Flight Delays Research Articles

Evaluation of Air Traffic Network Resilience: A UK Case Study

With growing air travel demand, weather disruptions cost millions in flight delays and cancellations. Current resilience analysis research has been focused on airports and airlines, rather than the en-route waypoints, and has failed to consider the impact of disruption scenarios. This paper analyses the resilience of the United Kingdom (UK) air traffic network to weather events that disrupt the network’s high-traffic areas. A Demand and Capacity Balancing (DCB) model is used to simulate adverse weather and re-optimise the cancellation, delay, and rerouting of flights. The model’s feasibility and effectiveness were evaluated under 20 concentrated and randomly occurring extreme disruption scenarios, lasting 2 h and 4 h. The results show that the network is vulnerable to extended weather events that target the network’s most central waypoints. However, the network demonstrates resilience to weather disruptions lasting up to two hours, maintaining operational status without any flight cancellations. As the scale of disruption increases, the network’s resilience decreases. Notably, there exists a threshold beyond which further escalation in disruption scale does not significantly impair the network’s performance.

Identification of Critical Nodes for Delay Propagation in Susceptible-Exposed-Infected-Recovered (SEIR) and Genetic Algorithm (GA) Route Networks

In response to the challenges associated with forecasting the trajectory of flight delay propagation, pinpointing pivotal nodes within the route network, and the substantial costs involved in enhancing operational efficiency, this study introduces an innovative approach to identifying critical nodes that influence delay propagation across route networks. The methodology commences by establishing a route network model for East China, leveraging the principles of complex network theory. It then incorporates the SEIR (Susceptible-Exposed-Infected-Recovered) model, typically used for analyzing the dynamics of infectious disease spread, to examine the propagation of delays between routes. Subsequently, the approach employs a GA to identify key nodes, which are then compared against those identified by network topology indices. The simulation outcomes demonstrate that the GA’s identification of key nodes offers superior insights into the overall network’s susceptibility to infection, thereby presenting operational managers with novel perspectives for analyzing the spread of flight delays.

Progress and challenges in airport slot allocation

Most of Level 3 airports worldwide face severe congestion and flight delays. Optimal allocating airport slots is one of the most effective ways in airport demand management. Here we systematically review the progress of research in airport slots allocation, as well as the main techniques in setting airport declared capacity and allocating slots.

The study on the Internet of Things implication in improving the punctuality of air transportation in bad weather

Air travel is a popular choice for people who seek to travel outside in the present. It greatly promotes communication and economic development. However, flight delays can have seriously disappointing effects on various aspects. For passengers, they not only need a long time to wait and additional expenses, but they also experience more psychological stress. And airlines face economic losses, reduced customer satisfaction, and disruptions in flight scheduling, which further affect operational efficiency. Therefore, this paper aims to explore potential methods that are combined with the Internet of Things to improve the punctuality of air transportation in order to ease the hard situation of flight delays caused by bad weather. And based on the previous related papers, the author of this paper thinks a kind of meteorological monitoring and early warning system can be built based on the Internet of Things to improve real-time weather monitoring and flight scheduling, as well as ground service optimization. Hence, people can improve the punctuality of air transportation by optimizing resource utilization and cost management so that the whole industry can bring significant economic benefits to airline companies and improved travel experiences to passengers.

Optimization of multi-path approach flight sequencing based on rolling horizon control

Arrival flight sequencing optimization is an effective method to improve the landing efficiency of incoming flights and reduce flight delays. Based on this, with the goal of maximizing landing efficiency, combined with multi-runway and multi-route selection, and considering the actual waypoint restrictions, a mixed integer programming model for multi-path and multi-runway integrated arrival flight sequencing optimization is proposed. In order to solve the real-time problem of large-scale flight sequencing calculation, a multi-waypoint rolling horizon control algorithm is proposed. The terminal area of Guangzhou Baiyun International Airport is used as an example for verification, and the actual arrival flight data is used to carry out calculation experiments. In terms of wake turbulence safety interval, the RECAT-CN operation standard is used. The calculation results show that when the number of small-scale flights (23 flights) is small, the maximum landing time of the proposed model is 55 s earlier than the first-come-first-served method and 271 s earlier than the non-optimized one; when the number of large-scale flights (104 flights) is large, the solver alone cannot find a feasible solution within 3600 s, and the proposed algorithm finds a solution within 128.65 s. The proposed model and algorithm are effective and can be applied to actual flight scheduling optimization.

