Search

Highlights • Field data from Montana used to assess impacts of posted speed limits (PSL) set below engineering recommendations (ER). • Speed limit compliance is high when difference between ER limit and PSL is small (5 mph). • Presence of heavy enforcement also improves speed limit compliance at these locations. • Reduction in crash frequency observed when difference between ER limit and PSL is small (5 mph). Abstract This study quantifies the operational and safety impacts of setting posted speed limits below engineering recommendations using field data from rural roads in Montana. Vehicle operating speeds and historical crash data were collected at multiple sites with posted speed limits set equal to engineering recommendations and sites with posted speed limits set lower than engineering recommendations. Linear, quantile and logistic regression models were estimated to predict mean operating speed, 85th percentile operating speed and speed limit compliance, respectively, as a function of various roadway characteristics and level of speed enforcement. The Empirical-Bayes before-after approach was also used to develop crash modification factors (CMFs) that describe the expected change in total and fatal + injury crash frequency when setting posted speed limits lower than engineering recommendations. Because safety data were collected over a long time period, temporal adjustments were incorporated to account for yearly changes in crash reporting, traffic characteristics and other variables. The results revealed that speed limit compliance worsened as the difference between the engineering recommended and posted speed limits increased. The presence of verified heavy police enforcement reduced both mean and 85th-percentile operating speeds by approximately 4 mph and increased speed limit compliance. The safety analysis found a statistically significant reduction in total, fatal + injury, and property damage only (PDO) crash frequency at locations with posted speed limits set 5 mph lower than engineering recommendations. Locations with posted speed limits set 10 mph lower than engineering recommendations experienced a decrease in total and PDO crash frequency, but an increase in fatal + injury crash frequency. The safety effects of setting speed limits 15 to 25 mph lower than engineering recommendations were less clear, as the results were not statistically significant, likely due to the small sample of sites included in the evaluation. Overall, the results suggest that setting posted speed limits 5 mph lower than the engineering recommended practice may result in operating speeds that are more consistent with the posted speed limits and overall safety benefits. [ABSTRACT FROM AUTHOR]

This study integrates a causal inference framework to the Empirical Bayes (EB) before-after method to develop generalizable safety effect estimates (i.e., crash modification factor (CMF)). The method considers approaches to estimate the average treatment effect for the treated (ATT), average treatment effect for the untreated (ATU), and average treatment effect (ATE). The current EB method is shown to estimate ATT while ATE is what is typically desired in traffic safety research. Modifications to the current EB method to estimate ATU and ATE are provided. The method is then applied to a dataset with a “no-treatment” scenario where the treatments were: 1) randomly selected and 2) selected based on crash history. Given the “no-treatment” outcome, it is known that the CMFs should have a value of 1 in order to be considered accurate. The standard negative binomial and mixed effects negative binomial regression models were applied in the analysis. It was found that, of the two regression methods, the ATE CMFs developed using the standard negative binomial were the most accurate. Finally, potential sources of bias in the EB method are discussed. [ABSTRACT FROM AUTHOR]

