Flight Arrival Delay Prediction And Analysis Using Ensemble Learning

In recent years, flight delay costs the air transportation industry millions of dollars and has become a systematic problem. Understanding the behavior of flight delay is thus critical. This paper focuses on how flight delay is affected by operation-, time-, and weather-related factors. Different econometric models are developed to analyze departure and arrival delay. The results show that compared to departure delay, arrival delay is more likely to be affected by previous delays and the buffer effect. Block buffer presents a reduction effect seven times greater than turnaround buffer in terms of flight delays. Departure flights suffer more delays from convective weather than arrival flights. Convective weather at the destination airport for flight delay has a greater impact than at the original airport. In addition, sensitivity analysis of flight delays from an aircraft utilization perspective is conducted. We find that the effect of delay propagation on flight delay differs by aircraft utilization. This impact on departure delay is greater than the impact on arrival delay. In general, specific to the order of flights, the previous delay increases the impact on flight on-time performance as a flight flies a later leg. Buffer time has opposite effects on departure and arrival delay, with the order increasing. A decrease in buffer time with the order increasing, however, still has a greater reduction effect on departure delay than arrival delay. Specific to the number of flights operated by an aircraft, the more flights an aircraft flies in a day, the more the on-time performance of those flights will suffer from the previous delay and buffer time generally.

The usage of big data analytics is booming today, with its ability to be used to draw useful insights from past data research. Its uses in the aviation industry have a wide array of applications ranging from predicting flight delays to detecting faults in airplane parts. In this paper, we conducted exploratory data analysis on flight dataset to draw inferences on arrival and departure delays and to identify relationships between flight timings and delays. Using the flight delay data, we identified which flight is mostly prone to delays. The arrived upon conclusions are useful for selecting flights in the future.

Data-Driven Robust Aircraft Assignment to Minimize Delay Propagation

Airplane transportation has many advantages compared to other means of transportation, where this means of transportation is able to cover long distances in a short time. Besides having advantages, of course, air transportation often experiences flight delays which can be caused by several things, including delayed flights due to late arrival (Arrival Delay), delays of the airline itself (Carrier Delay), previously late arrivals (Late Aircraft Delay), air traffic congestion (Nas Delay), security issues (Security Delay) and weather conditions (Weather Delay) The tests carried out were 101,315 US flight delay data from 2017 to 2022. By using the Machine Learning method, the results obtained were that the largest flight delays were caused by late arrivals, namely 386,124,672, where the largest flight delays were by airlines. The airline, namely Southwest Airlines, is 61,474,379 of the total airlines, which is 20, and the biggest departure delay is at Chicago O'Hare International airport, which is in the city of Chicago, IL province, which is 20,912,928 of the total airport, which is 596. This research aims to predict future flights by analyzing current flight data, so that future flights can be better by overcoming or avoiding previous flight delay problems.

Flight delays have become a major issue in the aviation industry, impacting operational efficiency and customer satisfaction. This study proposes a CatBoostClassifier-based approach combined with Feature Engineering, Bayesian Optimization, and Random Over Sampling techniques to improve the accuracy of flight delay predictions. Based on model evaluation results, the use of Feature Engineering and Bayesian Optimization enhances performance compared to the baseline CatBoost model. The CatBoost+FE+Bayes combination achieves an accuracy of 83.32%, higher than the unmodified CatBoost model, which only reaches 82.95%. However, applying the Random Over Sampling technique in the CatBoost+FE+Bayes+ROS combination decreases model performance, reducing accuracy to 81.44%. Regarding other metrics, the CatBoost+FE+Bayes model demonstrates the highest F1-score of 0.62, indicating a balance between precision and recall. Additionally, the Area Under Curve (AUC) analysis reveals that CatBoost+FE+Bayes has the highest AUC value of 0.7793, followed by CatBoost+FE at 0.7768, and the unmodified CatBoost model at 0.7643. Meanwhile, the application of ROS leads to a decrease in AUC value to 0.6787. These findings suggest that utilizing Feature Engineering and Bayesian Optimization significantly enhances flight delay predictions. However, resampling techniques such as ROS do not always positively impact the tested model and can even degrade classification performance. The objective of this research is to develop a more accurate flight delay prediction model through the application of appropriate optimization techniques. The resulting model is expected to improve prediction quality and benefit the aviation industry by optimizing operational efficiency and minimizing the negative impact of delays on passengers.

