Flight Departure Delay Research Articles

Prediction of flight departure delays caused by weather conditions adopting data-driven approaches

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.

Flight departure delay forecasting

The most accurate methods to predict delays have become essential to mitigate their generation and proliferation (and prevent their spread to other airports in the network). Given that both the delay time dependence function and the input variables on which it depends are not fully known, the objective of this research is to predict the delays in the departure of scheduled commercial flights through a methodology that uses predictive tools based on machine learning/deep learning (ML/DL), with supervised training in regression, based on the available flight datasets. The novel contribution of this work is, first, to make a comparison of the predictions in terms of means and statistical variance of the different ML/DL models implemented (ten in total) and, secondly, to determine the coefficients of the importance of the features or flight attributes. Using ML methods known as permutation importance, it is possible to rank the importance of flight attributes by their influence in determining the delay time and reduce the problem of selecting the most important flight attributes. The data for the analysis was obtained from the Colombian airport system for the year 2018. From the results obtained, it is worth mentioning that the model that presents the best performance is the Ensemble, or combination method of Random Forest Regressor models, configured with 2,000 trees within the forest, with an acceptable prediction range measured with the root-mean-square-error (RMSE) metric of 16 to 33 minutes (prediction of time of the flight departure delay) depending on the scenario analysed.

Characteristics of flight delays during solar flares

Solar flares are one of the severest solar activities that have important effects on near-Earth space. Previous studies have shown that flight arrival delays increase as a result of solar flares, but the intrinsic mechanism behind this relationship is still unknown. In this study, we conducted a comprehensive analysis of flight departure delays during 57 solar X-ray events by using a huge amount of flight data (~ 5 × 106 records) gathered over a 5-year period. It is found that the average flight departure delay time during solar X-ray events increased by 20.68% (7.67 min) compared to quiet periods. Our analysis also revealed apparent time and latitude dependencies, with flight delays being more serious on the dayside than on the nightside and longer (shorter) delays tending to occur in lower (higher) latitude airports during solar X-ray events. Furthermore, our results suggest that the intensity of solar flares (soft X-ray flux) and the Solar Zenith Angle directly modulate flight departure delay time and delay rate. These results indicate that communication interferences caused by solar flares directly affect flight departure delays. This work expands our conventional understanding of the impacts of solar flares on human society and provides new insights for preventing or coping with flight delays.

Flight delays due to air pollution in China

Using a newly-compiled dataset of airport traffic, air pollution, and weather from 2015 to 2017, we evaluate the effect of PM2.5 pollution levels on flight departure delays in China. We instrument for PM2.5 levels using changes in atmospheric thermal inversions and wind direction. We find that a one-standard-deviation increase in the daily PM2.5 concentration levels leads to roughly 4.5–6.4 additional minutes in departure delays per flight. We estimate that reductions in China's PM2.5 pollution levels due to the Air Pollution Prevention and Control Action Plan avoided 13–18% of the increases in national average flight delays and saved US $54 to $69 million in passenger time in 2017 alone.

Analysis of the effect of extreme weather on the US domestic air network. A delay and cancellation propagation network approach

Flight delays are one of the most discussed, yet not fully understood, topics in the aviation industry. In this paper, we shed more light into propagation of flight delays by providing a spatio-temporal analysis of flight departure delays of the US domestic air network for the year 2017. The analysis focuses on four US air carriers (full-service and low-cost) and two time events characterized by extreme weather conditions, in addition to a baseline case free of extreme weather conditions. We constructed a Delay Propagation Network (DPN) for each (time event, airline) pair detecting patterns of causality between hourly delays in airports using a Granger Causality approach. In addition, we identified four (time event, airline) pairs with a volume of cancellations large enough to construct a Cancellation Propagation Network (CPN), analogously to DPNs. For the baseline case, we observed that central nodes of the airport network (i.e., hubs) usually act as absorbers or intermediary nodes in the DPN. DPNs were more homogeneously distributed in space for point-to-point than for hub-and-spoke networks. For extreme weather events, we observed that the size of a DPN increases with the percentage of canceled flights as long as this stays below 10%. Conversely, it suddenly decreases when the percentage exceeds such tipping point because most causal relationships among delays are lost due to the volume of cancellations. We also observed that some airports located in the region of the extreme weather event were among the central nodes of the DPN. Those airports, together with the hub airports, acted as the top generators, absorbers, or intermediary nodes of the DPN. On the other hand, CPNs monotonously increased in size with the proportion of canceled flights. CPNs are less noisy and therefore easier to interpret than DPNs, as cancellations stem primarily from the extreme weather event only. In CPNs, hubs act as cancellation absorbers, due to the larger volume of resources that airlines allocate there.

