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- Title
- USING MODELING AND SIMULATION TO EVALUATE DISEASE CONTROL MEASURES.
- Creator
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Atkins, Tracy, Clarke, Thomas, University of Central Florida
- Abstract / Description
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This dissertation introduced several issues concerning the analysis of diseases by showing how modeling and simulation could be used to assist in creating health policy by estimating the effects of such policies. The first question posed was how would education, vaccination and a combination of these two programs effect the possible outbreak of meningitis on a college campus. After creating a model representative of the transmission dynamics of meningitis and establishing parameter values...
Show moreThis dissertation introduced several issues concerning the analysis of diseases by showing how modeling and simulation could be used to assist in creating health policy by estimating the effects of such policies. The first question posed was how would education, vaccination and a combination of these two programs effect the possible outbreak of meningitis on a college campus. After creating a model representative of the transmission dynamics of meningitis and establishing parameter values characteristic of the University of Central Florida main campus, the results of a deterministic model were presented in several forms. The result of this model was the combination of education and vaccination would eliminate the possibility of an epidemic on our campus. Next, we used simulation to evaluate how quarantine and treatment would affect an outbreak of influenza on the same population. A mathematical model was created specific to influenza on the UCF campus. Numerical results from this model were then presented in tabular and graphical form. The results comparing the simulations for quarantine and treatment show the best course of action would be to enact a quarantine policy on the campus thus reducing the maximum number of infected while increasing the time to reach this peak. Finally, we addressed the issue of performing the analysis stochastically versus deterministically. Additional models were created with the progression of the disease occurring by chance. Statistical analysis was done on the mean of 100 stochastic simulation runs comparing that value to the one deterministic outcome. The results for this analysis were inconclusive, as the results for meningitis were comparable while those for influenza appeared to be different.
Show less - Date Issued
- 2010
- Identifier
- CFE0003232, ucf:48535
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003232
- Title
- STOCHASTIC OPTIMIZATION AND APPLICATIONS WITH ENDOGENOUS UNCERTAINTIES VIA DISCRETE CHOICE MODELSl.
- Creator
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Chen, Mengnan, Zheng, Qipeng, Boginski, Vladimir, Vela, Adan, Yayla Kullu, Muge, University of Central Florida
- Abstract / Description
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Stochastic optimization is an optimization method that solves stochastic problems for minimizing or maximizing an objective function when there is randomness in the optimization process. In this dissertation, various stochastic optimization problems from the areas of Manufacturing, Health care, and Information Cascade are investigated in networks systems. These stochastic optimization problems aim to make plan for using existing resources to improve production efficiency, customer...
Show moreStochastic optimization is an optimization method that solves stochastic problems for minimizing or maximizing an objective function when there is randomness in the optimization process. In this dissertation, various stochastic optimization problems from the areas of Manufacturing, Health care, and Information Cascade are investigated in networks systems. These stochastic optimization problems aim to make plan for using existing resources to improve production efficiency, customer satisfaction, and information influence within limitation. Since the strategies are made for future planning, there are environmental uncertainties in the network systems. Sometimes, the environment may be changed due to the action of the decision maker. To handle this decision-dependent situation, the discrete choice model is applied to estimate the dynamic environment in the stochastic programming model. In the manufacturing project, production planning of lot allocation is performed to maximize the expected output within a limited time horizon. In the health care project, physician is allocated to different local clinics to maximize the patient utilization. In the information cascade project, seed selection of the source user helps the information holder to diffuse the message to target users using the independent cascade model to reach influence maximization. \parThe computation complexities of the three projects mentioned above grow exponentially by the network size. To solve the stochastic optimization problems of large-scale networks within a reasonable time, several problem-specific algorithms are designed for each project. In the manufacturing project, the sampling average approximation method is applied to reduce the scenario size. In the health care project, both the guided local search with gradient ascent and large neighborhood search with Tabu search are developed to approach the optimal solution. In the information cascade project, the myopic policy is used to separate stochastic programming by discrete time, and the Markov decision process is implemented in policy evaluation and updating.
