The Federal Aviation Administration (FAA) has been modernizing the United States' air transportation system within a series of initiatives called the Next Generation Air Transportation System (NextGen). The goal of NextGen is to increase the safety, efficiency, capacity, predictability, and resiliency of American Air Traffic Control (ATC) by implementing satellite-based communication, and navigation systems. Because of the vast oceanic areas controlled by Oakland, New York, and Anchorage air traffic control centers, improving oceanic operations is significant for the United States. According to the FAA, oceanic flights generate 31% of passenger revenue and 40% of cargo revenue in U.S.-controlled airspace. New NextGen procedures offer the opportunity for aircraft to save fuel consumption by allowing oceanic flights to fly at more efficient routes and flight levels. This dissertation investigates three areas to improve flight operations over oceanic airspace. The first area studies the operational benefits of providing satellite-based meteorological information to aircraft operating in remote and oceanic airspace. This research effort uses two approaches as follows: 1) statistical flight analysis, and 2) simulation-based analysis. The second area provides an optimization technique to improve the current procedures for assigning flights to the Organized Track System (OTS) in the Atlantic Ocean based on the Collaborative Decision Making (CDM) concept. The third area investigates the potential savings of "In-Trail Procedure" (ITP) as one of the advanced surveillance operations in the Pacific and Atlantic oceanic airspace. To quantify the operational benefits of the proposed procedures, a fast-time simulation tool, the Global Oceanic (GO) model, is developed and employed. The GO model is a microscopic flight simulation tool that has been developing by the Air Transportation Systems Laboratory at Virginia Tech offering realistic and inexpensive evaluations of novel technologies and procedures to improve flight operations over global oceanic airspace. the results of these studies are analyzed in terms of fuel consumption, travel distance, travel time, level of service, and potential air traffic controllers' workload. / Doctor of Philosophy / The economic growth and social connectivity of nations are highly correlated to effective and efficient air transportation systems. The Federal Aviation Administration (FAA) has initiated a program to modernize America's air transportation system and make flight operations safer, and more efficient. This program is called the Next Generation Air Transportation System (NextGen) and its goal is transforming the communication and navigation technologies to satellite-based systems. Improving oceanic flights is one of the main concerns of the NextGen program since the United States controls massive oceanic areas in the Atlantic and the Pacific Ocean. The FAA needs to evaluate the benefits and costs of advanced technologies and procedures to justify the NextGen initiatives. The FAA has employed computer simulation tools to support decisions for future infrastructure investments and encourage airlines to equip their aircraft with more advanced avionics. The Global Oceanic (GO) model is a microscopic flight simulation tool developed jointly by the Air Transportation Systems Laboratory at Virginia Tech and the FAA providing quick, realistic, and inexpensive evaluations of advanced procedures to improve flight operations over oceans. This dissertation investigates the operational benefit of three advanced procedures using the GO model. The areas to improve flight operations over oceanic airspace are as follows: 1) operational benefits of providing satellite-based meteorological information to aircraft operating in remote and oceanic airspace, 2) operational benefits of an optimization technique for flight assignments to the Organized Track System (OTS) in the Atlantic Ocean, 3) operational benefits of "In-Trail Procedure" (ITP) as one of the advanced surveillance operations in the Pacific and Atlantic oceanic airspace. These studies quantify the potential savings of these procedures in terms of reducing fuel consumption, travel distance, travel time, greenhouse gas emissions, and potential air traffic controllers' workload.

Present day air traffc control is reaching its operational limits and accommodating future traffic growth will be a challenging task for air traffic service providers and airline operators. Free Flight is a proposed transition from a highly-structured and centrally-controlled air traffic system to a self-optimized and highly-distributed system. In Free Flight, pilots will have the flexibility of real-time trajectory planning and dynamic route optimization given airspace constraints (traffic, weather etc.). A variety of advanced air traffc management (ATM) concepts are proposed as enabling technologies for the realization of Free Flight. Since these concepts can be exposed to unforeseen and challenging scenarios in Free Flight, they need to be validated and evaluated in order to implement the most effective systems in the field. Evaluation of advanced ATM concepts is a challenging task due to the limitations in the existing scenario generation methodologies and limited availability of a common platform (air traffic simulator) where diverse ATM concepts can be modeled and evaluated. Their rigorous evaluation on safety metrics, in a variety of complex scenarios, can provide an insight into their performance, which can help improve upon them while developing new ones. In this thesis, I propose a non-propriety, non-commercial air traffic simulation system, with a novel representation of airspace, which can prototype advanced ATM concepts such as conflict detection and resolution, airborne weather avoidance and cockpit display of traffic information. I then propose a novel evolutionary computation methodology to algorithmically generate a massive number of conflict scenarios of increasing complexity in order to evaluate conflict detection algorithms. I illustrate the methodology in detail by quantitative evaluation of three conflict detection algorithms, from the literature, on safety metrics. I then propose the use of data mining techniques for the discovery of interesting relationships, that may exist implicitly, in the algorithm's performance data. The data mining techniques formulate the conflict characteristics, which may lead to algorithm failure, using if-then rules. Using the rule sets for each algorithm, I propose an ensemble of conflict detection algorithms which uses a switch mechanism to direct the subsequent conflict probes to an algorithm which is less vulnerable to failure in a given conflict scenario. The objective is to form a predictive model for algorithm's vulnerability which can then be included in an ensemble that can minimize the overall vulnerability of the system. In summary, the contributions of this thesis are: 1. A non-propriety, non-commercial air traffic simulation system with a novel representation of airspace for efficient modeling of advanced ATM concepts. 2. An Ant-based dynamic weather avoidance algorithm for traffic-constrained enroute airspace. 3. A novel representation of 4D air traffic scenario that allows the use of an evolutionary computation methodology to evolve complex conflict scenarios for the evaluation of conflict detection algorithms. 4. An evaluation framework where scenario generation, scenario evaluation and scenario evolution processes can be carried out in an integrated manner for rigorous evaluation of advanced ATM concepts. 5. A methodology for forming an intelligent ensemble of conflict detection algorithms by data mining the scenario space.