Predicting flight delay with spatio-temporal trajectory convolutional network and airport situational awareness map

To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, contextual features and route information among different airports to predict flight delay are inaccurate and only predict arrival delay but not departure delay, which is essential to airlines. In this paper, we propose a vision-based solution to achieve a high forecasting accuracy, applicable to the airport. Our solution leverages a snapshot of the airport situational awareness map, which contains various trajectories of aircraft and contextual features such as weather and airline schedules. We propose an end-to-end deep learning architecture, TrajCNN, which captures both the spatial and temporal information from the situational awareness map. Additionally, we reveal that the situational awareness map of the airport has a vital impact on estimating flight departure delay. Our proposed framework obtained a good result (around 18 min error) for predicting flight departure delay at Los Angeles International Airport.

The Impact of Covid-19 on Flight Departure Delay Time and Cancellations at Philadelphia International Airport

The emergence of Covid-19 has deeply disturbed the aviation industry in many aspects. This study examines the effect of Covid-19 on departure delay time and flight cancellation at Philadelphia International Airport (PHL). By analyzing data of nearly 200,000 flights departed from PHL between January 2019 and February 2021, the research found that Covid-19 noticeably reduced flight departure delay time, and flight cancellations sharply rose then quickly dropped. Furthermore, flight departure delay time is highly correlated with the number of flights: the lower the number of flights, the lower the delay time. The ultimate cause is the lack of passengers, as the number of passengers dictates the number of flights an airline can successfully operate.

Hierarchical integrated machine learning model for predicting flight departure delays and duration in series

Flight delays may propagate through the entire aviation network and are becoming an important research topic. This paper proposes a novel hierarchical integrated machine learning model for predicting flight departure delays and duration in series rather than in parallel to avoid ambiguity in decision making. The paper analyses the proposed model using various machine learning algorithms in combination with different sampling techniques. The highly noisy, unbalanced, dispersed, and skewed historical high dimensional data provided by an international airline operating in Hong Kong was used to demonstrate the practical application of the model. The result shows that for a 4-h forecast horizon, a constructive neural network machine learning algorithm with the Synthetic Minority Over Sampling Technique-Tomek Links (SMOTETomek) sampling technique was able to achieve better average balanced recall accuracies of 65.5%, 61.5%, 59% for classifying delay status and predicting delay duration at thresholds of 60 min and 30 min, respectively. Similarly, for minority labels, the precision-recall and area under the curve showed that the proposed model achieved better results of 32.44% and 35.14% compared to the parallel model of 26.43% and 21.02% for thresholds of 60 min and 30 min, respectively. The effect of different sampling techniques, sampling approaches, and estimation mechanisms on prediction performance is also studied.

Analysis and Visualization of KLIA Flight Departure Delay Pattern

Analysis and Visualization of KLIA Flight Departure Delay Pattern

A novel hybrid method for flight departure delay prediction using Random Forest Regression and Maximal Information Coefficient

Flight departure delay prediction is one of the most critical components of intelligent aviation systems. The accurate prediction of flight departure delays can provide passengers with reliable travel schedules and enhance the service performance of airports and airlines. This article proposes a hybrid method of Random Forest Regression and Maximal Information Coefficient (RFR-MIC) for flight departure delay prediction. Random Forest Regression and Maximal Information Coefficient are inherently fused in terms of Information Consistency. Furthermore, this article focuses on utilizing flight information on multiple air routes for flight departure delay prediction. To validate the proposed flight departure delay prediction model, a numerical study is conducted using flight data collected from Beijing Capital International Airport (PEK). The proposed RFR-MIC model exhibits good performance compared with linear regression (LR), k-nearest neighbors (k-NN), artificial neural network (ANN), and standard Random Forest Regression (RFR). The results also show that flight information on multiple air routes can certainly improve the accuracy of flight departure delay prediction.

An Exploratory analysis of flight delay propagation in China

Delay propagation is the flight departure delay caused by the arrival delay of pre-segment flight. Chinese airline market has suffered very poor on-time performance (OTP) in recent years. It is, however, unclear whether delay propagation prevails as one major source for such problem. This study first aims to empirically quantify delay propagation in the Chinese airline market. Specifically, we shed light on heterogenous levels of delay propagations across different airports and airlines. Then, the distinct delay propagation patterns in China are also discussed and compared with other developed airline markets (e.g., the US and Europe). Our estimation is based on OTP data for over 12 million Chinese flights covering the 2015–2017 period. Specifically, it is found that 10 min arrival delay of pre-segment flight within 1 hr before the departure lead to an average of 7.49 mins delay propagation for subsequent departure flight. Arrival delay of earlier pre-segments (1–2 and 2–3 hr before the departure) leads to much less delay propagation, due to longer ground buffer. Chinese airlines arrange longer ground and flight buffer than that of the US airlines to prevent the delay propagation from accumulating along the subsequent flights in a day. Thus, unlike the US market, delay propagation is not the major reason for poor OTP in China. In addition, delay propagation is less prevailing at the Chinese hub airport. This is because China has relied on point-to-point network, which does not require sophisticated schedule coordination. And the local passengers at these Chinese hub airports have higher time value such that the Chinese airlines also try to improve OTP at these hub airports to better serve these lucrative but time-sensitive local passengers. Unlike the European LCCs, Spring Airlines, the largest low-cost carrier (LCC) in China, outperforms major full-service carriers (FSCs) in controlling delay propagation. This finding may also apply to other Northeast Asian LCCs sharing common operational characteristics as Spring Airlines. Last, we find that airlines purposely tolerate moderate departure delays of up to 15 min, which is the threshold that defines delays, no matter whether the pre-segment flight arrives late or on-time. The relevant policy and managerial implications are also discussed.

