# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 6 Conclusions  
With the rapid development of the civil aviation industry and growing demand for air traffic, flight delay is becoming more frequent. Flight delay not only affects passenger satisfaction, but can also cause huge economic losses. Therefore, development of a more advanced and accurate flight delay prediction method is an important research topic. Ensemble methods are introduced in this paper, including bagging, boosting, and stacking methods. RF and AdaBoost were selected as the base learners for the stacking model, which are categorized as bagging and boosting methods, respectively. In addition, other common algorithms were selected as base learners. The results showed that the best prediction performance for the test dataset was obtained using the stacking method (MAE, 12.58; RMSE, 22.97). In the prediction results of the test dataset, 95.7% of the predicted  
Figure 16: Cruise pressure of PEK–HGH flights (1 January 2019–31 January 2020). values were within 35 min deviation from the observed values, implying an acceptable prediction performance. This study focused on the use of macro and micro factors for predicting flight delay. We propose two novel explanatory variables from a microscopic perspective: departure pressure and cruise pressure. With the inclusion of these two variables, the MAE and RMSE for predictions using the stacking method were reduced by 20% and 12%, respectively. Moreover, cruise pressure can effectively characterize the congestion of the air route. To preserve the periodicity of data, values were converted to coordinates on a circle using the sine-cosine function. Compared with one-hot encoding, this type of encoding reduced MAE by 1.5% and RMSE by 1%. These were small improvements, but beneficial in predicting flight delay and deserving of further study. To reduce the dimensionality of data and speed up the model, airlines were coded using the Pareto encoding method. This study has many limitations. Flight delays are not only related to the departure airport, but information from the arrival airport also has a potential impact on flight delays. Due to the lack of data, information from the arrival airport was not used in these predictions. Therefore, information from arrival and departure airports should be utilized to enhance the performance of the model. During the calculation of cruise pressure, flights on five similar routes were considered; in future, more national and international routes can be considered. Additionally, this paper does not consider the effect of interactions between airports on the propagation of delays, which is a direction for future research. Conceptualization, X.W., Z.W., L.W. and Y.T. ; methodology, Z.W. ; software, X.W. ; validation, X.W., L.W. and Y.T. ; formal analysis, X.W. ; investigation, X.W. ; resources, Y.T. ; data curation, L.W. ; writing--original draft preparation, X.W. ; writing--review and editing, Z.W. ; visualization, X.W. ; supervision, Z.W., L.W. and Y.T. ; project administration, Y.T. ; funding acquisition, Y.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation Project of China (U1933119). The authors  
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