airports where laptops computers are carried by a significant number of passengers as carry-on items, our study indicates a likely impact on server performance ...
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The OR Society OR Insight Vol. 20 Issue 4 October - December 2007
Server Adaptation in an Airport Security System Queue by Clara ~ Marin, Colin G. Drury, Rajan Batta, Li Lin Industrial and Systems Engineering and Research Institute for Security and Safety in Transportation University at Buffalo (SUNY), Buffalo NY 14260, USA
Abstract We study the airport security queuing system for server behavior in response to queue length, and postulate its implications on security. To achieve this objective, an observational study was performed at the security screening process of a US airport. It was found that X-ray screeners (servers) did speed up with queue length for one type of item, laptop computers. Significant correlations between queue length and service time suggested Parkinson's Law as an explanation. Correlation of this finding with available data on speed-accuracy tradeoffs allows us to reach the following conclusion: For airports where laptops computers are carried by a significant number of passengers as carry-on items, our study indicates a likely impact on server performance as measured as by probability of threat detection.
system/queue, average time customer spends in the system/queue) and system information (i.e., Erlang's and Little's results) are most common (Kleinrock 1996). Within these queues, the customer arrival rate and server service rate are assumed to be independent. Much of the research in queuing theory has been devoted to such systems, and led to a significant understanding of the queuing phenomenon. All queues are not, however, composed of inanimate customers and mechanistic servers. Many queues have people as customers and as servers, which can have the effect of introducing new factors into the models as human behavior is present in both customers and servers. In considering performance of queues, perhaps the actual customer waiting time is not the objective to be minimized but rather customers' perceived waiting time. Just as there is a long tradition of queuing models, there is a tradition of examining behavior in queues stretching back at least 20 years to Hornik (1984). Fundamental papers by Larson (1987) and Maister (1985) examined the "psychology of queues" from the perspective of fairness or social justice. They provided lists of factors that could be assumed to affect customers' perception of waiting time, with their own and earlier examples of data supporting some of these factors. For example, Larson (1987) quoted a private communication from Ackoff (1987) about the efficacy of mirrors by hotel elevators in reducing complaints about elevator delays. Since the mid-1980's there have
Introduction, Literature Review and Background Queuing theory has a long history, exploring the mathematical consequences of many different queue configurations. Queuing theory has been used to model seemingly random processes (rush hour traffic, or the sorting of mail) with efficient and effective results (Newell 1982). With knowledge of the distribution of customer arrivals and service time (Poisson, exponential, etc.); it is possible to calculate many important facets of a queue's property (Ross 2003). Of these, information pertaining to the customer (average number of customers In the 22
been many modeling studies (e.g., Ho and Zheng 2004) and empirical studies (e.g.. Jones and Peppiatt 1996) of customer behavior in queues. Durrande-Moreau (1999) provided the nearest paper we have found to a meta-analysis of customer behavior studies III queues, summarizing 18 prior papers that covered both field and laboratory situations, to derive empirical support for hypothesized factors. Many of the factors expected to be important (e.g., actual waiting time) were found to be supported, while environmental factors (e.g. situational factors during the wait) were not. Since that review, research on customer behavior has continued, e.g., the works of Zhou and Soman (2003) on the effect of number of customers following a queue member, and a study of tele-queues by Zohar, Mandelbaum and Shimkin (2002).
of such behavior apart from the improved validity of their model of police dispatching, which proved superior to a standard model when many changes such as service time changes were incorporated. An explanation offered by Green and Kolesar (1987) was Parkinson's Law (Parkinson 1958), which states "Work expands to fill the time available for its completion." Such a law has been tested many times in studies beyond queuing, using techniques ranging from questionnaires (Peters, O'Connor, Pooyan and Quick 1984), through field studies (Latham and Locke 1975) to laboratory experiments (Bryan and Locke 1967; Peters, O'Connor and Rudolph 1980). A mixed set of results was obtained but they generally supported Parkinson's Law using all three study paradigms. Theoretical papers such as Gutierrez and Kouvelis (1991) and Austin (2001) provided ways to use Parkinson's Law concepts in operations research models, but did not support these models with further empirical data. No papers have been found since Green and Kolesar (1987) that apply Parkinson's Law to queuing phenomena, where the time devoted to work completion might be expected to decrease as queue length increases.
