assessment of General Aviation Pilot Performance During