PurposeThis paper aims to reduce flight delay propagation, improve flight punctuality rate and ensure aircraft maintenance opportunities by establishing an integrated aircraft scheduling model, aiming at minimizing the total propagated delay and direction operational cost.Design/methodology/approachIn this paper, flight data sets are obtained through automatic dependent detection broadcast. To accurately predict flight delay time, the flight delay prediction eXtreme gradient boosting model adds the data set obtained by random forest advance model learning and predicts the newly generated flight delays. Finally, based on the forecast results, the flight plan can be optimized and adjusted by using the improved column generation algorithm.FindingsIt is verified by the actual weekly planned operation data of an airline company, experiments show that the model established in this paper can reduce flight delay propagation by 30% in case tests and each aircraft has the opportunity to be repaired at the base airport.Originality/valueOptimize the aircraft scheduling plan, cover a wide range of data, not just a single route and airport, supplement the gap in the aircraft scheduling plan based on weather factors to predict flight delays.

Flight delay is a major issue in the aviation industry. In commercial aviation, if a flight reaches its destination 15 minutes later than the scheduled arrival, it is said to be delayed. Flight delays cause a great deal of bother to travelers. It could make them late to their booked occasions or miss a corresponding flight, accordingly prompting outrage and dissatisfaction. Likewise, travelers may not generally be entitled for a refund when a postponement happens. Carriers report that couple of the numerous reasons prompting most flight delays are carrier glitches, climate conditions, support issues with the airplane and congestion in air traffic. Rapid development in airline industry has led to an increased number of aircrafts in the skies, this has brought about air-gridlock causing flight delays. Flight delays are not only extremely undesirable financially but also have adverse environmental effects. Air-traffic management is becoming increasingly challenging. The aim of our research work is to predict the delay of flights due to various factors using machine learning and deep learning so as to minimize losses and increase customer satisfaction. This Machine Learning model could be integrated with Airlines systems for the use of staff and customers and it could also rank Airlines and flights based on delays.

Flight delays represent a significant challenge in the global aviation industry, resulting in substantial costs and a decline in passenger satisfaction. This study addresses the critical issue of predicting flight delays exceeding 15 min using machine learning techniques. The arrival delays at a Turkish airport are analyzed utilizing a novel dataset derived from airport operations. This research examines a range of machine learning models, including Logistic Regression, Naïve Bayes, Neural Networks, Random Forest, XGBoost, CatBoost, and LightGBM. To address the issue of imbalanced data, additional experiments are conducted using the Synthetic Minority Over-Sampling Technique (SMOTE), in conjunction with the incorporation of meteorological data. This multi-faceted approach ensures robust forecast performance under varying conditions. The SHAP (SHapley Additive exPlanations) method is employed to interpret the relative importance of features within the models. The study is based on a three-year period of flight data obtained from a Turkish airport. The dataset is sufficiently extensive and robust to provide a reliable foundation for analysis. The results indicate that XGBoost is the most proficient model for the dataset, demonstrating its potential to deliver highly accurate predictions with an accuracy of 80%. The impact of weather factors on the predictions is found to be insignificant in comparison to scenarios without weather data in this dataset.

Chaotic Phenomena and Quantitative Analysis of Aftereffect Delay Spread Time on Flights

Flight delay is the root of Delay Propagation, while delay propagated within a Flight Chain. Busy hub-airport plays an important role in a Flight Chain, since the Initial Delay often happens there. To research more possible impacting factors in the Flight Chain, a busy hub-airport is chosen as the object in this research. After analyzing the complex and uncertain relationship between flight delays and all possible impacting factors, Bayesian network (BN) adapt to model and estimate about flight delay in a busy hub-airport. We proposed two learning methods with three different models. Model I is an estimation model for Arrival Delay based on parameter learning of BN with the Expectation-Maximization (EM) algorithm. Model II is estimation model for Arrival Delay based on structure learning of BN with K2 algorithm. The model learned by K2 is proved more suitable for modeling the estimation of flight delay, with a higher estimating correct rate than Model I. Then Model II is estimation model for Departure Delay with the same algorithm of Model II.

