# An Assessment Model for Air Passenger Risk Classification
## 1 Introduction  
With the steady development of the social economy, the deepening reform of the civil aviation management system and the upgrading of the residents' consumption structure, the civil aviation industry has developed rapidly. However, the traditional security check mode is strict and cumbersome, and the contradiction between the increasing passenger throughput and the limited security check resources reduces the security check efficiency and passenger satisfaction. Moreover, with the development of society, the factors affecting civil aviation security have become more complex and diverse, which makes airport security checks face great challenges and pressure. Therefore, the optimization of airport security check mode is extremely important for improving security check efficiency, rationally allocating security check resources, and improving passenger satisfaction. To improve the current situation of airport security checks, the classified security check mode is proposed, which has been studied by many scholars. Some scholars mainly focus on the design of models for classified security check systems. [PERSON] et al. modeled the classified security check problems under three, five and eight passenger risk levels and used a heuristic algorithm based on the greedy strategy to solve them. The marginal costs, fixed costs and security levels under three, five and eight passenger risk levels were analyzed and compared. The experimental results showed that classified security checks with fewer passenger risk levels might be more effective [1]. [PERSON] et al., established a model to determine the number of passenger groups and the resource allocation for each group under the condition that the threat probability of all passengers is constant. The influence of the false alarm rate and the threat probability on the model was studied, concludingthat the grouping strategy is related to the threat probability [2]. Different from the study by [PERSON] et al., [PERSON] et al. relaxed the basic assumption that the threat probability of all passengers is constant and considered the risk levels of passengers. A mixed integer linear model with the minimum false alarm rate as the objective function was built to determine the grouping strategy of passengers and the staffing of each security channel. Compared with the model established by [PERSON] et al., the model considering passenger risk attributes makes the airport security system more effective [3]. [PERSON] et al., established the Bayesian decision model of the air passenger prescreening system, which divided passengers into two categories: fly passengers and no-fly passengers, and used cost parameters from the perspective of the government and passengers to evaluate classified security checks [4]. [PERSON] et al. simulated the queuing of classified security check channels to effectively utilize resources in airport security. According to the number of passengers in the classified security channels, passengers with different risk levels are allocated to different security channels [5]. [PERSON] et al., proposed an airport classified security check system that divided passengers into three categories according to historical security records. The two-dimensional Markov process and the Markov modulated Poisson process queue were used to build the model of the security system, and the simulated annealing method was used to solve the model. The effectiveness of the proposed classified security check system in improving service efficiency and security level was verified by examples [6]. [PERSON] et al. described an N-stage passenger screening model and analyzed it by using game theory and queuing theory. In this model, passengers have the chance to be passed or rejected at each stage. The functional relationships between application probabilities, screening probabilities, approver's benefits and the number of screening stages were also analyzed. In addition, some suggestions on the security check strategies and the optimal number of screening stages were given [7].