The busiest regions of airspace in the U.S. are congested during much of the day from traffic volume, weather, and other airspace restrictions. The projected growth in demand for airspace is expected to worsen this congestion while reducing system efficiency and safety. This dissertation focuses on providing methods to analyze en route airspace congestion during severe convective weather (i.e. thunderstorms) in an effort to provide more efficient aircraft routes in terms of: en route travel time, air traffic controller workload, aircraft collision potential, and equity between airlines and other airspace users. The en route airspace is generally that airspace that aircraft use between the top of climb and top of descent. Existing en route airspace flight planning models have several important limitations. These models do not appropriately consider the uncertainty in airspace demand associated with departure time prediction and en route travel time. Also, airspace capacity is typically assumed to be a static value with no adjustments for weather or other dynamic conditions that impact the air traffic controller. To overcome these limitations a stochastic demand, stochastic capacity, and an incremental assignment method are developed. The stochastic demand model combines the flight departure uncertainty and the en route travel time uncertainty to achieve better estimates for sector demand. This model is shown to reduce the predictive error for en route sector demand by 20\% at a 30 minute look-ahead time period. The stochastic capacity model analyzes airspace congestion at a more macroscopic level than available in existing models. This higher level of analysis has the potential to reduce computational time and increase the number of alternative routing schemes considered. The capacity model uses stochastic geometry techniques to develop predictions of the distribution of flight separation and conflict potential. A prediction of dynamic airspace capacity is calculated based on separation and conflict potential. The stochastic demand and capacity models are integrated into a graph theoretic framework to generate alternative routing schemes. Validation of the overall integrated model is performed using the fast time airspace simulator RAMS. The original flight plans, the routing obtained from an integer programming method, and the routing obtained from the incremental method developed in this dissertation are compared. Results of this validation simulation indicate that integer programming and incremental routing methods are both able to reduce the average en route travel time per flight by 6 minutes. Other benefits include a reduction in the number of conflict resolutions and weather avoidance maneuvers issued by en route air traffic controllers. The simulation results do not indicate a significant difference in quality between the incremental and integer programming methods of routing flights around severe weather. / Ph. D.

One way to address the need for increased airspace system capacity is to reduce the separation requirements between aircraft in-flight. A key limiting factor to any reduction in separation is wake turbulence. The potential for aircraft to encounter wake turbulence poses a threat to both safety as well as increased efficiency. This research effort seeks to develop a model that can be used to evaluate the potential for wake encounters in today's flight operations, as well as serve as a tool for evaluating future reduced separation scenarios. The wake encounter model (WEM) achieves this goal by integrating results from NASA's TDAWP wake turbulence prediction model with a flight operations model based on radar flight track data. Unique in this model's design, is the ability to evaluate the potential for wake encounters throughout the terminal area versus previous research which has largely been restricted to areas near the runway. Expanding the model's reach provides not only for a more thorough analysis of potential wake encounters, but also creates an effective tool for evaluating future reduced separation scenarios. The WEM model was used to evaluate operations at three metropolitan airspaces in the United States: Atlanta, Los Angeles and New York. The results from these model runs indicated that potential wake encounters in today's operations were few. More importantly, the results from the WEM create a baseline for wake turbulence exposure in today's system, by which future scenarios can be compared against as part of any comprehensive reduced separation safety analysis. / Master of Science

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No description available.

Aviation suffers many delays due to the lack of timely air traffic flow management. These delays are also caused by the uncertainty weather information; and the lack of efficient dissemination of weather products to pilots. It is clear that better models are needed to quantify air traffic flow in three flight regions - en-route, in the terminal, and on the ground, to determine aviation weather information requirements at each region, and to quantify their bandwidth requirements. Furthermore, the results from those models can be used to select alternative future aviation communication systems. In this research, the 'ITHINK' and 'MATLAB' software packages have been used to develop a lumped Air Traffic Flow Model (ATFM) and an Aviation Weather Information and Bandwidth Requirements Model (AWINBRM). The ATFM model is used to quantify the volume of air traffic in each phase of flight in three flight regions. This model can be used to study navigation, surveillance, and communication requirements. The AWINBRM model is used to study aviation weather information requirements in different flight phases of flight. Existing and potential communication systems used for transmitting aviation weather information are explored in this research. Finally, a usable and practical computer model - Aircraft Impacted and Detour Model (AIDM) around an aviation weather system is developed. This model is used to compare the costs between detoured flights around a weather system and delayed flights at the airports. The purpose of this research is to study air traffic flow and aviation weather information and bandwidth requirements through modeling. The ultimate goal of the models described here is to serve as a living laboratory where policies can be tried before implementing them into the real system. Moreover, these computer models can evolve dynamically through time allowing decision makers to exercise policies at various points in time to quantify results with ease. This research would be a first integrated model for combing air traffic flow and aviation weather requirements and determining the quantity of aviation weather information between pilot and ground service centers. This research would be a guideline for aviation industry to build an efficient and timely aviation weather information transmission system with minimum budget. Consequently, this research will reduce aviation delays and improve aviation safety. / Ph. D.