How Late Does Your Flight Depart? A Quantile Regression Approach for a Chinese Case Study

Flight departure delays cost airlines and airports millions of dollars and become a systematic problem. The on-time performance at an airport is connected to and easily affected by delay propagation from previous operations of flights using the airport. In this paper, we employ both Ordinary Least Square (OLS) and quantile regressions to investigate the impact of various influencing factors on flight departure delay. By using historical flight records and weather information, the impacts of delay propagation-related and other factors are quantified to study the correlations between the explanatory and response variables. Three variables, including previous arrival delay, turnaround buffer time, and the first order of a day, are used to examine the propagation effects. We find that aircraft type, flying on a weekday, and being the first flight of a day have significant impacts on short departure delays. Ground buffer is conducive to mitigating delay propagation. For long delays, however, ground buffer cannot work in an efficient way, and the previous arrival effect is more important. Convective weather and aircraft type are the crucial factors in this situation. Interestingly, flying on a weekday suddenly becomes one of the main components under extreme delays. Meanwhile, propagated delay and airport congestion remain significantly impactful on the on-time performance.

Modeling the flight departure delay using survival analysis in South Korea

The air transportation industry in South Korea has been growing since the 2000s. Although the number of air passengers has continuously increased, the flight delay rate has fluctuated from year to year. In this study, a survival analysis was conducted to compare each flight delay in terms of airport/time slot and to evaluate the actual impacts of major variables on flight delays. We performed multiple analyses using survival analysis methodologies, including the Kaplan–Meier estimator, log-rank test, and Cox proportional hazards model. In particular, we applied a turnaround buffer as a key variable in Cox proportional hazards model analysis. The survival curve showed that more international flights departed earlier than scheduled or with minimal delay than domestic flights. However, international flights tended to have more long-term delays. The log-rank test indicated that international flights had a wider distribution of delay times than domestic flights. In the Cox proportional hazards analysis, under the condition in which the actual flight turnaround time was less than 120 min, the results indicated that the longer the actual turnaround buffer was, the less frequently the delays occurred. By using these analyses, we could numerically verify the actual delay trends in South Korea. The results can be used as fundamental resources for political and economic decision-making processes in the aviation industry.

Machine Learning Approach for Flight Departure Delay Prediction and Analysis

The expected growth in air travel demand and the positive correlation with the economic factors highlight the significant contribution of the aviation community to the U.S. economy. On‐time operations play a key role in airline performance and passenger satisfaction. Thus, an accurate investigation of the variables that cause delays is of major importance. The application of machine learning techniques in data mining has seen explosive growth in recent years and has garnered interest from a broadening variety of research domains including aviation. This study employed a support vector machine (SVM) model to explore the non-linear relationship between flight delay outcomes. Individual flight data were gathered from 20 days in 2018 to investigate causes and patterns of air traffic delay at three major New York City airports. Considering the black box characteristic of the SVM, a sensitivity analysis was performed to assess the relationship between dependent and explanatory variables. The impacts of various explanatory variables are examined in relation to delay, weather information, airport ground operation, demand-capacity, and flow management characteristics. The variable impact analysis reveals that factors such as pushback delay, taxi-out delay, ground delay program, and demand-capacity imbalance with the probabilities of 0.506, 0.478, 0.339, and 0.338, respectively, are significantly associated with flight departure delay. These findings provide insight for better understanding of the causes of departure delays and the impacts of various explanatory factors on flight delay patterns.

Universal patterns in passenger flight departure delays

Departure delays are a major cause of economic loss and inefficiency in the growing industry of passenger flights. A departure delay of a current flight is inevitably affected by the late arrival of the flight immediately preceding it with the same aircraft. We seek to understand the mechanisms of such propagated delays, and to obtain universal metrics by which to evaluate an airline’s operational effectiveness in delay alleviation. Here we use big data collected by the American Bureau of Transportation Statistics to design models of flight delays. Offering two dynamic models of delay propagation, we divided all carriers into two groups exhibiting a shifted power law or an exponentially truncated shifted power law delay distribution, revealing two universal delay propagation classes. Three model parameters, extracted directly from dual data mining, help characterize each airline’s operational efficiency in delay mitigation. Therefore, our modeling framework provides the crucially lacking evaluation indicators for airlines, potentially contributing to the mitigation of future departure delays.

