Flight Disruption Research Articles

Improved NSGA2 algorithm for recovery of interrupted departure flights

To solve the problem of airline flight disruption caused by emergencies, the disrupted departing flights are restored, a dual-objective optimization model is constructed to minimize the total delay cost of airlines and minimize the total delay time of passengers. An adaptive non-dominated sorting genetic algorithm based on dominance intensity is designed, and three improved operations are proposed: a fast dominance sorting method, a new congestion distance and an adaptive elite retention strategy. The proposed algorithm is verified by the operation data of an airline at Fuzhou Airport. The experimental results show that compared with the traditional plan-first-serve method, the solution obtained by the proposed algorithm is greatly optimized. Compared with the constraint method, the solution time is generally lower than the constraint method and the solution result is close to the optimal result obtained by the constraint method. Compared with multi-objective optimization algorithms such as NSGA2 and MOEAD, the proposed algorithm shows better performance and can solve the problem effectively and efficiently, providing a basis for airlines to achieve optimized solutions.

Probabilistic risk assessment of civil aircraft associated failures under condition-based maintenance

Maintenance can improve an aircraft system's reliability over a long operation period or when a component has failed. However, inappropriate maintenance inspection intervals will cause latent failures to be covered or undetected, leading to a large number of unplanned flight disruptions for airlines. In this paper, we present a two-stage framework to assess the associated failure risk of civil aircraft under condition-based maintenance. In the first stage of the framework, the probability of primary functional failure across the lifecycles of the monitored component is determined by analyzing whether the current inspection interval prevents the component from progressing from latent failure to functional failure. In the second stage of the framework, the associated failure probability between components and related systems is formulated by the adjacency matrix. The structure and performance of the proposed model were tested on a case study by run-to-failure data associated with aircraft engines from a large airline. Focusing on the scenario of turbine disk cracking leading to fragment penetration of the fuel tank and causing fire as the consequential fault impact path, the results show that the risk of aircraft fire caused by turbine disk fragments falls within an acceptable range, necessitating the completion of inspections and subsequent monitoring within the stipulated timeframe. The method can be used to readjust the inspection interval, optimize the operation plan, improve the on-time performance of flights, and reduce the risk of aviation accidents.

Integrating AI support into a framework for collaborative decision-making (CDM) for airline disruption management

Airlines are regularly confronted with disruptions that interfere with their flight operations, resulting in financial losses and lower operational performance. While collaborative decision-making (CDM) is a commonly used approach to mitigate these airline disruptions, it is unclear how artificial intelligence (AI) can support CDM to manage airline disruption. This study's purpose is to identify how the adoption of AI can support CDM to mitigate operational flight disruptions. Using a theory building approach, this conceptual paper advances the literature in aviation management by delineating the relationship between AI and CDM in the context of airline disruption management. First, we propose an AI–CDM framework illustrating the factors that influence disruption management in airlines. Second, we highlight the implications of AI-supported CDM for disruption management in and for airlines. We found that to effectively use AI-supported CDM for disruption management, airlines need to a) introduce data-driven CDM, b) enable AI management of complex systems, and c) transform disruption management into AI-supported performance management. As one of the first studies linking AI and CDM, the framework provides a structured recognition of the role of AI in CDM and its implications for airline disruption management.

Analyzing Passenger Experience: A Study On Airline Mishandling Incidents At Ninoy Aquino International Airport Terminal 3

This research delves deep into the complex dynamics of airline mishandling incidents at NAIA Terminal 3, aiming to reveal their intricate effects on passenger experiences. With a focus on understanding the connections between mishandling, flight disruptions, and broader implications for airline operations, the study intends to offer a comprehensive view. Using a precise approach, the researchers adjusted Slovin's formula to pinpoint an exact sample size of 300 respondents, ensuring a detailed exploration of passenger encounters despite time constraints. They achieved a 14% response rate, providing insights into the diverse landscape of travelers. Going beyond traditional surveys, the study incorporated qualitative aspects through interviews with informants, including airport and airline personnel. This dual-method strategy facilitated a nuanced analysis of mishandling incidents, capturing the subtle challenges faced by both passengers and industry professionals. The results emphasized the significant impact of mishandling on operational efficiency, highlighting the need for improved baggage handling and security measures. The study concludes with practical recommendations, advocating for clear communication, technological advancements, and ongoing staff training to enhance the overall airport experience. This research serves as a valuable guide for aviation stakeholders, offering a roadmap to address mishandling incidents and improve passenger satisfaction.

