R.B. Adler, S.J. Fricker. / "August 13, 1951." / Bibliography: p. 50. / Air Navigation Development Board of the Department of Commerce Contract No. Cca-28152.

The current project is an examination of the effectiveness of decision-support graphics in a simulated real-world task, and of the role those graphics should play in training. It is also an attempt to apply a theoretical account of memory performance-transfer-appropriate processing-to naturalistic decision making. The task in question is a low-fidelity air traffic control simulation. In some conditions, that task includes decision-support graphics designed to explicitly represent elements of the task that normally must be mentally represented-namely, trajectory and relative altitude. The assumption is that those graphics will encourage a type of processing different from that used in their absence. If so, then according to the theory of transfer-appropriate processing (TAP), the best performance should occur in conditions in which the graphics are present either during both training and testing, or else not at all. For other conditions, the inconsistent presence or absence of the graphics should lead to mismatches in the type of processing used during training and testing, thus hurting performance. A sample of 205 undergraduate students were randomly assigned to four experimental and two control groups. The results showed that the support graphics provided immediate performance benefits, regardless of their presence during training. However, presenting them during training had an apparent overshadowing effect, in that removing them during testing significantly hurt performance. Finally, although no support was found for TAP, some support was found for the similar but more general theory of identical elements.

Uncertainty is inherent in complex socio-technical systems such as in aviation, military, and surface transportation domains. An improved understanding of how operators comprehend this uncertainty is critical to the development of operations and technology. Towards the development of a model of time of arrival (TOA) uncertainty, Experiment 1 was conducted to determine how air traffic controllers estimate TOA uncertainty and to identify sources of TOA uncertainty. The resulting model proposed that operators first develop a library of speed and TOA profiles through experience. As they encounter subsequent aircraft, they compare each vehicle’s speed profile to their personal library and apply the associated estimate of TOA uncertainty. To test this model, a normative model was adopted to compare inferences made by human observers to the corresponding inferences that would be made by an optimal observer who had knowledge of the underlying distribution. An experimental platform was developed and implemented in which subjects observed vehicles with variable speeds and then estimated the mean and interval that captured 95% of the speeds and TOAs. Experiments 2 and 3 were then conducted and revealed that subjects overestimated TOA intervals for fast stimuli and underestimated TOA intervals for slow stimuli, particularly when speed variability was high. Subjects underestimated the amount of positive skew of the TOA distribution, particularly in slow/high variability conditions. Experiment 3 also demonstrated that subjects overestimated TOA uncertainty for short distances and underestimated TOA uncertainty for long distances. It was shown that subjects applied a representative heuristic by selecting the trained speed profile that was most similar to the observed vehicle’s profile, and applying the TOA uncertainty estimate of that trained profile. Multiple regression analyses revealed that the task of TOA uncertainty estimation contributed the most to TOA uncertainty estimation error as compared to the tasks of building accurate speed models and identifying the appropriate speed model to apply to a stimulus. Two systematic biases that account for the observed TOA uncertainty estimation errors were revealed: Assumption of symmetry and aversion to extremes. Operational implications in terms of safety and efficiency for the aviation domain are discussed.

Uncertainty is inherent in complex socio-technical systems such as in aviation, military, and surface transportation domains. An improved understanding of how operators comprehend this uncertainty is critical to the development of operations and technology. Towards the development of a model of time of arrival (TOA) uncertainty, Experiment 1 was conducted to determine how air traffic controllers estimate TOA uncertainty and to identify sources of TOA uncertainty. The resulting model proposed that operators first develop a library of speed and TOA profiles through experience. As they encounter subsequent aircraft, they compare each vehicle’s speed profile to their personal library and apply the associated estimate of TOA uncertainty. To test this model, a normative model was adopted to compare inferences made by human observers to the corresponding inferences that would be made by an optimal observer who had knowledge of the underlying distribution. An experimental platform was developed and implemented in which subjects observed vehicles with variable speeds and then estimated the mean and interval that captured 95% of the speeds and TOAs. Experiments 2 and 3 were then conducted and revealed that subjects overestimated TOA intervals for fast stimuli and underestimated TOA intervals for slow stimuli, particularly when speed variability was high. Subjects underestimated the amount of positive skew of the TOA distribution, particularly in slow/high variability conditions. Experiment 3 also demonstrated that subjects overestimated TOA uncertainty for short distances and underestimated TOA uncertainty for long distances. It was shown that subjects applied a representative heuristic by selecting the trained speed profile that was most similar to the observed vehicle’s profile, and applying the TOA uncertainty estimate of that trained profile. Multiple regression analyses revealed that the task of TOA uncertainty estimation contributed the most to TOA uncertainty estimation error as compared to the tasks of building accurate speed models and identifying the appropriate speed model to apply to a stimulus. Two systematic biases that account for the observed TOA uncertainty estimation errors were revealed: Assumption of symmetry and aversion to extremes. Operational implications in terms of safety and efficiency for the aviation domain are discussed.

