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Electronic markets and automated trading have resulted in a drastic increase in the quantity and complexity of regulatory data. Reconstructing the limit order book and analyzing order flow is an emerging challenge for financial regulators. New order types, intra-market behavior and other exchange functionality further complicate the task of understanding market behavior at multiple levels. Data visualizations have proven to be a fundamental tool for building intuition and enabling exploratory data analysis in many fields. In this paper, we propose the incorporation of visualizations in the workflow of multiple financial regulatory roles, including market surveillance, enforcement, and supporting academic research.

Identifying and understanding the impact of algorithmic trading on financial markets has become a critical issue for market operators and regulators. Advanced data feed and audit trail information from market operators now make the full observation of market participants' actions possible. A key question is the extent to which it is possible to understand and characterize the behavior of individual participants from observations of trading actions. In this paper, we consider the basic problems of categorizing and recognizing traders (or, equivalently, trading algorithms) on the basis observed limit orders. Our approach, which is based on inverse reinforcement learning (IRL), is to model trading decisions as a Markov decision process and then use observations of an optimal decision policy to find the reward function. The approach strikes a balance between two desirable features in that it captures key empirical properties of order book dynamics and yet remains computationally tractable. Making use of a real-world data set from the E-Mini futures contract, we compare two principal IRL variants, linear IRL and Gaussian process IRL. Results suggest that IRL-based feature spaces support accurate classification and meaningful clustering.

Videology is an online video ad serving company. For Videology, browser cookies are an important tool that allows them to gather information about users in order to better target advertisements to relevant consumers. However, Videology is concerned that increased disabling or deletion of cookies by internet users will adversely affect their ability to accurately identify user demographics and therefore hurt their business. We will investigate whether cookie deletion is a problem and seek an alternative technology that could possibly replace browser cookies. To investigate the extent of cookie deletion, we integrated sample Videology and Experian data sets for online user traffic. Since unique users identified by Videology represent individual online users, and Experian labels identify individual households, we used this data to project the average number of Internet users identified per household. When compared to the average number of persons per household, it became clear that cookie deletion did not significantly inflate the number of identified users per household, which signifies that cookie deletion is not as prevalent an issue as previously thought. Additionally, we investigated alternative techniques for identifying Internet users, focusing particularly on device fingerprinting and examining how it could be adopted by Videology. At the time of project completion, we expect to have determined the impact of cookie deletion on Videology's business. We will also evaluate alternative identification technologies that Videology may pursue in an effort to mitigate the negative impact of cookie deletion.

Today the advertising industry is becoming increasingly dependent on the Internet to deliver advertisements to viewers. Third party advertising networks such as Advertising.com, a division of AOL Inc., are utilizing targeted advertising strategies to make Internet advertising more profitable. Targeted advertising utilizes cookies to track users and target them with advertisements based on attributes such as site history and past advertisements viewed. Chad Gallagher, the Mobile Team lead for Advertising.com, projects that mobile Internet usage will surpass computer Internet usage by 2015. This major shift will render the traditional cookie based targeting model obsolete, as the majority of mobile devices do not store cookies. Consequently, the development of new mobile targeting strategies has become a top priority for companies such as Advertising.com in order to increase the profitability of online advertising. Predictive segments power the Internet advertising targeting strategy, but with cookies no longer available new variables need to be used for the predictive segments aimed at mobile users. This analysis seeks to determine the predictive power of the information unique to mobile users, in particular cellular provider and model of phone. This analysis was conducted utilizing data from two telecom companies' advertising campaigns. The findings indicate that for telecom advertising, service provider and model of phone are statistically significant predictors of a consumer's likelihood to convert. From these findings, the authors of this paper recommend the incorporation of mobile variables into predictive segments as they provide significant insight into consumer patterns. With the addition of the team's work, Advertising.com will be able to boost their revenue per thousand impressions (RPM) from their mobile Internet traffic, which is currently their least valuable but most rapidly growing business segment. Future researchers should analyze the significance of these variables on advertising for nontelecom products, but as telecom companies are a major driver of mobile advertising, their campaigns proved to be a logical starting point.

