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Horizontal curves are locations that, as a result of the changing alignment, may be a contributing factor in roadway departure crashes. One low-cost countermeasure to mitigate crashes at these locations is the installation of the high friction surface treatment (HFST), which increases roadway friction and is intended to help keep drivers on the roadway when traversing a horizontal curve. This treatment has been implemented at numerous curves in Pennsylvania, but the overall safety effectiveness is not known. The purpose of this study is to estimate a suite of Crash Modification Factors (CMFs) for HFST applied to curve sections of undivided two-lane roadways. A novel combination of the empirical Bayes observational before-after study design and propensity score matching was used to estimate CMFs for multiple crash types, crash severities, and roadway settings (urban and rural). Propensity score matching was implemented to identify the most appropriate reference group to use within the empirical Bayes methodology. The results indicate that the installation of HFST is associated with a statistically significant decrease in all crash types and severities considered., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The American Association of State Highway and Transportation Officials' Highway Safety Manual (HSM) includes a collection of safety performance functions (SPFs) or statistical models to estimate the expected crash frequency of roadway segments, intersections, and interchanges. These models are applied in several steps of the safety management process, including to screen the road network for opportunities to improve safety and to evaluate the performance of safety countermeasure deployments. The SPFs in the HSM are generally estimated using negative binomial regression modeling. In some instances, they are estimated using annual crash frequency and site-specific (e.g., traffic volume) data, while in other instances they are estimated using aggregate crash frequency and site-specific data. This paper explores the differences that result from estimating SPFs using aggregate versus disaggregate data using the same methods as those used to estimate the SPFs in the HSM. A synthetic dataset was first used to conduct these comparisons - these data were generated in a manner that is consistent with the properties of the negative binomial distribution. Then, an observational dataset from Pennsylvania was used to compare the SPFs from both aggregate and disaggregate data. The results show that SPFs estimated using the panel (disaggregate) data and aggregated data provide similar model coefficients, although some differences may sometimes arise. However, the overdispersion parameter obtained using each dataset can differ significantly. These differences result in systematic biases in calculations of expected crash frequency when Empirical Bayes adjustments are applied, which - as the paper demonstrates - could lead to different outcomes in a network screening exercise. Overall, these results reveal that aggregating crash data might result in biased SPF outputs and lead to inconsistent Empirical Bayes adjustments., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Statistical models of expected crash frequency are referred to as Safety Performance Functions (SPFs) in the first edition of the American Association of State Highway and Transportation Officials' Highway Safety Manual (HSM). The SPFs in the HSM specify expected annual crash frequencies as a function of various roadway and roadside features, with the most important predictor variable being traffic volume, which serves as a measure of vehicle exposure to crashes. Traffic volumes are typically measured using the average annual daily traffic and are incorporated into the SPFs using a natural logarithm transformation. This specification suggests that the relationship between expected crash frequency and traffic volume increases non-linearly with a constant elasticity over the range of observed values. While researchers concur that the relationship between expected crash frequencies and traffic volume is non-linear, further exploration of the functional form of this relationship may offer additional insights concerning the association between safety performance and vehicle exposure. This paper proposes an alternative functional form for the traffic volume variable in SPFs that allows for different elasticities between traffic volume and expected crash frequency within different traffic volume ranges, while preserving the same general non-linear relationship in existing HSM SPFs. Although other forms-like the Hoerl function-have been proposed in the literature, the proposed model allows for natural breakpoints in the traffic volume for which roadway or geometric features might have varying effects on low- or high-volume roads. The proposed functional form was applied to SPFs developed for two-lane rural roadways in Pennsylvania. Comparisons with SPFs developed using the traditional and Hoerl functional forms suggest that this proposed functional form offers an improved fit and predictive performance, and thus might be considered for the development of future SPFs., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Adaptive traffic signal control (ATSC) is a novel traffic management system that is often deployed at