The Utah Department of Transportation (UDOT) Traffic and Safety Division continues to advance the safety of the state roadway network through network screening and decision making tools. In an effort to aid UDOT in meeting this goal, the Department of Civil and Environmental Engineering at Brigham Young University (BYU) has worked with the Statistics Department in developing analysis tools for highway safety, specifically the Utah Crash Prediction Model (UCPM) and the Utah Crash Severity Model (UCSM). Additional tools and methodologies, such as the "Hot Spot Identification and Analysis," have been created to summarize the roadway characteristics, crash data, and possible countermeasures of roadway segments with safety problems.This research focuses on the creation of a three part "Roadway Safety Analysis" methodology, which applies and automates the cumulative work of recently completed highway safety research conducted for UDOT. The first part is to prepare the roadway data and crash data for the statistical analysis. The second part is to perform the network screening statistical analysis; rank the segments by state, UDOT Region, and county; and select segments of interest. The third part is to compile and publish the Roadway Safety Analysis reports for the selected segments of interest. These parts are accomplished using the automation tools and graphical user interfaces (GUIs), which are documented in three respective volumes of user manuals. The automation tools and GUIs were developed with checks and processes to allow the Roadway Safety Analysis methodology to be completed with new, updated roadway and crash datasets.The Roadway Safety Analysis methodology allows future iterations of the UCPM and UCSM analysis and compilation of the Roadway Safety Analysis reports to be conducted in a user friendly environment. A series of critical data columns were identified to communicate the need for data consistency for future iterations of this safety research. An example of the entire process of the Roadway Safety Analysis methodology is given to illustrate how the three parts tie together. The overall process has automated data processing tasks, which saves time and resources for the analyst to investigate possible safety measures for segments of interest. Recommendations for future highway safety research are given, including continued development of the Roadway Safety Analysis methodology, an analysis of intersections and horizontal curves, the implementation of the Roadway Safety Analysis methodology to other states, and the advancement of safety countermeasures and geospatial tools for highway safety research.

Network screening and countermeasure selection are two crucial steps in the highway improvement process. In network screening, potential improvement locations are ranked and prioritized based on a specific method with a set of criteria. The most common practice by transportation agencies has been to use a simple scoring method, which, in general, weighs and scores each criterion and then ranks the locations based on their relative overall scoring. The method does not deal well with criteria that are qualitative in nature, nor does it account for the impacts of correlation among the criteria. The introduction of Analytic Hierarchy Process (AHP) provides agencies with a method to include both quantitative and qualitative criteria. However, it does not address the issue on correlation. This dissertation explores the use of both Analytic Network Process (ANP) and Fuzzy Analytic Network Process (FANP) for their potential capabilities to address both issues. Using urban four-lane divided highways in Florida for bicycle safety improvements, both ANP and FANP were shown to provide more reasonable rankings than AHP, with FANP providing the best results among the methods. After the locations are ranked and prioritized for improvements, the next step is to evaluate the potential countermeasures for improvements at the selected top-ranked locations. In this step, the standard practice has been to use Crash Modification Factors (CMFs) to quantify the potential impacts from implementing specific countermeasures. In this research, CMFs for bicycle crashes on urban facilities in Florida were developed using the Generalized Linear Model approach with a Zero-Inflated Negative Binomial (ZINB) distribution. The CMFs were tested for their spatial and temporal transferability and the results show only limited transferability both spatially and temporally. The CMFs show that, in general, wider lanes, lower speed limits, and presence of vegetation in the median reduce bicycle crashes, while presence of sidewalk and sidewalk barrier increase bicycle crashes. The research further considered bicycle exposure using the bicycle activity data from the Strava smartphone application. It was found that increased bicycle activity reduces bicycle crash probabilities on segments but increases bicycle crash probabilities at signalized intersections. Also, presence of bus stops and use of permissive signal phasing at intersections were found to increase bicycle crash probabilities.