Numerous transportation analysis and modeling tools have been developed over the last 30 years, and the data collection for these models was a personnel-intensive process requiring significant resources. As a result, there typically were insufficient amounts of data to verify transportation models under diverse operating conditions. However, with the proliferation of sensors to support intelligent transportation systems, researchers now find themselves in a data glut and an information shortage. With this data overload, there is a need to shift the modeling focus toward a state-based modeling approach. Fundamental to this is the need to explore the question of what constitutes a parsimonious state definition that effectively captures the dynamics of a large-scale, regional transportation system. Discussed are a proposed framework for a state-based modeling paradigm and, specifically, two key concepts to this new approach: case-based reasoning and simulation for model generation.

Intelligent transportation systems (ITS) include large numbers of sensors that collect enormous quantities of traffic-flow data. The data provided by ITS are necessary for advanced forms of control. However, more basic forms of control, such as time-of-day (TOD) signal-timing plans that are widely used in the United States, do not directly rely on the data. Thus, sensor data often are unused and discarded. The sensor data, however, can provide large amounts of information that can aid in the development of improved TOD signal-timing plans. Data-mining tools are necessary to extract information from the data necessary to improve timing-plan development and to allow the timing-plan development and monitoring processes to be automated. A research effort that investigates the application of data-mining tools, including statistical clustering and classification techniques, to aid in the development of traffic-signal-timing plans is described. A case study was conducted that illustrated that hierarchical cluster analysis can be used to identify temporal interval breakpoints that support the design of a TOD signal-control system. The cluster analysis approach was able to use a high-resolution system state definition that takes full advantage of the extensive set of sensors deployed in a traffic signal system. Finally, the case study also demonstrated that a classification and regression tree could be developed that can be used to monitor automatically the quality of TOD intervals as traffic conditions change. The results of this research indicate that advanced data-mining techniques have great potential to provide automated techniques that assist traffic engineers in signal-control-system design and operations.

The challenge of integrating multiple computer applications and systems is common in civil engineering. For example, civil engineers are working to integrate geographic information systems, database management systems, and hydraulic models for wastewater planning and design. This challenge also faces transportation engineers. Control systems, such as traffic signal systems and freeway management systems, play a central role in the intelligent transportation systems (ITS) program. While such systems provide benefit individually, their full benefit is not achieved until they are integrated regionally. Regional integration enables the systems to share information, allowing officials to manage the entire regional transportation system. Planning, designing, and developing integrated systems call for engineers to make sound computing decisions. The two critical areas in these decisions are the systems architecture and open systems design. For example, in ITS a sound regional architecture is needed to guide engineers in fitting individual systems into a regional context. In addition, individual system must possess documented, publicly available interfaces. This paper addresses these aspects of computing and provides an example of developing integrated regional ITS.

The development of individual intelligent transportation systems (ITS) that operate well independently is not sufficient to meet the growing transportation needs of urban areas. To realize the true potential of ITS, systems such as freeway management systems, transit scheduling and dispatch systems, signal control systems, emergency computer-aided dispatch systems, and even construction scheduling systems must be integrated to allow for coordinated regional decision making. The formidable task of realizing integrated ITS (IITS) must often be accomplished by various municipal systems, most with little or no automation, and several organizational bodies that attempt to coordinate these efforts into a comprehensive regional network. Each of these organizations and systems has its own set of objectives, and these objectives may be noncommensurate and conflicting. Regional integration allows the systems to share information, allowing transportation officials to manage the entire regional transportation system. A sound regional architecture must be developed to guide developers in fitting their individual systems into a regional context. An overview of a vision of IITS is presented, along with an example of an integrated system proposed for the Hampton Roads region of Virginia. The vision is based on a critical analysis of five principal design areas: regional architecture, open systems, the World Wide Web and extranets, data warehousing, and data mining.