Temporal variations in international air travel: implications for modelling the spread of infectious diseases.

The international flight network creates multiple routes by which pathogens can quickly spread across the globe. In the early stages of infectious disease outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common and can help inform disease control efforts. A challenge faced in this modelling is that the latest aviation statistics (referred to as contemporary data) are typically not immediately available. Therefore, flight patterns from a previous year are often used (referred to as historical data). We explored the suitability of historical data for predicting the spatial spread of emerging epidemics. We analysed monthly flight passenger data from the International Air Transport Association to assess how baseline air travel patterns were affected by outbreaks of Middle East respiratory syndrome (MERS), Zika and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) over the past decade. We then used a stochastic discrete time susceptible-exposed-infected-recovered (SEIR) metapopulation model to simulate the global spread of different pathogens, comparing how epidemic dynamics differed in simulations based on historical and contemporary data. We observed local, short-term disruptions to air travel from South Korea and Brazil for the MERS and Zika outbreaks we studied, whereas global and longer-term flight disruptions occurred during the SARS-CoV-2 pandemic. For outbreak events that were accompanied by local, small and short-term changes in air travel, epidemic models using historical flight data gave similar projections of the timing and locations of disease spread as when using contemporary flight data. However, historical data were less reliable to model the spread of an atypical outbreak such as SARS-CoV-2, in which there were durable and extensive levels of global travel disruption. The use of historical flight data as a proxy in epidemic models is an acceptable practice, except in rare, large epidemics that lead to substantial disruptions to international travel.

What can we learn from severity index on flight data monitoring? Analysis of safety resilience in flight operations during COVID-19 disruptions

The unexpected spread of the pandemic raised concerns regarding pilots’ skill decay resulting from the significant drops in the frequency of flights by about 70%. This research retrieved 4761 Flight Data Monitoring (FDM) occurrences based on the FDM programme containing 123,140 flights operated by an international airline between June 2019 and May 2021. The FDM severity index was analysed by event category, aircraft type, and flight phase. The results demonstrate an increase in severity score from the pre-pandemic level to the pandemic onset on events that occurred on different flight phases. This trend is not present in the third stage, which indicates that pilots and the safety management system of the airline demonstrated resilience to cope with the flight disruptions during the pandemic. Through the analysis of event severity, FDM enables safety managers to recommend measures to increase safety resilience and self-monitoring capabilities of both operators and regulators. Practitioner summary: The onset of the pandemic led to a rise in the severity of flight data monitoring events in a large airline, likely linked to a lack of operational practice and skills decay. This was demonstrated across different flight phases and aircraft types. In the settled pandemic period, the severity index returned to pre-pandemic levels, indicating that the resilience of individual pilots and safety management systems is critical to operational safety. HIGHLIGHTS The FDM event severity scores significantly increased following the pandemic onset, especially for event categories involving pilot core competencies. The FDM event severity scores stagnated or decreased during the later pandemic stage indicating resilience among the airline pilots and the airline’s safety management system. The airline and pilots demonstrated resilience by effectively mitigating the effects of proficiency decay which took place as the pandemic started. FDM analysis has shown to be effective in establishing a proactive SMS programme to mitigate the negative impacts of the pandemic on aviation safety.

Investigation of the Effect of the Instrument Landing System on Flights to Konya Airport between 2019-2022 Years

The Instrument Landing System (ILS) is the most widely used navigation aid system among the precision approach types, which enables the pilots to approach the runway in the appropriate direction and glide slope, especially in foggy and snowy weather when the visibility is very low. This study investigated the relationship between flight disruptions and ILS in Konya Airport. In this context, meteorological events such as snow, ice, fog, and cancellation, divert and delay events caused by humans (company policy) at Konya Airport in the period of 2019-2022 were revealed with numerical data. In light of these data, it has been revealed that ILS not only provides safe flights but also prevents possible passenger complaints and financial losses by reducing the number of diverts, and therefore ILS is also of great importance in terms of customer satisfaction.

