Testing Air Traffic Controllers Cognitive Complexity Limits. Design Proposal Fall Author: Howard Kleinwaks

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1 Testing Air Traffic Controllers Cognitive Complexity Limits Design Proposal Fall 2001 Author: Howard Kleinwaks Advisor: R. John Hansman Partner: Blake Gottlieb December 11,

2 Table of Contents 1.0 Introduction Previous Work Modeling the Structure of the Airspace Hypotheses Objective Technical Approach Experimental Set-Up Test Subjects Data Collection Traffic Pattern Development Mathematical Development of Traffic Pattern Objective Measurements Subjective Measurements Measurement Error Human Factors Errors Data Analysis Safety Issues Facilities and Resources Budget Schedule Acknowledgements References 28 2

3 Table of Figures Figure 1 Two types of merge points 7 Figure 2 Screenshot from TRACON II 10 Figure 3 Radar screens as viewed by test subjects 11 Figure 4 Relationship between sector length and percentage change in velocity 16 Figure 5 Traffic pattern 19 Figure 6 Modified Cooper-Harper chart 21 3

4 Table of Tables Table 1 Test Matrix 13 Table 2 Budget 26 Table 3 Schedule 27 4

5 1.0 Introduction Air traffic control (ATC) has become increasingly important in recent years. The number of planes in the sky has increased and, accordingly, the difficulty in ensuring that all these planes reach their destinations safely and in a timely matter has increased. Therefore, an increased pressure has been put on air traffic controllers to perform in more difficult and more complex situations. The question then arises, how difficult and how complex a situation can an air traffic controller safely handle? The key to answering this question lies in determining the relationship between the difficulty and complexity of an air traffic control situation and the performance of an air traffic controller. The first step is to determine the factors that make a situation difficult and complex. Once these factors are determined, they can be tested to evaluate the effect that they have on complexity. Understanding how air traffic control situations become complex will lead to improvements in the air traffic control process. Understanding complexity allows for complexity to be decreased. As complexity decreases, performance increases, and therefore the safety of the planes in the air will increase. Hence, developing an understanding of the factors that make an air traffic control situation complex will lead to safer air travel. 2.0 Previous Work Previous studies have shown that factors in an air traffic control situation can be grouped into the following categories: aircraft distribution factors, operational constraints, and airspace structure factors. Aircraft distribution factors depend on the 5

6 distribution of traffic within the airspace. Operational constraints are the rules that govern the traffic flow in the airspace, such as weather rules. Airspace factors include the factors that relate to the physical properties of the airspace, such as the placement of navigation beacons and the shape of the airspace 1. Previous work done on the complexity of air traffic situations has mostly focused on the free flight concept. This concept looks at airplanes flying in completely uncontrolled airspace, free to choose their own flight paths and speeds. However, this concept is not practical for the current air traffic control set-up, which is highly structured. Therefore, work needs to be done to determine how this structure relates to the complexity of the air traffic control situation in order to understand how to maximize the performance of the air traffic controllers Modeling the Structure of the Airspace Airplanes travel along flight paths. When these flight paths enter into an air traffic control sector, they are merged by the controllers into a single flight path at a point in space known as a merge point. For this experiment, a merge point is defined as a point in space where two flight paths come together. The merging is done so that the controllers can pass the planes out of their sectors in one stream, which is easier for the controller in the next sector to handle 2. There are two main arrangements of merge points, as shown in Figure 1. Figure 1(a) shows co-located merge points. Co-located merge points occur when two or more merge points are at the same location in space. Essentially, a co-located merge point is one where three or more incoming flight paths are merged together. Figure 1(b) shows 6

7 non-co-located merge points. Non-co-located merge points occur when two incoming flight paths are merged into a single stream of traffic, and then a third incoming flight path is merged into that new stream. Figure 1 - Two types of merge points. (a) Co-located merge points. (b) Non-co-located merge points 2.2 Hypotheses It is hypothesized that non-co-located merge points are easier for air traffic controls to handle, and therefore, less complex. The reasoning behind this hypothesis is that by separating out the merge points, controllers should be able to focus their attention on one stream at a time, which is a simpler task. Furthermore, it is hypothesized that the complexity of the situation will increase as the number of incoming flight paths increases. 3.0 Objective The objective of this project is to determine the relationship between structural factors and complexity in an air traffic control situation. Specifically, the structural factors that will be looked at are merge points and the number of incoming flight paths. Merge points are key structural features of the current air traffic control system. 7