Using an agent-based model of the limit order book, we explore how the levels of information available to participants, exchanges, and regulators can be used to improve our understanding of the stability and resiliency of a market. Ultimately, we want to know if electronic market data contains previously undetected information that could allow us to better assess market stability. Using data produced in the controlled environment of an agent-based model’s limit order book, we examine various resiliency indicators to determine their predictive capabilities. Most of the types of data created have traditionally been available either publicly or on a restricted basis to regulators and exchanges, but other types have never been collected. We confirmed our findings using actual order flow data with user identifications included from the CME (Chicago Mercantile Exchange) and New York Mercantile Exchange (NYMEX). Our findings strongly suggest that high-fidelity microstructure data in combination with price data can be used to define stability indicators capable of reliably signaling a high likelihood for an imminent flash crash event about one minute before it occurs.

SUMMARY A key aspect of design considers the effects of large groups of design variables on multiple measures of system performance or responses. Thus, a goal of design-aiding systems is to learn the contributions of each design variable to the response variables. This problem is particularly difficult when simulation runs are expensive in either time or money because conducting exhaustive searches over the design space is not possible. This situation occurs quite frequently for large-scale design problems where the number of design variables is large and their relationships to the response variables are not well understood. In this paper, we present a polynomial induction network (PIN) methodology that will support a designer in problems of this sort, where design variables are numerous and simulations are expensive. Our description shows how this approach was implemented in software and used to actually design a multi-processor system. Based on this experience, we conclude that PINs have excellent promi...

Videology is an online advertising company with a targeted advertising platform that more efficiently connects brands with target consumers. By obtaining and utilizing user data, advertisers have targeted specific groups through a process known as behavioral targeting. This process increases the odds that a user will click on an advertisement and reduces the odds that a customer will encounter irrelevant advertisements. While Videology has grown their market share in this space, inefficient forecasts have cost several hundred thousand dollars in lost opportunity costs. This paper addresses this problem by leveraging a systems engineering approach to suggest procedures for optimizing the validity and performance of two forecast variables for Videology. The paper first analyzes a volume forecast variable: the expected number of visitors, and second, a frequency forecast variable: the number of times a visitor comes back to the same website. Research used existing data to construct a pseudo-process to replicate Videology's algorithm in order to test the validity of and make enhancements to forecasts. Videology will then utilize findings from this process to continue forecast improvements.

The number of mobile-connected devices has been growing at a tremendous rate in recent years. These increasingly powerful tablets and smartphones are portable and personal, giving advertisers the potential to reach consumers on a one-on-one basis with personalized advertisements based on location, recent behaviors, and much more. A substantial difference between mobile media usage and mobile advertising spending suggests a significant growth opportunity in the mobile advertising market. Our work involves improving the decision-making technology used by Advertising.com, a large online advertising network, as it attempts to increase its presence in the mobile advertising market. We examined the factors that differentiate mobile consumers in order to target them more effectively, and drive the direction of Advertising.com's future mobile optimization technology development. Data was available to us from Advertising.com's back-end database, as well as in its front-end campaign reporting system. To investigate this data and determine the most valuable mobile variables, we performed data analysis utilizing tools including Microsoft Excel, R, SQL, and Minitab. We also leveraged Advertising.com's existing decision algorithm, AdLearn, as well as looked to existing mobile advertising studies. Our analyses indicate several factors are influential in the effectiveness of mobile advertisements including hour of day, day of week and device type. We found that mobile campaigns perform best during the morning hours and late at night in terms of both impressions and conversions. Also, we found that weekends have statistically superior conversion rates.

The technological advancement of financial markets has allowed for trade to move to being nearly entirely electronic, with the majority of trades placed by automated participants. This has resulted in drastic increases in the quantity and complexity of regulatory data. The processing and analyzing of these markets and participants have emerged as a serious challenge for financial regulators. New order types, intra-market behavior and other exchange functionality further complicate the task of understanding market behavior and events. Data visualizations have proven to be a fundamental tool for building intuition and enabling exploratory data analysis in many fields. In this paper, we propose the incorporation of visualizations in the workflow of multiple financial regulatory roles, including market surveillance, enforcement, and academic research.

