The present thesis aimed to investigate different traffic models and provide their analysis in context to traffic management decision support tools and evaluate the potential application of Dynameq software as a decision support system in Stockholm. A literature study, as well as a simulation case study, was used to meet the aim of the thesis. The main arguments for decision support systems were described in the context of traffic management and traffic control centres as a supportive tool for making informed decisions. The key features of the decision support system were identified as an advance knowledge-based system, inference system, or other AI system, and a simulation tool. In total, 18 traffic simulation tools were described and their applicability as decision support systems was assessed. Next, Dynameq traffic simulation software was examined both from the literature perspective and in context with a practical traffic simulation case study. The aim was to describe Dynameq software and its functionality in terms of a decision support system. Last, 5 incidents representing typical non-recurring congestion in forms of traffic incidents historically occurred during the Autumn 2019 were simulated and responses from Dynameq model were examined along with an investigation of a provided Stockholm traffic model in Dynameq. Limiting factors include the traffic data used, limited literature available for traffic software, and the quantity and quality of available incident data. The overall results were intended to provide answers to the formulated research questions. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

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The realization that the U.S. infrastructure is deteriorating and that there is a need to establish a strategy to prevent an infrastructure catastrophe have propelled the development of various infrastructure management systems. Often, the expansion of transportation facilities is regarded as a means to the improvement of the condition of transportation infrastructure. However, building more infrastructure than can be properly maintained causes serious deterioration of the existing infrastructure. Sustainable development from a highway management perspective can be equated with qualitative development, which improves the current condition of the highway system, rather than expanding its physical resources. The objective of this research is to develop a highway management strategy to help achieve sustainable development for the Commonwealth of Virginia. This research is performed by developing a transportation planning model for state highway management (TPMSHM) within the framework of a decision support system (DSS). The planning model consists of ten subsystems, including pavement and bridge management subsystems. These subsystems encompass various socioeconomic parameters that influence the physical status of highways. In the dynamic simulation model, these parameters are expressed in causal relationships using a system dynamics methodology. The types of trajectories for highway conditions that lead to sustainable development are provided. This research proposes a state-dependent prioritization strategy for calculating efficient budget shares by hierarchical levels of highway conditions. In this strategy, the proportions of the highway budget allocated to each level of management activity are determined by the physical conditions of the highways. Highways in the worst condition are given the first priority to receive the budget allocations. The model also addresses the policy of raising fuels tax to increase the state's transportation revenue. The adverse impact of a fuels tax increase is discussed in terms of revenue, the physical sufficiency of highways, and user benefits. The TPMSHM constitutes a leading component of the DSS and governs the building processes of other two components, which include a Data Base and a Display Base. A Data Base is constructed by listing all the parameters needed by the TPMSHM within a frame designed in terms of the records and fields of the parameters. A Display Base is demonstrated in a possible form using system dynamics' Powersim software. The graphical capability of representing the simulation results and the interactive user interface inherent in the software are examined. The emphasis of this research is placed on the development of the TPMSHM, which strives to manage the physical condition of the state highway system at an acceptable level through a state-dependent prioritization strategy to achieve sustainable development. / Ph. D.

This study presents a multi-attribute decision making (MADM) framework in support of strategic resource planning of electric utilities. Study efforts have focused on four technical issues identified to be essentially important to the process of strategic resource development, i.e., decision data expansion, MADM analysis with imprecise information, MADM analysis under uncertainty and screening applications. Main contributions from this study are summarized as follows. First, an automatic learning method is introduced for decision data expansion aiming at reducing the amount of computations involved in the creation of decision database. Test results have shown that the proposed method is feasible, easy to implement, and more accurate than the techniques available in the existing literature. Second, an interval-based MADM methodology is developed, which extends the traditional utility function model with the measure of composite utility variance, accounting for individual errors from inaccurate attribute measurements and inconsistent priority judgments. This enhanced decision approach would help the decision-maker (DM) gain insight into how the imprecise data may affect the choice toward the best solution and how a range of acceptable alternatives may be identified with certain confidence. Third, an integrated MADM framework is developed for multi-attribute planning under uncertainty which combines attractive features of utility function, tradeoff/risk analysis and analytical hierarchy process and thus provides a structured decision analysis platform accommodating both probabilistic evaluation approach and risk evaluation approach. Fourth, the application of screening models is investigated in the context of integrated resource planning of electric utilities as to identify cost effective demand-side options and robust generation expansion planning schemes. / Ph. D.

This research involves the development of a comprehensive decision support system for energy planning through the increased use of renewable energy sources, while still considering the role of existing electricity generating facilities. This dissertation focuses on energy planning at the regional level, with the Greater Southern Appalachian Mountain region chosen for analysis due to the dependence on coal as the largest source of generation and the availability of wind and solar resources within the region. The first stage of this planning utilizes a geographic information system (GIS) for the discovery of renewable energy sources. This GIS model analyzes not just the availability of wind and solar power based on resource strength, but also considers the geographic, topographic, regulatory, and other constraints that limit the use of these resources. The model determines potential wind and solar sites within the region based on these input constraints, and finally the model calculates the cost and generation characteristics for each site. The results of the GIS model are then input into the second section of the model framework which utilizes a multi-objective linear programming (MOLP) model to determine the optimal mix of new renewable energy sources and existing fossil fuel facilities. In addition to the potential wind and solar resources discovered in the GIS, the MOLP model considers the implementation of solid wood waste biomass for co-fire at coal plants. The model consists of two competing objectives, the minimization of annual generation cost and the minimization of annual greenhouse gas emissions, subject to constraints on electricity demand and capital investment, amongst others. The model uses the MiniMax function in order to find solutions that consider both of the objective functions. The third major section of this dissertation analyzes three potential public policies — renewable portfolio standard, carbon tax, and renewable energy production tax credit — that have been used to foster increased renewable energy usage. These policies require minor modifications to the MOLP model for implementation. The results of these policy cases are then analyzed to determine the impact that these policies have on generation cost and pollution emissions within the region. / Ph. D.

