Passenger grouping under constant threat probability in an airport security system

In a typical security system at a US airport, there are a series of inspections done on passengers as well as on baggage to check whether any item of threat is entering into the system. Considering the large number of passengers and items using our airports, one hundred percent check on all the items is not practical. This paper investigates the benefit of classifying passengers into different groups, with the idea that the number of checks and the degree of inspection may vary for different groups. The threat probability is assumed to be known and identical for all passengers. We develop a model to determine the number of groups, the fractions of passengers and the assignment of check stations for each group. The constraint is that the false clear probability is within Federal of Aviation Administration (FAA) specifications, and the objective is that the number of false alarms (a surrogate measure of passenger inconvenience) in the system is minimized. The model studies the effect of the system parameters on the number of items checked at various check stations and on the false alarm rate. The major conclusion of this paper is that passenger grouping is beneficial even when the threat probability is assumed constant across all passengers. A further conclusion is that the optimal grouping and check station assignments can depend on the overall threat probability.     

Simulation-based Selectee Lane queueing design for passenger checkpoint screening

There are two kinds of passenger checkpoint screening lanes in a typical US airport: a Normal Lane and a Selectee Lane that has enhanced scrutiny. The Selectee Lane is not effectively utilized in some airports due to the small amount of passengers selected to go through it. In this paper, we propose a simulation-based Selectee Lane queueing design framework to study how to effectively utilize the Selectee Lane resource. We assume that passengers are classified into several risk classes via some passenger prescreening system. We consider how to assign passengers from different risk classes to the Selectee Lane based on how many passengers are already in the Selectee Lane. The main objective is to maximize the screening system’s probability of true alarm. We first discuss a steady-state model, formulate it as a nonlinear binary integer program, and propose a rule-based heuristic. Then, a simulation framework is constructed and a neighborhood search procedure is proposed to generate possible solutions based on the heuristic solution of the steady-state model. Using the passenger arrival patterns from a medium-size airport, we conduct a detailed case study. We observe that the heuristic solution from the steady-state model results in more than 4% relative increase in probability of true alarm with respect to the current practice. Moreover, starting from the heuristic solution, we obtain even better solutions in terms of both probability of true alarm and expected time in system via a neighborhood search procedure.     

Probability models for access security system architectures

This paper presents probability models of access control security system architectures. Access control is the process of screening objects; for example people, baggage, entering a secured area in order to detect and prevent entry by threats such as unauthorized personnel, firearms, explosives. A security system architecture consists of device technologies, as well as operational policies and procedures for utilizing the technologies. The probability models are developed based on Type I (a false alarm is given) and Type II (a threat is not detected) errors. The concept of controlled sampling, in which objects may take different paths through the system, is introduced. New architectures consisting of multiple devices and controlled sampling are proposed and analyzed. The results presented indicate that for specific threat levels, multiple-device systems can be identified which outperform single-device systems for certain error probability measures.     

Multilevel,threshold-based policies for cargo container security screening systems

To mitigate the threat of nuclear terrorism within the US using nuclear material that has been smuggled into the country, the US Bureau of Customs and Border Protection has expanded its cargo container detection capabilities at ports of entry into the US This paper formulates a risk-based screening framework for determining how to define a primary screening alarm for screening cargo containers given a set of dependent primary screening devices. To do so, this paper proposes two linear programming models for screening cargo containers for nuclear material at port security stations using knapsack problem models. All cargo containers undergo primary screening, where they are screened by a given number of security devices. The objective is to identifying the primary security outcomes that warrant a system alarm for each container risk group such that the system detection probability is maximized, subject to a screening budget. The base model is compared to a second model that explicitly requires a threshold-based policy. The structural properties of the two models are compared, which indicates that all risk groups except at most one have deterministic screening policies. A computational example suggests that the detection probability is not significantly altered by enforcing a threshold policy.     

基于贝叶斯视角的地铁安检效果评估

综合地铁安检的技术可靠性、乘客违禁品携带比例等因素,界定了乘客通过安检时可能出现的4种场景,并由贝叶斯理论分析地铁安检的后验准确率.结果表明,由于地铁安检的特殊性,通常情况下后验准确率较低;即使可靠性可保持较高水平,但由于携带违禁品是小概率事件,后验准确性依然不会有显著改善;提示有违禁品携带情况多数是由于误判所造成的.安检等待时间的蒙特卡洛模拟结果进一步表明,由于误判所造成的无效检查所耗费的时间占比较高,可靠性的提高并不能改善这种情况,在人流高峰时段有可能对通行效率产生严重影响.因此,对地铁安检模式的改进,不能仅依赖于技术的升级,优化人机协同才是关键.     

