# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 1 Introduction  
In recent years, the air traffic transportation industry has developed rapidly. As an important part of the transportation system, air traffic transportation has greatly promoted economic development and culture exchange among cities. According to statistics from the Civil Aviation Administration of China (2019) [1], there were more than 238 transport airports and 5521 regular flight routes in China before the outbreak of the COVID-19 epidemic. The industry-wide transport airlines completed 4,966,200 transport take-off flights. The airport in the capital of Beijing has more than 300 fixed routes and more than 280 navigable airports. The Beijing-Shanghai route (A593) has 38,000 flights per year and carries more than 7.7 million passengers. Additionally, there were 875,902 abnormal flights. There are some reasons for abnormal flights: congestion, equipment failure, and meteorological conditions, etc. The operational situation of the air traffic system has an important influence on air safety and transportation. We can judge the safety level of air traffic operations by assessing the air traffic situation [2; 3; 4]. The air traffic situation is in a bad state, including route congestion and flight delay which seriously affect the stability and safety of air traffic. Andair transport revenue will decline [5]. Assessing the air traffic situation is helpful to find the risks and unstable factors. They affect the safe and efficient operation of air traffic. We can predict the evolutionary trends of air traffic flow and optimize the allocation of airspace resources [6]. And conflict detection and identification can be achieved by assessing the air traffic operational situation [7; 8]. Therefore, it is very important to assess the air traffic situation. However, research on the situation assessment of air traffic systems is still in its infancy. The air traffic system is a complex and gigantic system. Airports, routes, and aircraft interact with each other [9; 10]. The role of each element must be considered in assessing the air traffic situation. Thus, we proposed to construct an air traffic network model based on the complex network theory. The air traffic network including airports, routes, and operation data were weighted and constructed based on the complex network theory. The assessment of the air traffic situation is necessary to assess the operational situation of an air traffic network. At present, there are three methods to assess the network situation. The first is based on probability statistics. The weight of each safety factor is usually determined by expert qualitative knowledge or existing research results. A comprehensive weight index is constructed to calculate the network security situation, using methods such as the analytic hierarchy process (AHP), the Bayes network, and the entropy method [11; 12; 13], etc. [PERSON] and [PERSON] [14] proposed a network security situation assessment method based on an attack analytic hierarchy process. It took the change in the weighted index values after the network was attacked as the security situation results. [PERSON] and [PERSON] [15] considered the special network security requirements of the IIoT and proposed a quantitative evaluation method of network security based on the AHP. [PERSON] [16] combined support vector machines and improved the cuckoo algorithm to predict network security postures, and the method achieved a high accuracy in its performance on the data mining and knowledge discovery (KDD) data set. [PERSON] [17] proposed a Bayes network evaluation model based on an attacker's income, loss, cost, and risk-related indexes. These methods can reflect the influence of expert experience or existing knowledge of a network security situation effectively. Their calculation is simple and easy to implement. However, strong subjectivity will lead to inaccurate evaluation results. And it cannot be applied to the dynamic changes of the network. The second assessment method is based on information fusion and logical reasoning.