Analysis of Flight Disruption Data to Determine Problem Areas Utilizing Database Applications

Flight delays represent a significant issue to airline profits and passenger satisfaction. Many factors can lead to a flight being delayed and/or cancelled. To compound the issue, COVID restrictions have created a shortage of skilled, willing labor and a disturbance in the supply chain. All aspects of air travel have been affected. For example, aircraft maintenance and new plane constructions have increased lead times due to parts unavailability. Mass layoffs or early retirements as a knee-jerk response to reduced travel in 2020 has exacerbated the problem. Pilot strikes protesting mandates led to mass flight cancellations in 2021. The study will evaluate flight delays, cancellations, and incident data with the goal of visualizing which airports, airlines, cities, or states are experiencing the highest number of flight disruptions relative to others. In the era of new technological development, database applications are commonly used for data analysis to allow pattern recognition and large data distribution and organization. This study will mainly serve the purpose of flight data retrieval, the compilation of data and database design and finally output data visualizations. The outcome of the research will be presented in a user-friendly interface where the user can easily generate visualizations which can be used to analyze flight delay information.

ACHIEVING OPTIMAL FLIGHT DELAY PREDICTION AND ERROR CALCULATION USING MACHINE CLASSIFIERS

Flight delays are one of the main issues facing the aviation industry. Increased air traffic as a consequence of the aviation industry's expansion throughout the last two decades has led to flight delays. Flight delays not only waste time and money, but they also have an adverse effect on the environment. Airlines that operate commercial flights suffer huge losses as a result of flight delays. As a result, they make all necessary steps to prevent or minimize flight disruptions and delays. In this research, we apply algorithms based on machine learning to forecast when a specific flight's arrival will be postponed or not, including logistic regression, decision tree regression, bayesian ridge, random forest regression, and gradient boosting regression.

Predicting the Remaining Useful Life of Landing Gear with Prognostics and Health Management (PHM)

Landing gear is an essential part of an aircraft. However, the components of landing gear are susceptible to degradation over the life of their operation, which can result in the shimmy effect occurring during take-off and landing. In order to reduce unplanned flight disruptions and increase the availability of aircraft, the predictive maintenance (PdM) technique is investigated in this study. This paper presents a case study on the implementation of a health assessment and prediction workflow for remaining useful life (RUL) based on the prognostics and health management (PHM) framework of currently in-service aircraft, which could significantly benefit fleet operators and aircraft maintenance. Machine learning is utilized to develop a health indicator (HI) for landing gear using a data-driven approach, whereas a time-series analysis (TSA) is used to predict its degradation. The degradation models are evaluated using large volumes of real sensor data from in-service aircraft. Finally, the challenges of implementing a built-in PHM system for next-generation aircraft are outlined.

Refining an ensemble of volcanic ash forecasts using satellite retrievals: Raikoke 2019

Abstract. Volcanic ash advisories are produced by specialised forecasters who combine several sources of observational data and volcanic ash dispersion model outputs based on their subjective expertise. These advisories are used by the aviation industry to make decisions about where it is safe to fly. However, both observations and dispersion model simulations are subject to various sources of uncertainties that are not represented in operational forecasts. Quantification and communication of these uncertainties are fundamental for making more informed decisions. Here, we develop a data assimilation method that combines satellite retrievals and volcanic ash transport and dispersion model (VATDM) output, considering uncertainties in both data sources. The methodology is applied to a case study of the 2019 Raikoke eruption. To represent uncertainty in the VATDM output, 1000 simulations are performed by simultaneously perturbing the eruption source parameters, meteorology, and internal model parameters (known as the prior ensemble). The ensemble members are filtered, based on their level of agreement with the ash column loading, and their uncertainty, of the Himawari–8 satellite retrievals, to produce a constrained posterior ensemble. For the Raikoke eruption, filtering the ensemble skews the values of mass eruption rate towards the lower values within the wider parameters ranges initially used in the prior ensemble (mean reduces from 1 to 0.1 Tg h−1). Furthermore, including satellite observations from subsequent times increasingly constrains the posterior ensemble. These results suggest that the prior ensemble leads to an overestimate of both the magnitude and uncertainty in ash column loadings. Based on the prior ensemble, flight operations would have been severely disrupted over the Pacific Ocean. Using the constrained posterior ensemble, the regions where the risk is overestimated are reduced, potentially resulting in fewer flight disruptions. The data assimilation methodology developed in this paper is easily generalisable to other short duration eruptions and to other VATDMs and retrievals of ash from other satellites.

