Craig Lyon

EMPIRICAL BAYES PROCEDURE FOR RANKING SITES FOR SAFETY INVESTIGATION BY POTENTIAL FOR SAFETY IMPROVEMENT

The identification of sites requiring investigation for possible safety treatment is one of the most important aspects of infrastructure safety management and has been the subject of considerable research aimed at improving the efficiency of the process. The more recent techniques use the empirical Bayes (EB) method for estimating the safety of specific sites. A refinement of the EB method that is conceptually sound and inherently simple to apply is the focus of this research. With this refinement, the EB estimate of the safety of a site is compared with its expected safety to determine the potential for safety improvement, which forms the basis for ranking sites for safety investigation. A comparative evaluation is provided of the proposed method against other EB methods and the more conventional ones, with data for signalized intersections and two-lane rural highway sections. The refined EB method is shown to be comparatively efficient.

Comparison of empirical Bayes and full Bayes approaches for before-after road safety evaluations.

The empirical Bayes (EB) approach has now gained wide acceptance among researchers as the much preferred one for the before-after evaluation of road safety treatments. In this approach, the before period crash experience at treated sites is used in conjunction with a crash prediction model for untreated reference sites to estimate the expected number of crashes that would have occurred without treatment. This estimate is compared to the count of crashes observed after treatment to evaluate the effect of the treatment. This procedure accounts for regression-to-the-mean effects that result from the natural tendency to select for treatment those sites with high observed crash frequencies. Of late, a fully Bayesian (FB) approach has been suggested as a useful, though complex alternative to the empirical Bayes approach in that it is believed to require less data for untreated reference sites, it better accounts for uncertainty in data used, and it provides more detailed causal inferences and more flexibility in selecting crash count distributions. This paper adds to the literature on comparing the two Bayesian approaches through empirical applications. The main application is an evaluation of the conversion of road segments from a four-lane to a three-lane cross-section with two-way left-turn lanes (also known as road diets). For completeness, the paper also summarizes the results of an earlier application pertaining to the evaluation of conversion of rural intersections from unsignalized to signalized control. For both applications, the estimated safety effects from the two approaches are comparable.

Validation of FHWA Crash Models for Rural Intersections: Lessons Learned

A national-level safety analysis tool is needed to complement existing analytical tools for assessment of the safety impacts of roadway design alternatives. FHWA has sponsored the development of the Interactive Highway Safety Design Model (IHSDM), which is roadway design and redesign software that estimates the safety effects of alternative designs. Considering the importance of IHSDM in shaping the future of safety-related transportation investment decisions, FHWA justifiably sponsored research with the sole intent of independently validating some of the statistical models and algorithms in IHSDM. Statistical model validation aims to accomplish many important tasks, including (a) assessment of the logical defensibility of proposed models, (b) assessment of the transferability of models over future time periods and across different geographic locations, and (c) identification of areas in which future model improvements should be made. These three activities are reported for five proposed types of rural intersection crash prediction models. The internal validation of the model revealed that the crash models potentially suffer from omitted variables that affect safety, site selection and countermeasure selection bias, poorly measured and surrogate variables, and misspecification of model functional forms. The external validation indicated the inability of models to perform on par with model estimation performance. Recommendations for improving the state of the practice from this research include the systematic conduct of carefully designed before-and-after studies, improvements in data standardization and collection practices, and the development of analytical methods to combine the results of before-and-after studies with cross-sectional studies in a meaningful and useful way.

Validation of a Full Bayes methodology for observational before―after road safety studies and application to evaluation of rural signal conversions

The objective of the study on which the paper is based was to explore the application of fully Bayesian methods for before-after road safety studies. Several variations of the methodology were evaluated with a simulated dataset in which hypothetical treatments with no safety effect were randomly assigned to high accident locations to mimic the common site selection process in road jurisdictions. It was confirmed that the fully Bayesian method by estimating no safety effect can account for the regression-to-the-mean that results from this biased site selection process. The fully Bayesian method was then applied to California rural intersection data to evaluate the safety effect of conversion from stop to signalized control. The results were then compared with those from the empirical Bayesian method, currently the accepted approach for conducting unbiased before-after evaluations. This comparison was generally favorable in that FB can provide similar results as EB.

Multijurisdictional Safety Evaluation of Red Light Cameras

The use of red light camera (RLC) systems has risen dramatically in the United States in recent years. The size of the problem, the promise shown by RLC systems in other countries, and the paucity of definitive U.S. studies have motivated a multijurisdictional U.S. study. The fundamental objective of this study, which was sponsored by FHWA, was to determine the effectiveness of the RLC systems in reducing crashes at monitored intersections as well as jurisdictionwide. Phase I involved the development of a detailed experimental design that included collection of background information, establishment of study goals, selection of potential study jurisdictions, and specification of statistical methodology. In Phase 2, an empirical Bayes before-and-after study used data from seven jurisdictions across the United States, with a total of 132 treatment sites. Effects detected were consistent in direction with those found in many previous studies—a decrease in right-angle crashes and an increase in rear-end crashe...