Imperfect Inspection Games Over Time

We consider an inspection game played on a continuous finite time interval. The inspector wishes to detect a violation as soon as possible after it has been made by the operator. The loss to the inspector is assumed to be linear in the duration of the time elapsed between the violation and its detection. This paper is mostly an extension of Diamond's models for a single inspection, which includes the uncertainty aspect, by relaxing the assumption that the inspection is perfect. Here the inspection is imperfect; it has a Type One Error which means that the inspector may call a false alarm (with probability ), and a Type Two Error which means that the inspection may fail to detect (with probability ) a violation which did occur. In addition we will assume that the inspection is silent, i.e., the operator is unaware of the inspection when it takes place, unless the inspector calls a false alarm.     

Asymptotically optimal pointwise and minimax quickest change-point detection for dependent data

We consider the quickest change-point detection problem in pointwise and minimax settings for general dependent data models. Two new classes of sequential detection procedures associated with the maximal “local” probability of a false alarm within a period of some fixed length are introduced. For these classes of detection procedures, we consider two popular risks: the expected positive part of the delay to detection and the conditional delay to detection. Under very general conditions for the observations, we show that the popular Shiryaev–Roberts procedure is asymptotically optimal, as the local probability of false alarm goes to zero, with respect to both these risks pointwise (uniformly for every possible point of change) and in the minimax sense (with respect to maximal over point of change expected detection delays). The conditions are formulated in terms of the rate of convergence in the strong law of large numbers for the log-likelihood ratios between the “change” and “no-change” hypotheses, specifically as a uniform complete convergence of the normalized log-likelihood ratio to a positive and finite number. We also develop tools and a set of sufficient conditions for verification of the uniform complete convergence for a large class of Markov processes. These tools are based on concentration inequalities for functions of Markov processes and the Meyn–Tweedie geometric ergodic theory. Finally, we check these sufficient conditions for a number of challenging examples (time series) frequently arising in applications, such as autoregression, autoregressive GARCH, etc.     

Virtual queuing at airport security lanes

Airports continuously seek opportunities to reduce the security costs without negatively affecting passenger satisfaction. In this paper, we investigate the possibilities of implementing virtual queuing at airport security lanes, by offering some passengers a time window during which they can arrive to enter a priority queue. This process could result in a smoother distribution of arriving passengers, such that the required security personnel (costs) can be decreased. While this concept has received attention in a number of settings, such as theme parks, virtual queuing at airports bears an additional level of complexity related to the flight schedules, i.e., passengers can only be transferred forward in time to a limited extent, which we denote by the transfer time limit. We conducted a major simulation study in collaboration with a large international airport in Western Europe to determine the potential impact of virtual queuing and find that nearly one million Euro can be saved on security personnel cost without negatively impacting the passenger waiting time.     

Steady state results for the M/M(a,b)/c batch-service system

The transportation system considered in this paper has a number of vehicles with capacity constraint, which take passengers from a source terminal to various destinations and return to the terminal. The trip times are considered to be independent and identically distributed random variables with a common exponential distribution. Passengers arrive at the terminal in accordance with a Poisson process. The system is operated under the following policy: when a vehicle is available and there are at least ‘a’ passengers waiting for service, then a vehicle is dispatched immediately. A recursive algorithm is derived to obtain the steady-state probability P(m, j) that there are m idle vehicles and j waiting passengers in the queue. Analytical expressions have been derived for passenger queue length distribution, average passenger queue length, the r-th moment of passenger waiting time in the queue, service batch size distribution and the average service batch size, all in terms of P(0,0).     

