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primary topic: A comparative study on aviation arrival delay prediction using machine learning methods

A comparative study on aviation arrival delay prediction using machine learning methods
type: Thesis
type: Article
abstract: Flight delays have given rise to a great deal of redundant costs. According to the Federal Aviation Administration (FAA), it is estimated that the total cost of domestic flight delays in the United States (US) has increased from USD24 billion in 2016 to USD33 billion in 2019, a 39% increase over the years. These costs are a composition of costs to passengers and airlines, indirect costs of delay and cost of passenger decisions to avoid future air travel, in other words, lost demand. The top three delay causes are identified as aircraft arriving late, National Aviation System (NAS) delay and air carrier delay. This research aims to identify the most important features for flight delay prediction, build supervised machine learning algorithms (i.e., logistic regression (LR), random forest (RF) and artificial neural network (ANN)) for predicting flight arrival delay and compare the performances of the methods. Dataset from 2016 to 2020 with 35 variables for Southwest Airlines Co. carrier are sourced from the Bureau of Transportation Statistics (BTS) to be trained and validated as Southwest Airlines Co. holds the biggest share of number of flights as compared to other airlines across the years. The feature selection process is carried out via literature review approach and stepwise regression approach. Both approaches recommend departure delay as the most important feature for model building. From stepwise regression approach, taxi in and taxi out are important to be included as features for flight arrival delay prediction. Considering the afore-mentioned model building methods for both approaches, ANN model using stepwise regression approach performs the best for flight arrival delay prediction based on F1 score. The F1 score for ANN train set managed to achieve 0.9 and maintain at 0.81 for its test set despite having outlier data in 2020. Furthermore, 5-fold cross validation on ANN shows overall accuracy and misclassification rate of 0.952601 and 0.047399 respectively,…
date: 2023-12-22
author list: authors
type: EPrint
type: ThesisEPrint
is part of: repository
subject: Q Science (General)

Sunway University
type: Organization

School of Mathematical Sciences, Sunway University
type: Organization

Pui Ting Chew
type: Person
given name: Pui Ting
family name: Chew

HTML Summary of #3239 A comparative study on aviation arrival delay prediction using machine learning methods
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primary topic: A comparative study on aviation arrival delay prediction using machine learning methods

Q Science (General)
type: Concept