There are several theoretical indications that the quantum gravity approaches may have predictions for a minimal measurable length, and a maximal observable momentum and throughout a generalization for Heisenberg uncertainty principle. The generalized uncertainty principle (GUP) is based on a momentum-dependent modification in the standard dispersion relation which is conjectured to violate the principle of Lorentz invariance. From the resulting Hamiltonian, the velocity and time of flight of relativistic distant particles at Planck energy can be derived. A first comparison is made with recent observations for Hubble parameter in redshift-dependence in early-type galaxies. We find that LIV has two types of contributions to the time of flight delay $\Delta t$ comparable with that observations. Although the wrong OPERA measurement on faster-than-light muon neutrino anomaly, $\Delta t$, and the relative change in the speed of muon neutrino $\Delta v$ in dependence on redshift $z$ turn to be wrong, we utilize its main features to estimate $\Delta v$. Accordingly, the results could not be interpreted as LIV. A third comparison is made with the ultra high-energy cosmic rays (UHECR). It is found that an essential ingredient of the approach combining string theory, loop quantum gravity, black hole physics and doubly spacial relativity and the one assuming a perturbative departure from exact Lorentz invariance. Fixing the sensitivity factor and its energy dependence are essential inputs for a reliable confronting of our calculations to UHECR. The sensitivity factor is related to the special time of flight delay and the time structure of the signal. Furthermore, the upper and lower bounds to the parameter, $\alpha$ that characterizes the generalized uncertainly principle, have to be fixed in related physical systems such as the gamma rays bursts.

Delays in arrival of airline passengers should be the on-time performance metric of the airline passenger transportation system (not flight delays). A passenger trip can experience arrival delays, relative to the ticketed arrival time, as a result of a delayed flight, as well as a diverted flight, cancelled flight, denied boarding, and/or missed connection. This paper describes the results of analysis of annual passenger trip delays for U.S. domestic airline flights from 2007 to 2009. These results are based on estimated itineraries and load factors, and actual airline (flight) on-time performance data available from government websites.

A Classification Prediction Analysis of Flight Cancellation Based on Spark

The relationship between airport congestion and passenger delays at an airport is determined by many factors. Among these factors are scheduled and actual flight times, flight itineraries, airport capacity, and turn around time between flights. From the existing recorded data, it is possible to present a general profile of both airport congestion and passenger delays, measured with respect to the published schedule, at major airports within the US. This paper illustrates the discrepancy between the two measurements. Airport congestion may be masked by flight schedules which account for routine arrival delays and/or with slack time provided for turn around at an airport. It is possible for an airport to be heavily utilized, with little or unnoticeable at-gate delays (measured with respect to scheduled arrival time). On the other hand, at-gate delays can occur at under-utilized airports. Arrival and departure queuing delays are better indicators of airport congestion than passenger delays. Average flight at-gate delays are derived from performance data and compared with simulation results for 30 of the busiest airports in USA for a good weather day. The effects of variations in schedule flight time and slack time are studied parametrically in the simulation and calibrated to match the recorded baseline national airspace system (NAS) delay profiles. This paper illustrates a technique for estimating NAS-wide impact of propagated flight delays, based upon a method for calibration a system-level simulation against real world delays as well as delays derived from other simulations.

Many critical complex systems and networks are continuously monitored, creating vast volumes of data describing their dynamics. To understand and optimize their performance, we need to discover and formalize their dynamics to enable their control. Here, we introduce a multidisciplinary framework using network science and control theory to accomplish these goals. We demonstrate its use on a meaningful example of a complex network of U.S. domestic passenger airlines aiming to control flight delays. Using the real data on such delays, we build a flight delay network for each airline. Analyzing these networks, we uncover and formalize their dynamics. We use this formalization to design the optimal control for the flight delay networks. The results of applying this control to the ground truth data on flight delays demonstrate the low costs of the optimal control and significant reduction of delay times, while the costs of the delays unabated by control are high. Thus, the introduced here framework benefits the passengers, the airline companies and the airports.