A novel bi-objective model for sustainable and efficient airport logistics management: A case study on Copenhagen airport

Due to the growing volume of air transportation and the scarcity of airport resources, aviation planners are paying more attention to the efficiency of airport resource management. Optimizing the assignment and scheduling of ground facilities plays an important role in reducing ground congestion, flight delays, and passenger dissatisfaction. To prevent any collisions among aircraft on the apron, taxi network, and runways, safety considerations should be strictly followed by traffic controllers. Minimizing taxi time is another matter of importance in airport logistics management that helps mitigate environmental concerns (e.g., fuel consumption, tire abrasion, and noise pollution). In this study, a bi-objective mixed-integer linear programming model is developed to optimize the ground resource scheduling problem, taking into account the challenges of gate assignment, taxi planning, and runway scheduling. The model is developed in a way that includes safety and sustainability considerations. The validation of the proposed model is investigated through several examples based on Copenhagen airport using the augmented ε-constraint method. Since the simultaneous scheduling of multiple resources is an NP-hard problem, two multi-objective metaheuristic algorithms are employed to deal with the large-scale problems. The performance of the algorithms is then compared in terms of coverage and convergence rates. Additionally, the results are discussed in detail to provide managerial implications for airport authorities.

Analisis Pelaksanaan Tugas Ramp Handling dalam Menjaga On Time Performance (OTP) Pada Maskapai Garuda Indonesia di Bandara Soekarno Hatta Cengkareng

Soekarno-Hatta Airport is the main gateway for international and domestic air travel in Indonesia. As one of the busiest airports in Southeast Asia, Soekarno-Hatta Airport handles more than 60 million passengers per year (Soekarno-Hatta Airport Authority, 2023). According to the operational management theory by Suryanto (2020), efficiency in managing all operational processes, including ramp handling, is crucial to ensure smooth and punctual flights. With high traffic volume, the main challenge is to maintain On Time Performance (OTP) to minimize flight delays. This study aims to determine the implementation of ramp handling tasks during pre-flight activities, as well as to determine the obstacles experienced by Ramp Handling officers in pre-flight activities at Soekarno Hatta Cengkareng International Airport and to determine how efforts are made by PT Gapura Angkasa in improving the quality of On Time Performance (OTP) at Soekarno Hatta International Airport, Cengkareng. This study uses a qualitative method, namely through interviews, observations, and documentation of ramp handling personnel units, this study was conducted for 1 month at Soekarno Hatta International Airport The results of this study are that ramp handling officers at Soekarno-Hatta Cengkareng International Airport are responsible for checking the readiness of ground equipment, flight documents, and coordinating with the ground crew team. The use of checklists ensures that all pre-flight steps are carried out correctly and efficiently, which is important for smooth boarding and loading.

Flight Schedule Optimization Considering Fine-Grained Configuration of Slot Coordination Parameters

In response to the rapid growth of air passenger and cargo transportation services and the sharp increase in congestion at various airports, it is necessary to optimize the allocation of flight schedules. On the basis of reducing the total airport delay time and ensuring the total deviation of flight schedules applied by airlines, it is necessary to consider finely configuring flight schedules with slot coordination parameters, introducing a 5 min slot coordination parameter, and optimizing airport flight schedules in different time periods. This article considers factors such as flight schedule uniqueness, corridor flow restrictions, and time adjustment range limitations to establish a three-objective flight-schedule refinement configuration model, which is solved using the NSGA-II algorithm based on the entropy weight method. Taking Beijing Capital International Airport as an example, the optimized results show that the total flight delay was reduced from 4130 min to 1142 min, and the original delay of 389 flights was reduced to 283 flights. Therefore, flight schedule optimization considering the fine-grained configuration of slot coordination parameters can effectively reduce airport delays, fully utilize time resources, and reduce waste of time slot resources.

