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

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.本文提出了一种面向空域复杂网络中拥堵传播的传播动力学模型。本文通过对比空域拥堵传播与传染病传播的异同，探究了流行病学模型在复杂网络中的应用可行性。本文基于流行病学模型构建的模型，满足空域实际运行的约束条件。考虑到空域拥堵集群的演化始终具有动态性与异质性这一约束，本文将带Logistic增长的SIR流行病学模型（SIR epidemiology model）应用于空域拥堵传播问题，结果表明，相较于当前常用于拥堵传播预测的概率模型，该模型在预测拥堵峰值演化方面具有更高精度。样本数据验证结果显示，该模型不仅能够精准预测拥堵峰值的大小与出现时刻，还能准确刻画空域拥堵传播的各项特征。随后本文开展了数值仿真实验，证明网络结构对空域拥堵传播具有差异化影响。实验结果表明，在拥堵严重的空域区域，相较于传播速率调控，消散速率的调整对拥堵传播的抑制效果更为显著。