Airline Disruption Management: A Naturalistic Decision-Making Perspective in an Operational Control Centre

Operations Control Centres (OCCs) are critical structures used by airlines to oversee the execution of all planned flights, managing punctuality, regularity and customer support. In this study, we investigated the decision-making during flight disruptions inside an OCC from the naturalistic decision-making perspective. We conducted a mini-ethnography case study in a major South American airline, focussing on how functions critical to the flight disruption management cope with variability. Data collection included document analyses, field notes, direct observations and interviews. The functional description of work-as-done revealed how the OCC constantly and actively looks for signs of disruption while monitoring the normal operation and rebalancing resources. The decision-making process is distributed and decentralised across multiple functions, where experts from each function rely on a repertoire of strategies to deploy innovative solutions to dynamic scenarios. Five different mechanisms were identified that converge functions to disarm potential disruptions before they compromise the flight network, and continuously create and reinforce system buffers.

Jet fuel demand hit by Omicron; recovery delayed until 2023/2024

Jet fuel demand hit by Omicron; recovery delayed until 2023/2024

Providing an Integrated Framework for an Air-Cargo Network to Recover from Demand Uncertainties and Flight Disruptions

Providing an Integrated Framework for an Air-Cargo Network to Recover from Demand Uncertainties and Flight Disruptions

Statistical Analysis of Weather Parameters for Sustainable Flight Operation in Nigeria

Abstract The recent complications in the weather system, which oftentimes lead to flight cancellation, delay and diversion have become a critical issue in Nigeria. This study however considers the weather related parameters and their impacts on flight disruption in the country. Weather data (on thunderstorm, wind speed and direction, visibility and cloud cover) and flight data (delay, cancellation and diversion) were collected from Murtala International Airport, Ikeja-Lagos, Nigeria. The data covered the period between 2005 and 2020. However, Regional Climate Models (RCMs) were also used to run climate data projections between year 2020 and 2035 in the study region. The study employed Statistical Package for Social Sciences (SPSS) software for the descriptive and inferential analysis. Time series analysis, Pearson Moment Correlation for interrelationship among the weather parameters and the flight disruption data, and multiple linear regression analysis were applied to determine the influence of weather parameters on flight disruption data. Results show that cloud cover and high visibility are negatively correlated. Wind speed has positive relationship with wind direction; and an inverse relationship between visibility, thunderstorm, and fog. Direct relationship exists between highest visibility and thick dust, wind speed and cloud cover. Thick dust, wind speed and cloud cover indicate increased visibility level in the study area. Flight delay is prominent over flight diversion and cancellation, which indicates their relevance in air traffic of the study area. The prediction model indicates high degree of cloud cover at the beginning of every year and later declines sharply in 2035, the visibility flattens out by the year 2025, and low pattern of thick dust was calculated in the same pattern in 2011, 2016 and 2027. Based on this conclusion, the study recommends accurate weather reporting and strict compliance to safety regulations, and attention should be paid to changing pattern of weather parameters in order to minimize fight related disasters.

An Observational Constraint on Aviation-Induced Cirrus From the COVID-19-Induced Flight Disruption.

Global aviation dropped precipitously during the covid‐19 pandemic, providing an unprecedented opportunity to study aviation‐induced cirrus (AIC). AIC is believed to be responsible for over half of aviation‐related radiative forcing, but until now, its radiative impact has only been estimated from simulations. Here, we show that satellite observations of cirrus cloud do not exhibit a detectable global response to the dramatic aviation reductions of spring 2020. These results indicate that previous model‐based estimates may overestimate AIC. In addition, we find no significant response of diurnal surface air temperature range to the 2020 aviation changes, reinforcing the findings of previous studies. Though aviation influences the climate through multiple pathways, our analysis suggests that its warming effect from cirrus changes may be smaller than previously estimated.

