In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency’s safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs. In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from 2007-2010 for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models. The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors. The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests, represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R2 (R2FT), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibrated to Florida data. The performance of Florida-specific SPFs was further compared with that of the full SPFs, which include both traffic and geometric variables, in two major applications of SPFs, i.e., crash prediction and identification of high crash locations. The results showed that both SPF models yielded very similar performance in both applications. These empirical results support the use of the flow-only SPF models adopted in SafetyAnalyst, which require much less effort to develop compared to full SPFs.

Arguably the greatest issue facing the transportation profession is the ability to provide social equity with regards to both safety and mobility given the aging population. Given the overall dominance of the automobile within the transportation system, the ability to provide feasible alternatives is daunting. This fact, when coupled with the well-documented challenges of older drivers, underscores the need for improved safety features and system-wide safety approaches with a focus on the older driver. This paper describes an application of spatial crash analysis and road safety investigations that were employed in Massachusetts with a direct focus on the older driver. Specifically, the paper outlines an approach for identifying high crash locations for older drivers and presents the results of older driver focused road safety investigations for selected locations. The research approach targets both intersections and roadway segments identifying locations where older drivers are overrepresented in crashes. The road safety investigations resulted in recommended countermeasures aimed at mitigating the older driver crash problem at the identified locations. Although the resulting countermeasures, which were based upon established literature such as the Older Driver Design Handbook, included a full spectrum of recommendations, a specific emphasis was placed upon short-term and low cost measures that could be readily employed. Techniques to identify relationships between high crash location identification methods and the recommended countermeasures for the identified locations are considered. Ultimately the application of these techniques may provide transportation professionals with a means to associate specific older driver focused countermeasures with the results of particular methods of high crash location identification.