Application of Epidemiology Model on Complex Networks in Propagation Dynamics of Airspace Congestion

Dai, Xiaoxu; Hu, Minghua; Tian, Wen; Xie, Daoyi; Hu, Bin

doi:10.1371/journal.pone.0157945

Cited by 14 publications

(16 citation statements)

References 28 publications

(13 reference statements)

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“…For example, sector partitioning should take the system recovery into consideration. This paper builds on our previous paper [36] for modeling congestion propagation of airspace, by applying SIR with logistic, which reflects the evolution of congestion peaks. It focuses on the system of congestion propagation and analyzes how the factors of the system affect the propagation process.…”

Section: Resultsmentioning

confidence: 99%

“…Supposing the timespan as 5 minutes, the comparison between the historical data and prediction result with model (3) is shown in Figure 7 limitation of capacity, the comparison between the result of model (2) and the flow of GYA is shown in Figure 7(b). In our previous paper [36], we have used model (2), model of SIR with logistic, to describe the trend and maximum size of congestion in cross network. Compared with model based on probability [5], the prediction result of model (2) is more close to the historical data from amplitude difference and phase difference, which is shown in Figure 6.…”

Section: Comparison With Model Predictionmentioning

confidence: 99%

“…As the operation of flights depends on the airlines, the correlation between flights varies with the structure of air routes. This paper builds on our previous studies [29,36,37] in which we apply the model of the propagation of infectious diseases [38][39][40][41][42][43] to the airspace and airport to develop congestion propagation models that consider flight correlation. The ultimate goal of the research on congestion propagation is to provide theoretical foundations and strategic and tactical choices for congestion management.…”

Section: Introductionmentioning

confidence: 99%

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Systemic Congestion Propagation in the Airspace Network

Tian

Dai

2018

Mathematical Problems in Engineering

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To be different from the traditional concept of congestion, congestion propagation based on the correlation between aircraft is given. And the main resource shared and competed for in airspace is the air route network, especially the intersection linking the multiroute. The system composed of congestion propagation units operates in airspace network, which is limited by the network geometry and the correlation between aircraft. This paper presents models based on the congestion and propagation characteristics in complex network, predicting the trend of congestion propagation and the peak of congestion size. By analyzing the relationships between system parameters and congestion propagation and accounting for the effects of propagation across networks, this paper enhances the current dynamics models of congestion propagation in airspace. Firstly, a heterogeneous network model is introduced to reveal the propagation process of aircraft with different degrees of correlation. This is followed by the specification of two simplified models for short-term prediction, just taking the sector capacity, propagation rate, and dissipation rate into account. And the propagation rate and dissipation rate depend on the sector geometry and aircraft distribution. Using them (sector capacity, propagation rate, and dissipation rate), the prediction models are accurate in predicting the evolution of congestion peak and propagation trend in comparison with the sample data of intersections in the sector. Of them, the model with capacity limitation is more accurate on busy hour. And on non-busy hour, capacity is insensitive in predicting congestion clusters. Furthermore, the computing method of propagation rate and dissipation rate is given in our paper. Finally, a numerical analysis is performed, in which it is demonstrated that system capacity, propagation rate, and dissipation rate have different effects on congestion propagation in airspace. The results show that low propagation and high dissipation rates not only are nonlinear but also decrease the level of congestion in the propagation of congestion. In particular, of the three parameters, system capacity affects the rate of convergence, with a low-capacity system reaching a stable state quickly and therefore providing a basis for sector partitioning. The method proposed in this paper should enable air traffic controllers to better understand the characteristics of congestion and its propagation for the benefits of both congestion management and improvement of efficiency. Significantly, airspace designers can take congestion propagation into consideration for optimizing the airspace structure in the future.