high-volume intersections in order to mitigate traffic congestion and improve travel time reliability. While past studies have demonstrated its operational effectiveness, relatively few have focused on safety performance. Those that have tend to suffer from limitations including small sample sizes, insufficient study designs, or the lack of consideration of potential temporal and corridor effects after ATSC installation. Furthermore, results from previous studies are mixed: while many studies point to a safety improvement, more recent studies seem to indicate that ATSC systems might increase crash frequency. In light of this, a comprehensive Empirical Bayes (EB) before-after observational study was conducted using ATSC data collected throughout Pennsylvania. Crash modification factors (CMFs) were estimated based on the following different case scenarios: crash severity levels and crash types (total, fatal and injury, rear-end, and angle crashes); intersection locations (all intersections and intersections along corridors only); and, intersection configurations (3-leg and 4-leg). Temporal trends for intersection-level CMFs were examined using annual crash data in the after period. Corridor-level CMFs were also developed to quantify changes in safety performance along corridors with ATSC installed. The results suggest that ATSC is associated with a nominal increase in total and angle crashes, and an expected decrease in fatal plus injury crashes and rear-end crashes. However, the results were not statistically significant. The safety effect estimates are similar when considering intersection locations and configurations. In addition, the temporal trend analysis indicates that the safety effectiveness does not vary annually in the after period, suggesting no obvious novelty effect associated with ATSC. Finally, the magnitude of the corridor-level CMFs are slightly lower than the intersection-level CMFs, except for rear-end crashes., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Horizontal curves on two-way, two-lane rural roads pose critical safety concerns. Accurate prediction of safety performance at these locations is vital to properly allocate resources as a part of any safety management process. The current method of predicting safety performance on horizontal curves relies on the application of a safety performance function (SPF) developed using only tangent sections and adjusting this value using a crash modification factor (CMF). However, this process inherently assumes that safety performance on curves and tangent sections share the same general functional relationships with variables included in the SPF, notably traffic volumes and segment length, even though research suggests otherwise. In light of this, the goal of this paper is to systematically study the relationship between safety performance and traffic volumes on horizontal curves of two-lane, two-way rural roads and to compare this to the safety performance of tangent sections. The propensity scores-potential outcomes framework is used to help ensure similarity between tangent and curve sections considered in the study, while mixed-effects negative binomial regression is used to quantify safety performance. The results reveal that safety performance on horizontal curves differs significantly from that on tangent sections with respect to both traffic volumes and segment length. Significant differences were also found between the safety performance on tangents and curves relative to other roadway features. These results suggest that curve-specific SPFs should be considered in the next edition of the Highway Safety Manual., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Underreporting is a well-known issue in crash frequency research. However, statistical methods that can account for underreporting have received little attention in the published literature. This paper compares results from underreporting models to models that account for unobserved heterogeneity. The difference in the elasticities between the negative binomial underreporting model and random parameters negative binomial models, which accounts for unobserved heterogeneity in crash frequency models, are used as the basis for comparison. The paper also includes a comparison of the predicted number of unreported PDO crashes based on the negative binomial underreporting model with crashes that were reported to police but were not considered reportable to PennDOT to assess the ability of the underreporting models to predict non-reportable crashes. The data used in this study included 21,340 segments of two-lane rural highways that are owned and maintained by PennDOT. Reported accident frequencies over an eight year period (2005-2012) were included in the sample, producing a total of 170,468 segment-years of data. The results indicate that if a variable impacts both the true accident frequency and the probability of accidents being reported, statistical modeling methods that ignore underreporting produce biased regression coefficients. The magnitude of the bias in the present study (based on elasticities) ranged from 0.00-16.79%. If the variable affects the true accident frequency, but not the probability of accidents being reported, the results from the negative binomial underreporting models are consistent with analysis methods that do not account for underreporting., (Published by Elsevier Ltd.)

