Search Results (43)

Partially linear time series models often suffer from multicollinearity among regressors and autocorrelated errors, both of which can inflate estimation risk. This study introduces a generalized ridge-type kernel (GRTK) framework that combines kernel smoothing with ridge shrinkage and augments it through ordinary and positive-part Stein adjustments. Closed-form expressions and large-sample properties are established, and data-driven criteria—including GCV, AICc, BIC, and RECP—are used to tune the bandwidth and shrinkage penalties. Monte-Carlo simulations indicate that the proposed procedures usually reduce risk relative to existing semiparametric alternatives, particularly when the predictors are strongly correlated and the error process is dependent. An empirical study of US airline-delay data further demonstrates that GRTK produces a stable, interpretable fit, captures a nonlinear air-time effect overlooked by conventional approaches, and leaves only a modest residual autocorrelation. By tackling multicollinearity and autocorrelation within a single, flexible estimator, the GRTK family offers practitioners a practical avenue for more reliable inference in partially linear time series settings. Full article

Flight delays represent a significant challenge for airlines, airports, and passengers, impacting operational costs and customer satisfaction. Traditional prediction methods often rely on complex statistical analysis and mathematical models that may not be easily implementable. This study proposes scorecards as an innovative and simplified approach to forecast flight delays. Historical flight data from the United States were used, incorporating variables such as departure and arrival times, flight routes, aircraft types, and other factors related to delay. Exploratory data analysis identified key variables influencing delays, and scorecards were constructed by assigning weights, normalizing, and scaling variables to improve interpretability. The model was validated using test datasets, and predictive performance was evaluated by comparing forecast delays with actual results. The results indicate that scorecards provide accurate and interpretable predictions of flight delays. This method facilitates the identification of critical factors that contribute to delays and allows for an estimation of their likelihood and duration. Scorecards offer a practical tool for airlines and airport operators, potentially enhancing decision-making processes, reducing delay-related costs, and improving service quality. Future research should explore the integration of scorecards into operational systems and the inclusion of additional variables to increase model robustness and generalizability. Full article

Every year, airlines invest considerable resources in recovering from irregular operations caused by delays and disruptions to aircraft and crew. Consequently, the need to reschedule aircraft and crew to better address these problems has become pressing. The airline scheduling problem comprises two stages—that is, the Aircraft-Routing Problem (ARP) and the Crew-Pairing Problem (CPP). While the ARP and CPP have traditionally been solved sequentially, such an approach fails to capture their interdependencies, often compromising the robustness of aircraft and crew schedules in the face of disruptions. However, existing integrated ARP and CPP models often apply static rules for buffer time allocation, which may result in excessive and ineffective long-buffer connections. To bridge these gaps, we propose a robust integrated ARP and CPP model with two key innovations: (1) the definition of new critical connections (NCCs), which combine structural feasibility with data-driven delay risk; and (2) a spatiotemporal delay-prediction module that quantifies connection vulnerability. The problem is formulated as a sequential decision-making process and solved via a novel multi-agent reinforcement learning algorithm. Numerical results demonstrate that the novel method outperforms prior methods in the literature in terms of solving speed and can also enhance planning robustness. This, in turn, can enhance both operational profitability and passenger satisfaction. Full article

