Deciphering Air Travel Disruptions: A Machine Learning Approach

Aravinda Jatavallabha, Jacob Gerlach, Aadithya Naresh
8/5/2024
cs.LG

No official code found. Best implementation: aravinda-1402/Flight_Delay_Prediction_using_Machine_Learning (0 stars, Apache-2.0, last commit 2024-08-08).

Abstract

This research investigates flight delay trends by examining factors such as departure time, airline, and airport. It employs regression machine learning methods to predict the contributions of various sources to delays. Time-series models, including LSTM, Hybrid LSTM, and Bi-LSTM, are compared with baseline regression models such as Multiple Regression, Decision Tree Regression, Random Forest Regression, and Neural Network. Despite considerable errors in the baseline models, the study aims to identify influential features in delay prediction, potentially informing flight planning strategies. Unlike previous work, this research focuses on regression tasks and explores the use of time-series models for predicting flight delays. It offers insights into aviation operations by independently analyzing each delay component (e.g., security, weather).

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Code Implementations(1)

aravinda-1402/Flight_Delay_Prediction_using_Machine_LearningCommunity100%
Apache-2.0

This research investigates flight delay trends, examining departure time, airline, and airport factors. Regression machine learning meth- ods are utilized to predict delay contributions from various sources. Time-series models, including LSTM, Hybrid LSTM, and Bi-LSTM, are compared with baseline regression models such as Multiple Regression, Decisi

00Jupyter NotebookApr 20, 20242 years agoApache-2.0
bilstmdelaylstmlstm-neural-networksprediction+2 more

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Cite this paper

@article{jatavallabha2024deciphering,
  title  = {Deciphering Air Travel Disruptions: A Machine Learning Approach},
  author = {Aravinda Jatavallabha and Jacob Gerlach and Aadithya Naresh},
  year   = {2024},
  eprint = {2408.02802},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.02802v1}
}

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