PEDESTRIAN COLLISION PREDICTION MODELS FOR URBAN INTERSECTIONS

In more recent safety analysis methods, collision prediction models can be used for identifying intersections with promise of safety improvement and for evaluating the effects of treatment. Considerable effort has been directed at developing collision prediction models, but little has been directed at pedestrian collisions. Collisions involving motor vehicles and pedestrians pose a significant safety problem, principally in urban areas, where the levels of vehicle-pedestrian conflicts are high. Data from Toronto, Canada, are used in the development of pedestrian collision prediction models for three- and four-legged urban intersections, with and without signal control. These models, which relate safety to pedestrian and vehicle traffic volumes, can be used to identify locations that might be targeted for treatment and to help evaluate treatment effects. Models are developed by using pedestrian and vehicular volumes and vehicle volumes only. It is seen that the use of pedestrian volume information results in a much richer model, emphasizing the importance of collecting this information in routine traffic counting programs. An important issue for collision prediction models is transferability to other jurisdictions. This is especially important in the case of pedestrian collision models, because many jurisdictions may not have data sets containing sufficient collisions and pedestrian volume counts with which to calibrate reliable models. Data from the city of Hamilton, Ontario, were used to test the transferability of the Toronto four-legged signalized intersection model. The test was successful: the recalibrated Toronto models predicted collision numbers that were very close to those predicted by the model calibrated directly for the Hamilton data.

Empirical Investigation of Interactive Highway Safety Design Model Accident Prediction Algorithm: Rural Intersections

One major gap in transportation system safety management is the ability to assess the safety ramifications of design changes for both new road projects and modifications to existing roads. To fulfill this need, FHWA and its many partners are developing a safety forecasting tool, the Interactive Highway Safety Design Model (IHSDM). The tool will be used by roadway design engineers, safety analysts, and planners throughout the United States. As such, the statistical models embedded in IHSDM will need to be able to forecast safety impacts under a wide range of roadway configurations and environmental conditions for a wide range of driver populations and will need to be able to capture elements of driving risk across states. One of the IHSDM algorithms developed by FHWA and its contractors is for forecasting accidents on rural road segments and rural intersections. The methodological approach is to use predictive models for specific base conditions, with traffic volume information as the sole explanatory variable for crashes, and then to apply regional or state calibration factors and accident modification factors (AMFs) to estimate the impact on accidents of geometric characteristics that differ from the base model conditions. In the majority of past approaches, AMFs are derived from parameter estimates associated with the explanatory variables. A recent study for FHWA used a multistate database to examine in detail the use of the algorithm with the base model-AMF approach and explored alternative base model forms as well as the use of full models that included nontraffic-related variables and other approaches to estimate AMFs. That research effort is reported. The results support the IHSDM methodology.

Traffic Safety Evaluation of Video Advertising Signs

Road authorities are under increasing pressure from advertisers to allow video advertising in the right-of-way but are understandably concerned about whether video signs constitute a driving hazard. At the City of Torontos request, a comprehensive assessment of traffic safety impacts related to such signs was carried out in a series of studies involving three downtown intersections and an urban expressway site. An on-road eye fixation study was carried out to determine if drivers look at video advertising signs. Conflict studies were conducted to determine if there were more conflicts on intersection approaches with visible video signs than on those without such signs. A before-and-after sign installation study of headways and speeds on the urban expressway was carried out. Crashes were compared before and after sign installation at the expressway and three intersection sites. Finally, a public survey was conducted to determine if video advertising was perceived to affect traffic safety. On the basis of ...

Safety effectiveness of converting signalized intersections to roundabouts

Roundabouts may be new builds but often are conversions from existing intersections. When contemplating the later, there is a need to estimate the safety effects of conversions. Several studies have estimated large reductions in crashes and severity; however, these results pertain mainly to conversions from unsignalized intersections. Results for conversions from signalized intersections have been less conclusive or consistent and tend to be somewhat dated. The objective of this study was to fill this void by estimating the safety effectiveness of converting signalized intersections to roundabouts. Several states helped to identify signalized intersections that were converted to roundabouts in the recent past. In total, 28 conversions were identified in the United States. The empirical Bayes (EB) method was employed in an observational before-after study to estimate the safety effects. Data from select states were also used in a cross-sectional analysis to investigate the compatibility of results from cross-sectional and before-after studies. The EB results indicated a safety benefit for converting signalized intersections to roundabouts. There were reductions in both total and injury crashes, with a larger benefit for injury crashes. Further analysis indicated that the safety benefit of roundabouts for total crashes decreased as traffic volumes increase, a result that suggests the need for the development of a crash modification function, a task for which more data would be required. The safety benefit for injury crashes was sustained across all traffic volumes. Both trends were supported by the cross-sectional analysis. Based on the analysis, it appears that roundabouts have the potential to significantly reduce crashes and severity at signalized intersections. A key aspect of the study was the estimation of the standard deviation of the distribution of the CMF in addition to the conventionally estimated standard error of the mean CMF value. For some CMFs, especially the CMFs for total crashes, the standard deviation of the distribution was larger than the standard error of the mean value of the CMF, indicating substantial variation in the treatment effect across sites.

Safety Effectiveness of Selected Treatments at Urban Signalized Intersections

This study conducted a before–after evaluation by means of the empirical Bayes methodology for four types of treatments at signalized intersections with data from Winston-Salem, North Carolina. The results indicated that changing to protected left-turn phasing from permissive or permissive–protected phasing could lead to a virtual elimination of left-turn crashes but other crashes, which were likely to be less severe, could increase. Conversion from nighttime flashback to regular phasing seemed effective in reducing nighttime crashes. Replacing 8-in. signal heads with 12-in. heads seemed effective in reducing right-angle crashes, but this measure could increase other, less-severe crashes. Adding another red-signal lens to an existing one or changing from permissive to permissive–protected left-turn phasing did not seem particularly effective in reducing crashes, but these results were not definitive because they were based on a limited number of sites. Further research using data from other jurisdictions is needed, so that more definitive conclusions can be made about the safety effectiveness of these treatments.