基于DEA-博弈论模型的铁路电子车票实名制查验评价分析

将基于数据包络分析(DEA)模型和纳什讨价还价博弈论结合, 作为一种合作博弈方法, 对铁路电子车票实名制的查验进行了综合评价, 为准确评估铁路电子车票实名制查验的效率, 加强铁路客运服务水平提供决策判断。本文以20个不同等级的铁路车站为研究对象, 分人工和机器两大类, 选取身份证购票比例、通勤员工购票比例、临时身份证购票比例, 以及人工查验旅客数/小时/通道、机器查验的通过人数/闸机/小时、闸机人脸自动识别比例和延误比例等7个指标作为投入指标, 选取人、证、票三证合一的实名制查验比例作为产出指标, 基于数据包络分析(DEA)和纳什讨价还价博弈论结合的合作博弈模型, 从人工查验和机器查验两个角度对铁路电子车票实名制查验进行统一综合评价。案例结果表明, 铁路电子车票实名制查验效率呈现出人工查验和机器查验效率不均衡的态势; 车站人脸识别闸机的数量投入与所需闸机数量的不匹配是造成实名制查验效率下降的主要原因。根据评价结果, 从人工和机器查验两个角度提出了可行建议, 促进了铁路电子车票实名制查验效率的进一步提升。     

考虑成本、等待时间和安全水平的分类安检模式研究

近年来,随着民航机场旅客吞吐量快速增加,机场安检部门的工作压力正不断加大,旅客等待时间也正在逐渐增加。针对该问题,引入了分类安检模式。然而,分类安检模式通常需要额外的投资成本,可能会产生较大的财务负担。因此,以成本为目标,综合考虑旅客等待时间和安全水平的要求,研究了分类安检模式和传统安检模式的比较以及最优决策性质等相关问题。首先,考虑安全水平和旅客等待时间的约束,分别为传统安检模式和分类安检模式建立了模型,并分析了其最优运营决策性质。其次,从成本的角度,对两种安检模式做了比较研究。研究结果表明,当旅客数量多、安检犯错容忍度较高或旅客等待敏感时,分类安检模式比传统安检模式表现更优;反之,传统安检模式更优。此外,在分类安检模式中,分配到高风险安检通道的旅客比例存在一个最优值,并且其通常在20%到30%之间取到。最后,通过数值分析验证了分类安检模式的最优服务配置。     

Interdicting nuclear material on cargo containers using knapsack problem models

This paper introduces a framework for screening cargo containers for nuclear material at security stations throughout the United States using knapsack problem, reliability, and Bayesian probability models. The approach investigates how to define a system alarm given a set of screening devices, and hence, designs and analyzes next-generation security system architectures. Containers that yield a system alarm undergo secondary screening, where more effective and intrusive screening devices are used to further examine containers for nuclear and radiological material. It is assumed that there is a budget for performing secondary screening on containers that yield a system alarm. This paper explores the relationships and tradeoffs between prescreening, secondary screening costs, and the efficacy of radiation detectors. The key contribution of this analysis is that it provides a risk-based framework for determining how to define a system alarm for screening cargo containers given limited screening resources. The analysis suggests that highly accurate prescreening is the most important factor for effective screening, particularly when screening tests are highly dependent, and that moderately accurate prescreening may not be an improvement over treating all cargo containers the same. Moreover, it suggests that screening tests with high true alarm rates may mitigate some of the risk associated with low prescreening intelligence.     

Railway delay management with passenger rerouting considering train capacity constraints

Delay management for railways is concerned with the question of whether a train should wait for a delayed feeder train or depart on time. The answer should not only depend on the length of the delay but also consider other factors, such as capacity restrictions. We present an optimization model for delay management in railway networks that accounts for capacity constraints on the number of passengers that a train can effectively carry. While limited capacities of tracks and stations have been considered in delay management models, passenger train capacity has been neglected in the literature so far, implicitly assuming an infinite train capacity. However, even in open systems where no seat reservation is required and passengers may stand during the journey if all seats are occupied, physical space is naturally limited, and the number of standing seats is constrained for passenger safety reasons. We present a mixed-integer nonlinear programming formulation for the delay management problem with passenger rerouting and capacities of trains. Our model allows the rerouting of passengers missing their connection due to delays or capacity constraints. We linearize the model in exact and approximate ways and experimentally compare the different approaches with the solution of a reference model from the literature that neglects capacity constraints. The results demonstrate that there is a significant impact of considering train capacity restrictions in decisions to manage delays.     

