Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

Browse

Subject: search.filters.subject.Air Traffic Management

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    AIRSPACE PLANNING FOR OPTIMAL CAPACITY, EFFICIENCY, AND SAFETY USING ANALYTICS
    (2019) Ayhan, Samet; Samet, Hanan; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Air Navigation Service Providers (ANSP) worldwide have been making a considerable effort for the development of a better method for planning optimal airspace capacity, efficiency, and safety. These goals require separation and sequencing of aircraft before they depart. Prior approaches have tactically achieved these goals to some extent. However, dealing with increasingly congested airspace and new environmental factors with high levels of uncertainty still remains the challenge when deterministic approach is used. Hence due to the nature of uncertainties, we take a stochastic approach and propose a suite of analytics models for (1) Flight Time Prediction, (2) Aircraft Trajectory Clustering, (3) Aircraft Trajectory Prediction, and (4) Aircraft Conflict Detection and Resolution long before aircraft depart. The suite of data-driven models runs on a scalable Data Management System that continuously processes streaming massive flight data to achieve the strategic airspace planning for optimal capacity, efficiency, and safety. (1) Flight Time Prediction. Unlike other systems that collect and use features only for the arrival airport to build a data-driven model for predicting flight times, we use a richer set of features along the potential route, such as weather parameters and air traffic data in addition to those that are particular to the arrival airport. Our feature engineering process generates an extensive set of multidimensional time series data which goes through Time Series Clustering with Dynamic Time Warping (DTW) to generate a single set of representative features at each time instance. The features are fed into various regression and deep learning models and the best performing models with most accurate ETA predictions are selected. Evaluations on extensive set of real trajectory, weather, and airport data in Europe verify our prediction system generates more accurate ETAs with far less variance than those of European ANSP, EUROCONTROL’s. This translates to more accurately predicted flight arrival times, enabling airlines to make more cost-effective ground resource allocation and ANSPs to make more efficient flight scheduling. (2) Aircraft Trajectory Clustering. The novel divide-cluster-merge; DICLERGE system clusters aircraft trajectories by dividing them into the three standard major flight phases: climb, en-route, and descent. Trajectory segments in each phase are clustered in isolation, then merged together. Our unique approach also discovers a representative trajectory, the model for the entire trajectory set. (3) Aircraft Trajectory Prediction. Our approach considers airspace as a 3D grid network, where each grid point is a location of a weather observation. We hypothetically build cubes around these grid points, so the entire airspace can be considered as a set of cubes. Each cube is defined by its centroid, the original grid point, and associated weather parameters that remain homogeneous within the cube during a period of time. Then, we align raw trajectories to a set of cube centroids which are basically fixed 3D positions independent of trajectory data. This creates a new form of trajectories which are 4D joint cubes, where each cube is a segment that is associated with not only spatio-temporal attributes but also with weather parameters. Next, we exploit machine learning techniques to train inference models from historical data and apply a stochastic model, a Hidden Markov Model (HMM), to predict trajectories taking environmental uncertainties into account. During the process, we apply time series clustering to generate input observations from an excessive set of weather parameters to feed into the Viterbi algorithm. The experiments use a real trajectory dataset with pertaining weather observations and demonstrate the effectiveness of our approach to the trajectory prediction process for Air Traffic Management. (4) Aircraft Conflict Detection. We propose a novel data-driven system to address a long-range aircraft conflict detection and resolution (CDR) problem. Given a set of predicted trajectories, the system declares a conflict when a protected zone of an aircraft on its trajectory is infringed upon by another aircraft. The system resolves the conflict by prescribing an alternative solution that is optimized by perturbing at least one of the trajectories involved in the conflict. To achieve this, the system learns from descriptive patterns of historical trajectories and pertinent weather observations and builds a Hidden Markov Model (HMM). Using a variant of the Viterbi algorithm, the system avoids the airspace volume in which the conflict is detected and generates a new optimal trajectory that is conflict-free. The key concept upon which the system is built is the assumption that the airspace is nothing more than a horizontally and vertically concatenated set of spatio-temporal data cubes where each cube is considered as an atomic unit. We evaluate the system using real trajectory datasets with pertinent weather observations from two continents and demonstrate its effectiveness for strategic CDR. Overall, in this thesis, we develop a suite of analytics models and algorithms to accurately identify current patterns in the massive flight data and use these patterns to predict future behaviors in the airspace. Upon prediction of a non-ideal outcome, we prescribe a solution to plan airspace for optimal capacity, efficiency, and safety.
