Search

The study of the design of emergency trajectories of air vehicles is one of the key elements in improving airspace safety for air vehicles. The aim is to lighten pilots' workload, offering quick and effective solutions. However, almost all flight optimizers proposed in the literature still need to be completed when it comes to resolving emergency contexts, which presents a significant disadvantage to the advancement of scientific research. This resolution is based on the following problems: (a) finding paths free of obstacles, (b) ensuring their flight capacity, and finally, (c) calculating trajectories optimizing several criteria with a calculation time constraint (a few minutes). This document analyzes the safety landing problem and proposes an architecture that effectively reduces complexity and ensures solvability within a reasonable computational time. This architectural framework is designed to be adaptable, allowing for testing several algorithms to provide a quick overview of their strengths and weaknesses in this context. The primary aim of these tests is to benchmark the computational time of the overall architecture, ensuring that this adaptable framework is fully capable of handling the problem's complexity. It is important to note that the algorithms chosen address only a simplified version of the problem. The initial results are promising in terms of time response and the potential to enhance the representativeness and complexity of the problem. The next phase of our research will focus on striking the right balance between complexity, representativity, and computational time, aiming to impact emergency response significantly. [ABSTRACT FROM AUTHOR]

Data mining has achieved great success in air traffic management as a technology for learning knowledge from historical data that benefits people. However, data mining can rarely be embedded into the trajectory optimization process since regular optimization algorithms cannot utilize the functional and implicit knowledge extracted from historical data in a general paradigm. To tackle this issue, this research proposes a novel data mining-based trajectory generation method that is compatible with existing optimization algorithms. Firstly, the proposed method generates trajectories by combining various maneuvers learned from operation data instead of reconstructing trajectories with generative models. In such a manner, data mining-based trajectory optimization can be achieved by solving a combinatorial optimization problem. Secondly, the proposed method introduces a majorization–minimization-based adversarial training paradigm to train the generation model with more general loss functions, including non-differentiable flight performance constraints. A case study on Guangzhou Baiyun International Airport was conducted to validate the proposed method. The results illustrate that the trajectory generation model can generate trajectories with high fidelity, diversity, and flyability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Climate impact models of the non- CO 2 emissions of aviation are still subject to significant uncertainties. Condensation trails, or contrails, are one of these non- CO 2 effects. In order to validate the contrail simulation models, a dataset of observations covering the entire lifetime of the contrails will be required, as well as the characteristics of the aircraft which produced them. This study carries on the work on contrail observation from geostationary satellite by proposing a new way to track contrails and identify the flight that produced it using geostationary satellite infrared images, weather data as well as air traffic data. It solves the tracking and the identification problem as one, each process leveraging information from the other to achieve a better overall result. This study is a new step towards a consistent contrail dataset that could be used to validate contrail models. [ABSTRACT FROM AUTHOR]

A multiple-airport system (MAS) consists of more than two airports in a metropolitan area under a large block of terminal airspace that is managed by one or two air traffic control units. When the capacity of an airport or of the terminal airspace drops, flight delays occur in the MAS system. A quick estimation and predication of traffic congestion in the MAS is important yet challenging. This paper aims to develop a queuing network model of MAS using point-wise stationary queues. The model analyzes the changes of non-stationary queues under the principle of flow conservation to capture flight delay propagation in the system. Regression analyses are performed to examine the relationship between the arrival and departure efficiencies of different airports. The model is validated with the data of Guangdong–Hong Kong–Macao Greater Bay Area airports. Simulation results show that the model can effectively estimate flight delays in the MAS. [ABSTRACT FROM AUTHOR]

This paper presents a new air traffic complexity metric based on linear dynamical systems, of which the goal is to quantify the intrinsic complexity of a set of aircraft trajectories. Previous works have demonstrated that the structure and organization of air traffic are essential factors in the perception of the complexity of an air traffic situation. Usually, they were not able to explicitly address trajectory pattern organization. The new metric, by identifying the organization properties of trajectories in a traffic pattern, captures some of the key factors involved in ATC complexity. The key idea of this work is to find a linear dynamical system which fits a vector field as closely as possible to the observations given by the aircraft positions and speeds. This approach produces an aggregate complexity metric that enables one to identify high (low) complexity regions of the airspace and compare their relative complexity. The metric is very appropriate to compare different traffic situations for any scale (sector or country) by associating a complexity index to each trajectory sample in the airspace. For instance, to compute the complexity for a sector, one must just sum-up the complexity for trajectory samples intersecting such a sector. This computation can also be extended in the time dimension in order to estimate the average complexity in a given airspace for a period of time. [ABSTRACT FROM AUTHOR]