The timely identification of probable causes in aviation incidents is crucial for averting future tragedies and safeguarding passengers. Typically, investigators rely on flight data recorders; however, delays in data retrieval or damage to the devices can impede progress. In such instances, experts resort to supplementary sources like eyewitness testimonies and radar data to construct analytical narratives. Delays in this process have tangible consequences, as evidenced by the Boeing 737 MAX accidents involving Lion Air and Ethiopian Airlines, where the same design flaw resulted in catastrophic outcomes. To streamline investigations, scholars advocate for natural language processing (NLP) and topic modelling methodologies, which organize pertinent aviation terms for rapid analysis. However, existing techniques lack a direct mechanism for deducing probable causes. To bridge this gap, this study trains and evaluates the performance of a transformer-based model in predicting the likely causes of aviation incidents based on long-input raw text analysis narratives. Unlike traditional models that classify incidents into predefined categories such as human error, weather conditions, or maintenance issues, the trained model infers and generates the likely cause in a human-like narrative, providing a more interpretable and contextually rich explanation. By training the model on comprehensive aviation incident investigation reports like those from the National Transportation Safety Board (NTSB), the proposed approach exhibits promising performance across key evaluation metrics, including BERTScore with Precision: (M = 0.749, SD = 0.109), Recall: (M = 0.772, SD = 0.101), F1-score: (M = 0.758, SD = 0.097), Bilingual Evaluation Understudy (BLEU) with (M = 0.727, SD = 0.33), Latent Semantic Analysis (LSA similarity) with (M = 0.696, SD = 0.152), and Recall Oriented Understudy for Gisting Evaluation (ROUGE) with a precision, recall and F-measure scores of (M = 0.666, SD = 0.217), (M = 0.610, SD = 0.211), (M = 0.618, SD = 0.192) for rouge-1, (M = 0.488, SD = 0.264), (M = 0.448, SD = 0.257), M = 0.452, SD = 0.248) for rouge-2 and (M = 0.602, SD = 0.241), (M = 0.553, SD = 0.235), (M = 0.5560, SD = 0.220) for rouge-L, respectively. This demonstrates its potential to expedite investigations by promptly identifying probable causes from analysis narratives, thus bolstering aviation safety protocols. Full article

Delays are a global issue affecting both airports and airlines. Departure delays are particularly likely to propagate across airports, rendering the entire flight network susceptible to increased delay durations. The delay network and its duration fluctuate daily or even hourly across airports. This study investigates the association between departure delay duration and delay network properties. Using various network metrics, we apply a fixed-effect Prais–Winsten regression model within a panel data framework covering the period from 2018 to 2021 for two full-service carriers in Japan. The key findings reveal that higher in-degree centrality is associated with longer departure delays. Betweenness centrality suggests that, in addition to hub airports, some spoke airports may function as delay bridges, thereby increasing delay durations. Eigenvector centrality is linked to shorter but more frequent departure delays across the network, which are more likely to result in frequent delay propagations of shorter durations. The results indicate that some airports may form delay clusters among themselves, potentially extending departure delay durations among connected airports. During the COVID-19 pandemic, the state of emergency contributed to varying associations between network properties and departure delay durations. These outcomes are expected to provide valuable insights for airline delay and schedule management policymakers. Full article

Flight delays continue to pose a substantial concern in the aviation sector, impacting both operational efficiency and passenger satisfaction. Existing systems, while attempting to predict delays, often lack accurate predictive capabilities due to poor modeling setups, insufficient feature engineering, and inadequate feature selection processes, leading to suboptimal predictions and ineffective decision-making. Precisely forecasting flight arrival delays is essential for improving airline scheduling and resource allocation. The aim of our research is to create a superior prediction model that surpasses current modeling approaches. This study aims to forecast airline arrival delays by examining data from five prominent U.S. states in 2023—California (CA), Texas (TX), Florida (FL), New York (NY), and Georgia (GA). Our proposed modeling approach involves feature engineering to identify significant variables, followed by a novel feature selection algorithm (CFS) designed to retain only the most relevant features. Delay forecasts were generated using our proposed Deep Feed Forward Regression Network (DFFRN), a five-layer deep learning approach designed to enhance predictive accuracy by incorporating extensively selected features. The findings indicate that the DFFRN model substantially outperformed conventional models documented in the literature. The DFFRN had the highest R² score (99.916%), indicating exceptional predictive efficacy, highlighting the efficacy of the DFFRN model for predicting flight delays and establishing it as a significant asset for improving decision-making and minimizing operational delays in the aviation sector. Full article

