Many state agencies consider exclusive truck facilities to be an alternative to handle the safety and operational issues due to the increasing truck volumes. No such facilities exist, and there are no standard tools or procedures for measuring safety performance of an exclusive truck facility. This thesis aims at identifying factors that affect truck crashes, whose results could be used for better designing exclusive truck facilities. To accomplish the objectives of this thesis, five years’ roadway and crash data for Texas was collected to develop a comprehensive crash database. Negative binomial regression models were used to establish a relationship between truck crashes and various environmental, geometric and traffic variables. Separate models were developed for truck-related (involving at least one truck and another vehicle), truck-only (two trucks or more) and single-truck crashes. The results suggested that the percentage of trucks in Average Annual Daily Traffic (AADT), classification of the roadway (Rural/Urban), posted speed limit, surface condition, alignment and shoulder width are associated with truck crashes. It was observed that truck-related and truck-only crashes decreased as the percentage of trucks increased on freeway facilities. Based on conclusions derived from the literature review and statistical analyses, straight segments with wider shoulders and uniform grades are recommended for exclusive truck facilities. It is also recommended to provide ramps, horizontal and vertical curvature and signing based on truck size, driver eye height, braking ability and maneuverability. These models were developed using mixed-flow traffic data to understand the association of various factors with truck crashes. These models should not be used directly to estimate or predict truck crashes. Further analysis with more detailed data under different flow conditions might help in quantifying the safety performance of exclusive truck facilities.

Many state agencies consider exclusive truck facilities to be an alternative to handle the safety and operational issues due to the increasing truck volumes. No such facilities exist, and there are no standard tools or procedures for measuring safety performance of an exclusive truck facility. This thesis aims at identifying factors that affect truck crashes, whose results could be used for better designing exclusive truck facilities. To accomplish the objectives of this thesis, five years' roadway and crash data for Texas was collected to develop a comprehensive crash database. Negative binomial regression models were used to establish a relationship between truck crashes and various environmental, geometric and traffic variables. Separate models were developed for truck-related (involving at least one truck and another vehicle), truck-only (two trucks or more) and single-truck crashes. The results suggested that the percentage of trucks in Average Annual Daily Traffic (AADT), classification of the roadway (Rural/Urban), posted speed limit, surface condition, alignment and shoulder width are associated with truck crashes. It was observed that truck-related and truck-only crashes decreased as the percentage of trucks increased on freeway facilities. Based on conclusions derived from the literature review and statistical analyses, straight segments with wider shoulders and uniform grades are recommended for exclusive truck facilities. It is also recommended to provide ramps, horizontal and vertical curvature and signing based on truck size, driver eye height, braking ability and maneuverability. These models were developed using mixed-flow traffic data to understand the association of various factors with truck crashes. These models should not be used directly to estimate or predict truck crashes. Further analysis with more detailed data under different flow conditions might help in quantifying the safety performance of exclusive truck facilities.

This research project investigates the effectiveness of mentors on rates of self-reported criminal offending for released offenders. I use data from the Serious and Violent Offender Reentry Initiative (SVORI) study (2004-2007), which sought to evaluate factors relating to high-risk offenders outcomes post release in an effort to reduce the societal problem of mass incarceration. Previous research has examined the use of mentors to improve the delinquent and criminogenic behavior of youth, but little is known about the effectiveness of mentors used to aid imprisoned adult males. I utilize negative binomial analysis to compare the number of self-reported criminal activities among released offenders with mentors versus those without mentors, and assess if the values varied between different reported levels of need for mentoring. Results indicate that mentoring did not reduce the rate of post-release offending at a statistically significant level. Reasons for the lack of significant results and policy implications are discussed.

