Predicting Flight Delays Using Historical Data and Machine Learning Techniques
Year: 2025
Type: article
Abstract: This study investigates the application of machine learning (ML) techniques to predict flight delays using historical aviation data, with a focus on developing interpretable, scalable models that address operational inefficiencies in the aviation industry. Flight delays incur significant economic costs and disrupt passenger experiences, necessitating robust predictive analytics to enhance decision-making. By leveraging enriched datasets encompassing seasonal trends, carrier-specific performance metrics, and route-level probabilities, this research employs advanced ML algorithms such as gradient boosting machines (GBMs), deep neural networks (DNNs), and hybrid models to identify critical predictors of delays.
DOI: 10.26207/70hc-be53
Author: Dinesh Potla
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6927238138
FWCI:
Cites:
Cited by:
Related to: