Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

Visible Body Modification (VBM): Operationalizing Grooming Standards

The purpose of this study was to build on a previous one that focused on the effect of visible body modification (WM) on employment. In this study, samples from actual employee manuals used in the hospitality industry were collected and analyzed, specifically looking at policies regarding visible tattoos and body piercings. Examples from those employee manuals are presented, along with suggestions for operators looking to change or clarify their grooming standards.

The Effects of Attention Bias Modification Training on Cognitive Biases Perceived Threats in Children with Anxiety

It has been found in research that children and adults with anxiety have a bias toward interpreting ambiguous situations as threatening. This bias is thought to consequently maintain many symptoms of anxiety. An emergent computer treatment system called Attention Bias Modification Training (ABMT) has been used to try to reduce this bias. It is essential to understand whether this bias can be reduced with ABMT because of its feasibility and cost effective nature of treatment. In the current study, interpretation bias is measured using the Children's Opinions of Everyday Life Events (COELE). The ABMT treatment is given to children once a week for an hour and their answers to the COELE are recorded before and after treatment. The recorded procedures are transcribed by undergraduate students working at the Child Anxiety and Phobia lab, and then scored. Each of the situations of the COELE are rated 0 being neutral or 1 threatening interpretation of the situation. The hypothesis is that ABMT will reduce the negative interpretation bias in children over the course of 4 weeks of treatment. The study is still in the collection and transcription of data phase, and will expect to have analytical conclusions in the start of spring 2015.