In this paper, we propose the use of cases and instructional modules to teach invention, engineering design, and elements of technology management. One way to learn is to study and reflect upon the experience of others. Such experience may be captured in a case. Cases promote active learning by requiring students to assume the roles of participants in the decision making process. Cases are also a vehicle for raising business issues and human resources concerns not usually considered in traditional engineering courses. Real world design and engineering involves risk and uncertainty, tradeoffs and priorities, ethical issues, human elements, and impact assessment. Cases expose students to open ended, ill defined problems whose solution often depends on making assessments, judgments, and decisions about the technical competencies of the organization.

Videology is an online video advertising, optimization, and yield management solutions provider whose business strategy is to purchase online video advertising inventory from content providers and deliver ads to their clients using behavioral and demographic targeting to maximize value. The Capstone team used a diverse set of analytical tools, including Time Series models, Principal Component analysis, Decision Tree Optimization, Generalized Linear Models, K-Means clustering, and linear programming to improve efficiency in the online video advertisement space by connecting consumers with advertisements on which they are most likely to click. Trends, patterns, and anomalies in the data may lead to new targeting opportunities for Videology.

We propose a zero-intelligence agent-based model of the E-Mini S&P 500 futures market, which allows for a close examination of the market microstructure. Several classes of agents are characterized by their order speed and order placement within the limit order book. These agents' orders populate the simulated market in a way consistent with real world participation rates. By modeling separate trading classes the simulation is able to capture interactions between classes, which are essential to recreating market phenomenon. The simulated market is validated against empirically observed characteristics of price returns and volatility. We therefore conclude that our agent based simulation model can accurately capture the key characteristics of the nearest months E-Mini S&P 500 futures market. Additionally, to illustrate the applicability of the simulation, experiments were run, which confirm the leading hypothesis for the cause of the May 6th 2010 Flash Crash.

Electronic markets have emerged as popular venues for the trading of a wide variety of financial assets, and computer based algorithmic trading has also asserted itself as a dominant force in financial markets across the world. Identifying and understanding the impact of algorithmic trading on financial markets has become a critical issue for market operators and regulators. We propose to characterize traders' behavior in terms of the reward functions most likely to have given rise to the observed trading actions. Our approach is to model trading decisions as a Markov Decision Process (MDP), and use observations of an optimal decision policy to find the reward function. This is known as Inverse Reinforcement Learning (IRL), and a variety of approaches for this problem are known. Our IRL-based approach to characterizing trader behavior strikes a balance between two desirable features in that it captures key empirical properties of order book dynamics and yet remains computationally tractable. Using an IRL algorithm based on linear programming, we are able to achieve more than 90% classification accuracy in distinguishing High Frequency Trading from other trading strategies in experiments on a simulated E-Mini S&P 500 futures market. The results of these empirical tests suggest that High Frequency Trading strategies can be accurately identified and profiled based on observations of individual trading actions.

The advent of electronic financial markets and associated technologies has dramatically improved the trading functions that are available to market participants in terms of speed, capacity and sophistication. Advanced data feed and audit trail information from market operators also make the full observation of market participants’ behavior possible. The primary objective of this study is to model algorithmic trading behavior using Bayesian inference under the framework of inverse reinforcement learning (IRL). We model trader behavior as a Gaussian process in the reward space. With incomplete observations of different market participants, we aim to recover the optimal policies and the corresponding reward functions to explain trader behaviors under different circumstances. We show that algorithmic trading behavior can be accurately identified using the Gaussian Process Inverse Reinforcement Learning (GPIRL) algorithm developed by Qiao and Beling (2011), and that this algorithm is superior to the linear features maximization approach. Real market data experiments using the GPIRL model consistently give more than 95% trader identification accuracy using a classification method based on support vector machines (SVM). We also show that there is a clear connection between the existing summary statisticbased trader classification proposed by Kirilenko etc. (2011) and our behavior-based classification. To address the potential change in trading behavior over time, we propose a score-based classification approach to address variations of algorithmic trading behavior under different market conditions. We further argue that because our behavior-based identification is a better reflection of traders’ actions and value propositions under different market conditions than the summary statistic-based method, it is therefore more informative and robust than the summary statistic-based approach, and is well suited for discovering new behavior patterns of market participants.

