# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 2 Related Works  
Earlier studies used statistical modeling methods to estimate the probability of flight delays. For example, [PERSON] et al. [5] developed an econometric model for estimating average daily delays. [PERSON] et al. [6] decomposed delays into three patterns (propagation, seasonal, and random patterns), and proposed an estimation algorithm for flight-departure delay. However, the accuracy of the results obtained by the above methods was low, and they are infrequently applied in practice. Unlike the above studies, the current study proposes a practical flight-delay prediction model that aims to reduce problems such as airport congestion by accurately predicting the delay of each flight. ML has been widely used for flight-delay prediction. [PERSON] et al. [15] predicted airline delays caused by inclement weather conditions using decision trees, RF, AdaBoost, and K-nearest neighbor (KNN). They gathered flight schedules and weather forecasts to predict whether a scheduled flight would be delayed or on time. In addition to the binary classification of predicting whether or not a flight would be delayed, certain delay thresholdshave been set. [PERSON] et al. [8] set three delay thresholds (45, 60, and 90 min), and used RF to predict departure delays 2 to 4 h ahead. [PERSON] et al. [16] set up dichotomous, trichotomous, and quadruple classification problems according to different delay thresholds. They implemented RF-based and long short-term memory (LSTM)-based architectures to predict individual flight delays. [PERSON] et al. [17] divided flight time deviation into six intervals and used seven supervised learning algorithms including RF, SVM, and decision trees to predict time deviation of flights at Lithuanian airports. Because a specific delay-prediction value is more in line with the needs of passengers, airlines, and airports, many scholars have attempted to predict the specific delay values of flights and airports. [PERSON] et al. [18] predicted hourly aggregate flight-departure delays at Nanjing Lukou International Airport (NKG), using supervised learning methods. [PERSON] et al. [19] proposed a hybrid method of random forest regression and maximal information coefficient (RFR-MIC) for delay prediction. Deep learning (DL) is a new research direction in the field of ML, involving increasingly complex model structures. Many scholars have introduced it to flight-delay prediction. [PERSON] et al. [20] employed a deep belief network to predict flight delays. [PERSON] et al. [21] proposed a deep graph-embedded LSTM (DGLSTM) model to predict delays at 325 airports in the United States. [PERSON] et al. [22] developed a novel graph-to-sequence learning architecture with attention mechanism (AG2S-Net) to predict hourly delays in the airport network from multiple steps ahead. [PERSON] et al. [23] proposed a flight-delay prediction approach based on a graph convolutional neural network (GCN), from an airport network perspective. Most of the above studies predicted average hourly delays in airports from the perspective of airport networks. However, passengers and airlines are more concerned with specific delays to individual flights. Therefore, in this study, we used data from PEK to Hangzhou International Airport (HGH) as a case study, to predict specific delays for each flight on this route. As shown in Table 1, few studies have considered micro factors that can have a direct impact on flight delays. Although other factors such as weather and temporal data have an impact on flight delays, this impact is not immediate and requires a large amount of data to summarize. Therefore, the current study combined macro and micro factors, defining arrival/departure pressure and cruise pressure to predict specific delays for flights on a single route. To improve prediction performance, we applied ensemble methods that can form strong learners by combining several weak learners, and tested their effectiveness using a dataset obtained from CAAC.