The common thread in the above studies is that they have addressed the behavior of customers in queues but have not explored the consequences of changes in server behavior. In fact, the idea that servers may also adapt their behavior in queues is hardly new: Schwartz (1978) provided a social psychology-based model of queuing where he hypothesized such behaviors as "server balking," "server reneging" and a server changing the priority of an element in a queue. He highlighted the time-dependent character of queues, denying that organized activities "can be understood independently of temporal pressures" (page 4). Schwartz developed a theory to demonstrate pathological behavior as well as the potential for server stress due to time pressure, but did not link these concepts to any quantitative model. Similarly, Green and Kolesar (1987) noted some queuing systems in which "human interaction and the vagaries of human behavior are of the essence" (page 470), specifically expecting "speed-ups or slow-downs by servers" to occur (page 470). They gave a hypothetical example of "where congestion is severe, servers may cut corners in order to speed up service, thereby reducing the quality of service rendered." Again, they raised the concepts but offered no observational evidence
While Parkinson's Law provides an explanation of the speed-up effect of servers responding with greater effort to the fact of limited resources, it offers no insight into the quality of the work performed. Austin (2001) explicitly modeled quality reduction in software development under time pressure as the taking of short-cuts, treating the scenario in game-theoretic terms, i.e., whether a competing "agent" chooses to take a short-cut. However, no parametric relationship between speed of working and quality was postulated. In contrast, human factors engineers and psychologists have large amounts of empirical evidence and models of the time/quality relationship, typically called the Speed-Accuracy Trade-Off or SATO (Wickens and Hollands 2000). A review of SATO models in industrial applications (Drury 1994) drew together a number of mathematical models of 23
different tasks where increasing speed was likely to decrease accuracy. Such tasks have been termed "resource limited" in contrast to tasks that do not exhibit SATO that are "data limited" (Norman and Bobrow 1975; Berger and Posner 2006). One prime example of SATO relevant to security operations is visual search.
inspection tasks, which include security inspection, search is not the only function of the inspector, who must also reach a non-trivial decision about the disposition of each item inspected. These decisions are subject to two types of errors, Type I, where a non-threat is classified as a threat (False Alarm), and Type II, where a true threat is classified as a non-threat (False Clear or Miss). The probability of Miss (conditional on search success), Pd, acts as an
Given the importance of security inspections, we seek to explore the possibility of Parkinson's Law in an airport security queue and further explore implications on airport security. Since airport security screening is a human search task, we now review relevant papers in the area of search strategies and in human performance in search tasks.
asymptotic value of the SATO curve (the exponential equation above) giving a model of inspection as search plus decision (Drury, 1973). Explicitly the cumulative distribution function for probability of detection in an inspection task IS:
Search strategies and models have been a part of operations research since the classic papers by Koopman (1956a, 1956b, 1957), later updated in his book (Koopman, 1980). A parallel body of knowledge, although more data based than analytical, is part of the human factors engineering tradition, c.f Wickens and Hollands (2000). The typical approach is to model search as a sequence of glimpses or fixations, each of short duration and covering limited area. For human observers, eye movement data to verify these models shows a fixation time of about 0.20.5 s, with almost no information processing in the rapid eye movements between fixations. Most models assume what has come to be called "random search," where fixations are independent with no memory of prior fixation locations, but models of repeated "systematic search" of the same search area are also possible (Morawski, Drury and Karwan, 1980). In the random search model, performance, the cumulative probability (PJ of detection of a
From equations such as this, we can deduce that the time taken to inspect an area is extremely important in determining visual search success, The search component of i.e., SATo. inspection is typically the most time-consuming (Drury and Spencer, 1997). If insufficient time is spent in inspection, targets may be missed e.g., Drury (1994), Drury and Forsman (1996). Such models have been used to predict optimum stopping times in visual search, e.g., by Karwan, Morawski and Drury (1995) and Morawski, Drury, and Karwan (1992). They have also been fitted to the security task of X-ray inspection of carry-on baggage (Ghylin, Drury and Schwaninger, 2006) with model!data fits giving r 2 values averaging above 0.8.