8 4.0 Technical Approach This experiment will consist of test subjects acting as air traffic controllers using a computer-run simulation. The subjects will first undergo a period of training to get used to the keyboard inputs to the simulation and to get used to the display screen. The subject will then be given the task: to merge incoming planes onto a single flight path while maintaining a minimum separation between the planes. The test subject will only be allowed to adjust the speed of the planes. After each test, objective measures of the subject s performance, namely the number of times a separation violation occurred and the reaction time, will be measured. A separation violation occurs when plane are too close together after merging to be safe. The reaction time is the time it takes the test subject to react to a potentially dangerous situation after it appears in the sector. The subject will also fill out a subjective evaluation of the test using a Cooper-Harper chart, which gives a standardized subjective evaluation of complexity, which will be discussed later on. The first phase of the experiment will be to run preliminary tests using a couple of the test subjects. The preliminary tests will serve as a proving ground for the experimental process. These tests will show whether the traffic pattern is of significant difficulty and will give an indication of the results. If the results do not match to the expected results, the test process, including the traffic pattern will be altered. Preliminary tests will then be run again to evaluate the new traffic pattern and test process, until a usable test is developed. A usable test will be defined as one where the traffic pattern is 8

9 not deemed too simple and where the test subject feels significantly challenged in the completion of the task. 4.1 Experimental Set-Up The tests will be run using the Tracon II Air Traffic Control computer simulation software. A screenshot of the program can be seen in Figure 2. The right side of the screen displays information about the planes in the airspace, including flight number, speed, and heading. At the bottom of the right side is the name of the control sector and the score, which is a performance metric calculated by the software. The left side of the screen is the radarscope. On the scope can be seen the planes in the sector, each denoted by a small white airplane, with relevant information next to the plane. The plus signs represent intersections of airways and radio beacons. The small circles represent airports, with approach lines or funnels, depending on the approach status of the airport. The outline of the land underneath the airspace, in this case the California coastline, can also be seen. 9

10 Figure 2 - Screenshot from Tracon II. Courtesy of Tracon II Multi-Player Air Traffic Control Simulator Owner s Manual. Each test subject will be presented with a varying airspace structural arrangement in each test. They will then be given multiple incoming streams of traffic and will be instructed to merge the traffic into a single stream without violating the separation requirement. Test subjects will be able to accomplish this feat by adjusting the speed of the incoming aircraft only. The separation requirement for this experiment will be 5 miles. The reason for choosing five miles is twofold. First, the FAA restrictions call for a 5-mile separation between planes. Second, TRACON will record separation violations of 5 miles. On the TRACON screen, airways will be depicted for the test subjects. The airways will be present in order to help the test subjects understand where the planes will be headed, since they will not be actual air traffic controllers, as will be discussed in section 4.2. The test subjects will view a screen set up like those shown in Figure 3, with the only difference being that the screen shown in Figure 3 will be superimposed on the TRACON II screen in Figure 2. 10

11 Figure 3 - Radar screens as viewed by test subjects. a) 3 incoming flight paths, at a non-co-located merge point. b) Two incoming flight paths at a co-located merge point. As Figure 3 shows, the test subjects will have the flight paths as well as the distance markers shown. For example, in a), the test subject will have to merge planes from the first two flight paths and have them 5 miles apart by the time the planes cross the first vertical line. Then, they will have to merge that stream of planes with the third incoming flight path and have all the planes 5 miles apart by the time they cross the second vertical line. The same method applies in b), but with only one merge needing to be performed. 4.2 Test Subjects A total of twelve test subjects will be used for this experiment. However, if the preliminary tests show that the test is not usable, the number of test subjects will have to be increased. A test subject will not be used twice, as it is felt that test subjects will not likely be available for a second round of testing. Therefore, more than twelve test subjects will be recruited, although it may not be necessary to use all of them. 11