The continuous green T intersection is characterized by a channelized left-turn movement from the minor street approach onto the major street, along with a continuous through movement on the major street. The continuous flow through movement is not controlled by the three-phase traffic signal that is used to separate all other movements at the intersection. Rather, the continuous through movement typically has a green through arrow indicator to inform drivers that they do not have to stop. Past research has consistently shown that there are operational and environmental benefits to implementing this intersection form at three-leg locations, when compared to a conventional signalized intersection. These benefits include reduced delay, fuel consumption, and emissions. The safety effects of the conventional green T intersection are less clear. Past research has been limited to small sample sizes, or utilized only statistical comparisons reported crashes to evaluate the safety performance relative to similar intersection types. The present study overcomes past safety research evaluations by using a propensity scores-potential outcomes framework, with genetic matching, to compare the safety performance of the continuous green T to conventional signalized intersections, using treatment and comparison site data from Florida and South Carolina. The results show that the expected total, fatal and injury, and target crash (rear-end, angle, and sideswipe) frequencies are lower at the continuous green T intersection relative to the conventional signalized intersection (CMFs of 0.958 [95% CI=0.772-1.189], 0.846 [95% CI=0.651-1.099], and 0.920 [95% CI=0.714-1.185], respectively)., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The objective of this study is to quantify the safety performance of horizontal curves on two-way, two-lane rural roads relative to tangent segments. Past research is limited by small samples sizes, outdated statistical evaluation methods, and unreported standard errors. This study overcomes these drawbacks by using the propensity scores-potential outcomes framework. The impact of adjacent curves on horizontal curve safety is also explored using a cross-sectional regression model of only horizontal curves. The models estimated in the present study used eight years of crash data (2005-2012) obtained from over 10,000 miles of state-owned two-lane rural roads in Pennsylvania. These data included information on roadway geometry (e.g., horizontal curvature, lane width, and shoulder width), traffic volume, roadside hazard rating, and the presence of various low-cost safety countermeasures (e.g., centerline and shoulder rumble strips, curve and intersection warning pavement markings, and aggressive driving pavement dots). Crash prediction is performed by means of mixed effects negative binomial regression using the explanatory variables noted previously, as well as attributes of adjacent horizontal curves. The results indicate that both the presence of a horizontal curve and its degree of curvature must be considered when predicting the frequency of total crashes on horizontal curves. Both are associated with an increase in crash frequency, which is consistent with previous findings in the literature. Mixed effects negative binomial regression models for total crash frequency on horizontal curves indicate that the distance to adjacent curves is not statistically significant. However, the degree of curvature of adjacent curves in close proximity (within 0.75 miles) was found to be statistically significant and negatively correlated with crash frequency on the subject curve. This is logical, as drivers exiting a sharp curve are likely to be driving slower and with more awareness as they approach the next horizontal curve., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Many studies have proposed the use of a systemic approach to identify sites with promise (SWiPs). Proponents of the systemic approach to road safety management suggest that it is more effective in reducing crash frequency than the traditional hot spot approach. The systemic approach aims to identify SWiPs by crash type(s) and, therefore, effectively connects crashes to their corresponding countermeasures. Nevertheless, a major challenge to implementing this approach is the low precision of crash frequency models, which results from the systemic approach considering subsets (crash types) of total crashes leading to higher variability in modeling outcomes. This study responds to the need for more precise statistical output and proposes a multivariate spatial model for simultaneously modeling crash frequencies for different crash types. The multivariate spatial model not only induces a multivariate correlation structure between crash types at the same site, but also spatial correlation among adjacent sites to enhance model precision. This study utilized crash, traffic, and roadway inventory data on rural two-lane highways in Pennsylvania to construct and test the multivariate spatial model. Four models with and without the multivariate and spatial correlations were tested and compared. The results show that the model that considers both multivariate and spatial correlation has the best fit. Moreover, it was found that the multivariate correlation plays a stronger role than the spatial correlation when modeling crash frequencies in terms of different crash types., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