A variety of different study designs and analysis methods have been used to evaluate the performance of traffic safety countermeasures. The most common study designs and methods include observational before–after studies using the empirical Bayes method and cross-sectional studies using regression models. The propensity scores-potential outcomes framework has recently been proposed as an alternative traffic safety countermeasure evaluation method to address the challenges associated with selection biases that can be part of cross-sectional studies. Crash modification factors derived from the application of all three methods have not yet been compared. This paper compares the results of retrospective, observational evaluations of a traffic safety countermeasure using both before–after and cross-sectional study designs. The paper describes the strengths and limitations of each method, focusing primarily on how each addresses site selection bias, which is a common issue in observational safety studies. The Safety Edge paving technique, which seeks to mitigate crashes related to roadway departure events, is the countermeasure used in the present study to compare the alternative evaluation methods. The results indicated that all three methods yielded results that were consistent with each other and with previous research. The empirical Bayes results had the smallest standard errors. It is concluded that the propensity scores with potential outcomes framework is a viable alternative analysis method to the empirical Bayes before–after study. It should be considered whenever a before–after study is not possible or practical. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Past design consistency research has demonstrated the relationship between operating speeds and geometric design features on two-lane rural highways. However, little is known about the relationship between geometric design consistency and traffic safety. In this study, design consistency is referred to as the difference between operating speed and inferred design speed, and design consistency density is measured to account for the effect of elements upstream and downstream of the study element. To perform the design consistency-safety evaluation in the present study, geometric design, roadway inventory, crash, and operating speed data were collected along two case-study highways in central Pennsylvania (U.S. 322 and PA 350). Several count regression model formulations were used to explore the statistical association between design consistency and total crash frequency. A statistically significant positive association between geometric design consistency and safety was found. Design consistency surrounding the study elements was also found to increase the expected crash frequency in the study element. The significant effects of design consistency and design consistency density measures on crash frequency for the two case-study sites indicate that there is value in further considering the relationship between design consistency and safety in future research, particularly in the context of the highway safety manual crash-prediction algorithm. [ABSTRACT FROM AUTHOR]

The author outlines his study about roadway lighting and its impact on accident prevention. A comprehensive methodology was adopted to assess the implicit ties between roadway lighting, visibility, and crash frequency. The author said he had access to the U.S. Federal Highway Administration's Highway Safety Information System database. He created photometric distributions of lighted and unlighted intersections in Minnesota using lighting calculation software.

Abstract: 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 &y& Elsevier]

Abstract: More than 5.5 million police-reported traffic crashes occurred in the United States in 2009, resulting in 33,808 fatalities and more than 2.2 million injuries. Significant funds are expended annually by federal, state, and local transportation agencies in an effort to reduce traffic crashes. Effective safety management involves selecting highway and street locations with potential for safety improvements; correctly diagnosing safety problems; identifying appropriate countermeasures; prioritizing countermeasure implementation at selected sites; and, evaluating the effectiveness of implemented countermeasures. Accurate estimation of countermeasure effectiveness is a critical component of the safety management process. In this study, a statistical modeling framework, based on propensity scores and potential outcomes, is described to estimate countermeasure effectiveness from non-randomized observational data. Average treatment effects are estimated using semi-parametric estimation methods. To demonstrate the framework, the average treatment effect of fixed roadway lighting at intersections in Minnesota is estimated. The results indicate that fixed roadway lighting reduces expected nighttime crashes by approximately 6%, which compares favorably to other, recent lighting-safety research findings. [Copyright &y& Elsevier]

Abstract: 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 &y& Elsevier]

Abstract: National- and state-level guidance documents conclusively state that fixed lighting improves intersection safety. The sentiment is consistent with other design and safety manuals and is supported by a series of consistent safety findings; however, most published lighting-safety research is focused on rural, stop-controlled intersections and is limited by several methodological issues. The relationship between safety and intersection lighting at rural, signalized and urban locations is not as well documented. Methodological advancements in highway safety analysis justify new estimations of the safety effects of intersection lighting. This paper describes a proposed framework to estimate the safety effects of fixed lighting at a variety of intersection types and locations. Several key issues are explored including availability of relevant crash, lighting, and roadway inventory data; relevant data element structures; proposed analysis taxonomies to assess lighting-safety effects within and across different intersection classifications; specification and estimation of models to estimate expected crash frequencies during day and night; techniques to interpret model parameters, including variable elasticity; and tests of model transferability across states. A sample framework execution using Minnesota intersection data is provided. Results indicate a much lower overall safety benefit from lighting than published studies, but are consistent with estimates included in Highway Safety Manual research. [Copyright &y& Elsevier]

Abstract: 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. [ABSTRACT FROM AUTHOR]

