Assessing quality of crash modification factors estimated by empirical Bayes before-after methods

Before-after study with the empirical Bayes(EB)method is the state-of-the-art approach for estimating crash modification factors(CMFs).The EB method not only addresses the regression-to-the-mean bias,but also improves accuracy.However,the performance of the CMFs derived from the EB method has never been fully investigated.This study aims to examine the accuracy of CMFs estimated with the EB method.Artificial realistic data(ARD)and real crash data are used to evaluate the CMFs.The results indicate that:1)The CMFs derived from the EB before-after method are nearly the same as the true values.2)The estimated CMF standard errors do not reflect the true values.The estimation remains at the same level regardless of the pre-assumed CMF standard error.The EB before-after study is not sensitive to the variation of CMF among sites.3)The analyses with real-world traffic and crash data with a dummy treatment indicate that the EB method tends to underestimate the standard error of the CMF.Safety researchers should recognize that the CMF variance may be biased when evaluating safety effectiveness by the EB method.It is necessary to revisit the algorithm for estimating CMF variance with the EB method.

Developing crash modification factors for roundabouts using a cross-sectional method

The objective of the current study was to evaluate traffic and geometric features and their influences on the safety performance of roundabouts by developing suitable crash modification factors(CMFs).The cross-sectional method can be applied as an alternative method to estimate the CMFs when before-and-after studies are impractical to apply,e.g.,lack of data from the period after implementing treatments.To accomplish the study objective,CMFs were derived from generalised linear models(GLMs),i.e.,negative binomial(NB)regression,using data collected on regional roundabouts in Toowoomba City,Australia.Six years of crash data from 49 roundabouts included all recorded crashes as well as traffic and geometric features for the entire roundabouts.Several candidate models were developed using the GLMs.Five models were selected based on statistical significance,goodness-of-fit(GOF)measures,and cumulative residual(CURE)analysis.The results show that increasing the number of entry lanes,entry width,entry radius,traffic volume,circulatory roadway width,weaving width,and speed limit have positive effects on roundabout safety.On the other hand,increasing the number of legs,number of exit lanes,exit width,exit radius,weaving length,central island diameters,and presence of fixed object on a central island have negative effects on roundabout safety.The study shows that quantifying the risk factors can support road safety stakeholders to identify safety improvements at roundabouts more effectively and efficiently.

Bayesian spatial modeling to incorporate unmeasured information at road segment levels with the INLA approach:A methodological advancement of estimating crash modification factors

Estimating safety effectiveness of roadway improvements and countermeasures,using cross-sectional models,generally requires large amounts of data such as road geometric and traffic-related characteristics at road segment levels.These models do not consider all confounding crash contributory factors such as driving culture and environmental conditions at the segment level due to a lack of readily available data.This may result in inaccurate models representing actual conditions at road segment levels,followed by erroneous estimations of safety effectiveness.To minimize the effect of not including such variables,this study develops a new methodology to estimate safety effectiveness of roadway countermeasures,based on generalized linear mixed models,assuming zeroinflated Poisson distribution for the response,and adjusting for spatial autocorrelation using the spatial random effect.The Bayesian approach,with Integrated Nested Laplace Approximation,was used to make inference on this model with computational efficiency.Results showed that incorporating a spatial random effect into the models provided better model fit than non-spatial models;hence,estimated safety effectiveness based on such models is more accurate.The proposed approach is a methodological advancement in traffic safety,which allows evaluation of safety effectiveness or roadway improvements when data are not readily available.

Safety effectiveness of roadway conversion with a two way left turn lane

In urban or suburban areas with a large number of access points, four-lane undivided highways are prone to crashes due to left-turning and through movements in a single lane. Many studies recommended expensive countermeasures like conversion from undivided to divided road with physical separation. One inexpensive alternative is reconfiguring the existing roadways by either increasing or decreasing the number of lanes. This study investigated the safety impact of converting four lane undivided roadways （4U） to five lane undivided roadways （ST） with a two way left turn lane （TIkrLTL）. This study used Empirical Bayes method to determine the safety impact of this inexpensive countermeasure. In this study, data from eight sites from Louisiana were collected for investigation, and site- specific crash modification factor （CMF） values were calculated. Although ST is usually not preferable due to its exposure of higher number of crashes in the existing literature, the findings of the current study indicated a positive safety impact. The benefit-cost ratio of this conversion ranges from 97 to 379. The current findings indicate that conversion of 4U to 5T is a feasible inexpensive solution for urban roadways with lower volume and a limited number of driveways.