An Analysis Framework for Evaluation of Traffic Compliance Measures

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 increasing compliance predicted by our framework.;The first compliance strategy to be analyzed was increasing the size of the signage used. Specifically, the size of the Yield sign was increased to from 36 inches across to 48 inches, which increased its viewable area by 78%. After identifying the best prediction model for the behavior of drivers at this experimental location using the pre-change (or "before") data, the baseline compliance predictions were made for the post-change weeks. By comparing the observed levels of compliance to these predicted rates, we could then evaluate the improvement in compliance week by week in the "after" period due to the larger sign. Through curve-fitting techniques, we identified the driver response curve that best characterized the observed degree of improvement by week after the change, and from this we concluded that the larger Yield sign resulted in a 22% reduction in non-compliance at the on-ramp, with a 95% confidence interval of 10% to 26%. This measure of effectiveness excludes an estimated 11% of drivers who are deliberately noncompliant at this location and would not be expected to respond to anything apart from increased enforcement. The Driver Awareness effect was found to be significant in the model for the larger Yield sign at a 5% level, although the Novelty Effect was not.;Our results show that the traditionally-held notion of a driver response (following the implementation of a compliance strategy) that starts off strong and tapers off over time is inaccurate, and that the true driver response includes an initial period of increasing compliance instead. This has important consequences for analyses of compliance strategies, as it indicates that data immediately following a strategy's implementation is likely to under-represent, rather than over-represent, its long term effectiveness. (Abstract shortened by UMI.)...