Data-Driven Robust Aircraft Assignment to Minimize Delay Propagation

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.

With the development of the civil aviation transportation industry in recent years, the volume of civil aviation transportation has increased rapidly. Increased carrier costs and reduced airport operating efficiency caused by flight delays have become issues that need to be addressed. How to improve the accuracy of predicting flight arrival delay time is of great significance for improving airport transportation efficiency, rationally scheduling flights and improving passenger comfort. In this paper, the Cat-boost model is utilized on the U.S Domestic airline on-time performance data from U.S. Transportation Administration, combined with the characteristics of the model to determine the influencing factors, and to predict the arrival delays of flights within the United States. The accuracy;precision and some other criterion of the model are given to evaluate the performance on the data. A better effect is obtained: the accuracy reach 80.44% in this case. Finally, the specific delay time is predicted, we found that the support vector machine has the best prediction result for the flight delay time, the average prediction error is 9.733 min, which has a certain reference value for flight operation and airport scheduling.

An important decision faced by airline schedulers is how to adapt the flight schedule and aircraft assignment to unforeseen perturbations in an established schedule. In the face of unforeseen aircraft delays, schedulers have to decide which flights to delay, and when delays become excessive, which to cancel. Current scheduling models deal with simple decision problems of delay or cancellation, but not with both simultaneously. But in practice the optimal decision may involve results from the integration of both flight cancellations and delays. In Part I of this paper, a quadratic programming model for the integration decision problem is given. The model can formulate the integration of flight cancellations and delays as well as some special cases, such as the ferrying of surplus aircraft and the possibility of swapping different types of aircraft. In this paper, based on the special structure of the model, an effective algorithm is presented, sufficient computational experiments are conducted and some results are reported. These show that we can expect to obtain a sufficiently good solution in terms of reasonable CPU time.

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.

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.

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.

Concerning air traffic delays, air transport networks appear to have variable performance and stochastic nature. A delay incident in one airport may affect the operational efficiency of others and generate various side effects to the whole aviation network. Flight delays are a widespread phenomenon nowadays, costing billions to the air transportation economy and degrading passenger's quality of service. Dependency graphs have been proposed in the past to understand the delay propagation phenomenon and analyze such cascading events by using dependency chains. In this work, we propose a risk-based method to analyze interdependencies and congestions in the aviation network. The methodology and the developed tool can assess delay incidents in airports and produce weighted risk dependency graphs, presenting how a delay that occurred in one airport may affect other interconnected airports. Based on data collected from the US Bureau of Transportation Statistics, we analyze how flight delay risk propagates inside the aviation network. In addition, using historic flight performance data we provide predictions for flight chains, which are prone to delays. We implement a tool that can detect the most critical airports and congested connections based on their delay contribution in dependency chains. It also proposes the n-order dependency chains, which should be avoided by airline flight planners, to reduce delay impacts in the aviation network.

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.

Accurate flight delay prediction contains great reference value for airline business and passenger travel. Recent studies have been concentrated on applying machine learning methods to predict the probability of flight delay. Most of the previous prediction methods are built for a single air route or airport. This paper explores a broader spectrum of factors that may potentially affect the flight delay and proposes a gradient boosting decision tree (GBDT) based models for generalized flight delay prediction. To build a dataset for the proposed model, automatic dependent surveillance-broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition of airport, flight schedule, and airport information. Since the delay prediction results can be given with higher resolution, the designed prediction tasks contain four different classification tasks. Experimental results show that the proposed GBDT-based model can obtain higher prediction accuracy (87.72% for the binary classification) when handling our limited dataset.

INTRODUCTION: In-flight medical emergencies (IFMEs) average 1 of every 604 flights and are expected to increase as the population ages and air travel increases. Flight diversions, or the rerouting of a flight to an alternate destination, occur in 2 to 13% of IFME cases, but may or may not be necessary as determined after the fact. Estimating the effect of IFME diversions compared to nonmedical diversions can be expected to improve our understanding of their impact and allow for more appropriate decision making during IFMEs.METHODS: The current study matched multiple disparate datasets, including medical data, flight plan and track data, passenger statistics, and financial data. Chi-squared analysis and independent samples t-tests compared diversion delays and costs metrics between flights diverted for medical vs. nonmedical reasons. Data were restricted to domestic flights between 1/1/2018 and 6/30/2019.RESULTS: Over 70% of diverted flights recover (continue on to their intended destination after diverting); however, flights diverted due to IFMEs recover more often and more quickly than do flights diverted for nonmedical reasons. IFME diversions introduce less delay overall and cost less in terms of direct operating costs and passenger value of time (averaging around 38,000) than do flights diverted for nonmedical reasons.DISCUSSION: Flights diverted due to IFMEs appear to have less impact overall than do flights diverted for nonmedical reasons. However, the lack of information related to costs for nonrecovered flights and the decision factors involved during nonmedical diversions hinders our ability to offer further insights.Lewis BA, Gawron VJ, Esmaeilzadeh E, Mayer RH, Moreno-Hines F, Nerwich N, Alves PM. Data-driven estimation of the impact of diversions due to in-flight medical emergencies on flight delay and aircraft operating costs. Aerosp Med Hum Perform. 2021; 92(2):99105.

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.

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.

Delays in flights and other airline operations have significant consequences in quality of service, operational costs, and customer satisfaction. Therefore, it is important to predict the occurrence of delays and take necessary actions accordingly. In this study, we addressed the flight delay prediction problem from a supervised machine learning perspective. Using a real-world airline operations dataset provided by a leading airline company, we identified optimum dataset features for optimum prediction accuracy. In addition, we trained and tested 11 machine learning models on the datasets that we created from the original dataset via feature selection and transformation. CART and KNN showed consistently good performance in almost all cases achieving 0.816 and 0.807 F-Scores respectively. Similarly, GBM, XGB, and LGBM showed very good performance in most of the cases, achieving F-Scores around 0.810.

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.

Stochastic Route Choice Equilibrium Assignment for Travelers with Heterogeneous Regret Aversions