  • Thumbnail Image
    Item
    Optimization Models for Speed Control in Air Traffic Management
    (2015) Jones, James Calvin; Lovell, David J; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In a typical air traffic control environment, the precise landing times of en route aircraft are not set until each aircraft approaches the airspace adjacent to the destination airport. In times of congestion, it is not unusual for air traffic controllers to subject arriving aircraft to various maneuvers to create an orderly flow of flights onto an arrival runway. Typical maneuvers include flying in zig-zag patterns, flying in race track shaped patterns and tromboning. These maneuvers serve to delay the arrival time of the flight while also burning additional fuel. On the other hand, if the arrival time was established much earlier, then such delay could be realized by simply having flights fly slower while still at a higher altitude, which would incur much less fuel burn than the described maneuvers. Yet despite its potential benefit, thus far little has been done to promote the management of flights using speed control in the presence of uncertainty. This dissertation presents a set of models and prescriptions designed to use the mechanism of speed control to enhance the level of coordination used by FAA managers at the tactical and pre-tactical level to better account for the underlying uncertainty at the time of planning. Its models deal with the challenge of assigning delay to aircraft approaching a single airport, well in advance of each aircraft’s entry into the terminal airspace. In the first approach, we assume control of all airborne flights at a distance of 500 nm while assuming no control over flights originating less than 500 nm from the airport. We propose a set of integer programming models designed to issue arrival times for controlled flights in the presence of the uncertainty associated with the unmanaged flights. In the second approach, we assume control over all flights by subjecting flights to a combination of air and ground delay. Both approaches show strong potential to transfer delay from the terminal to the en route phase of flight and achieve fuel savings. Building on these ideas we then formulate an approach to incorporate speed control into Ground Delay Programs. We propose enhancements for equitably rationing airport access to carriers and develop a revised framework to allow carriers to engage in Collaborative Decision Making. We present new GDP control procedures and also new flight operator GDP planning models. While the ability to achieve all the benefits we describe will require NextGen capabilities, substantial performance improvements could be obtained even with a near-term implementation.
  • Thumbnail Image
    Item
    DETERMINING THE RELATIONSHIPS AMONG AIRPORT OPERATIONAL PERFORMANCE AREAS AND OTHER AIRPORT CHARACTERISTICS
    (2009) Chan, Kennis Yuen Man; Lovell, David J; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In this thesis, a methodology is proposed to investigate pair-wise relationships between different types of airport operational performance variables. The methodology represents a fundamental contribution for comparing airport performance between different air traffic management systems. Considerable attention is paid to analyzing the most appropriate techniques in an effort to produce the most reliable results. Additionally, a method to display the results in a simple and clear way is also suggested to allow users to understand the results visually. The key variables obtained from the proposed methodology not only serve as building blocks for developing models to answer a variety of air traffic questions, which allow policy makers to make decisions on allocating resources wisely, but also can be used as an evaluation tool to assist the FAA in selecting candidate projects.
  • Thumbnail Image
    Item
    IMPACT ASSESSMENT OF DYNAMIC SLOT EXCHANGE IN AIR TRAFFIC MANAGEMENT
    (2004-12-09) Sankararaman, Ravi; Ball, Michael; Decision and Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Since the inception of Collaborative Decision Making (CDM), the Federal Aviation Administration and the airlines have been striving to improve utilization of critical resources such as arrival slots and reduce flight delays during Ground Delay Programs. Two of the mechanisms that have been implemented for increasing utilization at resource-constrained airports are those of Compression and Slot Credit Substitution (SCS). SCS is a conditional, dynamic means of inter-airline slot exchange while compression can be considered a static means of achieving slot utilization. This thesis will be an attempt to develop theoretical models to understand the performance of compression to slot exchange requests from airlines. This thesis will also address the trends in these slot exchange procedures, the benefits in terms of delay savings realized by the airlines, and avenues for future applications for improving efficiency of the National Airspace System.