Enhancing Flight Delay Predictions Using Network Centrality Measures

Accurately predicting flight delays remains a significant challenge in the aviation industry due to the complexity and interconnectivity of its operations. The traditional prediction methods often rely on meteorological conditions, such as temperature, humidity, and dew point, as well as flight-specific data like departure and arrival times. However, these predictors frequently fail to capture the nuanced dynamics that lead to delays. This paper introduces network centrality measures as novel predictors to enhance the binary classification of flight arrival delays. Additionally, it emphasizes the use of tree-based ensemble models, specifically random forest, gradient boosting, and CatBoost, which are recognized for their superior ability to model complex relationships compared to single classifiers. Empirical testing shows that incorporating centrality measures improves the models’ average performance, with random forest being the most effective, achieving an accuracy rate of 86.2%, surpassing the baseline by 1.7%.

PREDICTING FLIGHT DELAYS WITH ERROR CALCULATION USING MACHINE LEARNED CLASSIFIERS

Worldwide, flight delays are becoming a major issue for the airline business. Due to air traffic congestion brought on by the airline industry's expansion over the last 20 years, flights have been delayed. In addition to hurting the economy, flight delays also have a detrimental effect on the environment since they increase fuel consumption and gas emissions. Thus, it is essential to take all reasonable precautions to avoid flight delays and cancellations. This paper's primary goal is to forecast an airline's delay utilizing a variety of variables. Thus, it is necessary to perform forward-looking analysis, which encompasses a variety of algorithmic predictive analytics approaches that use historical and current data to create models that are used for forecasts or simply to look at future delays using machine learning algorithms like Python 3's Gradient Boosting Regression technique, Bayesian Ridge, Random Forest Regression, and Logistic Regression. This will make it easier for the user to forecast whether an aircraft will arrive on time or not. Additionally, delay prediction analysis will assist airline industries in reducing their losses.

Air Passenger Rights Case Law Developments from 1 December 2023 to 15 June 2024

From 1 December 2023 to 15 June 2024, there have been several case developments in relation to air passenger rights. The first part of this article highlights recent decisions from the Court of Justice of the European Union that have shed light on the interpretation of EU Regulation No 261/2004, Package Travel Directive (EU) 2015/2302 and Council Directive 93/13/EEC of 5 April 1993 on Unfair Terms in Consumer Contracts.In the case Laudamotion GmbH v. flightright GmbH the Court stressed the standardized nature of compensation for a flight delay and held that for compensation claims under Regulation (EC) No 261/2004 due to a significant flight delay, passengers must adhere to the check-in requirements specified by the airline, even if they have already completed online check-in.In the case Eventmedia Soluciones SL v. Air Europa Líneas Aéreas SAU, the Court defined the rules governing the inclusion, in a contract of carriage, of a clause prohibiting the transfer of rights by passengers and the right of claim management companies to bring proceedings against airlines. In the case Cobult UG v. TAP Air Portugal SA (TAP), the Court clarified the rules applicable to passengers’ consent to the reimbursement of the cost of their ticket by a travel voucher rather than by a sum of money. In the case Touristic Aviation Services Limited (TAS) v. Flightright GmbH, the Court ruled that the shortage of staff at the airport responsible for baggage handling could be considered an extraordinary circumstance, provided the airline could demonstrate that they had taken all reasonable measures to prevent the delay.In the case QM v. Kiwi Tours GmbH, the Court held that to establish the existence of unavoidable and extraordinary circumstances significantly impacting the performance of a travel package, for the purpose of claiming a refund under Directive (EU) 2015/2302, only the situation prevailing on the date when the traveler terminated their travel contract should be considered. In the case Eventmedia Soluciones SL v. Air Europa Líneas Aéreas, the Court ruled on the fairness of clauses governing the assignment of rights by passengers to third parties under Directive 93/13/EEC on Unfair Terms in Consumer Contracts.

