Title:
A Data-Driven Approach using Machine Learning to Enable Real-Time Flight Path Planning

dc.contributor.author Kim, Junghyun
dc.contributor.author Briceno, Simon
dc.contributor.author Justin, Cedric Y.
dc.contributor.author Mavris, Dimitri N.
dc.contributor.corporatename Georgia Institute of Technology. Aerospace Systems Design Laboratory en_US
dc.contributor.corporatename American Institute of Aeronautics and Astronautics
dc.contributor.corporatename Georgia Institute of Technology. Aerospace Systems Design Laboratory
dc.date.accessioned 2020-06-15T13:36:31Z
dc.date.available 2020-06-15T13:36:31Z
dc.date.issued 2020-06
dc.description Presented at AIAA Aviation 2020 Forum en_US
dc.description.abstract As aviation traffic continues to grow, most airlines are concerned about flight delays, which increase operating costs for the airlines. Since most delays are caused by weather, pilots and flight dispatchers typically gather all available weather information prior to departure to create an efficient and safe flight plan. However, they may have to perform in-flight re-planning because weather information can significantly change after the original flight plan is created. One potential issue is that weather forecasts being currently used in the aviation industry may provide relatively unreliable information and are not accessible fast enough so that it challenges pilots to perform in-flight re-planning more accurately and frequently. In this paper, we propose a data-driven approach that uses an unsupervised machine learning technique to provide a more reliable and up-to-date area of convective weather. To evaluate the proposed methodology, we collect the American Airlines flight (AA1300) information and actual weather-related data on October 6th, 2019. Preliminary results show that the proposed methodology provides a better picture of the nearby convective weather activity compared to the most well-known convective weather product. en_US
dc.identifier.citation Kim, J.-H., Briceno, S. I., Justin, C. Y., & Mavris, D. (2020). A Data-Driven Approach using Machine Learning to Enable Real-Time Flight Path Planning. In AIAA AVIATION 2020 FORUM. American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2020-2873 en_US
dc.identifier.doi 10.2514/6.2020-2873 en_US
dc.identifier.uri http://hdl.handle.net/1853/62915
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher Georgia Institute of Technology
dc.publisher.original American Institute of Aeronautics and Astronautics (AIAA)
dc.relation.ispartofseries ASDL; en_US
dc.subject Machine learning en_US
dc.title A Data-Driven Approach using Machine Learning to Enable Real-Time Flight Path Planning en_US
dc.type Text
dc.type.genre Paper
dspace.entity.type Publication
local.contributor.author Mavris, Dimitri N.
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.contributor.corporatename Aerospace Systems Design Laboratory (ASDL)
local.contributor.corporatename College of Engineering
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relation.isOrgUnitOfPublication a8736075-ffb0-4c28-aa40-2160181ead8c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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