An agent-based model (ABM) has a structure, which includes a set of agents, a topology and an environment. A simplified conception of a financial market includes a set of market participants, a trading mechanism, and a set of securities. In a typical ABM of a financial market, the market participants are agents, the market mechanism is the topology and the exogenous flow of information into the market is the environment. A zero-intelligence ABM model of the E-Mini Futures Market is presented. Several classes of agents are characterized by their speed and placement of orders within the limit order book. The proposed minimum quote life rule is implemented in the simulation. The minimum quote life rule prevents new orders from being cancelled or modified before a given time limit. Through experimentation, trade-off curves are generated. Thereby, illustrating the usefulness of this ABM and its ability to inform ongoing financial policy debates.

The typical infrastructure maintenance decision‐making environment involves multiple objectives and uncertainty, and is dynamic. One of the most commonly used infrastructure models is a Markov decision process (MDP). MDP models have been applied to numerous sequential decision‐making situations involving uncertainty and multiple objectives, including applications related to infrastructure problems. In this paper we explore the use of Markov models for bridge management systems. In particular, we explore two critical issues associated with the use of MDP models. The first involves state‐space explosion, one of the most common problems with MDP models. We address the issues of state‐space cardinality and present approaches for dealing with the complexity. The second issue with MDP models is the compliance with the Markovian property. With both issues we use the Virginia bridge system and data to illustrate the concepts. Our research indicates that MDPs are a powerful and useful technique for bridge manageme...

We propose a near zero-intelligence agent-based model of the E-Mini S&P 500 futures market that allows for a close examination of market microstructure in the context of a flash crash. Several classes of agents are characterized by how fast they trade and where they place trades in the limit order book. These agents’ orders populate the simulated market in a way consistent with real world participation rates. The simulated market is validated against important empirically observed characteristics of price returns and volatility. Additionally, to illustrate the applicability of the simulation experimental results are present, which examine the leading hypothesis for the cause of the May 6th 2010 Flash Crash.

The Hard Rock Hotel & Casino (HRHC) in Las Vegas, NV hopes to implement customer relationship management (CRM) strategies to increase overall customer value. This paper discusses our process of analyzing historical customer data and creating incentives tests in order to create accurate and useful customer segments. These customer segments can be used to target customers based on their interests, increasing both revenue and the customer's satisfaction.

We apply systems thinking to the background, complexity and weaknesses of liquid biofuel lifecycle assessments and propose a new way of approaching the problem. The history of corn-based ethanol in North America is illustrative of the problematic interdependencies that can arise when decisions about liquid biofuel are made without comprehensive systems analysis [17]. At the direction of Congress [25], the EPA has proposed new standards for renewable fuels to be based on lifecycle assessment (LCA) methodology [6]. At the same time, the European Commission explicitly dictates the use of LCA for biofuel in their Renewable Energy Sources Directive [8]. Likewise, the United Nations Environment Program (UNEP) has directed that biofuel assessment be made based on LCA [1]. Recent lifecycle assessments of complex biofuel systems follow standard methodology such as EPA guidelines and ISO14040 standards, yet there are wide variations in the results. Studies conducted on the same liquid biofuel system reveal diverse and even conflicting results, some showing a net greenhouse gas (GHG) reduction, while others show a net increase for the same biofuel system [16], [18]. The results from these studies, therefore, cannot be considered viable for quantitative system impact comparisons. [While there are solid and gaseous biofuels, this paper focuses on liquid biofuel for simplicity and because of a strong economic need for liquid transportation fuels. The conclusions would apply to all three modes of biofuel.] We present an overview of the complexity of different liquid biofuel system processes and relate this complexity to the principal deficiencies in current biofuel LCA methodology: inconsistencies in LCA system boundaries, system boundaries that exclude relevant sub-processes, elements and environmental factors, a lack of standardized units and the reliance of commonly used LCA tools on industry average data without regard for relevant impact. We conclude that improved methodologies of determining system boundaries based on a comprehensive system impact assessment are necessary to adequately assess biofuels and their environmental effect. We propose work on a new approach based on an integrated hybrid assessment with unitized impact metrics (Renewable System Impact Rating - RSIR). This work will also lead to viable system impact optimization, permitting engineers and farmers to maximize the net energy balance of a biofuel system, while minimizing negative impacts on the environment and on world food supplies.

