Study of delay prediction in the US airport network
In modern business, Artificial Intelligence (AI) and Machine Learning (ML) have affected strategy and decision-making positively in the form of predictive modeling. This study aims to use ML and AI to predict arrival flight delays in the United States airport network. Flight delays carry severe soci...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/391935 |
| Acceso en línea: | https://hdl.handle.net/2117/391935 https://dx.doi.org/10.3390/aerospace10040342 |
| Access Level: | acceso abierto |
| Palabra clave: | Aeronautics, Commercial -- Planning -- United States Network analysis (Planning) Delay prediction Predictive modeling Flight delays Aviació comercial -- Planificació -- Estats Units d'Amèrica Anàlisi de xarxes (Planificació) Àrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Infraestructures i transport aeri Àrees temàtiques de la UPC::Economia i organització d'empreses::Direcció d'operacions::Modelització de transports i logística |
| Sumario: | In modern business, Artificial Intelligence (AI) and Machine Learning (ML) have affected strategy and decision-making positively in the form of predictive modeling. This study aims to use ML and AI to predict arrival flight delays in the United States airport network. Flight delays carry severe social, environmental, and economic impacts. Deploying ML models during the process of operational decision-making can help to reduce the impact of these delays. A literature review and critical appraisal were carried out on previous studies and research relating to flight delay prediction. In the literature review, the datasets used, selected features, selected algorithms, and evaluation tools used in previous studies were analyzed and influenced the decisions made in the methodology for this study. Data for this study comes from two public sets of domestic flight and weather data from 2017. Data are processed and split into training, validation, and testing data. Subsequently, these ML models are evaluated and compared based on performance metrics obtained using the testing data. The predictive model with the best performance (in choosing between logistic regression, random forest, the gradient boosting machine, and feed-forward neural networks) is the gradient boosting machine. |
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