Having the ability to predict enrollment is an important task for any school's recruiting team. The purpose of this study was to identify significant factors that can be used to predict the spatial distribution of enrollments. As a case study, we used East Tennessee State University (ETSU) pharmacy school, a regional pharmacy school located in the Appalachian Mountains. Through the application of a negative binomial regression model, we found that the most important indicators of enrollment volume for the ETSU pharmacy school were Euclidean distance, probability (based on competing pharmacy schools' prestige, driving distance between schools and home and tuition costs), and the natural barrier of the Appalachian Mountains. Using these factors, together with other control variables, we successfully predicted the spatial distribution of enrollments for ETSU pharmacy school. Interestingly, gender also surfaced as a variable for predicting the pharmacy school's enrollment. We found female students are more sensitive to the geographic proximity of home to school.

It is estimated that one third of the world's population is infected with tuberculosis. Though once thought a "dead" disease, tuberculosis is very much alive. The rise of drug resistant strains of tuberculosis, and TB-HIV coinfection have made tuberculosis an even greater worldwide threat. While HIV, poverty, and public health infrastructure are historically assumed to affect the burden of tuberculosis, recent research has been done to implicate smoking in this list. This analysis involves combining data from multiple sources in order determine if smoking is a statistically significant factor in predicting the number of incident tuberculosis cases in a country. Quasi-Poisson generalized linear models and negative binomial regression will be used to analyze the effect of smoking, as well as the other factors, on tuberculosis incidence. This work will enhance tuberculosis control efforts by helping to identify new hypotheses that can be tested in future studies. One of the main hypotheses is whether or not smoking increases the number of tuberculosis cases above and beyond the effects of other factors that are known to influence tuberculosis incidence. These known factors include TB-HIV coinfection, poverty and public health infrastructure represented by treatment outcomes.

The increasingly serious pedestrian safety issue in the City of Austin aroused the concern. Other than conducting quantitative analysis at aggregate level via collecting and examining the secondary data extracted from the existing datasets, the authors shifted towards the disaggregate level analysis, focusing on twenty-six hotspots of pedestrian collisions via mixed method research. Qualitative data was collected in the field survey to precisely capture the contextual features of collision locations, and was interpreted and coded as explanatory variables for the quantitative analysis. Instead of the frequency of pedestrian collision, crash rate measured by incident count per million pedestrians was the dependent variable to identify the factors truly influencing the pedestrian safety issue, not just the total number of walkers. The stepwise bivariate analysis and negative binomial regression examined the association between pedestrian collision rate and independent variables. Finally, the average block length, speed limit posted, sidewalk condition, and the degree of proximity to major pedestrian attractors were statistically significant factors correlating with the pedestrian collision risk. / text

Thesis advisor: Heather Rowan-Kenyon / According to the National Center for Health Statistics (2007), 18-24 year olds are most likely to report heavy drinking in the past year compared to other adults. Heavy alcohol use is problematic not only in itself, but also because it is associated with a host of other negative outcomes. Research has identified traditional-age college men (age 18-24), who are White, and members of a Greek organization or athletic team as the most likely to consume alcohol in excess (Ham & Hope, 2003; Hingson & White, 2012). White men, members of Greek organizations, and college athletes are also the populations least likely to change their behavior as a result of current alcohol interventions employed by colleges and universities (Fachini, Aliane, Martinez, & Furtado, 2012; LaBrie, Pedersen, Lamb, & Quinlan, 2007; Lundahl, Kunz, Brownell, Tollefson, & Burke, 2010; Mattern & Neighbors, 2004). The primary shortcoming of previous research into this problem, is that it has failed to take an intersectional approach to the phenomenon of college men’s alcohol use. To address this gap, this study surveyed 1,457 college men across five college in the Northeastern United States, using the Conformity to Masculine Norms Inventory (CMNI; Mahalik et al., 2003) the Revised Multigroup Ethnic Identity Measure (MEIM-R; Phinney & Ong, 2007) and the Social Dominance Orientation scale (SDO; Pratto, Sidanius, Stallworth, & Malle, 1994). Alcohol consumption was predicted using zero-inflated negative binomial regressions and zero-inflated Poisson regressions, and alcohol problems were predicted using logistic regressions. The study found that the college men’s drinking was primarily predicted by the masculine norms of risk taking, having power over women, emotional control, and desiring multiple sexual partners. Although the sample size was smaller, for non-White respondents in the study, men’s drinking was also predicted by a focus on heterosexual presentation, and the SDO factor of group based dominance. Alcohol problems were largely predicted by the same masculine norms. / Thesis (PhD) — Boston College, 2016. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Leadership and Higher Education.