Reliability of a Multiple-Demand Multistate Air Transport Network With Flight Delays and Budget Constraints

Reliability of a Multiple-Demand Multistate Air Transport Network With Flight Delays and Budget Constraints

Machine Learning Approach of Predicting Airline Flight Delay using Naïve Bayes Algorithm

Machine Learning Approach of Predicting Airline Flight Delay using Naïve Bayes Algorithm

Statistical Inference for Hüsler–Reiss Graphical Models Through Matrix Completions

The severity of multivariate extreme events is driven by the dependence between the largest marginal observations. The Hüsler–Reiss distribution is a versatile model for this extremal dependence, and it is usually parameterized by a variogram matrix. In order to represent conditional independence relations and obtain sparse parameterizations, we introduce the novel Hüsler–Reiss precision matrix. Similarly to the Gaussian case, this matrix appears naturally in density representations of the Hüsler–Reiss Pareto distribution and encodes the extremal graphical structure through its zero pattern. For a given, arbitrary graph we prove the existence and uniqueness of the completion of a partially specified Hüsler–Reiss variogram matrix so that its precision matrix has zeros on non-edges in the graph. Using suitable estimators for the parameters on the edges, our theory provides the first consistent estimator of graph structured Hüsler–Reiss distributions. If the graph is unknown, our method can be combined with recent structure learning algorithms to jointly infer the graph and the corresponding parameter matrix. Based on our methodology, we propose new tools for statistical inference of sparse Hüsler–Reiss models and illustrate them on large flight delay data in the United States, as well as Danube river flow data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Enhancing Flight Delay Prediction and Classification Using a Hybrid Bi-LSTM: Machine Learning

Enhancing Flight Delay Prediction and Classification Using a Hybrid Bi-LSTM: Machine Learning

Airport time profile construction driven by flight delay prediction

Slot structure is the equilibrium result of market demand side and slot resource supply side, while slot parameters reflect the operational support capacity of the aviation system. Time parameters reflect the operational support capability of the aviation system. Time structure should not only reflect changes in market demand, but also meet the constraints of operational efficiency. Constructing a reasonable 18–24 h timetable profile for busy airports that meets normal expectations for declared capacity and seasonal scheduling is a challenge in civil aviation slot management. This study utilizes historical data on airport flights and weather conditions to establish a regression prediction model for the time structure using K-means clustering and partial least squares regression. Additionally, ensemble learning is employed to forecast flight delay levels. The findings demonstrate that random forest yields favorable results in regression and prediction tasks, allowing for the integration of upper (good weather) and lower (severse weather) limits of the time profile with delay predictions as time parameter intervals. Consequently, the flights falling within these intervals achieve an average delay level of less than 15 min which meets the expectations of normal flight.

A Knee-Guided Evolutionary Algorithm for Multi-Objective Air Traffic Flow Management

Air traffic flow management plays a crucial role in efficient aviation. Most existing studies assume the flight speed as constant throughout the trip, leading to ineffective fixed-speed schedules. To address this issue, we propose a new problem model, which allows variable speed control to improve the flexibility and maneuverability of the management. In addition, we consider two conflicting objectives, which are minimizing the total flight delays and conflicts between flights, where the conflicts depend on the flight 4D trajectories (3D position plus time). To solve this new challenging problem, we propose a novel multi-objective evolutionary algorithm with new problem-specific individual representation and search operators. Specifically, the multi-chromosomes encoding scheme is designed to adapt to different types of operations. Then, to search the huge search space effectively, we develop a hybrid crossover operator that recombines the parents based on their flight routes. Furthermore, to balance the exploration and exploitation, we develop a new mutation strategy to utilize the heterogeneous search potential of different individuals. For exploitation, the knee individual in the Pareto front is improved by a new time shift operator for exploitation, and other non-dominated solutions are mutated by fixed-route mutation. For exploration, the dominated solutions are mutated randomly. To verify the effectiveness, we compare it with the real air traffic flow management schedules and the state-of-the-art algorithms on a range of real-world air traffic datasets. Extensive results show that the proposed algorithm can significantly outperform the baselines in generating safe and efficient 4D trajectories.