Ruin Probabilities in Cox Risk Models with Two Dependent Classes of Business

In this paper we consider risk processes with two classes of business in which the two claim-number processes are dependent Cox processes. We first assume that the two claim-number processes have a two-dimensional Markovian intensity. Under this assumption, we not only study the sum of the two individual risk processes but also investigate the two-dimensional risk process formed by considering the two individual processes separately. For each of the two risk processes we derive an expression for the ruin probability, and then construct an upper bound for the ruin probability. We next assume that the intensity of the two claim-number processes follows a Markov chain. In this case, we examine the ruin probability of the sum of the two individual risk processes. Specifically, a differential system for the ruin probability is derived and numerical results are obtained for exponential claim sizes.     

重尾索赔下二维风险模型有限时间的破产概率

在假定个体索赔额分布是重尾分布族的前提下,得到了带常利息力度二维风险模型有限时间内破产概率的渐进表达式.     

Predicting the false alarm rate in multi-institution mortality monitoring

Statistical process control is increasingly used by single hospitals or centres to monitor their performance, but national monitoring across multiple centres, measures and groups incurs higher false alarm rates unless the method is modified. We consider setting the threshold for cumulative sum charts to produce the desired false alarm rate, taking into account the centre volume and expected outcome rate. We used simulation to estimate the false alarm and successful detection rates for a variety of chart thresholds. We thereby calculated the ‘cost’ of a higher threshold compared with one set to give a false alarm rate of 5% for three clinical groups of common interest. The false alarm rate often showed non-linear relations with the threshold, volume and expected mortality rate but an equation was found with good approximation to the simulated values. The relation between these factors and the ‘cost’ of a higher threshold was not straightforward. The ‘cost’ (difference in number of deaths) incurred by raising the chart threshold provides an intuitive measure and is applicable to other settings.     

Adjusting a railway timetable in case of partial or complete blockades

Unexpected events, such as accidents or track damages, can have a significant impact on the railway system so that trains need to be canceled and delayed. In case of a disruption it is important that dispatchers quickly present a good solution in order to minimize the nuisance for the passengers. In this paper, we focus on adjusting the timetable of a passenger railway operator in case of major disruptions. Both a partial and a complete blockade of a railway line are considered. Given a disrupted infrastructure situation and a forecast of the characteristics of the disruption, our goal is to determine a disposition timetable, specifying which trains will still be operated during the disruption and determining the timetable of these trains. Without explicitly taking the rolling stock rescheduling problem into account, we develop our models such that the probability that feasible solutions to this problem exist, is high. The main objective is to maximize the service level offered to the passengers. We present integer programming formulations and test our models using instances from Netherlands Railways.     

A rolling stock circulation model for combining and splitting of passenger trains

This paper addresses the railway rolling stock circulation problem. Given the departure and arrival times as well as the expected numbers of passengers, we have to assign the rolling stock to the timetable services. We consider several objective criteria that are related to operational costs, service quality and reliability of the railway system.Our model is an extension of an existing rolling stock model for routing train units along a number of connected train lines. The extended model can also handle underway combining and splitting of trains.We illustrate our model by computational experiments based on instances of NS Reizigers, the main Dutch operator of passenger trains.     

具有常红利边界和延迟索赔的一类离散更新风险模型

考虑了具有常红利边界和延迟索赔的一类离散更新风险模型,其中间隔索赔到达时间从离散phase-type分布.定义了两种类型的索赔:主索赔和副索赔,主索赔以一定的概率引起副索赔且副索赔会以一定的概率被延迟到下一时段.通过引入辅助风险模型,推导了破产前红利折现期望满足的差分方程及其解.最后给出了当索赔额服从几何分布时的有关数值例子.     

网约车新政下平台和乘客的Stackelberg博弈模型及策略研究

近年来,滴滴和优步等网约车平台的出现给城市居民出行带来了新的选择,然而随着新政的实施以及垄断巨头的酝酿诞生,网约车平台是否会降补贴提车价成为消费者最为关心的问题。文章构建网约车平台和乘客之间的Stackelberg博弈模型,分析了网约车平台的补贴和抽成策略对乘客是否选择网约车出行的决策行为以及网约车市场均衡的影响。理论研究和仿真结果表明：网约车平台合理的补贴力度和抽成力度可以有效规避行政监管成本增加和乘客资源流失的风险,提高双方的收益,实现社会的帕累托最优。