Developing sound or reliable statistical models for analyzing vehicle crashes is very important in highway safety studies. A difficulty arises when crash data exhibit overdispersion. Over-dispersion caused by unobserved heterogeneity is a serious problem and has been addressed in a variety ways within the negative binomial (NB) modeling framework. However, the true factors that affect heterogeneity are often unknown to researchers, and failure to accommodate such heterogeneity in the model can undermine the validity of the empirical results. Given the limitations of the NB regression model for addressing over-dispersion of crash data due to heterogeneity, this research examined an alternative model formulation that could be used for capturing heterogeneity through the use of finite mixture regression models. A Finite mixture of Poisson or NB regression models is especially useful when the count data were generated from a heterogeneous population. To evaluate these models, Poisson and NB mixture models were estimated using both simulated and empirical crash datasets, and the results were compared to those from a single NB regression model. For model parameter estimation, a Bayesian approach was adopted, since it provides much richer inference than the maximum likelihood approach. Using simulated datasets, it was shown that the single NB model is biased if the underlying cause of heterogeneity is due to the existence of multiple counting processes. The implications could be poor prediction performance and poor interpretation. Using two empirical datasets, the results demonstrated that a two-component finite mixture of NB regression models (FMNB-2) was quite enough to characterize the uncertainty about the crash occurrence, and it provided more opportunities for interpretation of the dataset which are not available from the standard NB model. Based on the models from the empirical dataset (i.e., FMNB-2 and NB models), their relative performances were also examined in terms of hotspot identification and accident modification factors. Finally, using a simulation study, bias properties of the posterior summary statistics for dispersion parameters in FMNB-2 model were characterized, and the guidelines on the choice of priors and the summary statistics to use were presented for different sample sizes and sample-mean values.

This thesis focuses on a well-known topic in sports betting, predicting the number of goals in soccer games.The data set used comes from the top English soccer league: Premier League, and consists of games played in the seasons 2015/16 to 2017/18.This thesis approaches the prediction with the auxiliary support of the odds from the betting exchange Betfair. The purpose is to find a model that can create an accurate goal distribution. %The other purpose is to investigate whether Negative binomial distribution regressionThe methods used are Bayesian Negative Binomial regression and Bayesian Poisson regression. The results conclude that the Poisson regression is the better model because of the presence of underdispersion.We argue that the methods can be used to compare different sportsbooks accuracies, and may help creating better models.

Although mosquito monitoring systems in the form of dry-ice bated CDC light traps and sentinel chickens are used by mosquito control personnel in Polk County, Florida, the placement of these are random and do not necessarily reflect prevalent areas of vector mosquito populations. This can result in significant health, economic, and social impacts during disease outbreaks. Of these vector mosquitoes Culex nigripalpus, Culex erraticus, Coquillettidia perturbans, and Aedes vexans are present in Polk County and known to transmit multiple diseases, posing a public health concern. This study seeks to evaluate the effect of Land use Land cover (LULC) unique features and precipitation on spatial and temporal distribution of Cx. nigripalpus, Cx. erraticus, Cq. perturbans, and Ae. vexans in Polk County, Florida, during 2013 and 2014, using negative binomial regression on count data from eight environmentally unique light traps retrieved from Polk County Mosquito Control. The negative binomial regression revealed a statistical association among mosquito species for precipitation and LULC features during the two-year study period, with precipitation proving to be the most significant factor in mosquito count numbers. The findings from this study can aid in more precise targeting of mosquito species, saving time and resources on already stressed public health services.