Show less - Date Issued
- 2019
- Identifier
- CFE0007792, ucf:52347
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007792
- Title
- UTILIZING A REAL LIFE DATA WAREHOUSE TO DEVELOP FREEWAY TRAVEL TIME ELIABILITY STOCHASTIC MODELS.
- Creator
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Emam, Emam, Al-Deek, Haitham, University of Central Florida
- Abstract / Description
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During the 20th century, transportation programs were focused on the development of the basic infrastructure for the transportation networks. In the 21st century, the focus has shifted to management and operations of these networks. Transportation network reliability measure plays an important role in judging the performance of the transportation system and in evaluating the impact of new Intelligent Transportation Systems (ITS) deployment. The measurement of transportation network travel...
Show moreDuring the 20th century, transportation programs were focused on the development of the basic infrastructure for the transportation networks. In the 21st century, the focus has shifted to management and operations of these networks. Transportation network reliability measure plays an important role in judging the performance of the transportation system and in evaluating the impact of new Intelligent Transportation Systems (ITS) deployment. The measurement of transportation network travel time reliability is imperative for providing travelers with accurate route guidance information. It can be applied to generate the shortest path (or alternative paths) connecting the origins and destinations especially under conditions of varying demands and limited capacities. The measurement of transportation network reliability is a complex issue because it involves both the infrastructure and the behavioral responses of the users. Also, this subject is challenging because there is no single agreed-upon reliability measure. This dissertation developed a new method for estimating the effect of travel demand variation and link capacity degradation on the reliability of a roadway network. The method is applied to a hypothetical roadway network and the results show that both travel time reliability and capacity reliability are consistent measures for reliability of the road network, but each may have a different use. The capacity reliability measure is of special interest to transportation network planners and engineers because it addresses the issue of whether the available network capacity relative to the present or forecast demand is sufficient, whereas travel time reliability is especially interesting for network users. The new travel time reliability method is sensitive to the users' perspective since it reflects that an increase in segment travel time should always result in less travel time reliability. And, it is an indicator of the operational consistency of a facility over an extended period of time. This initial theoretical effort and basic research was followed by applying the new method to the I-4 corridor in Orlando, Florida. This dissertation utilized a real life transportation data warehouse to estimate travel time reliability of the I-4 corridor. Four different travel time stochastic models: Weibull, Exponential, Lognormal, and Normal were tested. Lognormal was the best-fit model. Unlike the mechanical equipments, it is unrealistic that any freeway segment can be traversed in zero seconds no matter how fast the vehicles are. So, an adjustment of the developed best-fit statistical model (Lognormal) location parameter was needed to accurately estimate the travel time reliability. The adjusted model can be used to compute and predict travel time reliability of freeway corridors and report this information in real time to the public through traffic management centers. Compared to existing Florida Method and California Buffer Time Method, the new reliability method showed higher sensitivity to geographical locations, which reflects the level of congestion and bottlenecks. The major advantages/benefits of this new method to practitioners and researchers over the existing methods are its ability to estimate travel time reliability as a function of departure time, and that it treats travel time as a continuous variable that captures the variability experienced by individual travelers over an extended period of time. As such, the new method developed in this dissertation could be utilized in transportation planning and freeway operations for estimating the important travel time reliability measure of performance. Then, the segment length impacts on travel time reliability calculations were investigated utilizing the wealth of data available in the I-4 data warehouse. The developed travel time reliability models showed significant evidence of the relationship between the segment length and the results accuracy. The longer the segment, the less accurate were the travel time reliability estimates. Accordingly, long segments (e.g., 25 miles) are more appropriate for planning purposes as a macroscopic performance measure of the freeway corridor. Short segments (e.g., 5 miles) are more appropriate for the evaluation of freeway operations as a microscopic performance measure. Further, this dissertation has explored the impact of relaxing an important assumption in reliability analysis: Link independency. In real life, assuming that link failures on a road network are statistically independent is dubious. The failure of a link in one particular area does not necessarily result in the complete failure of the neighboring link, but may lead to deterioration of its performance. The "Cause-Based Multimode Model" (CBMM) has been used to address link dependency in communication networks. However, the transferability of this model to transportation networks has not been tested and this approach has not been considered before in the calculation of transportation networks' reliability. This dissertation presented the CBMM and applied it to predict transportation networks' travel time reliability