Runway systems at airports have been identified as a major source of delay in the aviation system and efficient runway operations are, therefore, important to maintain and/or increase the capacity of the entire aviation system. The goal of the airport runway scheduling problem is to schedule a set of aircraft and minimize a given objective while maintaining separation requirements and enforcing other operational constraints. Uncertain factors such as weather, surrounding traffic and pilot behavior affect when aircraft can be scheduled, and these factors need to be considered in planning models. In this thesis we propose two stochastic programs to address the stochastic airport runway scheduling problem and similarly structured machine scheduling problems. In the first part, we develop a two-stage stochastic integer programming model and analyze it by developing alternative formulations and solution methods. As part of our analysis, we first show that a restricted version of the stochastic runway scheduling problem is equivalent to a machine scheduling problem on a single machine with sequence dependent setup times and stochastic due dates. We then extend this restricted model by considering characteristics specific to the runway scheduling problem and present two different stochastic integer programming models. We derive some tight valid inequalities for these formulations, and we propose a solution methodology based on sample average approximation and Lagrangian based scenario decomposition. Realistic data sets are then used to perform a detailed computational study involving implementations and analyses of several different configurations of the models. The results from the computational tests indicate that practically implementable truncated versions of the proposed solution algorithm almost always produce very high quality solutions. In the second part, we propose a sampling based stochastic program for a general machine scheduling problem with similar characteristics as the airport runway scheduling problem. The sampling based approach allows us to capture more detailed aspects of the problem, such as taxiway operations crossing active runways. The model is based on the stochastic branch and bound algorithm with several enhancements to improve the computational performance. More specifically, we incorporate a method to dynamically update the sample sizes in various parts of the branching tree, effectively decreasing the runtime without worsening the solution quality. When applied to runway scheduling, the algorithm is able to produce schedules with makespans that are 5% to 7% shorter than those obtained by optimal deterministic methods. Additional contributions in this thesis include the development of a global cost function, capturing all relevant costs in airport runway scheduling and trading off different, sometimes conflicting, objectives. We also analyze the impact of including environmental factors in the scheduling process.

This thesis addresses the possibility of using a delay-on-ground concept in which flights with less than 1 hour flying time (often referred to as pop-up flights) absorb their arrival sequencing delay at the departure gate by being issued their Arrival Manager (AMAN)-scheduled time as a Required Time of Arrival (RTA) that is inserted into the Flight Management System (FMS). Due to their short duration these flights are currently often inserted into the AMAN sequence shortly before Terminal Manoeuvring Area (TMA) entry and thereby often need to absorb their arrival sequencing delay in the inefficient manner of airborne holding or vectoring close to the arrival airport. The literature review examines current operational procedures of AMANs and Departure Managers (DMANs), the current FMS RTA function and live trials in which the delay-on-ground concept was tested in real operations. A case study airport in Europe that has potential to benefit from the concept is identified. The performance of the delay-on-ground concept for the case study airport is then assessed by performing 180 fast-time Monte Carlo simulation runs. For each run the arrival flow to the case study airport and the departure flows from two medium-sized airports from which the pop-up flights originate are simulated. Each run represents an operational day and variations in departure/arrivals time is put into the timetables to simulate the variation in actual departure/arrival times resulting from operational factors normally encountered in day-to-day operations. An algorithm is written in Matlab to simulate an AMAN-DMAN cooperation in which pop-up flights are locked to the required departure times to meet their RTAs. It is shown that a significant reduction in airborne delay time and fuel consumption can be achieved at the case study airport by using the concept. It is also shown that it is possible to ensure that the pop-up flights depart at the required times to meet their RTAs without negatively affecting the departure sequences.

The goal of this thesis is to improve pilot understanding of the Traffic alert and Collision Avoidance System (TCAS) by changing the Traffic Situation Display (TSD). This is supported by two objectives. The first objective is to create an integrated, realistic air traffic environment. This serves as an experimental platform for testing and evaluating future TCAS TSDs. The simulator environment includes a desktop flight simulator, background air traffic simulator, and intruder aircraft. The intruder aircraft uses seven dimensional waypoints to robustly follow trajectories and cause specific resolution advisories. Second, the relative benefits of, and potential concerns with, new TCAS TSDs are explored using a structured, iterative design process with subject matter ex- perts (SMEs). Incremental changes to the TSD were implemented into the simulator environment. SMEs evaluated the displays and potential points of confusion were identified. Several display features are discussed and implemented for future evaluations. These include boundary lines of TCAS variables depicted on the TSD and on a vertical situation display, speed lines which vary with the TCAS estimate of time to closest point of approach, and a prediction of the safe altitude target during a resolution advisory. Scenarios which may be confusing or misleading are discussed. These scenarios may be ameliorated or exacerbated by display features. This information is useful to guide both design and certification or operational approval and is a starting place for future TCAS experiments.

