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 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.

Analysis of the Multi-Dimensional Characteristics of City Weather Forecast Page Views and the Spatiotemporal Characteristics of Meteorological Disaster Warnings in China

“…When planning travel and daily routines on weekdays, individuals must adhere to stringent timetables for commuting, transporting children to school, attending professional meetings, and more. Consequently, weather conditions become a pivotal factor, influencing decisions like carrying rain gear or allowing extra time to navigate potential traffic delays [19][20][21]. Hence, staying informed about weekday weather is paramount for efficient trip planning.…”

Identification and Analysis of Flight Delay Based on Process Relevance

“…In recent years, scholars have used more intelligent methods to optimize this prediction process, using machine learning algorithms such as Support Vector Machine [1], Random Forest [1,2], Colored Petri Net [3,4], Bayesian network [5][6][7][8][9], and Long Short-Term Memory neural network [10,11] to make predictions. Deep learning algorithms such as the Gated Recurrent Unit (GRU) model [12] and Convolutional Long Short-Term Memory network [13,14] are used to predict flight delays.…”