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The study builds on previous rumble strip safety evaluations by providing some Canadian-specific experience and in the process offering some definitive insights into differences in safety effects between installations on curved and tangent segments. An empirical Bayes before–after study estimated crash modification factors (CMFs) for installing edge line rumble strips (ELRS) and centerline rumble strips (CLRS), separately and in combination, on two-lane rural roads in Ontario. Separate CMFs were estimated for ELRS and CLRS for curved and tangent segments. The estimated CMFs indicate that rumble strips can be beneficial, except for CLRS on curved segments, and especially if applied in combination on tangents. The results also indicate that edge line and dual rumble strips are more effective on curved segments, so priority should be given to their application on such segments. It is noteworthy that dual application is more safety effective than either CLRS or ELRS, for tangents and overall. [ABSTRACT FROM AUTHOR]

The article presents a study which assess the effects of maintenance treatments aimed at improving pavement condition of roads on road safety and pavement management. The research was made in two-lane rural roads in Ontario. Also cited are the use of the International Roughness Index (IRI) in the study, and the results showing that treatments were effective in reducing crashes, property damage only (PDO) crashes, and fatal plus injury (FI) crashes.

• This study explores the applicability of univariate and two-stage approaches for estimating crash frequency by severity. • The prediction accuracies of univariate and two-stage severity distribution function (SDF) methods were found to be similar. • The results highlight the potential of the two-stage SDF approach at least for curved two-lane road sections. • The two-stage SDF approach can be especially a useful method for the all too frequent cases where samples are too small. The Highway Safety Manual (HSM) procedures apply specific safety performance functions (SPFs) and crash modification factors (CMFs) appropriate for estimating the safety effects of design and operational changes to a roadway. Although the applicability of the SPFs and CMFs may significantly vary by crash severity, they are mainly based on total crash counts, with different approaches for estimation of crashes by crash severity. The variety of approaches in the HSM and in the literature in general suggests that there may be no one best approach for all situations, and that there is a need to develop and compare alternative approaches for each potential application. This paper addresses this need by demonstrating the development and comparison of alternative approaches using horizontal curves on two-lane roads as a case study. This site type was chosen because of the high propensity for severe crashes and the potential for exploring a wide range of variables that affect this propensity. To facilitate this investigation, a two-stage modeling approach is developed whereby the proportion of crashes for each severity level is estimated as a function of roadway-specific factors and traffic attributes and then applied to an estimate of total crashes from an SPF. Using Highway Safety Information System (HSIS) curve data for Washington state, a heterogeneous negative binomial (HTNB) regression model is estimated for total crash counts and then applied with severity distribution functions (SDFs) developed by a generalized ordered probit model (GOP). To evaluate the performance of this two-stage approach, a comparison is made with predictions directly obtained from estimated univariate SPFs for crash frequency by severity and also from a fixed proportion method that has been suggested in the HSM. The results revealed that, while the two-stage SDF approach and univariate approach adopt different procedures for model estimation, their prediction accuracies are similar, and both are superior to the fixed proportion method. In short, this study highlights the potential of the two-stage SDF approach in accounting for crash frequency variations by severity levels, at least for curved two-lane road sections, and especially for the all too frequent cases where samples are too small to estimate viable univariate crash severity models. [ABSTRACT FROM AUTHOR]

The combined application of centerline and shoulder rumble strips is intended to reduce the frequency of crashes by alerting drivers that they are about to leave the traveled lane. To date, this combination treatment has not been rigorously evaluated with a multistate database. To fill this void, this study evaluated treated two-lane rural road locations in three states. To account for potential site selection bias, an empirical Bayes (EB) before-after analysis was conducted. The combined results for all states indicate statistically significant crash reductions for all crash types analyzed. The crash type with the smallest crash modification factor (CMF) (i.e., the greatest crash reduction) is head-on with a CMF of 0.632 (i.e., a crash reduction of 36.8%). For run-off-road, head-on, and sideswipe-opposite direction crashes combined (i.e., lane departure crashes), the estimated CMF is 0.733. Benefit:cost (B:C) ratios were estimated to range from 28.2 to 67.7, depending on the treatment cost and service life assumption. [ABSTRACT FROM AUTHOR]

Abstract: In Europe, approximately 60% of road accident fatalities occur on two-lane rural roads. Thus, research to develop and enhance explanatory and predictive models for this road type continues to be of interest in mitigating these accidents. To this end, this paper describes a novel and extensive data collection and modeling effort to define accident models for two-lane road sections based on a unique combination of exposure, geometry, consistency and context variables directly related to the safety performance. The first part of the paper documents how these were identified for the segmentation of highways into homogeneous sections. Next, is a description of the extensive data collection effort that utilized differential cinematic GPS surveys to define the horizontal alignment variables, and road safety inspections (RSIs) to quantify the other road characteristics related to safety. The final part of the paper focuses on the calibration of models for estimating the expected number of accidents on homogeneous sections that can be characterized by constant values of the explanatory variables. Several candidate models were considered for calibration using the Generalized Linear Modeling (GLM) approach. After considering the statistical significance of the parameters related to exposure, geometry, consistency and context factors, and goodness of fit statistics, 19 models were ranked and three were selected as the recommended models. The first of the three is a base model, with length and traffic as the only predictor variables; since these variables are the only ones likely to be available network-wide, this base model can be used in an empirical Bayesian calculation to conduct network screening for ranking “sites with promise” of safety improvement. The other two models represent the best statistical fits with different combinations of significant variables related to exposure, geometry, consistency and context factors. These multiple variable models can be used, with caution, and in conjunction with results from other studies, to derive accident modification factors for these variables for design applications, and in safety assessment for smaller samples of sites for which these variables can be assembled with relative ease. [Copyright &y& Elsevier]