2. Methodology To test the hypothesis that service time was related to queue length, we sampled both variables during 24 one-hour observation sessions spread over about seven weeks at a US airport. Different expected passenger loads were sampled to ensure a wide enough distribution of queue lengths to provide correlations without range restriction problems. Expected passenger loads were taken from an earlier study (Paul, Lin, Batta and Drury 2007), which had measured passenger volumes from TSA-collected
single target in a time (t) is given by:
P, = 1- ex p( -;) where t is the mean search time. A repeated systematic search model gives a cumulative probability of detection as a series of line segments, with the exponential model as the asymptotic form as the lines become shorter. For 24
Figure 1. The Passenger Flow (load) Patterns for the Four-day Sets
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throughput data. This study, with 630 hourly data points, showed that there were different patterns of passenger load on Saturday, Sunday, Monday and Tuesday to Friday (Figure 1). Note that, throughout this paper, queue lengths and service times are presented as percentages of the maximum observed to preserve security of the data.
the servers, i.e. machine operators of the X-ray machines: Queue 1: All the passengers standing in line before the document check stations. Queue 2: All passengers standing after the document check stations and before the queues in front of the individual server lanes. Queue 3: Total number of passengers in the queues in front of all server lanes. Queue 4: Total number of passengers in front of the server in the lane where the service time was measured. From the viewpoint of the server (X-ray screener) Queue 1 is the most distant and Queue 4 the most proximal. The relative visibility of the four queues increases with queue number.
U sing Figure 1, three levels of expected passenger load were chosen: high, medium and low. High passenger load was between 0500 and 0800 and between 1600 and 1800, medium passenger load was around noon time and low passenger load was after 1800. Data were collected hourly with two replications for each pattern and passenger load, giving a total of 24 visits. During each observation period, one data collector observed the queue lengths while another independently observed service times for X-ray screening.
Service time: Prior observations showed that each item on the X-ray conveyor could be viewed for the whole time the image took to traverse the screen, plus any time that the X-ray operator stopped the conveyor. Two items could be on the screen together, and the observer had no way of telling which item the X-ray operator was attending to. Thus we defined the "service time" as the time an item stayed visible on the X-ray machine's screen, measured from its first appearance on the screen to its complete
Queue length: The number of passengers waiting in the various queues was measured by observations every five minutes. The airport layout had multiple queues, so that "queue length" was not a simple measurement. Four queues were defined and measured in front of
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disappearance. The X-ray machine is designed to leave the most recently processed images on the screen until new objects are fed through the conveyor. Therefore, when the passenger volume is low it is common to have an image on the screen for several minutes even though this item has already been processed by the screener. To provide accurate and consistent service time data collection, the observer ensured that at least three items were on the conveyor line, with the item in the middle selected to be measured. Item classification and sample size: when each item was on the screen, the observer classified it into four types:
Figure 2: Relative Mean Queue Length of each Queue as a Function of Expected Passenger Load 100
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A similar two way ANOVA (bag type by expected passenger load) applied to service time revealed only a significant main effect from bag type (F(3,88) = 6.4, p=O.OOl). No transform of times was required for normalization of residuals. The mean times and standard deviations for each bag type were: Type 1 = 21 s ± 4.5 s; Type 2 = 25 s ± 3.0 s; Type 3 = 24 s ± 5.5 s and Type 4 = 21s ± 4.8 s. Separate one-way ANOVAs of service times by predicted load were performed for each bag type. The service time for bag Type 4 (laptops) was affected by the expected load, p-value = 0.034 (Figure 3).