The interactive highway safety design model (IHSDM) is a suite of software analysis tools used to evaluate safety and operational effects of geometric design on two-lane rural highways. IHSDM checks existing or proposed two-lane highway designs against relevant design policy values and provides estimates of a design’s expected safety and operational performance. This paper describes efforts to apply IHSDM to two existing rural roadways (one an arterial and the other a collector) in Pennsylvania and to compare the crash prediction and speed profile results to observed speed and crash data for these same two roadway segments. When considering both case study roadways together, the use of site-specific crash history data, adjusted using the empirical Bayes procedure, and a county-level calibration factor, produced crash frequency predictions that most closely matched the observed crash frequencies. The design consistency module output on the arterial roadway closely matched the observed speeds. On the collector roadway, the predicted and observed speeds did not match closely, thus, underscoring the need to expand the design consistency module capabilities to consider low-speed, complex alignments. [ABSTRACT FROM AUTHOR]

Abstract: Cultural consensus analysis is a statistical method that can be used to assess participant responses to survey questions. The technique concurrently estimates the knowledge of each survey participant and estimates the culturally correct answer to each question asked, based on the existence of consensus among survey participants. The main objectives of this paper are to present the cultural consensus methodology and apply it to a set of median design and safety survey data that were collected using the Delphi method. A total of 21 Delphi survey participants were asked to answer research questions related to cross-median crashes. It was found that the Delphi panel had agreeable opinions with respect to the association of average daily traffic (ADT) and heavy vehicle percentage combination on the risk of cross-median crashes; relative importance of additional factors, other than ADT, median width, and crash history that may contribute to cross-median crashes; and, the relative importance of geometric factors that may be associated with the likelihood of cross-median crashes. Therefore, the findings from the cultural consensus analysis indicate that the expert panel selected to participate in the Delphi survey shared a common knowledge pool relative to the association between median design and safety. There were, however, diverse opinions regarding median barrier type and its preferred placement location. The panel showed a higher level of knowledge on the relative importance regarding the association of geometric factors on cross-median crashes likelihood than on other issues considered. The results of the cultural consensus analysis of the present median design and safety survey data could be used to design a focused field study of median safety. [Copyright &y& Elsevier]

Nighttime driving behavior differs from that during the day because of differences in the driver’s field of view. At night, drivers must rely on their vehicle headlamps to illuminate the roadway. It is essential then that the roadway delineation system provide the appropriate lane guidance to motorists when navigating a roadway, particularly one that is curvilinear. A nighttime driving experiment was conducted to collect user perception data of various pavement markings and markers applied to horizontal curves. The effectiveness of each pavement marking was rated using a subjective scale. A fuzzy inference system was used to analyze the subjective pavement marking and marker evaluation ratings provided by the research participants. Pavement marking effectiveness, horizontal curve sharpness, and driver age were used to develop a fuzzy index for nighttime driving condition (FIND). Based on the FIND, the results indicate that drivers prefer that a combination of treatments be applied to horizontal curves rather than only a single treatment. A bright centerline, bright edgeline, and bright retroreflective raised pavement maker combination treatment, and a bright centerline and bright edgeline combination treatment, resulted in the highest FIND score. A bright, 8-in. (20.3 cm) edgeline, applied alone to a horizontal curve, scored the lowest FIND. [ABSTRACT FROM AUTHOR]

• New SPF functional form proposed that allows traffic volume to have different elasticities in different ranges. • Proposed form allows for continuous relationship between predicted crash frequency and traffic volume. • Proposed models shown to better fit observed data that traditional and other forms. 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. [ABSTRACT FROM AUTHOR]

