Doctor of Philosophy

dissertationObservational studies are a frequently used "tool" in the field of road safety research because random assignments of safety treatments are not feasible or ethical. Data and modeling issues and challenges often plague observational road safety studies, and impact study results. The objective of this research was to explore a selected number of current data and modeling limitations in observational road safety studies and identify possible solutions. Three limitations were addressed in this research: (1) a majority of statistical road safety models use average annual daily traffic (AADT) to represent traffic volume and do not explicitly capture differences in traffic volume patterns throughout the day, even though crash risk is known to change by time of day, (2) statistical road safety models that use AADT on the "right-hand side" of the model equation do not explicitly account for the fact that these values for AADT are estimates with estimation errors, leading to potential bias in model estimation results, and (3) the current state-of-the-practice in road safety research often involves "starting over" with each study, choosing a model functional form based on the data fit, and letting the estimation results drive interpretations, without fully utilizing previous study results. These limitations were addressed by: (1) estimating the daily traffic patterns (by time of day) using geo-spatial interpolation methods, (2) accounting for measurement error in AADT estimates using measurement error models of expected crash frequency, and (3) incorporating prior knowledge on the safety effects of explanatory variables into regression models of expected crash frequency through informative priors in a Bayesian methodological framework. These alternative approaches to address the selected observational road safety study limitations were evaluated using data from rural, two-lane highways in the states of Utah and Washington. The datasets consisted of horizontal curve segments, for which crash data, roadway geometric features, operational characteristics, roadside features, and weather data were obtained. The results show that the methodological approaches developed in this research will allow road safety researchers and practitioners to accurately evaluate the expected road safety effects. These methods can further be used to increase the accuracy and repeatability of study results, and ultimately expand the current practice of evaluating regression models of expected crash frequency in observational road safety studies

Master of Science

thesisFor more than twenty years, the introduction of reliability-based analysis into roadway geometric design has been investigated. This type of probabilistic geometric design analysis is well suited to explicitly address the level of variability and randomness associated with design inputs when compared to a more deterministic design approach. In this study, reliability analysis was used to estimate the probability distribution of operational performance that might result from basic number of lanes decisions made to achieve a design level of service on a freeway. The concept is demonstrated using data from Interstate 15 and Interstate 80 in Utah. The basic traffic count data used for analysis were obtained from Utah Department of Transportation (UDOT). To account for the uncertainty in the design inputs, statistical distributions were developed and reliability analysis was carried out using Monte Carlo simulation. A statistical software Minitab was used to develop statistical distributions of design inputs involving variability from the traffic count data. Minitab was also used to run Monte Carlo simulation by generating random samples of the design inputs. The outcome of this probabilistic analysis is a distribution of vehicle density for a given number of lanes during the design hour. The main benefit of reliability analysis is that it enables designers to explicitly consider uncertainties in their decision-making and to illustrate specific values of the distributions that correspond their target level of service (e.g., the 65th through 85th percentile density corresponds to the design level of service). The results demonstrate how uncertainty in estimates of K (i.e., the percent of daily traffic in the design hour), directional distribution, percent heavy-vehicles, and free-flow speed significantly contribute to the variation in the vehicle density on a freeway