We describe the senior capstone design course in Systems Engineering at the University of Virginia. The capstone course is a major design experience for the students that involves the students in real design problems while working with real clients in order to expose the students to the practice of systems engineering. Working in groups with five or six members, the students apply the systems engineering aspects of systems and decision science, systems management and systems methodology toward open-ended design problems. Our experience with the program for the first three years has led to considerable success, with numerous capstone groups receiving commendations from clients and the Department of Systems Engineering receiving ABET's first Award for Educational Innovation.

Information analytics, wherein data and information are leveraged to improve efficiency and gain a competitive advantage, has great potential to revolutionize numerous industries. Internet-based businesses have particular potential in this arena due to the large amount of data available on consumer Internet usage. This paper explores opportunities to apply information analytics for growth in five major industries: Internet yellow pages, elderly services, advertising, Internet health sites, and Internet television. These opportunities will improve the effectiveness with which services are delivered to consumers, thereby making businesses in these industries more competitive.

The military value assessment conducted as part of the Army portion of the Base Realignment and Closure (BRAC) 2005 report provides a robust and realistic data set for demonstrating the usefulness and validity of SISMAUT. SISMAUT is a software tool built in Excel-VBA that implements the iterative methodology developed for the Imprecisely Specified Multi-Attribute Utility Theory (ISMAUT) with a simulation foundation that is both tractable for the general user and less brittle than the original linear programming underpinnings. Many decision analysis techniques require cognitive gymnastics of decision makers that do not take advantage of the innate judgment abilities of human beings, the gut feelings that many decision makers depend on and trust. ISMAUT attempts to harness the intuition of decision makers by allowing for behaviorally relevant preference statements that are translated into mathematical dominance relationships. These relationships drive the decision aiding technique that informs decision makers as to the implications of the preferences. Also, most methods in decision analysis result in an array of attribute weights that result in utility scores and total rankings of all alternatives, which may be more information than the decision maker requires or wants. A partial ordering of alternatives, based on dominance resulting from some limited attribute preferences, can provide a decision maker a breakdown of the best, the average, and the worst alternatives without a complete ranking which may be more difficult to break into such portions.

Five University of Virginia Systems Engineering students worked in partnership with Science Applications International Corporation (SAIC) and the Federal Highway Administration (FHWA) to develop a handbook detailing agency benefits from regional transportation collaboration. The UVa students were responsible for devising site selection metrics and tangible benefits metrics, applying the metrics to rank and select a list of sites, interviewing these sites, and finally presenting the data for the eventual incorporation into a handbook. This report details the methodology used in selecting the interview sites and the creation of the tangible benefits and preliminary interview strategy.

Ground stations receive data from satellites for processing and storage. The increasing amount of incoming satellite data and increases in the demand for the data are causing an excessive load on the system. Changes to the current data archiving architecture of ground stations may be necessary to continue providing data efficiently to users. A process model and simulation of the current system could reveal bottlenecks and constraints. The objective of this paper is to demonstrate whether process modeling is a useful method for analyzing data archiving systems within ground stations. This paper outlines the methods applied during the model development process and examines the overall effectiveness of process modeling in this capacity. The architecture of NASA's Goddard Distributed Active Archive Center (DAAC) was used as a basis for developing the process model, which demonstrates the value of such a method for analyzing data archiving systems. The value of this particular model would be improved with a formal empirical data collection for model validation.

We investigated how online advertising could be made more receptive to Internet users' needs, thereby improving the efficacy. Only three to five viewers in a thousand click on a given online banner advertisement. To improve the low response rate, marketers need to reach the right audience, which will yield a higher return on advertising dollars by eliminating wasted ads and maximizing campaign effectiveness. Users will see relevant ads based on their preferences and fewer ads that do not interest them. We identified and modeled user behavior and characterized a subpopulation of users to help predict advertising response. We then developed an exploratory optimization and targeting technology for use by Advertising.com by serving users ads probabilistically on the basis of their online behaviors. After preliminary algorithm validation, the increase in early user clicks indicates the potential effectiveness of these models in improving the online advertising response rate. The resultant potential increase in action could lead to increased revenue.

