Title:
Development of a Framework for the Analysis and Assessment of Daily Airport Operations
Development of a Framework for the Analysis and Assessment of Daily Airport Operations
| dc.contributor.author | Mangortey, Eugene | |
| dc.contributor.committeeMember | Schrage, Daniel | |
| dc.contributor.committeeMember | Fischer, Olivia | |
| dc.contributor.committeeMember | Tessitore, Tom | |
| dc.contributor.committeeMember | Paglione, Mike | |
| dc.contributor.department | Aerospace Engineering | |
| dc.date.accessioned | 2022-01-14T16:04:36Z | |
| dc.date.available | 2022-01-14T16:04:36Z | |
| dc.date.created | 2020-12 | |
| dc.date.issued | 2020-12-01 | |
| dc.date.submitted | December 2020 | |
| dc.date.updated | 2022-01-14T16:04:37Z | |
| dc.description.abstract | Tremendous progress has been made over the last two decades towards modernizing the National Airspace System (NAS) by way of technological advancements, and the introduction of procedures and policies that have maintained the safety of the United States airspace. However, as with any other system, there is a need to continuously address evolving challenges pertaining to the sustainment and resiliency of the NAS. One of these challenges involves efficiently analyzing and assessing daily airport operations for the identification of trends and patterns to inform better decision making so as to improve the efficiency and safety of airport operations. Efforts have been undertaken by stakeholders in the aviation industry to categorize airports as a means to facilitate the analysis of their operations. However, a comprehensive, repeatable, and robust approach for this very purpose is lacking. In addition, these efforts have not provided a means for stakeholders to assess the impacts and effectiveness of traffic management decisions and procedures on daily airport operations. Furthermore, an efficient and secure framework for extracting, processing, and storing the data needed for the analysis and assessment of daily airport operations is needed, as the current process employed by FAA analysts is manual, time-consuming, and prone to human error. Consequently, this dissertation addresses these gaps through a set of methodologies that 1) leverage unsupervised Machine Learning algorithms to categorize daily airport operations, 2) leverage a supervised Machine Learning algorithm to determine the category that subsequent daily airport operations belong to, 3) facilitate the comparison of similar and different daily airport operations for the identification of trends and patterns, 4) enable stakeholders to analyze and assess the impacts and effectiveness of traffic management decisions and procedures on daily airport operations, and 5) develop a framework to facilitate the efficient and secure extraction, processing and storage of data needed for the analysis and assessment of daily airport operations. The developed framework automates the flow of data from extraction through storage, and enables users to track the flow of data in real time. It also facilitates data provenance by logging the history of all processes and is equipped with the capability to log errors and their causes, and to notify analysts via email whenever they occur. In addition, it has the capacity to automatically extract, process, and store the data needed for the analysis and assessment of the daily operations of all airports in the NAS. Indeed, this framework will be one of the first of its kind to be deployed into the FAA's Enterprise Information Management platform and will serve as a template for leveraging cloud-based services and technologies to improve operations in the NAS. Finally, this framework will enable FAA analysts to analyze and assess daily airport operations in an efficient manner to facilitate the identification of trends and patterns for better decision making, which will lead to improved airport operational performance. | |
| dc.description.degree | Ph.D. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/66000 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Machine Learning | |
| dc.subject | Aviation Research | |
| dc.title | Development of a Framework for the Analysis and Assessment of Daily Airport Operations | |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Daniel Guggenheim School of Aerospace Engineering | |
| local.contributor.corporatename | Aerospace Systems Design Laboratory (ASDL) | |
| local.contributor.corporatename | College of Engineering | |
| local.relation.ispartofseries | Doctor of Philosophy with a Major in Aerospace Engineering | |
| relation.isOrgUnitOfPublication | a348b767-ea7e-4789-af1f-1f1d5925fb65 | |
| relation.isOrgUnitOfPublication | a8736075-ffb0-4c28-aa40-2160181ead8c | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 | |
| relation.isSeriesOfPublication | f6a932db-1cde-43b5-bcab-bf573da55ed6 | |
| thesis.degree.level | Doctoral |