that an origin demand can reach a specified destination under multimodal dependency link failure conditions. The new model studied the multi-state system reliability analysis of transportation networks for which one cannot formulate an "all or nothing" type of failure criterion and in which dependent link failures are considered. The results demonstrated that the newly developed method has true potential and can be easily extended to large-scale networks as long as the data is available. More specifically, the analysis of a hypothetical network showed that the dependency assumption is very important to obtain more reasonable travel time reliability estimates of links, paths, and the entire network. The results showed large discrepancy between the dependency and independency analysis scenarios. Realistic scenarios that considered the dependency assumption were on the safe side, this is important for transportation network decision makers. Also, this could aid travelers in making better choices. In contrast, deceptive information caused by the independency assumption could add to the travelers' anxiety associated with the unknown length of delay. This normally reflects negatively on highway agencies and management of taxpayers' resources.
Show less - Date Issued
- 2006
- Identifier
- CFE0000965, ucf:46709
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000965
- Title
- Development of Regional Optimization and Market Penetration Models For the Electric Vehicles in the United States.
- Creator
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Noori, Mehdi, Tatari, Omer, Oloufa, Amr, Nam, Boo Hyun, Xanthopoulos, Petros, University of Central Florida
- Abstract / Description
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Since the transportation sector still relies mostly on fossil fuels, the emissions and overall environmental impacts of the transportation sector are particularly relevant to the mitigation of the adverse effects of climate change. Sustainable transportation therefore plays a vital role in the ongoing discussion on how to promote energy insecurity and address future energy requirements. One of the most promising ways to increase energy security and reduce emissions from the transportation...
Show moreSince the transportation sector still relies mostly on fossil fuels, the emissions and overall environmental impacts of the transportation sector are particularly relevant to the mitigation of the adverse effects of climate change. Sustainable transportation therefore plays a vital role in the ongoing discussion on how to promote energy insecurity and address future energy requirements. One of the most promising ways to increase energy security and reduce emissions from the transportation sector is to support alternative fuel technologies, including electric vehicles (EVs). As vehicles become electrified, the transportation fleet will rely on the electric grid as well as traditional transportation fuels for energy. The life cycle cost and environmental impacts of EVs are still very uncertain, but are nonetheless extremely important for making policy decisions. Moreover, the use of EVs will help to diversify the fuel mix and thereby reduce dependence on petroleum. In this respect, the United States has set a goal of a 20% share of EVs on U.S. roadways by 2030. However, there is also a considerable amount of uncertainty in the market share of EVs that must be taken into account. This dissertation aims to address these inherent uncertainties by presenting two new models: the Electric Vehicles Regional Optimizer (EVRO), and Electric Vehicle Regional Market Penetration (EVReMP). Using these two models, decision makers can predict the optimal combination of drivetrains and the market penetration of the EVs in different regions of the United States for the year 2030.First, the life cycle cost and life cycle environmental emissions of internal combustion engine vehicles, gasoline hybrid electric vehicles, and three different EV types (gasoline plug-in hybrid EVs, gasoline extended-range EVs, and all-electric EVs) are evaluated with their inherent uncertainties duly considered. Then, the environmental damage costs and water footprints of the studied drivetrains are estimated. Additionally, using an Exploratory Modeling and Analysis method, the uncertainties related to the life cycle costs, environmental damage costs, and water footprints of the studied vehicle types are modeled for different U.S. electricity grid regions. Next, an optimization model is used in conjunction with this Exploratory Modeling and Analysis method to find the ideal combination of different vehicle types in each U.S. region for the year 2030. Finally, an agent-based model is developed to identify the optimal market shares of the studied vehicles in each of 22 electric regions in the United States. The findings of this research will help policy makers and transportation planners to prepare our nation's transportation system for the future influx of EVs.The findings of this research indicate that the decision maker's point of view plays a vital role in selecting the optimal fleet array. While internal combustion engine vehicles have the lowest life cycle cost, the highest environmental damage cost, and a relatively low water footprint, they will not be a good choice in the future. On the other hand, although all-electric vehicles have a relatively low life cycle cost and the lowest environmental damage cost of the evaluated vehicle options, they also have the highest water footprint, so relying solely on all-electric vehicles is not an ideal choice either. Rather, the best fleet mix in 2030 will be an electrified fleet that relies on both electricity and gasoline. From the agent-based model results, a deviation is evident between the ideal fleet mix and that resulting from consumer behavior, in which EV shares increase dramatically by the year 2030 but only dominate 30 percent of the market. Therefore, government subsidies and the word-of-mouth effect will play a vital role in the future adoption of EVs.
