Vulnerability analysis for airport networks based on fuzzy soft sets: From the structural and functional perspective

Recently,much attention has been paid to the reliability and vulnerability of critical infrastructure.In air traffic systems,the vulnerability analysis for airport networks can be used to guide air traffic administrations in their prioritization of the maintenance and repair of airports,as well as to avoid unnecessary disturbances in the planning of flight schedules.In this paper,the evaluation methods of airport importance and network efficiency are established.Firstly,the evaluation indices of airport importance are proposed from both the topological and functional perspectives.The topological characteristics come from the structure of airport network and the functional features stem from the traffic flow distribution taking place inside the network.Secondly,an integrated evaluation method based on fuzzy soft set theory is proposed to identify the key airports,which can fuse together importance indices over different time intervals.Thirdly,an airport network efficiency method is established for the purpose of assessing the accuracy of the evaluation method.Finally,empirical studies using real traffic data of US and China’s airport networks show that the evaluation method proposed in this paper is the most accurate.The vulnerability of US and China’s airport networks is compared.The similarities and differences between airport geography distribution and airport importance distribution are discussed here and the dynamics of airport importance is studied as well.     

Empirical analysis of airport network and critical airports

Air transport network, or airport network, is a complex network involving numerous airports. Effective management of the air transport system requires an in-depth understanding of the roles of airports in the network. Whereas knowledge on air transport network properties has been improved greatly, methods to find critical airports in the network are still lacking. In this paper, we present methods to investigate network properties and to identify critical airports in the network. A novel network model is proposed with airports as nodes and the correlations between traffic flow of airports as edges. Spectral clustering algorithm is developed to classify airports. Spatial distribution characteristics and intraclass correlation of different categories of airports are carefully analyzed. The analyses based on the fluctuation trend of distance-correlation and power spectrum of time series are performed to examine the self-organized criticality of the network. The results indicate that there is one category of airports which dominates the self-organized critical state of the network. Six airports in this category are found to be the most important ones in the Chinese air transport network. The flights delay occurred in these six airports can propagate to the other airports, having huge impact on the operation characteristics of the entire network. The methods proposed here taking traffic dynamics into account are capable of identifying critical airports in the whole air transport network.     

Dynamics of air transport networks: A review from a complex systems perspective

Air transport systems are highly dynamic at temporal scales from minutes to years. This dynamic behavior not only characterizes the evolution of the system but also affect the system's functioning. Understanding the evolutionary mechanisms is thus fundamental in order to better design optimal air transport networks that benefits companies, passengers and the environment. In this review, we briefly present and discuss the state-of-the-art on time-evolving air transport net-works. We distinguish the structural analysis of sequences of network snapshots, ideal for long-term network evolution (e.g. annual evolution), and temporal paths, preferred for short-term dynamics (e.g. hourly evolution). We emphasize that most previous research focused on the first modeling approach (i.e. long-term) whereas only a few studies look at high-resolution temporal paths. We conclude the review highlighting that much research remains to be done, both to apply already available methods and to develop new measures for temporal paths on air transport networks. In particular, we identify that the study of delays, network resilience and optimization of resources (aircraft and crew) are critical topics.     

A novel reliability analysis method for engineering problems:Expanded learning intelligent back propagation neural network

Estimating the failure probability of highly reliable structures in practice engineering,such as aeronautical components,is challenging because of the strong-coupling and the small failure probability traits.In this paper,an Expanded Learning Intelligent Back Propagation(EL-IBP)neu-ral network approach is developed:firstly,to accurately characterize the engineering response cou-pling relationships,a high-fidelity Intelligent-optimized Back Propagation(IBP)neural network metamodel is developed;furthermore,to elevate the analysis efficacy for small failure assessment,a novel expanded learning strategy for adaptive IBP metamodeling is proposed.Three numerical examples and one typical practice engineering case are analyzed,to validate the effectiveness and engineering application value of the proposed method.Methods comparison shows that the EL-IBP method holds significant efficiency and accuracy superiorities in engineering issues.The current study may shed a light on pushing the adaptive metamodeling technique deeply toward complex engineering reliability analysis.     

DDQNC-P:A framework for civil aircraft tactical synergetic trajectory planning under adverse weather conditions

Adverse weather during aircraft operation generates more complex scenarios for tactical trajectory planning,which requires superior real-time performance and conflict-free reliability of solving methods.Multi-aircraft real-time 4D trajectory planning under adverse weather is an essen-tial problem in Air Traffic Control(ATC)and it is challenging for the existing methods to be applied effectively.A framework of Double Deep Q-value Network under the Critic guidance with heuristic Pairing(DDQNC-P)is proposed to solve this problem.An Agent for two aircraft syner-getic trajectory planning is trained by the Deep Reinforcement Learning(DRL)model of DDQNC,which completes two aircraft 4D trajectory planning tasks preliminarily under dynamic weather conditions.Then a heuristic pairing algorithm is designed to convert the multi-aircraft synergetic trajectory planning into multi-time pairwise synergetic trajectory planning,making the multi-aircraft trajectory planning problem processable for the trained Agent.This framework compresses the input dimensions of the DRL model while improving its generalization ability significantly.Sub-stantial simulations with various aircraft numbers,weather conditions,and airspace structures were conducted for performance verification and comparison.The success rate of conflict-free trajectory resolution reached 96.56％with an average calculation time of 0.41 s for 350 4D trajectory points per aircraft,finally confirming its applicability to make real-time decision-making support for con-trollers in real-world ATC systems.