Prediction of flight departure delays caused by weather conditions adopting data-driven approaches
Abstract In this study, we utilize data-driven approaches to predict flight departure delays. The growing demand for air travel is outpacing the capacity and infrastructure available to support it. In addition, abnormal weather patterns caused by climate change contribute to the frequent occurrence...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-01-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-023-00867-5 |
| _version_ | 1797355741179281408 |
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| author | Seongeun Kim Eunil Park |
| author_facet | Seongeun Kim Eunil Park |
| author_sort | Seongeun Kim |
| collection | DOAJ |
| description | Abstract In this study, we utilize data-driven approaches to predict flight departure delays. The growing demand for air travel is outpacing the capacity and infrastructure available to support it. In addition, abnormal weather patterns caused by climate change contribute to the frequent occurrence of flight delays. In light of the extensive network of international flights covering vast distances across continents and oceans, the importance of forecasting flight delays over extended time periods becomes increasingly evident. Existing research has predominantly concentrated on short-term predictions, prompting our study to specifically address this aspect. We collected datasets spanning over 10 years from three different airports such as ICN airport in South Korea, JFK and MDW airport in the United States, capturing flight information at six different time intervals (2, 4, 8, 16, 24, and 48 h) prior to flight departure. The datasets comprise 1,569,879 instances for ICN, 773,347 for JFK, and 404,507 for MDW, respectively. We employed a range of machine learning and deep learning approaches, including Decision Tree, Random Forest, Support Vector Machine, K-nearest neighbors, Logistic Regression, Extreme Gradient Boosting, and Long Short-Term Memory, to predict flight delays. Our models achieved accuracy rates of 0.749 for ICN airport, 0.852 for JFK airport, and 0.785 for MDW airport in 2-h predictions. Furthermore, for 48-h predictions, our models achieved accuracy rates of 0.748 for ICN airport, 0.846 for JFK airport, and 0.772 for MDW airport based on our experimental results. Consequently, we have successfully validated the accuracy of flight delay predictions for longer time frames. The implications and future research directions derived from these findings are also discussed. |
| first_indexed | 2024-03-08T14:15:24Z |
| format | Article |
| id | doaj.art-aed02de08a8f46ebb7ec633b0bb1dee1 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-03-08T14:15:24Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-aed02de08a8f46ebb7ec633b0bb1dee12024-01-14T12:26:18ZengSpringerOpenJournal of Big Data2196-11152024-01-0111112510.1186/s40537-023-00867-5Prediction of flight departure delays caused by weather conditions adopting data-driven approachesSeongeun Kim0Eunil Park1Department of Semiconductor and Display Engineering, Sungkyunkwan UniversityDepartment of Interaction Science, Sungkyunkwan UniversityAbstract In this study, we utilize data-driven approaches to predict flight departure delays. The growing demand for air travel is outpacing the capacity and infrastructure available to support it. In addition, abnormal weather patterns caused by climate change contribute to the frequent occurrence of flight delays. In light of the extensive network of international flights covering vast distances across continents and oceans, the importance of forecasting flight delays over extended time periods becomes increasingly evident. Existing research has predominantly concentrated on short-term predictions, prompting our study to specifically address this aspect. We collected datasets spanning over 10 years from three different airports such as ICN airport in South Korea, JFK and MDW airport in the United States, capturing flight information at six different time intervals (2, 4, 8, 16, 24, and 48 h) prior to flight departure. The datasets comprise 1,569,879 instances for ICN, 773,347 for JFK, and 404,507 for MDW, respectively. We employed a range of machine learning and deep learning approaches, including Decision Tree, Random Forest, Support Vector Machine, K-nearest neighbors, Logistic Regression, Extreme Gradient Boosting, and Long Short-Term Memory, to predict flight delays. Our models achieved accuracy rates of 0.749 for ICN airport, 0.852 for JFK airport, and 0.785 for MDW airport in 2-h predictions. Furthermore, for 48-h predictions, our models achieved accuracy rates of 0.748 for ICN airport, 0.846 for JFK airport, and 0.772 for MDW airport based on our experimental results. Consequently, we have successfully validated the accuracy of flight delay predictions for longer time frames. The implications and future research directions derived from these findings are also discussed.https://doi.org/10.1186/s40537-023-00867-5Flight delayDelay prediction weatherMachine learningLSTM |
| spellingShingle | Seongeun Kim Eunil Park Prediction of flight departure delays caused by weather conditions adopting data-driven approaches Journal of Big Data Flight delay Delay prediction weather Machine learning LSTM |
| title | Prediction of flight departure delays caused by weather conditions adopting data-driven approaches |
| title_full | Prediction of flight departure delays caused by weather conditions adopting data-driven approaches |
| title_fullStr | Prediction of flight departure delays caused by weather conditions adopting data-driven approaches |
| title_full_unstemmed | Prediction of flight departure delays caused by weather conditions adopting data-driven approaches |
| title_short | Prediction of flight departure delays caused by weather conditions adopting data-driven approaches |
| title_sort | prediction of flight departure delays caused by weather conditions adopting data driven approaches |
| topic | Flight delay Delay prediction weather Machine learning LSTM |
| url | https://doi.org/10.1186/s40537-023-00867-5 |
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