The Utah Department of Transportation (UDOT) manages and maintains a complex state-wide network of highways. Recent incidents such as the collapse of the I-35W bridge in Min- neapolis, Minnesota, and the I-85/Piedmont Road fire and subsequent bridge collapse in Atlanta, Georgia, have brought identification of transportation network vulnerabilities to the forefront of UDOT’s planning efforts. Traditional estimates of transportation network impacts have focused on increases to user travel time or the volume of affected traffic, but studies of these disasters have revealed that when facing a degraded transportation network, people adjust their trip making in terms of destination, mode, and route choice. The objective of this thesis is to evaluate the relative systemic criticality of highway links on Utah’s highway network using a logit-based model sensitive to changes in destination choice, mode choice, and route path. The current Utah Statewide Travel Model (USTM) does not incorporate user mode or destination choice, making it unsuitable for this task in its present condition. Consequently, this thesis develops a logit-based model structure that evaluates the cost of impaired destination choices and mode choices for home-based and non-home-based personal trips resulting from a damaged highway network. The choice model logsums capture the total value of user choices and can be readily converted to monetary values, making them ideal for this purpose. The logit-based model is then applied to 40 highway links located at strategic locations on Utah’s network. When compared with a more traditional travel time increase estimation, the logsum and travel time models provide categorically different cost estimates, where the logsum results are typically lower than travel time estimates, with implications for policy making and UDOT’s planning strategy. The results further suggest that freight trips are likely more important considerations than passenger trips, and should be considered in future research.

The purpose of this study is to empirically investigate if the municipal decision on wind power development can be explained by a model including socioeconomic variables and proxies for the natural environment, using a pooled cross-section data set for Swedish municipalities for the period 2010-2019. The study poses the question whether politicians' decisions-making can be explained by socioeconomic factors. In order to analyse the approving or denying of wind power development in Swedish municipalities, three models are used: the linear probability model, the probit and the logit model. The results show that the Green political party (positively affecting wind power development) and that the unemployment rate, income, population density, protected areas and the affiliations with the Sweden Democrats (negatively affecting the approval rate), has a statistical significant effect on the permissionprocess. Installed capacity of wind power plants seemingly have no impact. Our findings suggest that the municipal decision making is less random than the critics of the municipal veto proposes.

Detta examensarbete syftar till att svara på ett av Trafikverket fastställt problem: en gemensam regional cykelplanerings process skulle göra dem billigare och mer jämförbara. De erbjuder för närvarande planerarna en modell som utvecklades av Kågeson 2007. Denna modell har formen av en rapport som ger råd om när man ska bygga en cykelväg mellan städer eller platser i en region. Ändå används den bara i endast 6 av de 21 svenska länen. Trafikverket kräver ett nytt planeringsstödverktyg, mer interaktivt och komplett än Kågeson-modellen. Några nya önskade funktioner är separationen av efterfrågan per syfte, införandet av e-cyklar, olika resesyfte och en prioritering av investeringarna.  Examensarbetet är att designa och implementera det här verktyget, även kallat Planning Support System (PSS), som syftar till att jämföra utbud och efterfrågan på cykelväg till prioritering av infrastrukturförbättringar. En huvudbegränsning för modellen är att den måste vara billig datavis, men så komplett och exakt som möjligt. Det baseras på flera öppna dataleverantörer, till exempel OpenStreetMap, den svenska nationella vägdatabasen (NVDB) eller reseundersökningar från Sverige och Nederländerna. Resultatet är en modell, uppdelad efter turändamål och typ av cykel.  Del för efterfrågeuppskattning anpassar en klassisk fyrsteg transportmodell till cykelplanering och begränsad data. För olika resändamål genereras och distribueras resor tack vare en ursprungs begränsad gravitationsmodell. Valet av cykelläge är anpassat till det faktiska resebeteendet genom logistisk regression med en binär logit-modell. Resorna tilldelas sedan nätverket med tilldelnings metoden "allt-eller-ingenting" genom Dijkstras algoritm. För att utvärdera cykelförsörjningen använde vi ett mått som heter Level of Traffic Stress (LTS), som uppskattar den potentiella användningen av en nätverkslänk för olika delar av befolkningen som en funktion av vägnätvariablerna. Prioriteringsrankningen är då förhållandet mellan mått på efterfrågan och utbud.  Detta nya verktyg implementeras med opensource Geographic Information System (GIS) som heter QGIS och med Python 3 och testas i Södermanlands län / This Master Thesis main objective is to answer a problem set by the Swedish Transport Administration: a common regional bicycle planning process would them cheaper and more comparable. They currently offer the planners a model developed by Kågeson in 2007. This model takes the form of a report which advises on when to build a bicycle path between cities or places of a region. Still, it is only used in only 6 of the 21 Swedish counties. Trafikverket requires a new planning support tool, more interactive and complete than the Kågeson model. Some new desired features are the separation of demand per purpose, the inclusion of e-bikes, different trip purposes, and a prioritization of the investments.  The Degree Project work is to design and implement this tool, also called Planning Support System (PSS), which compares supply and demand for bicycle path to prioritizing infrastructure improvements. A main constraint for the model is that it needs to be cheap data-wise, but as complete and precise as possible. It bases on several open data providers, such as OpenStreetMap, the Swedish National Road Database (NVDB), or Travel Surveys from Sweden and the Netherlands. The result is a model, disaggregated by trip purpose and type of bicycle.  The demand estimation part adapts a classic four-step transportation model to bicycle planning and limited data. For different trip purposes, trips are generated and distributed thanks to an origin-constrained gravity model. Bicycle mode choice is fit to actual travel behaviour through logistic regression with a binary logit model. The trips are then assigned to the network using the "all-or-nothing" assignment method through the Dijkstra algorithm. To evaluate bicycle supply, we used a metric called Level of Traffic Stress (LTS), which estimates the potential use of a network link by different parts of the population as a function of the road network variables. The prioritization ranking is then the ratio between demand and supply metrics.  This new tool is implemented with the opensource Geographic Information System (GIS) called QGIS and with Python 3, and it is tested on Södermanland County.