When making important decisions, most companies and organizations require the input of several individuals in order to generate new ideas and make judgments. However, when the complexities of a group decision problem exceed normal decision making approaches, a more formal collaborative process and decision support environment is required. The primary goal of this project is to develop a group decision support system (GDSS) recommendation for the aerospace corporation that allow users to generate ideas electronically, make quantitative judgments, and identify the group's preferred outcome(s) using robust decision analysis and facilitated group collaboration methods. This paper outlines the overall GDSS recommendation and supporting objectives that were met, including the completion of an industrywide software trade survey, a ''rate and weight" decision model of prospective software solutions, and an examination of group collaboration techniques and GDSS facility designs.

In an effort to stimulate greater emphasis on Transportation Systems Management & Operations (TSM&O), the Federal Highway Administration (FHWA) is sponsoring projects in three metropolitan areas to demonstrate development and implementation of a regional concept of transportation operations (RCTO). The RCTO requires regional collaboration of multiple entities - local and state governments, departments of transportation, police and rescue squads, etc. n order to accomplish various objectives that are beneficial to regional transportation operators and transportation system users in the metropolitan areas. The University of Virginia, working as part of a team led by Science Applications International Corporation (SAIC), is supporting the FHWA by assisting in evaluating the success of these demonstration projects and providing assistance to the demonstration project participants as they seek to develop their RCTO through regional collaboration and coordination. More specifically, the team assisted FHWA by reviewing and commenting on demonstration applications and in designing and developing metrics and a score-card to assess the extent to which the demonstration projects achieve their objectives.

Modeling collateral damage has challenged military simulation officers for some time. Past equations-based models calculate attrition of forces and battlefield effects but do not model complex environments [Delaney 2003]. Thus they do not approximate the interactive nature of crowds, information operations, communications infrastructure effects on battlefield performance, and long-term attitudes towards military operations. This effort demonstrates that the U.S. Air Force's agent-based systems effectiveness analysis simulation (SEAS) tool can model collateral damage. By creating and testing two distinct scenarios under different rules of engagement, a tradeoff between collateral damage and mission effectiveness was demonstrated. Future research includes modifying SEAS to better model urban conflict and dynamically updating agent behavior

The purpose of this research was to apply formal human-computer interaction (HCI) analysis techniques to investigate visualization in transportation management and control systems, specifically traffic information websites provided to the general public in major U.S. cities. HCI is a discipline concerned with the design, evaluation and implementation of interactive computing systems for human use. This effort employed formal HCI system usability testing of current traffic information websites as a means to identify specific areas for improvement in transportation system status visualization. The testing results indicated that users preferred simple presentations of information, and, in many cases, were unable to effectively utilize more advanced information technology-based visualization tools. In addition, users indicated a strong preference to receive information on abnormal traffic conditions. This category of information was not readily available on the majority of traffic websites examined by the study team. Finally, it is recommended that developers of new transportation condition visualization tools use formal HCI testing in the development stage to refine designs based on user preferences.

The authors present a tool for general purpose local optimization. Randomized combinatorial search heuristics such as simulated annealing (SA) are an effective means of exploring function space to find regions of high performance. Their ability to optimize functions once regions of high performance are found is limited. To improve, and accelerate, their local optimization capabilities, a perturbation operator that selectively perturbs the solution string was created. During the initial phases of optimization, the significance of each bit position is monitored. As the search continues, the probability of perturbations is shifted away from the most significant bits to the least. As the strength of techniques such as SA is achieved through their ability to sample a diverse set of hyperplanes, the perturbation probability is never allowed to decrease to zero for any bit position. This technique was tested on twelve problems, including problems in which the significance of each bit position was nonstatic through the search progression. The local optimization tool was also tested on K. DeJong's five-problem test set (1975). >

Current technology enables multifaceted federations to increase the availability of privileged information. Unfortunately, the availability of such information also significantly increases security risks. We present a system design for monitoring the security of an integrated public safety network called capital wireless integration network (CapWIN). CapWIN is a diverse mixture of public and private networks that share information in order to provide public safety and transportation services for metropolitan Washington, DC. It is imperative that the members of integrated systems such as CapWIN be aware of the security status of the other participants in order to anticipate security breaches and respond accordingly. As of now, there are few means by which the members of such systems can obtain real-time information pertaining to the security status of all other participating parties. The system presented here integrates these disparate agencies by utilizing existing security information available from each agency within the enterprise.

