Agencies and practitioners are often testing new and innovative strategies for improving driver compliance with traffic regulations. However, in evaluating these strategies, researchers often rely on simple before-and-after methods that suffer from several flaws that can result in misleading results and an inaccurate assessment of a strategy's effectiveness. Specifically, such studies frequently omit control groups to account for other factors that may influence driver behavior aside from the experimental change. Furthermore, these studies often focus on only one compliance measure, and their results are often poorly suited for making comparisons to other compliance strategies that have been evaluated through other before-and-after studies due to the unique details of the experimental sites chosen in each case. Finally, analyses based on the traditional before-and-after approach do not properly account for the period of instability following the experimental change, nor do they make any attempt to characterize it-rather, these studies typically rely on assumptions about how long the instability will last beforehand (and subsequently ignore this period), or fail to account for it at all.In this dissertation, we examine these flaws and propose a framework that avoids or corrects for them. Among the key features of our proposed framework are a model to describe the driver response to an experimental change (e.g., the increase in compliance following the implementation of a compliance strategy), the inclusion of a baseline prediction model that incorporates control group compliance rates along with other relevant covariates to project what the behavior of the experimental group would have been in the absence of the experimental change, and a measure of effectiveness based on the estimated long-term performance of the compliance strategy after accounting for the period of instability immediately following the experimental change. The framework incorporates the previously documented Novelty Effect, which refers to a short-term boost in compliance due to the novelty of the change, and combines it with a Driver Awareness or driver learning effect, which describes the tendency of the behavioral response (e.g., the boost in compliance) to occur gradually after the experimental change-rather than instantaneously after it-as a result of users taking some time to become aware of the change and to respond appropriately to it. The result is a characteristic driver response curve that is initially increasing after the experimental change, rather than decreasing as is conventionally assumed. When we take a detailed look at compliance data following the implementation of two compliance strategies, we find that the data support this pattern of initially 2 An Analysis Framework for Evaluation of Traffic Compliance Measures Campbell increasing compliance predicted by our framework.To illustrate the use of this framework, we consider the case of drivers failing to yield on a series of freeway entrance ramps along Interstate 10 in Los...