Type 3: Small purses or camera cases (n 292) Type 4: Laptop computers (n = 215) for a total of 2337 items. Note that our hypothesis was tested using the average values of queue lengths and service times in each of the one-hour periods. Item-toitem analysis of this data was not possible as coordination of service and queue measures in real time was not possible, while video recording was not permitted in the security operations. Results A two-way ANOVA of queue lengths by expected passenger load was performed on the log of queue length to normalize the residuals. This showed that the sampling plan was effective as predicted, with significant differences between expected passenger load levels (F(3,82) = 59.3, P < 0.001). There was also a significant effect of queue (Ql to Q4) and a significant interaction between load level and queue length, both were also at p < 0.001. Figure 2 shows the mean queue lengths (converted back from logs by exponentiation, and scaled to percentage of maximum length) for all queues as a function of expected passenger load. The interaction is seen to be merely a differing slope by queue, with all queues increasing in length with predicted load.
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Figure 3. Relative Laptop Service TIme as a Function of Expected Passenger Load
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All correlation coefficients were negative indicating that the higher the number of passengers in the queue, the faster the service times for laptops. Figure 4 shows the largest of these significant correlations-for Queue 3. Note that for the significant correlations of laptop service times, higher correlation coefficients were found where the queue was closer to server's view.
Figures 2 and 3 illustrate that the significant passenger load effects on queue length and service time are opposite. Under conditions of high passenger load, the length of all queues increases while service time for laptops decreases. This finding suggests an inverse correlation between queue length and service time, which we now explore directly. Inter-correlations across one-hour periods of the four queue lengths and the service times for the four bag types showed that correlations were significant for laptops (the other three types of bags were not significant under any of the four queues). See Table 1. Table 1: Inter-correlation Analysis of Queue Length and Service TIme for Laptop Cornputes-s
MeanQl MeanQ2 MeanQ3 MeanQ4
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Laptop Corrrpurera = -0.499, p = 0.011 = -0.406, p = 0.044 = -0.643, p = 0.001 = -0.639, p = 0.001
Discussion and Irnpficarions to Airport Security The main finding of the observational study was that human servers at X-ray machines in an airport security system are not blind to queue length. There were significant correlations between all the queue lengths and the particular service time to process a laptop computer. The two queues closest to the server (the actual queue in front of the observed server lane and the sum of all queues in front of all server lanes) were larger (r > 0.6) than the two more remote queues, which gave r = 0.4 to 0.5. Thus the speculations of Schwartz (1978) and Green and Kolesar (1987) are confirmed. It is further
Figure 4: Relative Laptop Service TIme versus Relative Mean Queue 3 Length
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Figure 5a: Scaled Probability of Threat Detection versus Search TUne
observed that servers speed up in apparent response to the buildup at the most immediately This speed-up could be visible queues. considered as a prediction of Parkinson's law, i.e., slowing down when the workload is low, or an inverse of this, i.e., speeding-up when workload is high.
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TSA's screeners are instructed to take sufficient time to inspect each item, i.e., not to speed up. One explanation of server speed up on laptop computers is that laptops are relatively simple to process as their internal layout is more predictable than that of other items such as carry-on bags. This may be why they are the type of item where speed-up is possible without the screener perceiving that screening effectiveness would suffer.
