This paper aims to provide a resiliency management strategy of the air transportation network through network stability theory. To ensure the network to recover quickly from an upset condition, an optimization problem has been introduced through network stability upon the meta-population epidemic process model, which is a low-dimensional approximate model of the air traffic flow network. The delay propagation over air traffic network has been modeled as an epidemic spreading process model, which allows to define the whole network through a few states of the individuals (i.e., flights) and recovery rates of the nodes (i.e., airports). The physical parameters of the network extracted from real flight data set are transformed into a parameter set of the epidemic model enabling to simulate the propagation of delay. Moreover, self-organizing maps are used, generating the discretized representation of the input space through an artificial neural network to analyze the European air traffic network with regard to resiliency metrics. Through examples with historical real flight data, it is shown that the applied methodology to control the infection rates, which has the direct projection to operational applications such as ground holding and flight cancellation, effectively enhances the resiliency of the air traffic network under disruptive events.

本文旨在通过网络稳定性理论提供一种航空运输网络的弹性管理策略。为了保证网络从扰动状态中快速恢复，在元种群流行过程模型上引入了网络稳定性优化问题，该模型是空中交通流网络的低维近似模型。空中交通网络上的延迟传播已被建模为流行病传播过程模型，它允许通过个人（即航班）的几种状态和节点（即机场）的恢复率来定义整个网络。从真实飞行数据集中提取的网络物理参数被转换为流行模型的参数集，能够模拟延迟的传播。而且，使用自组织地图，通过人工神经网络生成输入空间的离散表示，以分析欧洲空中交通网络的弹性指标。通过历史真实飞行数据的例子，表明控制感染率的应用方法可以直接预测地面等待和航班取消等操作应用，有效地增强了空中交通网络在破坏性事件下的弹性。