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Advances in information technology are making the close integration of vehicles and the infrastructure in the surface-transportation system feasible. In particular, one approach to this integration receiving significant attention and effort in the United States is referred to as vehicle-infrastructure integration (VII). While few would argue that VII possesses the potential to support applications that improve traffic safety and mobility, the level of benefits, as well as strengths/weaknesses of various design alternatives, cannot be effectively assessed without the availability of an appropriate simulation environment. The purpose of this research is to develop a simulator that can fully model the functional aspects of VII deployment. This paper identifies key requirements for development of the simulation architecture, and proposes an architecture for a VII simulation system after identifying several alternatives. The proposed architecture components include a commercial off-the-shelf (COTS) traffic simulator, database, VII interface module, and multiple applications. Finally, the paper presents a prototype application of the architecture to explore the computing efficacy of the architecture. The results indicate that simulation run times are linear with respect to the number of COTS traffic simulator interruptions required to extract vehicular data, and that database access can increase computational requirements at a ratio of 14:1--reasonable for analytical purposes. DOI: 10.1061/(ASCE)0887-3801(2007)21:6(434) CE Database subject headings: Integrated systems; Intelligent transportation systems; Simulation; Architecture; Vehicles; Infrastructure.

This paper presents a new problem-solving approach, termed Simulation-based Policy Generation (SPG), that is able to generate solutions to problems that may otherwise be computationally intractable. When trying to optimize large-scale sequential stochastic problems, it is often easier to simulate the system under consideration and then to perform some type of simulation/optimization procedure, such as pseudorandom search or a response surface methodology. The SPG method builds on this idea by using a simulation of the original problem to create an approximating Markov decision process (MDP) model which is then solved via traditional MDP solution approaches. Since this approximating MDP is a fairly rich and robust sequential optimization model, solution policies can be created which represent an intelligent and structured search of the policy space. An important feature of the SPG approach is its adaptive nature, in that it uses the original simulation model to generate improved aggregation schemes, allowing the approach to be applied in situations where the underlying problem structure is largely unknown. In order to illustrate the performance of the SPG methodology, we apply it to a common but computationally complex problem of inventory control, and we briefly discuss its application to a large-scale telephone network routing problem. Index Terms--Markov decision processes (MDPs), simulation, state-space aggregation, value iteration.

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 IT'S A sound regional architecture is needed to guide engineers in fitting individual systems into a regional context. In addition, individual systems must possess documented, publicly available interfaces. This paper addresses these aspects of computing and provides an example of developing integrated regional ITS.

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 management systems; however, the data collection for repair and maintenance history can be improved in order to build more accurate and complete MDP-based models.

The journal Systems has made updates to its sections to align with developments in the research community. A new section called "Artificial Intelligence and Digital Systems Engineering" has been added, which combines AI and digital technologies with systems engineering to create more efficient and optimized systems. Other sections have been combined, such as "Systems Design" being merged into the new section, and "Systems Practice in Social Science" and "Systems Practice in Natural Science" being combined into one section. The updated sections in the Systems journal now include Systems Theory and Methodology, Artificial Intelligence and Digital Systems Engineering, Systems Practice in Social Science, Systems Engineering, Supply Chain Management, and Complex Systems and Cybernetics. The journal encourages submissions in these fields to continue its progress. [Extracted from the article]

Byline: Susan K. Donohue, William T. Scherer, K. Preston White Jr We discuss the practical, professional education in information systems and operations management available to undergraduate systems engineers at the University of Virginia through participation in relevant Capstone projects in this paper. Thus, its perspective is more experiential than theoretical. The structure and administration of the Department of Systems and Information Engineering's award-winning Capstone programme, a unique model at the time of its inception, are presented as an overall introduction to the programme. The model's robustness is demonstrated both in its longevity and adoption by other institutions. Case studies of Capstone projects conducted during academic year 2002-2003 provide examples of the types of educational experiences students can elect to receive in information systems and operations management. We end with reflections on the lessons learned through participation in our Department's Capstone. Throughout, the goal is to provide sufficient information to allow those interested to adopt similar practices.

Byline: Douglas N. Fuller, William T. Scherer, Thomas A. Pomroy The healthcare industry currently accounts for more than 12% of the US Gross Domestic Product (GDP). The industry is struggling with a shift from the 'fee for service' compensation model prevalent during the 20th Century to a delivery model based on some form of 'managed care'. Efforts at implementing capitation rate contracts with physicians have been significantly hampered by the inability to accurately forecast and adjust for risk differences among various practice populations. We approach the problem within the framework of state-based modelling to develop state definitions that represent homogeneous sub-populations. We use the state definitions to develop a First Order Markov Model for healthcare expense prediction and compare performance to the current state of the art expense prediction models on actual data from a family practice in Calgary.