A Methodology for Predicting Aggregate Flight Departure Delays in Airports Based on Supervised Learning

This paper proposes a new methodology for predicting aggregate flight departure delays in airports by exploring supervised learning methods. Individual flight data and meteorological information were processed to obtain four types of airport-related aggregate characteristics for prediction modeling. The expected departure delays in airports is selected as the prediction target while four popular supervised learning methods: multiple linear regression, a support vector machine, extremely randomized trees and LightGBM are investigated to improve the predictability and accuracy of the model. The proposed model is trained and validated using operational data from March 2017 to February 2018 for the Nanjing Lukou International Airport in China. The results show that for a 1-h forecast horizon, the LightGBM model provides the best result, giving a 0.8655 accuracy rate with a 6.65 min mean absolute error, which is 1.83 min less than results from previous research. The importance of aggregate characteristics and example validation are also studied.

A Method of Reducing Flight Delay by Exploring Internal Mechanism of Flight Delays

This paper explores the internal mechanism of flight departure delay for the Delta Air Lines (IATA-Code: DL) from the viewpoint of statistical law. We roughly divide all of delay factors into two sorts: propagation factor (PF), and nonpropagation factors (NPF). From the statistical results, we find that the distribution of the flight departure delay caused by only NPF exhibits obvious power law (PL) feature, which can be explained by queuing model, while the original distribution of flight departure delay follows the shift power law (SPL). The mechanism of SPL distribution of flight departure delay is considered as the results of the aircraft queue for take-off due to the airports congestion and the propagation delay caused by late-arriving aircraft. Based on the above mechanism, we develop a specific measure for formulating flight planning from the perspective of mathematical statistics, which is easy to implement and reduces flight delays without increasing operational costs. We analyze the punctuality performance for 10 of the busiest and the highest delay ratio airports from 155 airports where DL took off and landed in the second half of 2017. Then, the scheduled turnaround time for all flights and the average scheduled turnaround time for all aircraft operated by DL has been counted. At last, the effectiveness and practicability of our method is verified by the flights operation data of the first half of 2018.

Factors influencing charter flight departure delay

This study aims to identify the main factors leading to charter flight departure delay through data mining. The data sample analysed consists of 5484 flights operated by a European airline between 2014 and 2017. The tuned dataset of 33 features was used for modelling departure delay (e.g., if the flight delayed >15 min). The results proved the value of the proposed approach by an area under the receiver operating characteristic curve of 0.831 and supported knowledge extraction through the data-based sensitivity analysis. The features related to previous flight delay information were considered as being the most influential toward current flight being delayed or not, which is consistent with the propagating effect of flight delays. However, it is not the reason for the previous delay nor the delay duration that accounted for the most relevance. Instead, a computed feature indicating if there were two or more registered reasons accounted for 33% of relevance. The contributions include also using a broader data mining approach supported by an extensive data understanding and preparation stage using both proprietary and open access data sources to build a comprehensive dataset.

The dynamic programming algorithm for management of real-time aircraft assignments

The schedule is the basis of any airline. In this paper, we provide one of the problems of schedule theory. This problem of optimal management of flight schedule is to minimize flight departure delays and to improve flight on-time performance. This paper provides a conceptual and mathematical formulation of optimizing an operating flight schedule problem. The problem solution is to manage the assignments of specific air-crafts on flights considering the operational information about the implementation of the planned flight schedule. This problem is NP-hard and can not be solved exactly for any real dimensions. We propose the parametric effective algorithm for its approximate solution and provide the statistical data for testing.

Study of Flight Departure Delay and Causal Factor Using Spatial Analysis

Analysis of flight delay and causal factors is crucial in maintaining airspace efficiency and safety. However, delay samples are not independent since they always show a certain aggregation pattern. Therefore, this study develops a novel spatial analysis approach to explore the delay and causal factors which is able to take dependence and the possible problem involved including error correlation and variable lag effect of causal factors on delay into account. The study first explores the delay aggregation pattern by measuring and quantifying the spatial dependence of delay. The spatial error model (SEM) and spatial lag model (SLM) are then established to solve the error correlation and the variable lag effect, respectively. Results show that the SEM and SLM achieve better fit than ordinary least square (OLS) regression, which indicates the effectiveness of considering dependence by employing spatial analysis. Moreover, the outcomes suggest that, aside from the well-known weather and flow control factors, delay-reduction strategies also need to pay more attention to reducing the impact of delay at the previous airport.