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| DOI | 10.1186/s40537-023-00867-5 |
| Prediction of flight departure delays caused by weather conditions adopting data-driven approaches | |
| 发表日期 | 2024 |
| EISSN | 2196-1115 |
| 起始页码 | 11 |
| 结束页码 | 1 |
| 卷号 | 11期号:1 |
| 英文摘要 | 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. |
| 英文关键词 | Flight delay; Delay prediction weather; Machine learning; LSTM |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Theory & Methods |
| WOS记录号 | WOS:001138572300002 |
| 来源期刊 | JOURNAL OF BIG DATA
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/299790 |
| 作者单位 | Sungkyunkwan University (SKKU); Samsung; Samsung Electronics; Sungkyunkwan University (SKKU) |
| 推荐引用方式 GB/T 7714 | . Prediction of flight departure delays caused by weather conditions adopting data-driven approaches[J],2024,11(1). |
| APA | (2024).Prediction of flight departure delays caused by weather conditions adopting data-driven approaches.JOURNAL OF BIG DATA,11(1). |
| MLA | "Prediction of flight departure delays caused by weather conditions adopting data-driven approaches".JOURNAL OF BIG DATA 11.1(2024). |
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