This project addresses a current need of The Aerospace Corporation, a federally-funded research and development (R&D) center, to make appropriate project selections on a yearly basis. The technology roadmap concept was integrated with a discrete sequential stochastic dynamic program to model the project selection problem. We have created a software package (SWORD): Simulation With Optimization for Research and Development to implement the proposed model. SWORD utilizes a combination of COTS (Commercial Off-The-Shelf) tools: Microsoft Project, Excel, Crystal Ball, and OptQuest to produce heuristic solutions to the NP complete problem. Included in the model are inputs, such as project cost and project probability of failure, that allow for the analysis of different R&D project selection strategies. The model will output a suggested R&D investment strategy for Aerospace for a variable number of years. Along with this, the model estimates the total utility, or total benefit, of pursuing that specific portfolio of projects along with numerous other performance statistics.

In this paper we explore the errors associated with an inappropriate Markov assumption or approximation. We examine problems where the Markovian property is known to be true for a certain level of augmentation and is then approximated by a reduced state space. The focus of this paper is on the structural characteristics of the Markov chain for the augmented problem and preliminary analysis of the error measurement. >

A methodology to support design synthesis is described which features intelligent search and generation of alternatives. The search is directed by degree of closeness of multiple responses to their known ideal and imprecise designer preferences. A key objective of the methodology is to minimize the number of probes of the design model which is assumed to be in the form of a simulation program. A multiprocessor system design problem with features representing typical design synthesis problems is presented to demonstrate application. >

Engine, aircraft, and environmental data collected during the operation of eight GE TF-39 engines installed in US Air Force C-5 Galaxy cargo aircraft were used in a two part program to demonstrate, test, and evaluate the capability of polynomial neural networks (PNNs) to predict maximum turbine inlet temperatures (Max TITs). Input parameters were measured before, during, and shortly after the takeoff throttle advance. The average absolute value of the prediction error for Max TITs averaging 787 degrees F was 8.5 degrees F based on as many as 13 input variables available immediately before the takeoff throttle advance. Better accuracy was then achieved by collecting data from one input variable (TIT) for several seconds after the takeoff throttle advance before making Max TIT predictions. These results, based on models derived from the performance of eight engines on two aircraft during a total of five flights, suggest the possibility of significantly improving aviation safety and reducing engine maintenance by enabling on-board engine monitoring equipment to notify aircrews of off-normal engine performance in sufficient time to avoid operating the engines at potentially destructive TITs. However, additional data for PNN training and evaluation is needed to develop models applicable to the entire C-5 fleet. >

First introduced in 1965, the concept of a "desirability function" has gained wide acceptance as a method of determining parameter settings to optimize a process with multiple output measurements. Unfortunately, little attention has been paid to the underlying assumptions of decision theory on which the procedure is based. This paper demonstrates critical issues that can arise from blind adherence to this procedure.

This paper presents a new approach to solving large-scale Markov decision processes (MDP) which uses simulation to generate empirical estimates of the parameters for an approximating MDP model. We begin with a brief description of this simulation for model generation algorithm and discuss some theoretical concerns regarding its implementation. We then discuss the application of the approach to a problem of choosing the optimal routes for calls within a telephone network. Although an MDP approach to this problem has been shown to be effective on small problems, no optimal procedure has yet been developed for solving this problem when the network has a nontrivial number of nodes. The ability of the algorithm to handle the complexity of such larger networks is demonstrated through some experimental results, and a comparison of its relative performance with respect to several existing solution methodologies is provided.