12 Test subjects for this experiment will be MIT students. Air traffic controllers will not be used, nor is it necessary to use air traffic controllers, because the goal of this project is understand how the structural factors affect complexity, not how well an air traffic controller can do in a given situation. It is intentionally simplified so that the relationship between the specific structural factors and the complexity can be determined. 4.3 Data Collection There will be six different tests run in this experiment, as can be seen in Table 1, the test matrix. Each test will have a varying arrangement of incoming flight paths and merge points, the two independent variables. It should be noted that for one or two incoming flight paths, the co-location status of the merge points does not vary. Recalling that a merge point is defined as a point in space where two flight paths are merged, it is readily seen that the number of merge points is always one less than the number of incoming flight paths. Therefore, for the tests with one or two incoming flight paths, there are zero and one merge points, respectively. Hence, there is only one possible arrangement of the merge points, and therefore the co-located and non-co-located arrangements are the exact same. That leaves only six different structural arrangements. Furthermore, it is possible to have different arrangements for the four incoming flight path, non-co-located test. However, it is rare that an air traffic controller would encounter a different arrangement, and therefore only the arrangement shown will be tested. These six arrangements will be run on each subject and cover the spectrum of the possible tests with the two independent variables. It should also be noted that a total of 12

13 seven tests will be run per test subject, in order to account for fatigue effects, as is discussed in section 4.5. Table 1: Test Matrix Traffic Pattern Development The same baseline traffic pattern will be used for each test. In order to develop this pattern, certain requirements had to be satisfied. First of all, the time for each test needs to be limited. A total of seven tests will be run. Including training time, it is not desirable to keep test subjects for more than 3 hours. After estimating that training time will amount to approximately 45 minutes, it can be seen that each test cannot exceed twenty minutes. The second consideration is the regulations and controls that the subject will be able to use. Since the subject can change the speed of the aircraft, it is possible that the subject could adjust the speed of each aircraft as soon as they come on the screen so that the aircraft shoots off the other side of the screen. This action cannot be allowed, since it defeats the purpose of running the test. Therefore, speed control needs to be factored into the test development. 13

14 Finally, the traffic pattern that is developed needs to be mathematically possible to solve. It would do little good to run a test where it is not possible for the subject to successfully complete the test Mathematical Development of Traffic Pattern The mathematical solution resulted from some basic calculations. In order to properly develop a traffic pattern, the total length of the airspace sector needs to be known. The sector length depends on the amount of time it takes for two planes that entered the airspace at the same time to separate by the required five miles. Therefore, the first step in determining the sector length is to determine the time it takes for two planes to separate by the required distance. The only control that the controller has over the planes is speed control, so the time to reach the required separation is dependent on the speed that the planes are flying at. Since each plane will be entering the airspace at the same speed, it becomes necessary to evaluate the time based on the percentage of the speed that the controller can change. If this percentage is labeled α, then the following equation results: Ndsep 2αv 0t = (1) In equation 1, t is the time it takes for two planes, which entered the airspace at the same exact time traveling at speed v 0, to reach the required separation, d sep. The two represents the fact that the percentage velocity change can be either positive or negative, and thus covers a span of 2α times the velocity. The N represents the number of planes that need to have their speed altered in order to avoid a separation violation. Increasing the number of planes increases the distance required between the first and last planes, and therefore increases the total separation distance for the sector. In other words, having 14

15 three planes requires that, for a 5 mile separation requirement, the first and the last plane be 10 miles apart after they are merged. Therefore, the separation distance has doubled. The doubling makes sense, because in order to avoid a separation violation with three planes, only two need to have their speed altered, and hence N=2. Rearranging equation 1 results in equation 2, which gives the time for the two planes to separate by the minimum required distance. Ndsep t = (2) 2αv 0 The minimum length of the sector depends on the time just calculated as well as the percentage change in velocity allowed. The sector length must at least be long enough for a plane traveling at the maximum allowable speed to separate from a second plane. Hence, the sector length can be given by equation 3. d sec tor ) 0 = ( 1+ α v t (3) Then, by substituting equation 2 into equation 3, a result for the sector length as a function of the percentage change in velocity, α, and the required separation distance, d sep, can be found, as shown in equation 4. d 1 + α = (4) 2α sector Nd sep With this relationship determined, it is now possible to determine the minimum sector length for the minimum separation distance of 5 miles. The relationship between α and ( 1+ α) N is shown in Figure 4. The scale works such that for a given percentage 2α change in velocity, α, a value for the total sector length divided by the separation distance is gathered. The length of the sector can then be calculated. For example, two planes are 15

