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Addressing Passenger Risk Uncertainty for Aviation Security Screening

机译:解决航空安全检查中的旅客风险不确定性

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Within the aviation security system, the preeminent objective of passenger and baggage screening is to prevent prohibited items from entering the airport terminal and getting put onboard a commercial aircraft. Because of limited budget and personnel resources, as well as constraints on the screening device capacities, only a fraction of the passengers may be screened at the highest security levels. Sequential passenger assignment policies have been formulated through dynamic programming and nonlinear control. However, both of these approaches rely on a known distribution of passenger risk. This paper presents estimation algorithms that address various levels of uncertainty in the passenger risk distribution, which can be applied to existing passenger assignment policies. Simulation results are reported to illustrate the sensitivity to variations in the unknown distribution parameter and to demonstrate that the prudent practice of overestimating the overall population risk level produces a larger number of improperly screened passengers and a lower level of security in comparison to underestimating passenger risk. The key contribution of this work is the finding that integrating online estimation of passenger risk into security screening assignment decisions increases the overall expected security and decreases the sensitivity to variations in the overall population risk level.
机译:在航空保安系统内,对旅客和行李进行检查的主要目的是防止违禁物品进入机场航站楼并被带上商用飞机。由于预算和人力资源有限,并且筛查设备的容量受到限制,因此只能以最高的安全级别筛查一部分乘客。通过动态编程和非线性控制已经制定了顺序乘客分配策略。但是,这两种方法都依赖于已知的乘客风险分布。本文提出了估计算法,该算法可解决乘客风险分布中各种不确定性水平,该算法可应用于现有的乘客分配策略。报道了模拟结果,以说明对未知分布参数变化的敏感性,并证明与低估旅客风险相比,高估总体人口风险等级的审慎做法会产生大量未经适当检查的乘客,以及较低的安全级别。这项工作的主要贡献在于发现将对乘客风险的在线估计整合到安全筛选分配决策中可以提高总体预期安全性,并降低对总体人群风险水平变化的敏感性。

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