Show less - Date Issued
- 2015
- Identifier
- CFE0005852, ucf:50927
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005852
- Title
- Biophysical Sources of 1/f Noises in Neurological Systems.
- Creator
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Paris, Alan, Vosoughi, Azadeh, Atia, George, Wiegand, Rudolf, Douglas, Pamela, Berman, Steven, University of Central Florida
- Abstract / Description
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High levels of random noise are a defining characteristic of neurological signals at all levels, from individual neurons up to electroencephalograms (EEG). These random signals degrade the performance of many methods of neuroengineering and medical neuroscience. Understanding this noise also is essential for applications such as real-time brain-computer interfaces (BCIs), which must make accurate control decisions from very short data epochs. The major type of neurological noise is of the so...
Show moreHigh levels of random noise are a defining characteristic of neurological signals at all levels, from individual neurons up to electroencephalograms (EEG). These random signals degrade the performance of many methods of neuroengineering and medical neuroscience. Understanding this noise also is essential for applications such as real-time brain-computer interfaces (BCIs), which must make accurate control decisions from very short data epochs. The major type of neurological noise is of the so-called 1/f-type, whose origins and statistical nature has remained unexplained for decades. This research provides the first simple explanation of 1/f-type neurological noise based on biophysical fundamentals. In addition, noise models derived from this theory provide validated algorithm performance improvements over alternatives.Specifically, this research defines a new class of formal latent-variable stochastic processes called hidden quantum models (HQMs) which clarify the theoretical foundations of ion channel signal processing. HQMs are based on quantum state processes which formalize time-dependent observation. They allow the quantum-based calculation of channel conductance autocovariance functions, essential for frequency-domain signal processing. HQMs based on a particular type of observation protocol called independent activated measurements are shown to be distributionally equivalent to hidden Markov models yet without an underlying physical Markov process. Since the formal Markov processes are non-physical, the theory of activated measurement allows merging energy-based Eyring rate theories of ion channel behavior with the more common phenomenological Markov kinetic schemes to form energy-modulated quantum channels. These unique biophysical concepts developed to understand the mechanisms of ion channel kinetics have the potential of revolutionizing our understanding of neurological computation.To apply this theory, the simplest quantum channel model consistent with neuronal membrane voltage-clamp experiments is used to derive the activation eigenenergies for the Hodgkin-Huxley K+ and Na+ ion channels. It is shown that maximizing entropy under constrained activation energy yields noise spectral densities approximating S(f) = 1/f, thus offering a biophysical explanation for this ubiquitous noise component. These new channel-based noise processes are called generalized van der Ziel-McWhorter (GVZM) power spectral densities (PSDs). This is the only known EEG noise model that has a small, fixed number of parameters, matches recorded EEG PSD's with high accuracy from 0 Hz to over 30 Hz without infinities, and has approximately 1/f behavior in the mid-frequencies. In addition to the theoretical derivation of the noise statistics from ion channel stochastic processes, the GVZM model is validated in two ways. First, a class of mixed autoregressive models is presented which simulate brain background noise and whose periodograms are proven to be asymptotic to the GVZM PSD. Second, it is shown that pairwise comparisons of GVZM-based algorithms, using real EEG data from a publicly-available data set, exhibit statistically significant accuracy improvement over two well-known and widely-used steady-state visual evoked potential (SSVEP) estimators.