The current project is an examination of the effectiveness of decision-support graphics in a simulated real-world task, and of the role those graphics should play in training. It is also an attempt to apply a theoretical account of memory performance-transfer-appropriate processing-to naturalistic decision making. The task in question is a low-fidelity air traffic control simulation. In some conditions, that task includes decision-support graphics designed to explicitly represent elements of the task that normally must be mentally represented-namely, trajectory and relative altitude. The assumption is that those graphics will encourage a type of processing different from that used in their absence. If so, then according to the theory of transfer-appropriate processing (TAP), the best performance should occur in conditions in which the graphics are present either during both training and testing, or else not at all. For other conditions, the inconsistent presence or absence of the graphics should lead to mismatches in the type of processing used during training and testing, thus hurting performance. A sample of 205 undergraduate students were randomly assigned to four experimental and two control groups. The results showed that the support graphics provided immediate performance benefits, regardless of their presence during training. However, presenting them during training had an apparent overshadowing effect, in that removing them during testing significantly hurt performance. Finally, although no support was found for TAP, some support was found for the similar but more general theory of identical elements.

The air traffic system in the United States is currently undergoing a complete overhaul known as "NextGen". NextGen is the FAA's initiative to update the antiquated National Airspace System (NAS) both procedurally and technologically to reduce costs to the users and negative impacts on the general public. There are currently numerous studies being conducted that are focused on finding optimal solutions to the problems of congestion, delay, and the high fuel and noise footprints associated aircraft operations. These studies require accurate simulation techniques to assess the potential benefits and drawbacks for new procedures and technology. One common method uses air traffic control radar data. As an aircraft travels through the air traffic control system, its latitude, longitude, and altitude are recorded at set intervals. From these values, estimates of groundspeed and heading can be derived. Researchers then use this data to estimate aircraft performance parameters such as engine thrust and aircraft configuration, variables essential to estimate fuel burn, noise, and emissions. This thesis creates a more accurate method of simulating aircraft performance based solely on air traffic control radar data during the arrival process. This tool will allow the benefits of different arrival procedures to be compared at a variety of airports and wind conditions before costly flight testing is required. The accuracy of the performance estimates will be increased using the Tool for Assessing Separation and Throughput (TASAT), a fast-time Monte Carlo aircraft simulator that can simulate multiple arrivals with a mixture of different aircraft types. The tool has succeeded in matching various recorded radar profiles and has produced fuel burn estimates with an RMS error of less than 200 pounds from top of descent to landing when compared to high fidelity operational data. The output from TASAT can also be ported to FAA software tools to make higher quality predictions of aircraft noise and emissions.

The current air traffic management system has primarily evolved via incremental changes around historic control, navigation, and surveillance technologies. As a result, the system as a whole is not capable of handling air traffic capacities well beyond current levels, despite recent developments, such as ADS-B, that could potentially enable new concepts of operation. Methods of analyzing air traffic for safety and performance have also evolved around current-day operating constructs. Thus, attempts to examine future systems tend to use different analysis methods developed for each. Most notably, questions of 'locus of control' - whether the control should be centralized or de-centralized and distributed - have no common framework by which to judge relative costs and benefits. For instance, a completely centralized control paradigm is commonly asserted to provide an airspace-wide optimal traffic management solution due to a more complete picture of the state of the airspace, whereas a completely decentralized control paradigm is commonly asserted to provide a more user-specific optimal traffic management solution, to distribute the traffic management workload, and potentially be more robust. Given the disparate nature of these assertions and the different types of evaluations commonly used with each, some shared framework must be established to allow comparisons between very different control paradigms. The objective of this thesis was to construct a formal framework to examine the relative costs and benefits of shifting the locus of control in a novel air traffic management environment. This framework provides useful definitions and quantitative measures of flexibility and robustness with respect to various control paradigms ranging between, and including, completely centralized and completely decentralized concepts of operation. Multi-agent dynamic analysis and simulation was used to analyze the range of dynamics found in the different control paradigms. In addition, futuristic air traffic management concepts were developed in sufficient detail to demonstrate the framework. In other words, the objectives were met because the framework was demonstrated to have the ability to identify (or dispel) hypotheses about the relative costs and benefits of locus of control.