A sufficient understanding of the safety impact of lane widths in urban areas is necessary to produce geometric designs that optimize safety performance for all users. The overarching trend found in the research literature is that as lane widths narrow, crash frequency increases. However, this trend is inconsistent and is the result of multiple cross-sectional studies that have issues related to lack of control for potential confounding variables, unobserved heterogeneity or omitted variable bias, or endogeneity among independent variables, among others. Using ten years of mid-block crash data on urban arterials and collectors from four cities in Nebraska, crash modification factors (CMFs) were estimated for various lane widths and crash types. These CMFs were developed using the propensity scores-potential outcomes methodology. This method reduces many of the issues associated with cross-sectional regression models when estimating the safety effects of infrastructure-related design features. Generalized boosting, a non-parametric modeling technique, was used to estimate the propensity scores. Matching was performed using both Nearest Neighbor and Mahalanobis matching techniques. CMF estimation was done using mixed-effects negative binomial or Poisson regression with the matched data. Lane widths included in the analysis included 9ft, 10ft, 11ft, and 12ft. Some of the estimated CMFs were point estimates while others were functions of traffic volume (i.e., the CMF changed depending on the traffic volume). Roadways with 10ft travel lanes were found to experience the highest crash frequency relative to other lane widths. Meanwhile, roads with 9ft travel lanes were found to experience the lowest relative crash frequency. While this may be due to increased driver caution when traveling on narrow lanes, it is possible that unobserved factors influenced this result. CMFs for target crash types (sideswipe same-direction and sideswipe opposite-direction) were consistent with the values currently used in the Highway Safety Manual (HSM)., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Accurate estimation of the expected number of crashes at different severity levels for entities with and without countermeasures plays a vital role in selecting countermeasures in the framework of the safety management process. The current practice is to use the American Association of State Highway and Transportation Officials' Highway Safety Manual crash prediction algorithms, which combine safety performance functions and crash modification factors, to estimate the effects of safety countermeasures on different highway and street facility types. Many of these crash prediction algorithms are based solely on crash frequency, or assume that severity outcomes are unchanged when planning for, or implementing, safety countermeasures. Failing to account for the uncertainty associated with crash severity outcomes, and assuming crash severity distributions remain unchanged in safety performance evaluations, limits the utility of the Highway Safety Manual crash prediction algorithms in assessing the effect of safety countermeasures on crash severity. This study demonstrates the application of a propensity scores-potential outcomes framework to estimate the probability distribution for the occurrence of different crash severity levels by accounting for the uncertainties associated with them. The probability of fatal and severe injury crash occurrence at lighted and unlighted intersections is estimated in this paper using data from Minnesota. The results show that the expected probability of occurrence of fatal and severe injury crashes at a lighted intersection was 1 in 35 crashes and the estimated risk ratio indicates that the respective probabilities at an unlighted intersection was 1.14 times higher compared to lighted intersections. The results from the potential outcomes-propensity scores framework are compared to results obtained from traditional binary logit models, without application of propensity scores matching. Traditional binary logit analysis suggests that the probability of occurrence of severe injury crashes is higher at lighted intersections compared to unlighted intersections, which contradicts the findings obtained from the propensity scores-potential outcomes framework. This finding underscores the importance of having comparable treated and untreated entities in traffic safety countermeasure evaluations., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

To approach the goal of "Toward Zero Deaths," there is a need to develop an analysis paradigm to better understand the effects of a countermeasure on reducing the number of severe crashes. One of the goals in traffic safety research is to search for an effective treatment to reduce fatal and major injury crashes, referred to as severe crashes. To achieve this goal, the selection of promising countermeasures is of utmost importance, and relies on the effectiveness of candidate countermeasures in reducing severe crashes. Although it is important to precisely evaluate the effectiveness of candidate countermeasures in reducing the number of severe crashes at a site, the current state-of-the-practice often leads to biased estimates. While there have been a few advanced statistical models developed to mitigate the problem in practice, these models are computationally difficult to estimate because severe crashes are dispersed spatially and temporally, and cannot be integrated into the Highway Safety Manual framework, which develops a series of safety performance functions and crash modification factors to predict the number of crashes. Crash severity outcomes are generally integrated into the Highway Safety Manual using deterministic distributions rather than statistical models. Accounting for the variability in crash severity as a function geometric design, traffic flow, and other roadway and roadside features is afforded by estimating statistical models. Therefore, there is a need to develop a new analysis paradigm to resolve the limitations in the current Highway Safety Manual methods. We propose an approach which decomposes the severe crash frequency into a function of the change in the total number of crashes and the probability of a crash becoming a severe crash before and after a countermeasure is implemented. We tested this approach by evaluating the effectiveness of shoulder rumble strips on reducing the number of severe crashes. A total of 310 segments that have had shoulder rumble strips installed during 2002-2009 are included in the analysis. It was found that shoulder rumble strips reduce the total number of crashes, but have no statistically significant effect on reducing the probability of a severe crash outcome., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