This paper addresses the generation of aircraft emergency trajectories with obstacle avoidance. After presenting in detail the fast marching tree algorithm, in this paper we propose an improvement of its performance. First, the free space checking function is sped up. Then, the algorithm is used twice, firstly with the sampling of a few points to generate an approximate trajectory, and secondly with a sampling of points close to the first computed trajectory to refine it. The proposed method significantly reduces the computing time of the emergency geometric trajectory generation. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to minimize the airspace congestion of aircraft trajectories based on slot allocation techniques. The traffic assignment problem is modeled as a combinatorial optimization problem for which a selective simulated annealing has been developed. Based on the congestion encountered by each aircraft in the airspace, this metaheuristic selects and changes the time of departure of the most critical flights in order to target the most relevant aircraft. The main objective of this approach is to minimize the aircraft speed vector disorder. The proposed algorithm was implemented and tested on simulated trajectories generated with real flight plans on a day of traffic over French airspace with 8800 flights. [ABSTRACT FROM AUTHOR]

Air traffic controllers have to make quick decisions to keep air traffic safe. Their behaviors have a significant impact on the operation of the air traffic management (ATM) system. Automation tools have enhanced the ATM system's capability by reducing the controller's task-load. Much attention has been devoted to developing advanced automation in the last decade. However, less is known about the impact of automation on the behaviors of air traffic controllers. Here, we empirically tested the effects of three levels of automation—including manual, attention-guided, and automated—as well as varying traffic levels on eye movements, situation awareness and mental workload. The results showed that there are significant differences in the gaze and saccade behaviors between the attention-guided group and automated group. Traffic affected eye movements under the manual mode or under the attention-guided mode, but had no effect on eye movements under the automated mode. The results also supported the use of automation for enhancing situation awareness while reducing mental workload. Our work has potential implications for the design of automation and operation procedures. [ABSTRACT FROM AUTHOR]

Paris Charles de Gaulle Airport was the second European airport in terms of traffic in 2019, having transported 76.2 million passengers. Its large infrastructures include four runways, a large taxiway network, and 298 aircraft parking stands (131 contact) among three terminals. With the current pandemic in place, the European air traffic network has declined by −65% flights when compared with 2019 traffic (pre-COVID-19), having a severe negative impact on the aviation industry. More and more often taxiways and runways are used as parking spaces for aircraft as consequence of the drastic decrease in air traffic. Furthermore, due to safety reasons, passenger terminals at many airports have been partially closed. In this work we want to study the effect of the reduction in the physical facilities at airports on airspace and airport capacity, especially in the Terminal Manoeuvring Area (TMA) airspace, and in the airport ground side. We have developed a methodology that considers rare events such as the current pandemic, and evaluates reduced access to airport facilities, considers air traffic management restrictions and evaluates the capacity of airport ground side and airspace. We built scenarios based on real public information on the current use of the airport facilities of Paris Charles de Gaulle Airport and conducted different experiments based on current and hypothetical traffic recovery scenarios. An already known optimization metaheuristic was implemented for optimizing the traffic with the aim of avoiding airspace conflicts and avoiding capacity overloads on the ground side. The results show that the main bottleneck of the system is the terminal capacity, as it starts to become congested even at low traffic (35% of 2019 traffic). When the traffic starts to increase, a ground delay strategy is effective for mitigating airspace conflicts; however, it reveals the need for additional runways. [ABSTRACT FROM AUTHOR]

The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft's six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future. [ABSTRACT FROM AUTHOR]

The bottleneck of today's airspace is the Terminal Maneuvering Areas (TMA), where aircraft leave their routes to descend to an airport or take off and reach the en-route sector. To avoid congestion in these areas, an efficient design of departure and arrival routes is necessary. In this paper, a solution for designing departure and arrival routes is proposed, which takes into account the runway configuration, the surroundings of the airport and operational constraints such as limited slopes or turn angles. The routes consist of two parts: a horizontal path in a graph constructed by sampling the TMA around the runway, to which is associated a cone of altitudes. The set of all routes is optimized by the Simulated Annealing metaheuristic. In the process and at each iteration, each route is computed by defining adequately the cost of the arcs in the graph and then searching a path on it. The costs are chosen so as to avoid zigzag behaviors as much as possible. Two tests were performed, one on an instance taken from the literature and the other on an artificial problem designed specifically to test this approach. The obtained results are satisfying with regard to the current state of air operations management and constraints. [ABSTRACT FROM AUTHOR]