Resilience assessment of airport aircraft area network operations under thunderstorm weather

Thunderstorms can cause a decrease in airports' dynamic capacity, and taxiway runway capacity is a key element of airport airfield operations. In this paper, based on the cell transmission model (CTM), the meteorological operation parameters of thunderstorms are set according to the airport aircraft taxiing rules, and a CTM is proposed for evaluating the resilience of the airport aircraft area operation network under thunderstorm weather. Based on the CTM, the network performance parameters are obtained, and the number of aircraft waiting for the network and the network operation efficiency are used as the basic performance indicators to establish a resilience index that reflects the dynamic change in the resilience of the thunderstorm meteorological interference network. Finally, this paper collects and analyzes the operation data of Tianjin Binhai International Airport in 2019 to build a CTM for aircraft operation in the flight area. The results show that the model's simulated airport departure operation data is almost consistent with the trend of the actual departure operation data, which verifies that the model can be used to simulate traffic for the airport airfield operation network. In addition, based on the resilience index, the sensitivity analysis of different parameters to network performance under thunderstorm weather was also studied. Based on the evaluation results of different parameter scenarios and indicators, we drew the resilience map of the airport ground operation network, and found that the network shows different resilience levels in different thunderstorm level scenarios. This study provides a great method and evaluation index for modeling and resilience evaluation of aircraft operation networks in the airfield, which is expected to improve airport operations, reduce flight delays, and improve service quality.

Arrival and Departure Sequencing, Considering Runway Assignment Preferences and Crossings

Aircraft sequencing has the potential to decrease flight delays and improve operational efficiency at airports. This paper presents the aircraft sequencing problem (ASP) on multiple runways with complex interactions by allocating flights on runways and optimizing landing times, take-off times, and crossing times simultaneously in a uniform framework. The problem was formulated as a mixed-integer program considering realistic operational constraints, including runway assignment preferences based on the entry/exit fixes of the terminal maneuvering area (TMA), minimum runway separation, time window, and arrival crossing rules. Variable-fixing strategies were applied, to strengthen the formulation. A first-come-first-served (FCFS) heuristic was proposed for comparison. Various instances from the literature and from realistic data sets were tested. Our computational study showed that the solution approach optimizes runway schedules, to achieve significantly fewer flight delays, taking runway assignment preferences and arrival crossings into account.

Principle of corresponding states of particle gases for passenger flights

Passenger flight delays in civil aviation systems cause much harm to people’s daily lives. Recent works confirmed that the simple laws of delays emerge from the complex interactions of the systems’ elements, the patterns of which can be traced by the actual operations of passenger flights. Figuring out the underlying mechanism of the collective behaviours of flights is required to capture the law of delays, and to alleviate them. In the present work, based on the historical records of the United States from 1995 to 2020, we propose state equations to describe the motion of Arrival/Departure(AD) type flights by considering the attractions from airports and air routes together with the repulsions between flights. We erect the universal gaseous constant R by removing the intercepts representing the systemic errors. With the linear relationship between the number of air routes and the mole number, we calculate the critical thermodynamic quantities. Introducing the reduced ones with them, we generalize the principle of corresponding states of passenger flight particle gases. According to it, we think increasing the number of AD type flights between airports would be a proper strategy for alleviating flight delays. We hope the general ideas presented here can stimulate much more work in civil aviation and be used to solve practical problems in other fields.