• Horizontal Curve Warning Pavement Markings in Pennsylvania were Evaluated. • Empirical Bayes Before-After Analysis was Used. • Multiple crash types and severities along horizontal curves were considered. • Findings indicate that the pavement markings reduce run-off road crashes by 23–35 %. Roadway departure crashes contribute to a large proportion of fatal and injury crashes in the United States. These crash types are more likely to occur along horizontal curve sections of a roadway. Countermeasures that prevent vehicles from departing the roadway is one method to mitigate roadway departure crashes. Pennsylvania has deployed on-pavement horizontal curve warning markings in advance of horizontal curves on two-lane rural highways as a roadway departure crash reduction strategy. This study used an Empirical Bayes (EB) before-after study design to evaluate the safety effects of the horizontal curve warning pavement markings. A total of 263 treatment sites and more than 21,000 reference sites were included in the evaluation. Crash modification factors were developed for total, fatal plus injury, run-off-road, nighttime, nighttime run-off-road, and nighttime fatal plus injury crashes. The point estimates for each of these crashes ranged from 0.65 to 0.77 – the results were statistically significant for total and fatal plus injury crashes at the 95th-percentile confidence level. [ABSTRACT FROM AUTHOR]

• A Bayesian propensity score analysis is compared to a frequentist propensity score analysis. • A no-treatment study design using observational data was used for the comparison. • The Bayesian nominally outperformed the frequentist method in the largest sample. • The methods produced near-identical results in the medium sample. • Neither method closely approximated assumed, true safety effect using small sample. The propensity score matching method has been used to estimate safety countermeasure (treatment) effects from observational crash data. Within the counterfactual framework, propensity score matching is used to balance the covariates between treatment and control groups. Recent studies in traffic safety research have demonstrated the strength of this method in reducing the bias caused by treatment site selection. However, several general issues associated with safety effect estimates may still influence the effectiveness and robustness of this method. In the present study, Bayesian methods were integrated into the propensity score matching method. Bayesian models are known for their ability to capture heterogeneity and modeling uncertainty. This may help mitigate unobserved variable effects in the roadway and crash data. Furthermore, the sampling-based algorithm used for Bayesian estimation yields more consistent estimates in small region analysis than estimates from frequentist modeling. In this study, a dataset that was used to evaluate the safety effects of the dual application of shoulder and centerline rumble strips on two-lane rural highways was acquired. Only data from the before treatment period were used in a no-treatment effect analysis in order to compare the results of a Bayesian propensity score analysis to a frequentist propensity score analysis. Because no treatment was applied during the analysis period, it was assumed that there would be no treatment effect, or a crash modification factor equal to 1.0. The Bayesian propensity score matching method nominally outperformed the frequentist propensity score matching method in the largest sample and produced near-identical results in the medium sample, but neither method closely approximated the assumed, true crash modification factor in the small sample analysis. A simulation study is recommended to further study the effects of sample size and confounding factors when comparing the Bayesian and frequentist propensity score matching methods. [ABSTRACT FROM AUTHOR]

• Model-based recursive partitioning (MOB) produces similar crash frequency model estimation compared to heterogeneity model. • MOB algorithm improves predictive power relative to traditional safety and heterogeneity models based on hold-out sample. • MOB is promising alternative to traditional count regression models to identify covariate subgroup patterns in safety data. • The posted speed limit and the presence of a passing zone were key splitting variables in the MOB algorithm. • Traffic volume and horizontal curve density were identified as random parameters in heterogeneity model. Count regression models have been applied widely in traffic safety research to estimate expected crash frequencies on road segments. Data mining algorithms, such as classification and regression trees, have recently been introduced into the field to overcome some of the assumptions associated with statistical models. However, these data-driven algorithms usually provide non-parametric output, making it difficult to draw statistical inference or to evaluate how independent variables are associated with expected crash frequencies. In this paper, the model-based recursive partitioning (MOB) algorithm is applied in a crash frequency application. The algorithm incorporates the concept of recursive partitioning data in tree models and develops user-defined statistical models as outputs. The objective of this paper is to explore the potential of the MOB algorithm as a methodological alternative to parametric modeling methods in crash frequency analysis. To accomplish the objective, a standard negative binomial (NB) regression model, a NB model developed using the MOB algorithm, adjusted NB models which incorporate variables identified by the MOB algorithm, and a random parameters NB model are compared using 8 years of data collected from two-lane rural highways in Pennsylvania. The models are compared in terms of data fitness, sign and magnitude of statistical association between the independent and dependent variables, and predictive power. The results show that the MOB-NB model yields better data fitness than other NB models, and provides similar performance to the RPNB model, suggesting that the MOB-NB model may be capturing unobserved heterogeneity by dividing the data into subgroups. The presence of a passing zone and posted speed limit are two covariates identified by the MOB algorithm that differentiate variable effects among subgroups. In addition, the MOB-NB model provides the highest prediction accuracy based on the training and test data sets, although the difference among models is small. The comparison results reveal that the MOB algorithm is a promising alternative to identify covariates, evaluate variable associations and instability, and make predictions in a crash frequency context. [ABSTRACT FROM AUTHOR]