A Case Study on the Detection and Prognosis of Internal Leakages in Electro-Hydraulic Flight Control Actuators

Electro-hydraulic servo-actuators (EHSAs) are currently considered the state-of-the art solution for the control of the primary flight control systems of civil and military aircraft. Combining the expected service life of a commercial aircraft with the fact that electro-hydraulic technology is employed in the vast majority of currently in-service aircraft and is planned to be used on future platforms as well, the development of an effective Prognostic and Health Management (PHM) system could provide significant advantages to fleet operators and aircraft maintenance, such as the reduction of unplanned flight disruptions and increased availability of the aircraft. The occurrence of excessive internal leakage within the EHSAs is one of the most common causes of return from the field of flight control actuators, making this failure mode a priority in the definition of any dedicated PHM routine. This paper presents a case study on the design of a prognostic system for this degradation mode, in the context of a wider effort toward the definition of a prognostic framework suitable to work on in-flight data. The study is performed by means of a high-fidelity simulation model supported by experimental activities. Results of both the simulation and the experimental work are used to select a suitable feature, then implemented within the prognostic framework based on particle filtering. The algorithm is at first theoretically discussed, and then tested against several degradation patterns. Performances are evaluated through state-of-the-art metrics, showing promising results and providing the basis towards future applications on real in-flight data.

Estimation of Discretionary Fuel for Airline Operations

Fuel costs represent one of the most substantial expenses for airlines, accounting for 20% – 36% of the airline’s total operating cost. The present study discusses the so-called discretionary fuel that is additionally loaded at the discretion of airlines to cover unforeseen variations from the planned flight operations. The proper range of the discretionary fuel to be loaded for economic flight operations was estimated by applying Monte Carlo simulation technique. With this simulation model for loading discretionary fuel, airlines cannot only reduce the total amount of fuel to be consumed but also minimize the risk of unplanned flight disruptions caused by insufficient fuel on board. Airlines should be able to guarantee proper risk management processes for fuel boarding by carrying enough fuel to high-risk airports. This study would provide a practical guideline for loading proper amounts of discretionary fuel. Future researchers should be encouraged to improve this study by elaborating the weather variable.

AIR PASSENGERS’ RIGHTS IN THE EUROPEAN UNION

During the age of mass tourism, legislation is necessary to cover the rights and obligations of both passengers and airlines. Air passengers’ rights are enshrined in specific laws that support travelers and provide for protection and compensation when people face flight disruptions. These rules may differ depending on the region. The main problem in this field is that many people are unaware that the law is on their side and even passenger rights exist. Experience shows that 85% of air passengers are unaware of their rights. The legal basis of the European Union air passengers’ rights is Articles 91 and 100 of the Treaty on the Functioning of the European Union (TFEU). Regulation (EC) No 261/2004 of the European Parliament and of the Council of 11 February 2004 establish common rules on compensation and assistance to passengers in the event of denied boarding and of cancellation or long delay of flights. Key words: passenger rights, flight delay; flight cancellation; overbooked flight; missed connecting flight; airline strike; delayed, lost, or damaged baggage.

Calculating block time and consumed fuel for an aircraft model

ABSTRACTIn this paper we present a novel approach to calculate Block Time and Fuel (BTF) consumed for an aircraft model during a flight. The BTF model computes the ground distance between the origin and destination airports, derives the flight’s cruise altitude and by integrating two institutional data sets calculates the duration and the fuel consumed for the whole of taxi-out, take-off, climb, cruise, descent, approach, landing and taxi-in phases. We use the French Association for Operational Research and Decision Support (ROADEF) 2009 Challenge flight rotation to sample our model. The statistical analysis of the results consisted of comparing BTF results for the block time and those from the ROADEF Challenge 2009 with the real ones retrieved from Flightaware® for the same origin and destination airports and aircraft model. Statistical results are reported for percentile and root mean square error, and we show that, using simple calculations, the BTF computational results for block time are in a lower percentile and have lower root mean square error than the block times used by the ROADEF 2009 Challenge. To compare the fuel consumed, we used the values for the real flights published in the literature review. We were able to verify a good fit between the BTF results and those values. Since the BTF model computational results are obtained within a few seconds, we also conclude that the BTF model is suited for flight planning and disruption recovery in commercial aviation.