16 being considered, with a percentage velocity change of 20%, then the total sector length must be three times as long as the separation distance requirement. For a requirement of 5 miles, the sector must be at least 15 miles long. Figure 4 also shows what happens as N is increased. The graph moves vertically, and therefore, the sector length must be increased proportionally. To illustrate this point, again consider the velocity change of 20% and a 5 mile separation distance, but this time with 4 planes. The sector must then be 9 times as long as the separation distance, or 45 miles. Sector Length vs Increment of Velocity Change Sector Length Divided By Minimum Separation Distance Percentage of velocity that can be changed 2 aircraft 3 aircraft 4 aircraft Figure 4 - Relationship between sector length and percentage change in velocity The time of the test has yet to be accounted for. The planes will fly at about 300 miles per hour. In a 45 mile long sector, each plane would then take, on average, 9 minutes to cross the sector. In order to limit the test to twenty minutes, the last plane would then have to enter the sector 2 minutes after the first plane leaves the sector. Doing so does not leave much time for the addition of planes, and would therefore require a test that was too simple. Therefore, it becomes desirable to reduce the time it takes for each plane to cross the sector to 5 minutes. By reducing the time to 5 minutes, 16

17 the traffic pattern can be broken up into 3 equal sections that are each five minutes long. The time for each test then becomes twenty minutes five minutes for each section of the test pattern, and an additional five minutes for the last plane to cross the sector. There are two ways to accomplish this feat: changing the size of the sector and changing the size of the sector. Changing the size of the sector such that it takes five minutes for a plane traveling at 300 miles per hour to cross the sector results in a sector length of 25 miles. Using equation 4, with a 20% change in velocity and only two planes, it can be seen that for this sector to be viable, the separation requirement would have to be 8.33 miles. While it would be possible to change the separation requirement, it is not ideal, since TRACON II can record separation violations at a separation requirement of 5 miles, thereby making data collection more accurate. The other option is to change the speed of the planes. Increasing the speed of the planes to 400 miles per hour allows for planes to cross the 45 mile sector in 6.75 minutes. This time is still longer than desired. Unfortunately, it is not possible, within the bounds of TRACON II, to increase the speed further and still allow the test subject to be able to increase the speed during the test run. Therefore, it is necessary to also adjust the sector length, in addition to the speed of the planes. Decreasing the time to cross the sector to 5 minutes results in a sector length of 33.3 miles, if the planes are flying at 400 miles per hour. This sector length can be usable in terms of time, but the question arises as to its usability in terms of problem situations. 17

18 Earlier, it was calculated that for four planes entering the sector at the same time, a minimum of 45 miles was needed to allow the planes to separate properly. Using a sector length of 33 miles necessitates that four planes cannot be allowed to enter the sector at the exact same time. Therefore, it becomes necessary to space the planes out. There are infinite workable combinations in terms of spacing for up to four planes, and therefore the solution that fit with the rest of the traffic pattern was chosen, as shown below. The resulting traffic pattern is shown in Figure 5 below. The pattern is divided up into three sections that will be rearranged in order to account for learning effects. For example, one structural arrangement will have the pieces of the traffic pattern in the order ABC, while another will have BCA, and so on. Each vertical line represents a plane that will flow through the pattern, and the X represents two planes that will be in a separation violation after they are merged unless they are acted upon by the controller. It can be seen that each structural arrangement will have ten problem situations. 18

19 Figure 5 - Traffic pattern Objective Measurements The complexity of the situation will be measured in two ways. First, since performance is related to complexity, the objective performance of the test subject will be measured. Two measurements will determine the performance of the subject: the number of separation violations and the reaction time. A separation violation occurs when two planes get too close together for the situation to be safe. At the start of each test, the subject will be given instructions to merge the incoming planes onto a single flight path while maintaining a minimum distance between the planes. A separation violation will occur anytime that this minimum distance fails to be met. A larger number of separation 19