Guidelines to screen and select common items for vertical scaling have been adopted from equating. Differences between vertical scaling and equating suggest that these guidelines may not apply to vertical scaling in the same way that they apply to equating. For example, in equating the examinee groups are assumed to be randomly equivalent, but in vertical scaling the examinee groups are assumed to possess different levels of proficiency. Equating studies that examined the characteristics of the common-item set stress the importance of careful item selection, particularly when groups differ in ability level. Since in vertical scaling cross-level ability differences are expected, the common items' psychometric characteristics become even more important in order to obtain a correct interpretation of students' academic growth. This dissertation applied two screening criteria and two selection approaches to investigate how changes in the composition of the linking sets impacted the nature of students' growth when creating vertical scales for two elementary mathematics tests. The purpose was to observe how well these equating guidelines were applied in the context of vertical scaling. Two separate datasets were analyzed to observe the impact of manipulating the common items' content area and targeted curricular grade level. The same Rasch scaling method was applied for all variations of the linking set. Both the robust z procedure and a variant of the 0.3-logit difference procedure were used to screen unstable common items from the linking sets. (In vertical scaling, a directional item-difficulty difference must be computed for the 0.3-logit difference procedure.) Different combinations of stable common items were selected to make up the linking sets. The mean/mean method was used to compute the equating constant and linearly transform the students' test scores onto the base scale. A total of 36 vertical scales were created. The results indicated that, although the robust z procedure was a more conservative approach to flagging unstable items, the robust z and the 0.3-logit difference procedure produced similar interpretations of students' growth. The results also suggested that the choice of grade-level-targeted common items affected the estimates of students' grade-to-grade growth, whereas the results regarding the choice of content-area-specific common items were inconsistent. The findings from the Geometry and Measurement dataset indicated that the choice of content-area-specific common items had an impact on the interpretation of students' growth, while the findings from the Algebra and Data Analysis/Probability dataset indicated that the choice of content-area-specific common items did not appear to significantly affect students' growth. A discussion of the limitations of the study and possible future research is presented.

