It is becoming widely recognised that shiftwork has significant implications for the health, safety and quality of life of shiftworkers. To date, little research has been carried out on how individuals cope with the problems caused by shiftwork and how effective coping strategies maintain their health and well-being. It has been proposed, by Monk (1994), that there are a number of aspects of a shiftworker's life which are important in determining the ability of an individual to cope with shiftwork. These factors are an individual's circadian rhythms, sleep patterns, and social and domestic situation. Further literature also suggests that workplace factors and coping style are an important part of tolerating shiftwork. The primary aim of the present study was to determine which factors are important in predicting the physical and mental well-being of Air Traffic Services staff working on shifts. It was hypothesised that individuals who are evening types, have few social, domestic, sleep, and work place difficulties will be physically and mentally healthy. In addition, it was hypothesised that the use of engagement strategies in dealing with shiftwork related problems will relate to better physical and mental health. To test the hypotheses, 183 Air Traffic Services staff from Melbourne centre, Australia were surveyed by questionnaire. The results of the regressions showed that physical health was predicted by variables from each of the five areas considered; circadian typology, the social and domestic situation, work place factors, sleep patterns and coping style. Mental well-being was best predicted by a single domestic variable, which is the extent to which shiftwork caused domestic problems and the two coping variables of engagement and disengagement. The results support the suggestion that in order for an individual to be able to tolerate shiftwork they must have strategies in place to help them deal with the effect of shiftwork variables on a range of factors in their lives. An additional aim of the present study was to determine the reliability of a questionnaire for use with Air Traffic Services staff. This was due to a lack of suitable questionnaires for use in this occupational context. Overall the items in the questionnaire were found to have acceptable reliability, although the collection of sleep data by subjective reporting is not recommended.

Non-controlled airports are literally that - uncontrolled. Safe separation is achieved by pilot vigilance. The consensus of reports on incidences at noncontrolled airports generally conclude that pilots cannot rely entirely on vision to avoid collision and attempts should be made to obtain all available traffic information to enable a directed traffic search. Ideally, a system is required which has the ability to provide advice to all parties to ensure separation minima is maintained. Provision of a such a system would remove a measure of pressure from the pilot to allow that person to devote their attention to their prime responsibility of flying the aircraft. To this end, research on use of intelligent remote advisory systems for non-controlled airports was undertaken with emphasis on those systems which could minimize human resources and associated recurring costs, to provide a measure of repeatability and to provide an acceptable level of safety. A rule based system was developed and evaluated. The evaluation showed that use of a rule based system as the basis of an intelligent remote air traffic control system for non-controlled airports is a viable proposition. In test scenarios, collision hazards were identified and evasion tactics generated. For a full operational system, the application of the rules and definition of the aircraft circuit area may need refining; however, the results are certainly encouraging.

The aim of the present research was to examine the impact of mental workload on conflict resolution decision making in air traffic control (ATC). While previous studies have examined the effect of workload on performance (Averty, 2004; Kopardekar & Magyrits, 2002) and conflict detection (Mogford, 1997; Seamster, Redding, Cannon, Ryder & Purcell, 1993), limited research examines the effect of workload on conflict resolution decisions. The aim of the first study was to gain an initial understanding of how controllers manage their airspace. Results demonstrated that controllers scan repetitively, in a clockwise and top-bottom pattern; group aircraft with similar characteristics and use at least five lateral and eight vertical conflict resolution heuristics. Study two examined the effect of conflict type on conflict resolution under different levels of workload. Under moderate workload controllers used a mix of solutions, while under high workload, solutions became more conservative. Study three examined the effect of other contextual factors on conflict resolution. Results again suggested conflict type affects conflict resolution decisions and also that other contextual parameters, such as aircraft performance may play a role in solution preferences. Study four examined the effect of workload on conflict resolution using a realistic ATC task. Workload not only impacted on controllers’ performance scores, but interacted with conflict type to determine whether an efficient solution was preferred over a less efficient solution. This research identifies some of the heuristics experts use when competing priorities are present and provides an understanding of how conflict type, contextual factors and workload affect decisions. Findings contribute to the naturalistic decision making (NDM) literature by demonstrating how the situation can influence decision making.

<p>As air traffic is forecasted to increase, air traffic control software subsequently needs to be more sophisticated. To efficiently push development forward, testing is important in order to determine usability. The tests need to be adapted to fit a particular purpose and carried out with methods that preserve the validity of the results. </p><p>This thesis describes an implementation project carried out at the EUROCONTROL Experimental Centre, Bretigny-sur-Orge, France. The purpose of the project is to create an application that enables a user to create datasets of air traffic to be used for these tests. The application allows for manual work or bulk imports from external data sources. Furthermore it compiles scenarios as output datasets intended for prototype air traffic control software developed at Linköping University. </p><p>The application design rationale and development process is described. Some time is spent on demonstrating the flexibility of the application and how its usage fits in a bigger picture.</p>

<p>Now a day, one of the most attractive research topics in the area of Intelligent Traffic Control is </p><p>Inter-vehicle communication (V2V communication). In V2V communication, a vehicle can </p><p>communicate to its neighbouring vehicles even in the absence of a central Base Station. The </p><p>concept of this direct communication is to send vehicle safety messages one-to-one or one-to- </p><p>many vehicles via wireless connection. Such messages are usually short in length and have very </p><p>short lifetime in which they must reach the destination. The Inter-vehicle communication system </p><p>is an ad-hoc network with high mobility and changing number of nodes, where mobile nodes </p><p>dynamically create temporary sensor networks and transferring messages from one network to </p><p>others by using multiple hops due to limitation of short range. </p><p> </p><p>The goal of the project is to investigate some basic research questions in order to organize such </p><p>sensor networks and at the same time highlight the appropriate routing protocol that support </p><p>mobile ad hoc networks in an efficient and reliable manner. </p><p> </p><p>In our investigation, we have answered the technical issues in order to construct a V2V </p><p>communication system. We have also studied some mobile ad hoc network routing protocols in </p><p>detail and then selected the DSR (Dynamic Source Routing) for our V2V communication and </p><p>then simulated it according to our system requirements. We are quite satisfied by the result of </p><p>DSR, but at the same time much more work is required to come up with an absolute application </p><p>for the end user.</p>

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.