20 violations indicates a worse performance. Separation violations will be determined by the computer simulation. Reaction time is the time it takes for the subject to notice and act upon a problem situation after it has entered the airspace. A problem situation is a situation where two planes on incoming flight paths will violate the minimum separation distance after they are merged, unless the controller acts upon the planes. Presumably, a controller will have a harder time noticing problem situations, and thus a longer reaction time, in more complex scenarios. Combined together, reaction time and the number of separation violations will give an objective measure of the subject s performance Subjective Measurements The second measure of complexity will be to have the subject subjectively evaluate the complexity of the situation using a modified Cooper-Harper chart, as shown in Figure 6. The Cooper-Harper chart originated as a way to subjectively evaluate the controllability of experimental aircraft. Test pilots would rate each plane by answering a series of questions and following descriptions of the plane s behavior, resulting in a numerical rating for the controllability. A rating of 10 represented an uncontrollable plane, while a rating of one represented a plane that was easy to control. By modifying the Cooper-Harper chart, it is possible to make the chart work for this test. Then, the test subjects will be able to give a numerical rating of the complexity of the test scenario, with ten being a very complex scenario and a one being a very simple scenario. 20

21 Figure 6 - Modified Cooper-Harper chart The test subject using the chart shown in Figure 6 would quickly come to an estimate of the complexity of the situation. First, the subject answers the question as to whether or not the situation was controllable. If the answer is no, the subject moves to the right, and gives the simulation a rating of 10, meaning that control of the situation was lost during some portion of the required task. If the answer is yes, the subject moves up to the next question, is adequate performance attainable with maximum tolerable controller workload. Answering no to this question leads to the overworked section of responses, with ratings from 7-9. These responses described the structural characteristics of the airspace as having major 21

22 difficulties. Furthermore, while the situation was controllable, it required more effort from the controller than what is considered tolerable. Answering yes to the second question leads to the third question, is it satisfactory without simplification? Answering no to this question leads to responses that describe the structural characteristics as having difficulties ranging from small, but annoying to major, but tolerable, and to ratings of 4-6. This section is the hard section. The controllers are not exceeding their maximum workload capability, but it is not easy to maintain the separation of the planes. Answering yes to the third question leads to ratings of 1-3 and the easy section. The structural characteristics of the airspace in this section consist only of small difficulties, and the controller can maintain separation without having to exert much effort. 4.4 Measurement Error In taking the measurements described in section 4.3.2, there are sources of error. The error in the calculation of the number of separation violations should be minimal, since it is measured by the computer. The reaction time estimate may be more error prone. The current plan is to adapt the simulation software to record the keystrokes of the test subject. If this method proves feasible, then the error should be minimal. However, if the software cannot be adapted, then a video camera set-up will be used to record the simulation, and the reaction time will be garnered from the videotape. This method is significantly more error prone, with errors of up to a second in the estimation of reaction time being likely. 22

23 4.5 Human Factors Errors Human subjects learn as the test progresses and then get better at the test. There are three steps that will be taken to counteract these learning effects. First, the test matrix will be counter-balanced. Counter-balancing is the rearrangement of the test scenarios so that the subject cannot anticipate what is coming next. In order to fully counter-balance the test matrix, the number of test subjects needs to be a multiple of the number of test points. Since there are six test points in the test matrix for this experiment, twelve test subjects will be used. Twelve is a multiple of six, and is also a number that is high enough to provide a viable range of results while being low enough to accomplish during the allotted period of time. If the twelve subjects do not provide a useful amount of data, more test subjects will be gathered and more test will be run. The second step that will be taken to counteract learning effects is the training of each test subject. Subjects will come into the test with a different skill level at the simulation, due to their prior experiences. To ensure that prior skill is not a factor in this experiment, each test subject will spend practice time on the simulation before participating in the actual tests. To participate in the actual test, the subject will have to demonstrate a minimum level of proficiency at the simulation. In this way, all of the subjects will begin the test at the same ability. Another effect that comes from dealing with human subjects is the fatigue effect. As humans tire, their performance tends to decrease. Therefore, a decrease in performance in the test may be due to fatigue instead of an increase in complexity. As a result, fatigue needs to be tested for. To test for fatigue, the simplest test, the single 23

