Application of Airline Data in Aviation Flight Management

  • 江 奇勳

Student thesis: Master's Thesis

Abstract

Big data analysis has rapid develop recently In various fields are trying to bring the new insight into their enterprise for increasing revenue or finding potential patterns Basis for big data analysis is on the volume variety velocity and veracity of data It needs to be well collected analyzed and applied that can reveal its value For aviation there is a strict regulation for collecting preserving data Nowadays many public information including event reports and digital flight data depends on accurate global satellite positioning (GPS) to obtain aircraft location and the airline has the flight arrival data including on-time or delay information Moreover Weather bureau has situation of the weather wind direction moisture and so on There is more accurate reliable and comprehensive It will make the aviation more efficient and security The purpose of this guide is to provide information on existing analytical methods and tools that can help the airline community turn their data into valuable information to improve safety and flight management Random Forest is a popular machine learning algorithms It is a decision tree model consists of multiple trees Then we predict the final result by majority voting of the results Random forest in R software package “random forest” is very easy and comprehensive to use Due to the data rapidly changing in the aviation random forest models can be used to explore the assumptions of various conditions If we can collect more external information the model can improve the degree of accuracy In this study based on random forest algorithm applied in aviation data to predict delayed flights as the target The data is collected by the US Department of Transportation and use it to build the model and explore the important features to find the delay flight In the future data analysis will continue to receive attention If we cultivate judgment and intuition will become strong and generate great insights
Date of Award2018 Aug 21
Original languageEnglish
SupervisorChin-E. Lin (Supervisor)

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