Improving Traffic Safety Culture in Iowa, 2011

Vehicle crashes rank among the leading causes of death in the United States. In 2006, the AAA Foundation for Traffic Safety “made a long- term commitment to address the safety culture of the United States, as it relates to traffic safety, by launching a sustained research and educational outreach initiative.” An initiative to produce a culture of safety in Iowa includes the Iowa Comprehensive Highway Safety Plan (CHSP). The Iowa CHSP “engages diverse safety stakeholders and charts the course for the state, bringing to bear sound science and the power of shared community values to change the culture and achieve a standard of safer travel for our citizens.” Despite the state’s ongoing efforts toward highway safety, an average of 445 deaths and thousands of injuries occur on Iowa’s public roads each year. As such, a need exists to revisit the concept of safety culture from a diverse, multi-disciplinary perspective in an effort to improve traffic safety. This study summarizes the best practices and effective laws in improving safety culture in the United States and abroad. Additionally, this study solicited the opinions of experts in public health, education, law enforcement, public policy, social psychology, safety advocacy, and traffic safety engineering in a bid to assess the traffic safety culture initiatives in Iowa. Recommendations for improving traffic safety culture are offered in line with the top five Iowa CHSP safety policy strategies, which are young drivers, occupant protection, motorcycle safety, traffic safety enforcement and traffic safety improvement program, as well as the eight safety program strategies outlined in the CHSP. As a result of this study, eleven high-level goals were developed, each with specific actions to support its success. The goals are: improve emergency medical services response, toughen law enforcement and prosecution, increase safety belt use, reduce speeding-related crashes, reduce alcohol-related crashes, improve commercial vehicle safety, improve motorcycle safety, improve young driver education, improve older driver safety, strengthen teenage licensing process, and reduce distracted driving.

From Empirical Bayes to Full Bayes: Methods for Analysing Traffice Safety Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this slight, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.

From Empirical Bayes to Full Bayes: Methods for Analyzubg Traffic Safey Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this light, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.