The purpose of this thesis was to evaluate the impact of dynamic message signs (DMS) on the Orlando-Orange County Expressway Authority (OOCEA) toll road network using a Pre and Post-Deployment DMS Survey (henceforth referred to as "pre and post-deployment survey") analysis. DMS are electronic traffic signs used on roadways to give travelers information about travel times, traffic congestion, accidents, disabled vehicles, AMBER alerts, and special events. The particular DMS referred to in this study are large rectangular signs installed over the travel lanes and these are not the portable trailer mount signs. The OOCEA have been working over the past two years to add several fixed DMS on their toll road network. At the time of the pre-deployment survey, only one DMS was installed on the OOCEA toll road network. At the time of the post-deployment survey, a total of 30 DMS were up and running on the OOCEA toll road network. Since most of the travelers on the OOCEA toll roads are from Orange, Osceola, and Seminole counties, this study was limited to these counties. This thesis documents the results and comparisons between the pre and post-deployment survey analysis. The instrument used to analyze the travelers' perception of DMS was a survey that utilized computer aided telephone interviews. The pre-deployment survey was conducted during early November of 2006, and the post-deployment survey was conducted during the month of May, 2008. Questions pertaining to the acknowledgement of DMS on the OOCEA toll roads, satisfaction with travel information provided on the network, formatting of the messages, satisfaction with different types of messages, diversion questions (Revealed and Stated preferences), and classification/socioeconomic questions (such as age, education, most traveled toll road, county of residence, and length of residency) were asked to the respondents. The results of both the pre and post-deployment surveys are discussed in this thesis, but it should be noted that the more telling results are those of the post-deployment survey. The results of the post-deployment survey show the complete picture of the impact of DMS on travelers' experience on the OOCEA toll road network. The pre-deployment results are included to show an increase or decrease in certain aspects of travel experience with relation to DMS. The results of the pre-deployment analysis showed that 54.4% of the OOCEA travelers recalled seeing DMS on the network, while a total of 63.93% of the OOCEA travelers recalled seeing DMS during the post-deployment analysis. This showed an increase of almost 10% between the two surveys demonstrating the people are becoming more aware of DMS on the OOCEA toll road network. The respondents commonly agreed that the DMS were helpful for providing information about hazardous conditions, and that the DMS are easy to read. Also, upon further research it was found that between the pre and post-deployment surveys the travelers' satisfaction with special event information provided on DMS and travel time accuracy on DMS increased significantly. With respect to formatting of the DMS, the following methods were preferred by the majority of respondents in both the pre and post-deployment surveys: ● Steady Message as a default DMS message format ● Flashing Message for abnormal traffic information (94% of respondents would like to be notified of abnormal traffic information) ● State road number to show which roadway (for Colonial - SR 50, Semoran - SR 436 and Alafaya - SR 434) ● "I-Drive" is a good abbreviation for International Drive ● If the distance to the international airport is shown on a DMS it thought to be the distance to the airport exit The results from the binary logit model for "satisfaction with travel information provided on OOCEA toll road network" displayed the significant variables that explained the likelihood of the traveler being satisfied. This satisfaction model was based on respondents who showed a prior knowledge of DMS on OOCEA toll roads. With the use of a pooled model (satisfaction model with a total of 1775 responses - 816 from pre-deployment and 959 from post-deployment), it was shown that there was no statistical change between the pre and post-deployment satisfaction based on variables thought to be theoretically relevant. The results from the comparison between the pre and post-deployment satisfaction models showed that many of the coefficients of the variables showed a significant change. Although some of the variables were statistically insignificant in one of the two survey model results: Either the pre or post-deployment model, it was still shown that every variable was significant in at least one of the two models. The coefficient for the variable corresponding to DMS accuracy showed a significantly lower value in the post-deployment model. The coefficient for the variable "DMS was helpful for providing special event information" showed a significantly higher value in the post-deployment model. The final post-deployment diversion model was based on a total of 732 responses who answered that they had experienced congestion in the past 6 months. Based on this final post-deployment diversion model, travelers who had stated that their most frequently traveled toll road was either SR 408 or SR 417 were more likely to divert. Also, travelers who stated that they would divert in the case of abnormal travel times displayed on DMS or stated that a DMS influenced their response to congestion showed a higher likelihood of diversion. These two variables were added between the pre and post-deployment surveys. It is also beneficial to note that travelers who stated they would divert in a fictitious congestion situation of at least 30 minutes of delay were more likely to divert. This shows that they do not contradict themselves in their responses to Revealed Preference and Stated Preference diversion situations. Based on a comparison between pre and post-deployment models containing similar variables, commuters were more likely to stay on the toll road everything else being equal to the base case. Also, it was shown that in the post-deployment model the respondents traveling on SR 408 and SR 417 were more likely to divert, but in the pre-deployment model only the respondents traveling on SR 408 were more likely to divert. This is an expected result since during the pre-deployment survey only one DMS was located on SR 408, and during the post-deployment survey there were DMS located on all toll roads. Also, an interesting result to be noted is that in the post-deployment survey, commuters who paid tolls with E-pass were more likely to stay on the toll road than commuters who paid tolls with cash. The implications for implementation of these results are discussed in this thesis. DMS should be formatted as a flashing message for abnormal traffic situations and the state road number should be used to identify a roadway. DMS messages should pertain to information on roadway hazards when necessary because it was found that travelers find it important to be informed on events that are related to their personal safety. The travel time accuracy on DMS was shown to be significant for traveler information satisfaction because if the travelers observe inaccurate travel times on DMS, they may not trust the validity of future messages. Finally, it is important to meet the travelers' preferences and concerns for DMS.

