数据驱动的网络方法在解决生物、经济、信息等领域的实际问题中发挥了重要作用，是网络科学发展的一个重要方向。航空运输是关系到国计民生的具有巨大经济价值的领域，可通过网络科学研究提升其运行效率。然而，少有学者深入该领域研究其结构、动力学和系统韧性等问题。本项目针对中美航空运输系统，以实际运行的大数据为基础，构建由机场、航路和空中交通管理三个主要子系统组成的航空运输多层时变网络，研究多层网络的结构特征和演化机制，挖掘网络中级联故障的时空模式；基于系统运行的结构和性能，研究系统的韧性。通过中美数据对比，验证所提模型和方法的普适性和差异性。本项目不仅对于航空运输系统的实际运行具有指导作用，而且对于理解多层时变网络结构和动力学具有更重要的意义。

Data-driven network approach, as a burgeoning research interest in network science, has been proving to be an effective way to tackle practical problems in various fields, ranging from biology, through economics, to information science. Air transportation is crucial to the national economics and the efficiency for its operation can be improved by leveraging on network science. However, very little research has been mentioned to study the structure and dynamics of air transportation system. The resilience of the system is less addressed. This project aims to explore the structure and the resilience of air transportation system using a data-driven approach. Two large empirical datasets from US and China are collected and cleaned to form a complete, consistent database capturing the operating characteristics of air transportation systems. A multilayer temporal complex network of air transportation system is construct, including airports, air routes, and air traffic management as three major sub-systems. The structure and evolution mechanisms of the networks are first investigated. The temporal spatial characteristics of the cascading failure behavior are explored. Then the resilience of the network is analyzed based on the operational structure and the performance of the system. The universality and difference are validated for the proposed approaches through the comparison of the China and US data. The outcomes of this project will provide the guidance for the planning and management of the air transportation systems and contributions to our understanding of temporal, multilayer complex networks.