24 incoming flight path, will be run as the first and last test for each subject. If the performance on this test deteriorates significantly from the first run to the last run, it can be inferred that the subject has become fatigued, and the data therefore becomes questionable. If fatigue is noticed in one subject, another test subject will be run, and the fatigued data removed. If fatigue is noticed in multiple subjects, a new testing system will be devised and the subjects retested. A third effect of human subjects is preconceived notions of complexity. As stated in section the advantage of the Cooper-Harper chart is that it removes the subjects preconceived notions of complexity by steering the subject to a numerical value for complexity based on the subject s experiences during the test. 4.6 Data Analysis After each test is performed, the data needs to be analyzed. The number of separation violations is the crudest measurement of the performance of the controller, and therefore will be looked at first. As the complexity of the situation increases, it is expected that the number of separation violations would increase. A rough estimate of the relationship between the airspace structure and the complexity of each scenario will then be developed. This estimate will be fine-tuned by using the reaction time data. The reaction time should also increase as the situation becomes more complex. The larger reaction times should correspond to the scenarios with more separation violations and a more detailed definition of the complexity of the situation will evolve. Finally, the subjective assessment data will be compared to the objective data. The situations where the controllers performed the worst, objectively, should correspond to the situations that 24

25 the controllers felt were the most difficult. By comparing the subjective and objective assessments, a relationship between the structure and complexity of an airspace situation will be developed. 5.0 Safety Issues In running this experiment, there are no strong safety issues that need to be addressed. However, in order to utilize human test subjects, permission must be received from the Committee on the Use of Humans as Experimental Subjects. Since this test falls under the auspices of the Aerospace Information study area, the test is covered by that area s COUHES permission. 6.0 Facilities and Resources This experiment will require the use of computers and an isolated area to perform the test. The computer needs to be equipped with Windows 2000 to run the simulation. Such a computer has been obtained in the back room of the Gelb Lab, which should serve as a good area for testing. 7.0 Budget The budget for this experiment will be limited, as can be seen in Table 2. Previously, money was needed to buy other software to evaluate as opposed to TRACON II. However, after searching for software, it was found that no program exceeded the capabilities of TRACON II. Furthermore, Jon Histon has experience using TRACON II, and can therefore serve as a useful reference in the development of the software. 25

26 Therefore, money only needs to be allocated for videotapes. These tapes will serve as a backup to the data collection as well as a means for reviewing the actual experiment, in order to note any interesting trends. The video camera has been made available by MIT. The total budget then comes to $36. Table 2: Budget Item Quantity Needed Price Per Unit Cost Software 0 Donated $0 Video camera 1 Donated $0 Video cassette tapes 12 $3 $36 Computers 1 Donated $0 Work space Donated $0 Total Cost: $ Schedule Table 3 shows the schedule for the following term. The project is currently on schedule, having completed the detailed scenario design, which consisted of designing the traffic pattern, and having completed the subjective analysis development, which consisted of modifying the Cooper-Harper chart. For next term, the coding of the traffic pattern into the simulation will take place in the first two weeks, and all of the test subjects will have been gathered by this time. Additional test subjects will also be gathered in case the preliminary tests show that changes need to be made. After the traffic pattern is coded into the simulation and the 26

27 scenarios are set up, the preliminary tests will begin. If everything goes well with the preliminary tests, which will be found out in the preliminary data analysis, the actual training and testing will begin. The training and testing is scheduled to end two weeks before the last day to collect data. These two weeks will serve as a buffer zone, in case the preliminary tests show that the traffic pattern needs to be changed, or other unexpected problems occur in the testing process. Table 3 - Schedule Month Novmeber December January February March April May Task Date Oral Proposal Evaluate other ATC sims Detailed Scenario Design Subjective analysis development Oral Design Review Gather Test Subjects End of First Term Start of Second Term Coding of scenario into sim Preliminary Training and Testing Preliminary Data Analysis Oral Progress Report 1 Training and Testing Data Analysis Oral Progress Report 2 Last Day to Collect Data Final Oral Report 27

28 9.0 Acknowledgements The author would like to thank the following people for their help and support: Professor John Hansman Andrea McKenzie Professor Earll Murman Professor Kim Blair Col. Pete Young Jonathan Histon 28

29 10.0 References 1. J.M. Histon, et al. Introducing Structural Considerations into Complexity Metrics, Extended Abstract for ATM J.M. Histon. Personal Communication. October 4, Wesson, R. B. (1990). TRACON II: Multi-Player Air Traffic Control Simulator, Version 2.0. Wesson International: Austin TX. 29

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