Show less - Date Issued
- 2016
- Identifier
- CFE0006485, ucf:51418
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006485
- Title
- Multi-Level Safety Performance Functions for High Speed Facilities.
- Creator
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Ahmed, Mohamed, Abdel-Aty, Mohamed, Radwan, Ahmed, Al-Deek, Haitham, Mackie, Kevin, Pande, Anurag, Uddin, Nizam, University of Central Florida
- Abstract / Description
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High speed facilities are considered the backbone of any successful transportation system; Interstates, freeways, and expressways carry the majority of daily trips on the transportation network. Although these types of roads are relatively considered the safest among other types of roads, they still experience many crashes, many of which are severe, which not only affect human lives but also can have tremendous economical and social impacts. These facts signify the necessity of enhancing the...
Show moreHigh speed facilities are considered the backbone of any successful transportation system; Interstates, freeways, and expressways carry the majority of daily trips on the transportation network. Although these types of roads are relatively considered the safest among other types of roads, they still experience many crashes, many of which are severe, which not only affect human lives but also can have tremendous economical and social impacts. These facts signify the necessity of enhancing the safety of these high speed facilities to ensure better and efficient operation. Safety problems could be assessed through several approaches that can help in mitigating the crash risk on long and short term basis. Therefore, the main focus of the research in this dissertation is to provide a framework of risk assessment to promote safety and enhance mobility on freeways and expressways. Multi-level Safety Performance Functions (SPFs) were developed at the aggregate level using historical crash data and the corresponding exposure and risk factors to identify and rank sites with promise (hot-spots). Additionally, SPFs were developed at the disaggregate level utilizing real-time weather data collected from meteorological stations located at the freeway section as well as traffic flow parameters collected from different detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMS). These disaggregate SPFs can identify real-time risks due to turbulent traffic conditions and their interactions with other risk factors.In this study, two main datasets were obtained from two different regions. Those datasets comprise historical crash data, roadway geometrical characteristics, aggregate weather and traffic parameters as well as real-time weather and traffic data.At the aggregate level, Bayesian hierarchical models with spatial and random effects were compared to Poisson models to examine the safety effects of roadway geometrics on crash occurrence along freeway sections that feature mountainous terrain and adverse weather. At the disaggregate level; a main framework of a proactive safety management system using traffic data collected from AVI and RTMS, real-time weather and geometrical characteristics was provided. Different statistical techniques were implemented. These techniques ranged from classical frequentist classification approaches to explain the relationship between an event (crash) occurring at a given time and a set of risk factors in real time to other more advanced models. Bayesian statistics with updating approach to update beliefs about the behavior of the parameter with prior knowledge in order to achieve more reliable estimation was implemented. Also a relatively recent and promising Machine Learning technique (Stochastic Gradient Boosting) was utilized to calibrate several models utilizing different datasets collected from mixed detection systems as well as real-time meteorological stations. The results from this study suggest that both levels of analyses are important, the aggregate level helps in providing good understanding of different safety problems, and developing policies and countermeasures to reduce the number of crashes in total. At the disaggregate level, real-time safety functions help toward more proactive traffic management system that will not only enhance the performance of the high speed facilities and the whole traffic network but also provide safer mobility for people and goods. In general, the proposed multi-level analyses are useful in providing roadway authorities with detailed information on where countermeasures must be implemented and when resources should be devoted. The study also proves that traffic data collected from different detection systems could be a useful asset that should be utilized appropriately not only to alleviate traffic congestion but also to mitigate increased safety risks. The overall proposed framework can maximize the benefit of the existing archived data for freeway authorities as well as for road users.
Show less - Date Issued
- 2012
- Identifier
- CFE0004508, ucf:49274
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004508