Abstract
This paper presents a propagation dynamics model for congestion propagation in complex networks of airspace. It investigates the application of an epidemiology model to complex networks by comparing the similarities and differences between congestion propagation and epidemic transmission. The model developed satisfies the constraints of actual motion in airspace, based on the epidemiology model. Exploiting the constraint that the evolution of congestion cluster in the airspace is always dynamic and heterogeneous, the SIR epidemiology model (one of the classical models in epidemic spreading) with logistic increase is applied to congestion propagation and shown to be more accurate in predicting the evolution of congestion peak than the model based on probability, which is common to predict the congestion propagation. Results from sample data show that the model not only predicts accurately the value and time of congestion peak, but also describes accurately the characteristics of congestion propagation. Then, a numerical study is performed in which it is demonstrated that the structure of the networks have different effects on congestion propagation in airspace. It is shown that in regions with severe congestion, the adjustment of dissipation rate is more significant than propagation rate in controlling the propagation of congestion.
Highlights
Air traffic congestion represents a greater need for airspace capacity
We obtain the conclusion that the model of congestion propagation is more accurate than the model based on probability in describing the value and time of the congestion peak
This paper has modeled the congestion propagation to predict the evolution of flow in crowded airspace, applying the epidemiology models (SIR and SIR with logistic)
Summary
Air traffic congestion represents a greater need for airspace capacity. Its propagation in complex air transport networks [1,2,3,4,5,6] is a paradigmatic example of the way in which a distributed transport system moves towards collapsing. Research on the evolution of peak congestion value in the amplitude and phase difference is more important in the congestion propagation. Ahi and Api are the ith peak amplitude value of SIR with logistic, historical data and probability model, respectively. Ηsi, ηhi and ηpi are the ith peak phase of SIR with logistic, historical data and probability model, respectively. We obtain the conclusion that the model of congestion propagation (the model of SIR with logistic) is more accurate than the model based on probability in describing the value and time of the congestion peak. Assuming that the air transport networks are homogenous, the mechanism and trend of congestion propagation in airspace can be described by expression (3). Research on the propagation dynamics model on homogenous networks is useful in understanding flow management congestion and dissipation trends. The dissipation rate of congestion is more significant in controlling congestion than the propagation rate
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
