scholarly journals Community Detection for Air Traffic Networks and Its Application in Strategic Flight Planning

2021 ◽  
Vol 13 (16) ◽  
pp. 8924
Author(s):  
Silvia Zaoli ◽  
Giovanni Scaini ◽  
Lorenzo Castelli
Keyword(s):  
Community Detection ◽  
Traffic Management ◽  
Air Traffic ◽  
Computational Time ◽  
Acceptable Solution ◽  
Flight Planning ◽  
Detection Algorithms ◽  
Traffic Management System ◽  
Definition Of ◽  
The Cost

An environmentally and economically sustainable air traffic management system must rely on fast models to assess and compare various alternatives and decisions at the different flight planning levels. Due to the numerous interactions between flights, mathematical models to manage the traffic can be computationally time-consuming when considering a large number of flights to be optimised at the same time. Focusing on demand–capacity imbalances, this paper proposes an approach that permits to quickly obtain an approximate but acceptable solution of this problem. The approach consists in partitioning flights into subgroups that influence each other only weakly, solving the problem independently in each subgroup, and then aggregating the solutions. The core of the approach is a method to build a network representing the interactions among flights, and several options for the definition of an interaction are tested. The network is then partitioned with existing community detection algorithms. The results show that applying a strategic flight planning optimisation algorithm on each subgroup independently reduces significantly the computational time with respect to its application on the entire European air traffic network, at the cost of few and small violations of sector capacity constraints, much smaller than those actually observed on the day of operations.

Ensuring the implementation of the modernized structure and automation of air traffic control processes in the area of responsibility of the Moscow district of air traffic control

2018 ◽  
pp. 46-54
Author(s):  
A. V. Strukova
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Air Traffic Control ◽  
Management System ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Security System ◽  
System Description ◽  
Functional Capabilities ◽  
Traffic Management System

The article considers the new automated air traffic management system «Synthesis AR4», as well as a system description for ensuring the implementation of a modernized airspace structure, navigation and surveillance that provides technical capabilities. A number of functional capabilities and advantages of the airspace security system are presented.

Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

2021 ◽  
pp. 1-17
Author(s):  
Mohammed Al-Andoli ◽  
Wooi Ping Cheah ◽  
Shing Chiang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Complex Networks ◽  
Learning Community ◽  
Community Detection ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Real World Datasets ◽  
The Cost

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

Strategic and Tactical Functions in an Autonomous Air Traffic Management System

2021 ◽  
Author(s):  
Robert D. Windhorst ◽  
Todd A. Lauderdale ◽  
Alexander V. Sadovsky ◽  
James Phillips ◽  
Yung-Cheng Chu
Keyword(s):  
Traffic Management ◽  
Management System ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Traffic Management System

scholarly journals Space Traffic Management (STM) – Legal Aspects

2021 ◽  
Vol 50 (1) ◽  
pp. 1-11
Author(s):  
Małgorzata Polkowska ◽  
Keyword(s):  
Traffic Management ◽  
Outer Space ◽  
Air Traffic Management ◽  
Legal Aspects ◽  
Air Traffic ◽  
Civil Aviation ◽  
International Civil Aviation Organization ◽  
Definition Of ◽  
Security Regulations ◽  
Space Activities

Space Traffic Management (STM) is a new concept referring to space activities. The highest priority is the safety and security of outer space and all conducted operations. There is no definition of STM. There is an urgent need to regulate STM providing safety and security regulations at the international, regional, and national levels. Because there is no STM definition, the regulator might use the example of existing regulations of the International Civil Aviation Organization on Air Traffic Management (ATM). European EUSST is a good example of being a “precursor” of STM. However, many questions are still open regarding specific regulations needed to create an STM system, such as at which level they should be made: globally, regionally, or nationally.

Sign in / Sign up

Export Citation Format

Share Document