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. Full article

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. Full article

Flight delay identification is an important way to coordinate the operation time of airport ground service providers and improve the efficiency of airport operations. By analyzing the flight turnaround operation process, considering the randomness and synchronization of the turnaround process, and using Colored Petri Nets and Python (4.0.1), we explore the correlation between various links in the flight turnaround process and the take-off delay at the next station. This paper is committed to improving the service performance of airports and airlines, dynamically predicting flight delays, and providing guidance for avoiding excessive time in the actual operation of bad combinations. The results show that there are six kinds of bad combinations in the departure slip-out link, which is the most likely to affect the transit time. The maximum lifting degree in the bad combination is 2.043, and the maximum average delay time in the bad combination is 22.5 min. When the combination of passenger boarding and departure slip-out time is too long, it has a great positive correlation with delay. When the other links are in a state of being able to pass the station on time, the departure time and baggage loading and unloading are the two links that most affect the flight delay value. Full article

Over-capacity flight scheduling by commercial airlines due to the surging demand in recent years creates congestion and significant delays at major airports. This attitude towards maximizing throughput calls for tactical flight rescheduling to comply with airports’ capacity limitations and distribute the peak hour demand over the course of a day. Such displacements of flights may cause significant problems and costs for airlines and some cancellations or missed connections for passengers. This paper presents an optimization model for flight rescheduling at a schedule-coordinated airport to minimize congestion and flight delays at peak hours. The optimization model is used to make better scheduling intervention decisions considering airport resource constraints and safety of operation. A simulation algorithm is also developed to replicate arrival and departure processes in such an airport. The simulation adheres to a first come first served (FCFS) discipline and enforces runway capacity constraints and minimum turnaround times. We compare the delays caused by an ad hoc FCFS operation with those of the optimization model. Computational results from a case study demonstrate that a reduction of 52.6% and 61% in total delay times for arrival and departure flights, respectively, can be achieved. The optimization model also facilitates the implementation of a collaborative decision-making system for better coordination of airport traffic flow management with commercial airlines. Full article

The novel coronavirus outbreak has significantly heightened environmental costs and operational challenges for civil aviation airlines, prompting emergency airport closures in affected regions and a substantial decline in ridership. The consequential need to reassess, delay, or cancel flight itineraries has led to disruptions at airports, amplifying the risk of disease transmission. In response, this paper proposes a spatial approach to efficiently address pandemic spread in the civil aviation network. The methodology prioritizes the use of a static gravity model for calculating route-specific infection pressures, enabling strategic flight rescheduling to control infection levels at airports (nodes) and among airlines (edges). Temporally, this study considers intervals between takeoffs and landings to minimize crowd gatherings, mitigating the novel coronavirus transmission rate. By constructing a discrete space–time network for irregular flights, this research generates a viable set of routes for aircraft operating in special circumstances, minimizing both route-specific infection pressures and operational costs for airlines. Remarkably, the introduced method demonstrates substantial savings, reaching almost 53.4%, compared to traditional plans. This showcases its efficacy in optimizing responses to pandemic-induced disruptions within the civil aviation network, offering a comprehensive solution that balances operational efficiency and public health considerations in the face of unprecedented challenges. Full article

To further enhance fairness in the allocation of en-route space–time resources in the collaborative trajectory selection program, a study on the plan preferences between air traffic control (ATC) and airlines in the selection process of the final plan is conducted based on the initial resource allocation plans, considering the roles of airlines in resources allocation decisions. By using Stackelberg game theory, a game model is established for the roles played by ATC and airlines in the process of selecting plans. Then, combining the overall consideration of ATC for all affected flights, the preferences of airlines for initial allocation plans are obtained, and the option range of selectable plans is narrowed down to determine the optimal allocation plan. The results of the example analysis show that the proposed model and method can effectively select the optimal allocation plan from the six initial allocation plans, select the trajectories and entry slots in the congestion areas for airlines that better meet the operation demand, and provide the decision basis with more preferences for ATC to select the final allocation plan. When ATC prefers the lowest overall delay cost, the delay cost of the selected optimal allocation plan is 267.7 min, which is 23.84% lower than the traditional RBS algorithm; when considering the preferences of the main base airline in East China, the delay cost of the selected optimal allocation plan is 287.7 min, which is 18.15% lower than the traditional RBS algorithm. Full article