At the present time, millions of dollars are being lost by major airlines each year because of the inability of high density air terminals to efficiently service all of the demands placed upon them during peak periods of demand. Up to the present time, studies involving congestion have been aimed primarily at the implementation of computerized techniques to aid the air traffic controller during peak demand periods. By scheduling aircraft in a given system in a different manner, delay, caused by congestion, could possibly be reduced at high density terminals even more than it has been reduced by the results obtained from previous studies. The approach taken in this study involves testing different heuristic scheduling algorithms, based on what has been done previously, to determine what extent total system delay can be reduced. The method of approach which was followed was based on a simulator which models aircraft movement between N major terminals. For each scheduling algorithm developed, hourly statistics related to the number of aircraft demanding service, average departure delay, and average arrival delay were calculated along with total system delay times for arriving and departing aircraft. The results obtained from these algorithms were analyzed and compared with the scheduling algorithm which resulted in a reduction in delay being examined in greater detail to determine whether or not such a schedule would actually be feasible and worthwhile. / Master of Science

This thesis deals with innovative concept of air traffic operations such as Free Flight. First, a baseline is established to determine how controllers operate under the current operation guidelines. Then flight trajectories are developed for different alternatives to the Free Flight operational concept. Finally, a comparison of these Free Flight alternatives with current operational concept is conducted to investigate an impact of Free Flight on Air Traffic Control. With the powerful features of optimization, graphics, and hierarchical modeling, the MATLAB toolboxes proved to be effective in the modeling process involved in this research. / Master of Science

Master of Science

A multitude of wireless technologies are used by air traffic communication systems during different flight phases. From a conceptual perspective, all of them are insecure as security was never part of their design and the evolution of wireless security in aviation did not keep up with the state of the art. Recent contributions from academic and hacking communities have exploited this inherent vulnerability and demonstrated attacks on some of these technologies. However, these inputs revealed that a large discrepancy between the security perspective and the point of view of the aviation community exists. In this thesis, we aim to bridge this gap and combine wireless security knowledge with the perspective of aviation professionals to improve the safety of air traffic communication networks. To achieve this, we develop a comprehensive new threat model and analyse potential vulnerabilities, attacks, and countermeasures. Since not all of the required aviation knowledge is codified in academic publications, we examine the relevant aviation standards and also survey 242 international aviation experts. Besides extracting their domain knowledge, we analyse the awareness of the aviation community concerning the security of their wireless systems and collect expert opinions on the potential impact of concrete attack scenarios using insecure technologies. Based on our analysis, we propose countermeasures to secure air traffic communication that work transparently alongside existing technologies. We discuss, implement, and evaluate three different approaches based on physical and data link layer information obtained from live aircraft. We show that our countermeasures are able to defend against the injection of false data into air traffic control systems and can significantly and immediately improve the security of air traffic communication networks under the existing real-world constraints. Finally, we analyse the privacy consequences of open air traffic control protocols. We examine sensitive aircraft movements to detect large-scale events in the real world and illustrate the futility of current attempts to maintain privacy for aircraft owners.

<div> <div> <div> <p>As the demand and complexity of air traffic increase, it becomes crucial to maintain the safety and efficiency of the operations in airspaces, which, however, could lead to an increased workload for Air Traffic Controllers (ATCs) and delays in their decision-making processes. Although terminal airspaces are highly structured with the flight procedures such as standard terminal arrival routes and standard instrument departures, the aircraft are frequently instructed to deviate from such procedures by ATCs to accommodate given traffic situations, e.g., maintaining the separation from neighboring aircraft or taking shortcuts to meet scheduling requirements. Such deviation, called vectoring, could even increase the delays and workload of ATCs. This thesis focuses on developing a framework for trajectory pattern identification and classification that can provide ATCs, in vectored airspace, with real-time information of which possible vectoring pattern a new incoming aircraft could take so that such delays and workload could be reduced. This thesis consists of two parts, trajectory pattern identification and trajectory pattern classification. </p> <p>In the first part, a framework for trajectory pattern identification is proposed based on agglomerative hierarchical clustering, with dynamic time warping and squared Euclidean distance as the dissimilarity measure between trajectories. Binary trees with fixes that are provided in the aeronautical information publication data are proposed in order to catego- rize the trajectory patterns. In the second part, multiple recurrent neural network based binary classification models are trained and utilized at the nodes of the binary trees to compute the possible fixes an incoming aircraft could take. The trajectory pattern identifi- cation framework and the classification models are illustrated with the automatic dependent surveillance-broadcast data that were recorded between January and December 2019 in In- cheon international airport, South Korea . </p> </div> </div> </div>