A two-pronged effort to quantify the impact of lighting on traffic safety is presented. In the statistical approach, the effects of lighting on crash frequency for different intersection types in Minnesota were assessed using count regression models. The models included many geometric and traffic control variables to estimate the association between lighting and nighttime and daytime crashes and the resulting night-to-day crash ratios. Overall, the presence of roadway intersection lighting was found to be associated with an approximately 12% lower night-to-day crash ratio than unlighted intersections. In the parallel analytical approach, visual performance analyses based on roadway intersection lighting practices in Minnesota were made for the same intersection types investigated in the statistical approach. The results of both approaches were convergent, suggesting that visual performance improvements from roadway lighting could serve as input for predicting improvements in crash frequency. A provisional transfer function allows transportation engineers to evaluate alternative lighting systems in the design phase so selections based on expected benefits and costs can be made., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Problem: While observational before-after studies are considered the industry standard for developing crash modification factors (CMFs), there are practical limitations that may preclude their use in highway safety analysis. There is a need to explore alternative methods for estimating CMFs., Method: This paper employs case-control and cross-sectional analyses to estimate CMFs for fixed roadway lighting and the allocation of lane and shoulder widths., Results: Based on the case-control method, the CMF for intersection lighting is 0.886, while the cross-sectional study indicates a CMF of 0.881. The CMFs developed for lane and shoulder widths are also similar when comparing the two methods., Conclusions: This paper suggests that case-control and cross-sectional studies produce consistent results if care is taken in the study design and model development., Impact on Industry: Case-control and cross-sectional studies may provide a viable alternative to estimate CMFs when a before-after study is impractical due to data restrictions., (Copyright © 2011. Published by Elsevier Ltd.)

Although vehicle dynamics simulations have long been used in vehicle design and crash reconstruction, their use for highway design is rare. This paper investigates the safety of highway medians through iterative simulations of off-road median encroachments. The commercially available software CarSim was used to simulate over one hundred thousand encroachments, representing the entire passenger vehicle fleet and a wide range of encroachment angles, departure speeds, steering inputs, and braking inputs. Each individual simulation output was then weighted using data from previous studies to reflect the probability of each specific accident scenario occurring in a real-life median encroachment. Results of this analysis illustrate the relative influence of median cross-section geometry on the resulting accident outcomes. The simulations indicate that the overall safety of a highway median depends on the occurrence of both vehicle rollover and median crossover events, and the cross-section shape, slope, and width are all shown to greatly affect each of these incidents. An evaluation of the simulation results was conducted with vehicle trajectories from previous experimental crash tests. Further assessment of the aggregate simulation results to actual crash data was achieved through comparison with several databases of crash statistics. Both efforts showed a strong agreement between the simulations and the real-life crash data., (2010 Elsevier Ltd. All rights reserved.)

Median barrier is used to prevent cross-median crashes on divided highways. Although it is well documented that crash frequencies increase after installing median barrier, little is known about median barrier crash severity outcomes. The present study estimated a nested logit model of median barrier crash severity using 5 years of data from rural divided highways in North Carolina. Vehicle, driver, roadway, and median cross-section design data were factors considered in the model. A unique aspect of the data used to estimate the model was the availability of median barrier placement and median cross-slope data, two elements not commonly included in roadway inventory data files. The estimation results indicate that collisions with a cable median barrier increase the probability of less-severe crash outcomes relative to collisions with a concrete or guardrail median barrier. Increasing the median barrier offset was associated with a lower probability of severe crash outcomes. The presence of a cable median barrier installed on foreslopes that were between 6H:1V and 10H:1V were associated with an increase in severe crash probabilities when compared to cable median barrier installations on foreslopes that were 10H:1V or flatter., (2010 Elsevier Ltd. All rights reserved.)