1,720,981 research outputs found
Weather-aware aircraft arrival characterization at terminal maneuvering area with data-driven methodologies
No full textAir traffic management (ATM) attempts to assist aircraft’s approach and landing procedures with a safety-first operation. It can be challenging to evaluate aviation economics, environmental issues, and safety operations all at once while making decisions inside the terminal maneuvering area (TMA). A comprehensive arrival plan that considers weather factors and aircraft trajectory configuration is essential to increase the job efficiency of air traffic controllers and alleviate the negative environmental impact. Current state-of-the-art solutions do not fully consider unfavorable weather circumstances and unusual aircraft paths in arrival scheduling, arrival airborne congestion, and arrival time prediction. This thesis proposes a framework for the aircraft trajectory aspect to extract features from data and predict arrival transit time, with consideration of adverse weather and non-standard flight trajectory operations. The developed procedures can reveal more air-traffic information and patterns from data, which can be used to predict arrival transit time in various situations better. The spatio-temporal pattern identification for aircraft congestion and arrival transit time within the Hong Kong International Airport TMA was presented in this thesis. To include weather factor, the hourly recorded weather radar images were also included in the analysis. To investigate weather impact, the author proposed a scheme to quantify the weather impact on airport arrival on-time performance, by using a growth function to represent the performance deterioration with increasingly more adverse weather conditions. The model parameters and hyperparameters were derived based on actual data via a Bayesian approach. The developed scheme could also quantify the impact of dangerous weather phenomena, which were often excluded in existing aviation weather impact studies. Results exhibited the generality and versatility of the developed weather impact quantification model, which could also be used to compare the aviation weather impact at different airports. The updateability of the Bayesian approach also allows us to consider the future climate change impact on arrivals. Lastly, the author developed a model architecture to predict arrival transit time by considering inputs pertaining to three important aspects in air traffic operations, namely aircraft, airport, and weather. The results indicated that the developed model structure could reduce the prediction errors by 5.63% when all weather conditions were considered and 8.45% under extreme weather scenarios. The arrival transit time prediction was demonstrated with several metrics to represent weather conditions. In particular, two metrics were introduced in this work that not only contained weather information, but also its interaction with airport traffic and individual flight time. These metrics were shown to yield better arrival transit time prediction accuracy than other weather metrics used in the study.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Gradient-based smart predict-then-optimize framework for aircraft arrival scheduling problem
Get PDFThis paper introduces a gradient-based Smart Predict-then-Optimize (SPO) framework for solving the Aircraft Arrival Scheduling Problem (ASP) in Terminal Maneuvering Area. Traditional approaches to ASP typically separate arrival time prediction from scheduling optimization, potentially leading to incomplete solutions. We address this limitation by developing an end-to-end learning framework that directly integrates prediction with optimization objectives. Our methodology introduces the concept of traffic instances for simultaneous prediction of multiple aircraft arrival times, coupled with a Mixed Integer Programming (MIP) model for scheduling optimization. We evaluate our approach using real-world data from London Gatwick Airport, analyzing 47452 arrival flights from June to September 2024, organized into 2404 traffic instances. The framework incorporates comprehensive weather data through the ATMAP algorithm, considering factors such as wind, visibility, precipitation, and dangerous phenomena. Experimental results demonstrate that the MLP+SPO+ framework shows particular effectiveness in adapting to adverse weather conditions, strategically balancing transit times with operational efficiency. While the minimum time window is required, the MLP+SPO+ will reach around 85.0% and 43.4% lower costs compared with the First-Come-First-Serve (FCFS) cost and optimized true cost, respectively. These findings suggest significant potential for improving arrival scheduling efficiency through integrated SPO approaches
3D Optimal Airspace Sector Design with Mixed Integer Linear Programming
No full textThe increasing complexity of air traffic management and the growing demand for efficient airspace utilization have driven the development of smart upper airspace systems that leverage advanced operations research methodologies. This presentation present the application of operations research techniques in the design and configuration of intelligent upper airspace frameworks in the SMARTS project, focusing on optimization models that enhance traffic flow management and improve overall system performance. We explore two key applications: First, the optimal design of airspace sectors. Mathematical programming models, such as Mixed Integer Programming (MIP), are employed to create sectors that balance controller workload and ensure smooth traffic flow, with the connectivity and traffic flow convexity consideration. Complementary heuristic approaches demonstrate the ability to achieve near-optimal solutions rapidly, crucial for operational agility. Second, dynamic airspace configuration. Here, OR techniques, including integer linear programming and graph-based algorithms, enable the selection of optimal airspace configurations in response to fluctuating traffic demand and uncertainty. These methods aim to maximize capacity, minimize delays, and provide robust plans adaptable to unforeseen demand surges. Validated with real-world data, these OR applications provide theoretically sound and practically implementable tools. They offer significant improvements in ATM efficiency, workload distribution, and system robustness, paving the way for more adaptive and resilient airspace management in the face of evolving challenges
Gradient-based smart predict-then-optimize framework for aircraft arrival scheduling problem
No full textThis paper introduces a gradient-based Smart Predict-then-Optimize (SPO) framework for solving the Aircraft Arrival Scheduling Problem (ASP) in Terminal Maneuvering Area. Traditional approaches to ASP typically separate arrival time prediction from scheduling optimization, potentially leading to incomplete solutions. We address this limitation by developing an end-to-end learning framework that directly integrates prediction with optimization objectives. Our methodology introduces the concept of traffic instances for simultaneous prediction of multiple aircraft arrival times, coupled with a Mixed Integer Programming (MIP) model for scheduling optimization. We evaluate our approach using real-world data from London Gatwick Airport, analyzing 47452 arrival flights from June to September 2024, organized into 2404 traffic instances. The framework incorporates comprehensive weather data through the ATMAP algorithm, considering factors such as wind, visibility, precipitation, and dangerous phenomena. Experimental results demonstrate that the MLP+SPO+ framework shows particular effectiveness in adapting to adverse weather conditions, strategically balancing transit times with operational efficiency. While the minimum time window is required, the MLP+SPO+ will reach around 85.0% and 43.4% lower costs compared with the First-Come-First-Serve (FCFS) cost and optimized true cost, respectively. These findings suggest significant potential for improving arrival scheduling efficiency through integrated SPO approaches
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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