# Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model
## 1 Introduction
Air Traffic Control (ATC) plays an important role in Air Traffic Management (ATM), and ATC service does hold the purpose of preventing collision between aircrafts and obstacles and expediting and maintaining an orderly flow of air traffic [12]. There are a lot of discussion about collision risk in traffic safety assessment [13; 14]. ICAO uses collision risk estimates safety in a given en-route airspace [13; 15], and the level of risk that is deemed acceptable is termed the Target Level of Safety (TLS), which is 2.5 \(\times\) 10\({}^{-9}\) collisions per flight hour [13]. A combination of LSTM model and DNN model is used to predict the en-route flight safety, in which probabilistic separation distance is used as a quantitative safety metric [16]. However, the uncertainty in air traffic (such as weather uncertainty) increases the difficulty in recognizing conflicts issues, and the nonlinear characteristic of flight conflict has been verified, which means that the changes in flight conflict are complex and hard to understand and predict [17]. On the air traffic flow theory side, which can help in explaining traffic phenomenon from the perspective of intrinsic traffic properties, so as to provide a theoretical reference for transportation management. Since the Euler mathematical model of air traffic flow was established [18], research about air traffic flow theory largely concentrated in the field of theoretical modeling and simulation. With traffic parameters, the car-following model of high-speed flight [19] and the correlation between traffic flow parameters has been verified [20]. There are a lot of discussion about air traffic parameters without a flow model, many researchers focus the nonlinear characteristics of parameters [10; 17; 21; 22] and specific variant on basic parameters, such as dynamic density [23; 24]. With the development of surveillance services, high quality data can be collected easily. More and more researchers apply air traffic parameters to describe flight operation, which makes air traffic flow theory more practical. The distribution of traffic parameters in the terminal area [25] and time-headway of nose in en-route airspace have been proposed [26]. On the basis of traffic flow model and traffic data, optimization research about controlling traffic flow in terminal airspace and en-route airspace also attract researchers' attention [27; 28]. In the field of transportation, volume, density, and velocity are the basic parameters that could be used to describe traffic flow, which can effectively characterize traffic status in some way. Therefore, this research believes that some information that related to air traffic safety issues do exist in traffic flow parameters, and the relationship between traffic parameters and traffic safety should be studied precisely, so as to realize connection between air traffic flow theory and flight operation theoretically and practically. Aiming at promoting safety problems identification and safety prediction, this paper proposes prediction models for forecasting flight conflicts. Based on the model with highest accuracy, adding traffic parameters as new input variable into current prediction model to realize performance improvements and further observation. The research framework of this paper is shown in Figure 1. The remaining part of the paper proceeds as follows: Section 2 introduces the definition and calculation process of required parameters. Section 3 proposes time series forecasting based on LSTM. Section 4 presents a case study based on a real data set from Guangzhou Area Control Center (GACC) and compares model performance under different input conditions (without traffic parameters, with all traffic parameters and with single traffic parameter), and results and discussion are included. Conclusions are subsequently discussed in Section 5.