• Simultaneous estimation of crash frequency by either type or severity considered. • Univariate and bivariate (without and with copulas) negative binomial regression models compared. • Bivariate models with copulas shown to outperform traditional bivariate and univariate models. • Bivariate model with normal copulas outperforms other copulas tested. Statistical models of crash frequency typically apply univariate regression models to estimate total crash frequency or crash counts by various categories. However, a possible correlation between the dependent variables or unobserved variables associated with the dependent variables is not considered when univariate models are used to estimate categorized crash counts—such as different severity levels or numbers of vehicles involved. This may lead to inefficient parameter estimates compared to multivariate models that directly consider these correlations. This paper compares the results obtained from univariate negative binomial regression models of property-damage only (PDO) and fatal plus injury (FI) crash frequencies to models using traditional bivariate and copula-based bivariate negative binomial regression models. A similar comparison was made using models for the expected crash frequency of single- (SV) and multi-vehicle (MV) crashes. The models were estimated using two-lane, two-way rural highway segment-level data from an engineering district in Pennsylvania. The results show that all bivariate negative binomial models (with or without copulas) outperformed the corresponding univariate negative binomial models for PDO and FI, as well as SV and MV, crashes. Second, the statistical association of various traffic and roadway/roadside features with PDO and FI, as well as SV and MV crashes, were not the same relative to their corresponding relationships in the univariate models. The bivariate negative binomial model with normal copula outperformed all other models based on the goodness-of-fit statistics. The results suggest that copula-based bivariate negative binomial regression models may be a valuable alternative for univariate models when simultaneously modeling two disaggregate levels of crash counts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 9 ft, 10 ft, 11 ft, and 12 ft. 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 10 ft travel lanes were found to experience the highest crash frequency relative to other lane widths. Meanwhile, roads with 9 ft 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). [ABSTRACT FROM AUTHOR]

• CMFs for adaptive traffic signal control (ATSC) systems developed using Empirical Bayes before-after study. • CMFs for ATSC system impacts at individual intersections and along entire adaptive corridors are estimated. • Temporal trends in safety performance of ATSC system also studied. • Results reveal minor increase in total crash frequency and decrease in fatal + injury crash frequency. 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. [ABSTRACT FROM AUTHOR]

Reducing the potential for crashes involving front line service workers and passing vehicles is important for increasing worker safety in work zones and similar locations. Flashing yellow warning beacons are often used to protect, delineate, and provide visual information to drivers within and approaching work zones. A nighttime field study using simulated workers, with and without reflective vests, present outside trucks was conducted to evaluate the effects of different warning beacon intensities and flash frequencies. Interactions between intensity and flash frequency were also analyzed. This study determined that intensitiesof 25/2.5 cd and 150/15 cd (peak/trough intensity) provided the farthest detection distances of the simulated worker. Mean detection distances in response to a flash frequency of 1 Hz were not statistically different from those in response to 4 Hz flashing. Simulated workers wearing reflective vests were seen the farthest distances away from the trucks for all combinations of intensity and flash frequency. [ABSTRACT FROM AUTHOR]

Abstract: 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. [ABSTRACT FROM AUTHOR]