Alzheimer's Disease (AD) affects nearly 5.4 million Americans as of 2016 and is the most common form of dementia. The disease is characterized by the presence of neurofibrillary tangles and amyloid plaques [1]. The amount of plaques are measured by Braak stage, post-mortem. It is known that AD is positively associated with hypercholesterolemia [16]. As statins are the most widely used cholesterol-lowering drug, there may be associations between statin use and AD. We hypothesize that those who use statins, specifically lipophilic statins, are more likely to have a low Braak stage in post-mortem analysis. In order to address this hypothesis, we wished to fit a regression model for ordinal outcomes (e.g., high, moderate, or low Braak stage) using data collected from the National Alzheimer's Coordinating Center (NACC) autopsy cohort. As the outcomes were matched on the length of follow-up, a conditional likelihood-based method is often used to estimate the regression coefficients. However, it can be challenging to solve the conditional-likelihood based estimating equation numerically, especially when there are many matching strata. Given that the likelihood of a conditional logistic regression model is equivalent to the partial likelihood from a stratified Cox proportional hazard model, the existing R function for a Cox model, coxph( ), can be used for estimation of a conditional logistic regression model. We would like to investigate whether this strategy could be extended to a regression model for ordinal outcomes. More specifically, our aims are to (1) demonstrate the equivalence between the exact partial likelihood of a stratified discrete time Cox proportional hazards model and the likelihood of a conditional logistic regression model, (2) prove equivalence, or lack there-of, between the exact partial likelihood of a stratified discrete time Cox proportional hazards model and the conditional likelihood of models appropriate for multiple ordinal outcomes: an adjacent categories model, a continuation-ratio model, and a cumulative logit model, and (3) clarify how to set up stratified discrete time Cox proportional hazards model for multiple ordinal outcomes with matching using the existing coxph( ) R function and interpret the regression coefficient estimates that result. We verified this theoretical proof through simulation studies. We simulated data from the three models of interest: an adjacent categories model, a continuation-ratio model, and a cumulative logit model. We fit a Cox model using the existing coxph( ) R function to the simulated data produced by each model. We then compared the coefficient estimates obtained. Lastly, we fit a Cox model to the NACC dataset. We used Braak stage as the outcome variables, having three ordinal categories. We included predictors for age at death, sex, genotype, education, comorbidities, number of days having taken lipophilic statins, number of days having taken hydrophilic statins, and time to death. We matched cases to controls on the length of follow up. We have discussed all findings and their implications in detail.

This thesis covers two separate studies. The first study, chapter 2, was conducted to evaluate whether there are additional economic returns from producing strawberries in the high tunnel compared to the open-field in Southeast Virginia. We develop and compare budgets for eight strawberry cultivars grown in the two environments and sold under three marketing strategies (pre-pick wholesale, pre-pick retail, and U-pick). Almost all cultivars in the high tunnel generated negative net revenues regardless of the marketing strategy. In contrast, net revenues from open-field cultivars were always positive. In the second study, chapter 3, we used a fractional multinomial logit model to estimate the effect of crop revenues, input costs, and fuel ethanol production on the joint acreages planted to eight U.S major crops (barley, corn, cotton, peanuts, rice, sorghum, soybeans, and wheat). We found a positive and statistically significant marginal effect of the expected peanuts' revenue on its acreage share. The expected corn revenue had a negative average marginal effect on soybean acreage share, and the effect of expected wheat revenue was positive on cotton acreage share and negative on rice acreage share. / Master of Science / This thesis covers two separate research studies. The first study, chapter 2}, was conducted to evaluate whether growing strawberries in a simple, low-cost, and passive heat structure known as a "high tunnel" would yield more profit (the difference between total revenues and total costs) compared to growing them outside in an open-field in Southeast Virginia. We estimate and compare differences between total revenues and costs for eight strawberry cultivars grown in the two environments and sold under three marketing strategies. The first two marketing strategies involve growers harvesting strawberries themselves. They can then either retail them (farm stands, farmers' markets, or in a similar setting: pre-pick retail) or sell them in bulk to be retailed by others (pre-pick wholesale). A third marketing strategy involves consumers visiting a farm and picking their own strawberries (U-pick). Almost all cultivars grown in the high tunnel structure resulted in a loss (negative difference between total revenues and costs) regardless of the marketing strategy. In contrast, cultivars produced in the open-field always generated a profit (positive total revenues and costs difference). In the second study, chapter 3, we estimated the effect of crop revenues, input costs, and fuel ethanol production on acreages planted to eight U.S major crops (barley, corn, cotton, peanuts, rice, sorghum, soybeans, and wheat), and we considered the fact that acreages allocated to one crop affect other crops' acreages. We found that increasing the expected revenue of peanuts leads to an increase in its acreage share. Increasing the expected revenue of corn leads to a decrease in soybeans' acreage share. Finally, increasing the expected revenue of wheat leads to an increase in the cotton acreage share and a decrease in the rice acreage share.

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