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Section: Resultsmentioning

confidence: 99%

Section: Comparison With Model Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Systemic Congestion Propagation in the Airspace Network

Tian

Dai

2018

Mathematical Problems in Engineering

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“…Our previous papers have described daily congestion propagation and modeled the evolution of congestion clusters in airports [27] and at the intersection of sectors [28] using some classic epidemic models [29][30][31], based on the similarity between congestion propagation and disease transmission. And the prediction of congestion propagation is a complex work, due to the polytrope of operational environment.…”

Section: Introductionmentioning

confidence: 99%

Modeling Congestion Propagation in Multistage Schedule within an Airport Network

Dai

Tian

et al. 2018

Journal of Advanced Transportation

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In order to alleviate flight delay it is important to understand how air traffic congestion evolves or propagates. In this context, this paper focusses on the aggravation of airport congestion by the accumulation of delayed departure flights. We start by applying a heterogeneous network model that takes congestion connection/degree into consideration to predict departure congestion clusters. This is on the basis of the fact that, from a micro perspective, the connection between congestion and discrete clusters can be embodied in models. However, the results show prediction to be of high accuracy and time consuming due to the complexities in capturing the connection in congested flights. The problem of being highly time consuming is resolved in this paper by improving the models by stages. Stage partitioning based on the variation of delay clusters is similar to the typical infectious cycle. For heterogeneous networks the model can describe the congestion propagation and its causes at the different stages of operation. If the connection between flights is homogeneous, the model can describe a more indicative process or trend of congestion propagation. In particular, for single source congestion, the simplified multistage models enable short-term prediction to be fast. Furthermore, for the controllers, the accuracy of prediction using simplified models can be acceptable and the speed on the prediction is significantly increased. The simplified models can help controllers to understand congestion propagation characteristics at different stages of operation, make a fast and short-term prediction of congestion clusters, and facilitate the formulation of traffic control strategies.

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“…How virus spread through a network has a direct parallelism to the way failures can happen at infrastructures [82]. Epidemiology models have been already adapted to aerospace infrastructure [83], transportation networks [84], and urban water networks [85], among others.…”

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confidence: 99%

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

et al. 2020

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Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.Processes 2020, 8, 312 2 of 29 in a telecommunication system network, nodes (e.g., routers and switches) may suffer over time a degradation in their properties that may lead them to diminish their performance. However, just the daily variation on traffic demand also has an impact on such nodes performance. Unexpected, external intervention such as cyber-attacks or extreme weather conditions may affect the properties, even the topology of a telecommunication system.Intelligent distributed systems are capable of modelling how different parts of the network might work individually and collectively [10]. Ultimately, this is used as support for any decision-making system aiming to achieve better overall system functioning and its consequent performance. Intelligent distributed systems encompass autonomous learning units that can be associated with the nodes of a complex network. These intelligent network nodes are able to act independently and also interact with other nodes to pursue both individual (local) and general system-level targets. The necessary communication between individuals can be represented by links of the complex network connecting their nodes. It is natural to understand such nodes as intelligent agents within a MAS framework. Each agent separately obeying simple rules but working together with other agents makes it possible to approach complex engineering challenges. Intelligent distributed systems have been proposed in a broad range of engineered complex systems [11,12]. Precisely, the review proposed herein focuses only on engineered systems and manufacturing processes, given the wide spectrum of the approached topic. Out of this review fall, then, topics related to social systems analysis such as migration models [13,14] or online social media [15,16]. Similarly, natural systems are out of this review scope. This is the case of computational chemistry [17,18], biology [19,20], and metabolic networks [21,22].Automatic optimisation and control in an engineering system is associated with a near real-time data acquisition and an optimal decision-making. Th...

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Application of Epidemiology Model on Complex Networks in Propagation Dynamics of Airspace Congestion

Cited by 14 publications

References 28 publications

Systemic Congestion Propagation in the Airspace Network

Systemic Congestion Propagation in the Airspace Network

Modeling Congestion Propagation in Multistage Schedule within an Airport Network

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

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