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.

Safety Effectiveness of High-Speed Expressway Signals, 2005

The safety benefit of signalizing intersections of high-speed divided expressways is considered. Analyses were conducted on 50 and 55 mph and on 55 mph only intersections, comparing unsignalized and signalized intersections. Results of the 55 mph analysis are included in this report. Matched-pair analysis indicates that generally, signalized intersections have higher crash rate but lower costs per crash. On the other hand, before-and-after analysis (intersections signalized between 1994 and 2001) indicates lower crash rates (~30 percent) and total costs (~10 percent) after signalization. Empirical Bayes (EB) adjusted before-and-after analysis reduces estimates of safety benefit (crash rate) to about 20 percent. The study shows how commonly used analyses can differ in their results, and that there is great variability in the safety performance of individual signalized locations. This variability and the effect of EB adjustment are demonstrated through the use of innovative graphics.

Changing Diets in China’s Cities: Empirical Fact or Urban Legend, November 2006

China’s economic reforms, which began in 1978, resulted in remarkable income growth, and urban Chinese consumers have responded by dramatically increasing their consumption of meat, other livestock products, and fruits and by decreasing consumption of grain-based foods. Economic prosperity, a growing openness to international markets, and domestic policy reforms have changed the food marketing environment for Chinese consumers and may have contributed to shifts in consumer preferences. The objective of this paper is to uncover evidence of structural change in food consumption among urban residents in China. Both parametric and nonparametric methods are used to test for structural change in aggregate household data from 1981 to 2004. The tests provided a reasonably clear picture of changing food consumption over the study period.

A Transportation Safety Planning Tool for the City of Ames, August 2011

The historically-reactive approach to identifying safety problems and mitigating them involves selecting black spots or hot spots by ranking locations based on crash frequency and severity. The approach focuses mainly on the corridor level without taking the exposure rate (vehicle miles traveled) and socio-demographics information of the study area, which are very important in the transportation planning process, into consideration. A larger study analysis unit at the Transportation Analysis Zone (TAZ) level or the network planning level should be used to address the needs of development of the community in the future and incorporate safety into the long-range transportation planning process. In this study, existing planning tools (such as the PLANSAFE models presented in NCHRP Report 546) were evaluated for forecasting safety in small and medium-sized communities, particularly as related to changes in socio-demographics characteristics, traffic demand, road network, and countermeasures. The research also evaluated the applicability of the Empirical Bayes (EB) method to network-level analysis. In addition, application of the United States Road Assessment Program (usRAP) protocols at the local urban road network level was investigated. This research evaluated the applicability of these three methods for the City of Ames, Iowa. The outcome of this research is a systematic process and framework for considering road safety issues explicitly in the small and medium-sized community transportation planning process and for quantifying the safety impacts of new developments and policy programs. More specifically, quantitative safety may be incorporated into the planning process, through effective visualization and increased awareness of safety issues (usRAP), the identification of high-risk locations with potential for improvement, (usRAP maps and EB), countermeasures for high-risk locations (EB before and after study and PLANSAFE), and socio-economic and demographic induced changes at the planning-level (PLANSAFE).

Safety and Mobility Impacts of Winter Weather --Phase 3, SPR RB01-012, 2014

Highway agencies spend millions of dollars to ensure safe and efficient winter travel. However, the effectiveness of winter-weather maintenance practices on safety and mobility are somewhat difficult to quantify. Safety and Mobility Impacts of Winter Weather - Phase 1 investigated opportunities for improving traffic safety on state-maintained roads in Iowa during winter-weather conditions. In Phase 2, three Iowa Department of Transportation (DOT) high-priority sites were evaluated and realistic maintenance and operations mitigation strategies were also identified. In this project, site prioritization techniques for identifying roadway segments with the potential for safety improvements related to winter-weather crashes, were developed through traditional naïve statistical methods by using raw crash data for seven winter seasons and previously developed metrics. Additionally, crash frequency models were developed using integrated crash data for four winter seasons, with the objective of identifying factors that affect crash frequency during winter seasons and screening roadway segments using the empirical Bayes technique. Based on these prioritization techniques, 11 sites were identified and analyzed in conjunction with input from Iowa DOT district maintenance managers and snowplow operators and the Iowa DOT Road Weather Information System (RWIS) coordinator.