Optimal operation of water reclamation facilities (WRFs) is critical for an indirect potable reuse (IPR) system, especially when the reclaimed water constitutes a major portion of the reservoir's safe yield. It requires timely and informed decision-making in response to the fluctuating operational conditions, e.g., weather patterns, plant performance, water demand, etc. Advanced integrated modeling techniques can be used to develop reliable operational strategies to mitigate future risks associated with water quality without needing high levels of financial investment. The Upper Occoquan Service Authority (UOSA) WRF, located in northern Virginia, discharges nitrified reclaimed water directly into a tributary of the Occoquan Reservoir, one of the major water supply sources for Fairfax County. Among the many operational challenges at UOSA, one is to regulate the nitrate concentration in its reclaimed water based on the denitrifying capacity of the reservoir. This study presents an integrated model that is used to predict future reservoir conditions based on the weather and streamflow forecasts obtained from the Climate Forecast System and the National Water Model. The application captures the dynamic transformations of the pollutant loadings in the streams, withdrawals by the water treatment plant, WRF effluent flows, and plant operations to manage the WRF performance. It provides plant operators with useful feedback for correctly targeting the effluent nitrates using an intelligent process simulator called IViewOps. The platform is powered by URUNME, a new software that fully automates the operation of the reservoir and process models integrating forecasting products, and data sources. URUNME was developed in C#.NET to provide out-of-the-box functionality for model coupling, data storage, analysis, visualization, scenario management, and decision support systems. The software automatically runs the entire integrated model and outputs data on user-friendly dashboards, displaying historical and forecasting trends, on a periodic basis. This decision support system can provide stakeholders with a holistic view for the design, planning, risk assessments, and potential improvements in various components of the water supply chain, not just for the Occoquan but for any reservoir augmentation type IPR system. / Doctor of Philosophy / In an indirect potable reuse (IPR) system, reclaimed water from an advanced wastewater treatment facility is blended with a natural water source, such as a reservoir, to augment drinking water supply. Reliable operation of such a system is critical, especially when the reclaimed water constitutes a major portion of the withdrawals from the reservoir for treatment and distribution. One example of such an IPR system is the Upper Occoquan Service Authority (UOSA) water reclamation facility (WRF) which discharges its reclaimed water into the Occoquan Reservoir, a key water resource for Fairfax County. Integrated environmental modeling (IEM) provides a comprehensive approach towards the design and operation of water resource systems in which water supply, drainage, and sanitation are simulated as a single entity rather than independent units. In IEM, different standalone models, each representing a single subsystem, are linked together to analyze the complex interactions between various components of the system. This approach can be used for developing operational support tools for an IPR system to ensure timely and informed decision-making in response to the fluctuating conditions, e.g., weather patterns, plant performance, water demand, etc. The overarching goal of this research was to integrate different models and the data sources and develop a decision support system (DSS) to manage the UOSA-WRF performance. This resulting integrated model is used to predict future reservoir conditions based on the weather and streamflow forecasts obtained from the National Weather Service. The application runs various future scenarios to capture the possible variations of the pollutant loadings in the streams, withdrawals by the water treatment plant, WRF effluent flows, and plant operations and provide feedback to plant operators. The entire integrated model is operated periodically to output data on user-friendly dashboards, displaying historical and forecasting trends. The DSS provides stakeholders with a holistic view for the design, planning, risk assessments, and potential improvements in various components of the water supply chain, not just for the Occoquan but for any reservoir augmentation type IPR system.

Yes / To identify a subset of software issues occurring in daily Swedish healthcare practice and devise a set of local solutions to overcome the challenges. Methods: A sample of 46 incident reports was collected from one of Sweden's national incident reporting repositories, ranging from June 2019 to December 2021. The reports were first subjected to an algorithm to identify if they were health information technology-related incidents and were analysed using an existing framework, i.e., the Health Information Technology Classification System, to identify the software-related incidents. The incidents associated with software issues were then subjected to thematic analysis, in which themes were extracted and presented under the category assigned by the existing framework used. Results: Of 46 reports, 45 (with one exception) were included using the algorithm. Of 45 incidents, 31 software-related incidents were identified using the classification system. Six types of software issues were identified, including software functionality (n = 10), interface with other software systems or components (n = 10), system configuration (n = 7), interface with devices (n = 2), record migration (n = 1) and increased volume of transactions (n = 1). Each issue was further categorised into different themes; for example, software interface-related problems were grouped into ‘two patients being active in the system simultaneously’ (n = 6) and ‘transfer of patient information’ (n = 4). Conclusions: The study provided some insights into software issues and relevant consequences. A set of local solutions were devised to overcome the present challenges encountered in Swedish healthcare in their daily clinical practice. Systematic identification and characterisation of such software challenges should be a routine part of clinical practice for all major health information technology implementations. / This work has been part of being an Affiliated Researcher at the Department of Medicine and Optometry, Linnaeus University, Sweden. A publishing grant was received from Linnaeus University as a part of the University Library's research support.