In the aviation industry, the issuance of airside passes often encounters significant delays, posing logistical challenges and hindering crucial operations. This study delves into the potential of implementing blockchain technology, particularly smart contracts, to streamline and expedite airport security processes. Our analysis of data from leading UK airports reveals notable inefficiencies in the current airside pass issuance procedures, necessitating a transformative solution. We advocate for the integration of blockchain smart contracts as a pioneering approach to substantially reduce processing times. By automating execution based on predefined conditions, smart contracts have the potential to revolutionize airport security operations. This research signifies a groundbreaking advancement in the use of smart contracts within the airline industry, underscoring the substantial efficiency improvements that can be achieved. As we conclude this study, we foresee further research and practical implementations to unlock the full transformative impact of blockchain technology on aviation security. Full article

According to the Federal Aviation Administration (FAA), the number of runway incursions is increasing. Over the last two decades, the number of runway incursions at U.S. airports has increased from 987 in 2002 to 25,036 in 2020. Runway incursions are a major threat to aviation safety, causing major delays and financial consequences for airlines, as well as injury or death through incidents such as aircraft collisions. The FAA promotes the implementation of runway safety technology, infrastructure, procedural methods, alterations to airport layouts, and training practices to reduce the frequency of runway incursions. In this paper, the relationship between airport geometry factors, mitigating technologies, and the number of runway incursions at large hub airports in the United States was investigated using a random effects Poisson model for analyses of panel data. Airport operations data from the FAA Air Traffic Activity System, runway incursion data from the FAA Aviation Safety Information Analysis and Sharing System from 2002 to 2020, and airport geometry data created using airport geometry features from the FAA airport diagrams were collected. Thirty large hub airports with FAA-installed mitigating technologies were investigated. The model identified significant variables that correlate with runway incursions for large hub airport categories defined by the National Plan of Integrated Airport Systems (NPIAS). The model results indicate that airports with significant numbers of runway-to-runway intersection points increase runway incursion rates and mitigating technologies Runway Status Lights (RWSLs) and Airport Surface Detection Equipment, Model X (ASDE-X), can help reduce runway incursions at severity levels A and B. Full article

Accurate prediction of traffic demand and airport capacity plays a crucial role in minimising ground delays and airborne holdings. This paper focuses on the latter aspect. Adverse weather conditions present significant challenges to airport operations and can substantially reduce capacity. Consequently, any predictive model, regardless of its complexity, should account for weather conditions when estimating the airport capacity. At present, the sole shared platform for airport capacity information in Europe is the EUROCONTROL Public Airport Corner, where airports have the option to voluntarily report their capacities. These capacities are presented in tabular form, indicating the maximum number of hourly arrivals and departures for each possible runway configuration. Additionally, major airports often provide a supplementary table showing the impact of adverse weather in a somewhat approximate manner (e.g., if the visibility is lower than 100 m, then arrival capacity decreases by 30%). However, these tables only cover a subset of airports, and their generation is not harmonised, as different airports may use different methodologies. Moreover, these tables may not account for all weather conditions, such as snow, strong winds, or thunderstorms. This paper presents a machine learning approach to learn mapping from weather conditions and runway configurations to the 99th percentile of the delivered throughput from historical data. This percentile serves as a capacity proxy for airports operating at or near capacity. Unlike previous attempts, this paper takes a novel approach, where a single model is trained for several airports, leveraging the generalisation capabilities of cutting-edge machine learning algorithms. The results of an experiment conducted using 2 years of historical traffic and weather data for the top 45 busiest airports in Europe demonstrate better alignment in terms of mean pinball error with the observed departure and arrival throughput when compared to the operational capacities reported in the EUROCONTROL Public Airport Corner. While there is still room for improvement, this system has the potential to assist airports in defining more reasonable capacity values, as well as aiding airlines in assessing the impact of adverse weather on their flights. Full article