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We explore the impact of this speed-up in screening further by examining the speedaccuracy trade-off (SATO) in inspection tasks. As noted by Drury (1973, 1994), such a SATO effect has been found for many inspection tasks in different industries. Recently, Ghylin, Drury and Schwaninger (2006) used existing data on screener performance to measure the same effect for carry-on bag screening. Their results are summarized in Figures 5a and 5b. The data have again been scaled (to protect security data), but using the same scaling factor as Figure 4 so that service times can be compared directly. Figure 5a shows the scaled detection probability with search time for carry-on bags. Figure 5b shows the scaled probability of correct rejection with stopping time for carryon bags. Finally, Figure 5c shows the range of speedup effects from laptops as discovered in this study. From the range of speedup predicted in Figure 5c we can use Figures 5a and 5b to recognize that for laptop passengers there is a significant decrease in the detection probability and in the probability of correct rejection. In an airport setting where many of the of travelers carry laptops as part of their carry-on items, the effect on probability of detection appears to be significant. Such a situation is likely to arise at airports that have high business traveler traffic.
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Figure 5: Cumulative distributions of search times and stopping times (from Ghylin et al, 2007) shown in 5a and 5b on same time scale as the range of the speed-up effect on laptops from the current study (5c).
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Acknowledgernenrs This work is supported by a grant from the National Science Foundation; grant number DMI-0500241. We also wish to acknowledge the generous assistance of the Transportation Security Administration at the airport used in this study. For security reasons they wish to remam anonymous.
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Bios and Pictures Clara V; Marin is a PhD student in Human Factors Engineering, GPA (4.0/4.0), in the Department of Industrial and Systems Engineering, State University of New York at Buffalo. Since 2004 she has been working as a Research Assistant at the Research Institute for Safety and Security in Transportation (RISST) in important projects funded by FAA and TSA. She has four publications in topics such as Ergonomics, Aviation Maintenance Safety and Aviation Security. She is an active member of the Human Factors and Ergonomics Society. She is working in her dissertation related to emergency responders' decision making in the disaster domain.
Colin G. Drury is SUNY Distinguished Professor and Director of Research Institute for Safety and Security in Transportation (RISST) in the Department of Industrial and Systems Engineering at the State University of New York at Buffalo, where his work is concentrated on the application of human factors techniques to inspection and maintenance processes. Since 1989 he has been leading a team applying human factors techniques to reduce errors in aviation maintenance and inspection at RISST. He has over 200 publications on topics in industrial process control, quality control, aviation maintenance, security and safety. He is a Fellow of the Institute of Industrial Engineers, the Ergonomics Society, the International Ergonomics Association and the Human Factors & Ergonomics Society, receiving the Bartlett medal of the Ergonomics Society and both the Fitts and Lauer Awards of the Human Factors Ergonomics Society. In 2005 he received that FAA's Excellence in Aviation Research award.
Rajan Batta is Professor of Industrial and Systems Engineering at the State University of New York at Buffalo, where he has been a faculty member since he obtained his Ph.D. in 1984 from the Massachusetts Institute of Technology. Dr. Batta's research interests are in the area of the applications of Operations Research techniques to problems in military and homeland security applications. He is a recipient of the SUNY Chancellor's Award for Excellence in Teaching (2007), the SUNY Research Foundation Award for Research and Scholarship (2006), the UB Sustained Scholar Award (2002), the Best Paper Award for the Journal Military Operations Research (2004), and the Fellow Award from the Institute of Industrial Engineers (2006). He serves as a Departmental Editor for lIE Transactions: Scheduling & Logistics, and is a member of the Editorial Advisory Boards of the journals Computers & Operations Research and the International Journal of Operational Research.
Li Lin is Professor of Industrial and Systems Engineering at University State University of New York at Buffalo, where he has been a faculty member since he obtained his Ph.D. in 1989 from Arizona State University. Dr. Lin's research areas include manufacturing and healthcare systems simulation and concurrent engineering and design, including environmentally conscious design and manufacturing. Dr. Lin research has been supported by the National Science Foundation (NSF), the Environmental Protection Agency (EPA) and the Agency for Healthcare Research and Quality (AHRQ). He has also worked extensively with many manufacturing companies and healthcare organizations in helping them improve operational efficiency, cost and quality of products and services.
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