# Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model
## 1 Introduction  
For the civil aviation transportation industry, safety is the basis cornerstone for the sustainable development, which are always highly valued. In order to significantly improve safety, the aviation industry has been moving towards a proactive approach to safety problems identification, which could increase awareness of emerging risks among the operators. With the development of data recording techniques in commercial aircrafts, more and more safety related data can be applied to investigate safety issues and recognize risks, which is collected from different data sources, such as safety reports, on-board recorders, and flight-data records. Numerous applications of machine learning based on different data have gained popularity in the aviation safety domain recently. Generally, these data can be divided into two different types according to theirs formats, textual data or numerical data. Safety data are collected from proactive or reactive safety-related activities, such as safety reporting [1]. Aviation Safety Reporting System (ASRS) was first built in the U.S in the 1970s, and the Civil Aviation Administration of China (CAAC) has established the Sino Confidential Aviation Safety Reporting System (SCASS), which are both aimed at collecting voluntarily submitted incident reports from pilots, air traffic controllers, dispatchers, and others. Processing these texts can benefit identification, analysis, and evaluation of risks [2]. There has been recent progress in establishing theories and tools for performing largely automated and relatively rapid analysis of a corpus [3]. Many researchers process safety information in large text corpora by using artificial intelligence algorithms, such as latent Dirichlet allocation (LDA), which could be applied to obtain the probability distribution of words and topics in unsafeincident reports, and draw out the hazard source classification [4]. The structural topic modeling also helps in identifying known issues and evaluating the usefulness of a novel method based on ASRS data [3]. On the other side, safety issues are also found in numerical data, such as safety assessment data or other time-series data. On the basis of the numerical data, artificial intelligence can provide insights into identifying safety related factors and providing predictive capabilities, so as to move toward proactive safety management. The Random Forest regression algorithm is used to predict the critical landing parameters, which aid in risk assessment during the approach and landing phases of flight [5]. Based on the data from the flight data recorder, a cluster-based anomaly detection method to detect abnormal flights, which can support in detecting anomalies and associated risks from routine operations [6]. The Human Factors Analysis and Classification System (HFACS) framework and Bayesian Network Model (BNM) are proposed to provide a systematic method for aviation safety research [7]. The LSTM model trained with historical flight data are used to predict the flight safety level based on percentile velocity bounds of historical flights [8]. A Multi-Head Convolutional Neural Networks-Recurrent Neural Networks (MHCNN-RNN) architecture is proposed, which enables the prediction of different adverse events for any given flight [9]. The Extreme Learning Model (ELM) is used to predict the evolution of time series, and the prediction results of different algorithms at different dimensional levels and periods were compared [10]. Based on the safety evaluation, performance assessment about delay and predictability has been put forward in recent research [11]. Besides the safety related issues that have been mentioned, such as incidents, landing parameters, and human workload, conflict issues are directly relevant to unsafe events, which means that the flight separation violates the minimum separation standard.