An investigation of brake failure related crashes and injury severity on mountainous roadways in Wyoming.

INTRODUCTION: Although the braking system plays a key role in a safe and smooth vehicular operation, it has not been given proper attention and hence brake failures are still underrepresented in traffic safety. The current body of literature on brake failure-related crashes is very limited. Moreover, no previous study was found to extensively investigate the factors associated with brake failures and the corresponding injury severity. This study aims to fill this knowledge gap by examining brake failure-related crashes and assessing the factors associated with the corresponding occupant injury severity. METHOD: The study first performed a Chi-square analysis to examine the relationship among brake failure, vehicle age, vehicle type, and grade type. Three hypotheses were formulated to investigate the associations between the variables. Based on the hypotheses, vehicles aged more than 15 years, trucks, and downhill grade segments seemed to be highly associated with brake failure occurrences. The study also applied the Bayesian binary logit model to quantify the significant impacts of brake failures on occupant injury severity and identified various vehicle, occupants, crash, and roadway characteristics. CONCLUSIONS AND PRACTICAL APPLICATIONS: Based on the findings, several recommendations regarding enhancing statewide vehicle inspection regulation were outlined.

Suitability Study of Using UAVs to Estimate Landfilled Fly Ash Stockpile.

The decrease in fly ash production due to the shift in coal industries toward a green environment has impacted many concrete industries as fly ash is a significant component in cement and concrete. It is critical for concrete industries to identify the availability of fly ash in landfills to meet their demand if the supply decreases. This paper aims to analyze the suitability of UAVs in determining the fly ash stockpile volumes. A laboratory test is performed to validate the proposed UAV method. Then, a real quarry site is selected to demonstrate the suitability in a large scale. The results indicate that the UAVs estimate the most accurate volume of the stockpile when the flight height is about five times the stockpile height. A considerable range of 3.5-5 times the stockpile height is most suitable for quantity takeoff. The findings of this study provide a recommendation for choosing the most appropriate technology for the quantitative estimation of fly ash in existing landfills on a large scale.

The mixed-mixed multinomial logit model for identification of factors to the passengers' seatbelt use.

A better understanding of the underlying factors to the choice of seatbelt use could contribute to the policy solutions, which consequently enhance the rate of seatbelt usage. To achieve that goal, it is important to obtain unbiased and reliable results by employing a valid statistical technique. In this paper, the latent class (LC) model was extended to account for unobserved heterogeneity across parameters within the same class. The random parameter latent class, or mixed-mixed (MM) model, is an extension of the mixed and LC models by adding another layer to the LC model, with an objective of accounting for heterogeneity within a same class. The results indicated that although the LC model outperformed the mixed model, the standard LC model did not account for the whole heterogeneity in the dataset and adding an extra layer for changing the parameter across the observations result in an improvement in a model fit. The results indicated that seatbelt status of the driver, vehicle type, day of a week, and driver gender are some of factors impacting whether or not passengers would wear their seatbelts. It was also observed that accounting for day of a week, drivers' gender, and type of vehicle heterogeneities in the second layer of the MM model result in a better fit, compared with the LC technique. The results of this study expand our understanding about factors to the choice of seatbelt use while capturing extra heterogeneity of the front-seat passengers' choice of seatbelt use. This is one of the earliest studies implemented the technique in the context of the traffic safety, with individual-specific observations.

Contributory factors to the severity of single-vehicle rollover crashes on a mountainous area, generalized additive model.

The purpose of this study was to identify contributory factors to severity of rollover crashes in the mountainous state of Wyoming. These crashes account for more than half of all roadway fatalities in Wyoming, compared with the average of the U.S. rollover-related fatality crashes, which stands at 33%. In this study, the standard generalized linear model (GLM) was extended to the method of generalized additive model (GAM) to determine if giving more flexibility provides more realistic point estimates of the factors to the rollover crash severity. The results highlighted the superiority of the GAM compared with the GLM in terms of confusion matrix accuracy and Akaike Information Criterion (AIC). The results of the GAM highlighted that the majority of important factors that contribute to rollover crash severity are related to drivers' characteristics such as driving while under influence of drugs, being under an emotional condition, driving with no valid driver license, and driving with suspended drivers' license. Also, it was found that the impact of passenger vehicles on the severity of rollover crashes is not stable and varies based on the gender of drivers. Only two predictors were considered based on the smooth functions including posted speed limit and drivers' age. We accounted for non-linearity of those two predictors by means of cubic spline smooth function.

Impact of mountainous interstate alignments and truck configurations on rollover propensity.

INTRODUCTION: Combined horizontal and vertical alignments are frequently utilized in mountainous interstates in Wyoming. The roll stability of trucks on these challenging terrain conditions is of great concern for transportation officials. The impact of curve characteristics combined with truck configurations has not been considered in the literature due to data availability issues related to the weight and Center of Gravity (CG) payload height of trucks. METHOD: High-fidelity vehicle dynamics simulation modeling is employed to investigate the rollover propensity of trucks navigating curves of varying geometric design and truck characteristics. A multinomial regression model was then developed to further quantify the impact of these key factors and the effect of their interactions on rollover safety margins. RESULTS: It was shown that complying with the assigned speed limits of the curved roadways is not enough to navigate a curve without experiencing a rollover under some circumstances. The CG payload height and the operating speeds have the highest impact on the safety margins of a truck rollover. Steeper downgrades would amplify the impact of the gross weight of a truck. Tighter curves would also raise the impact of the truck configurations. CONCLUSIONS: This study assessed the curve speed limits and revealed that the exciting approach to assigning safe speed limits should be modified according to the aforementioned factors. For the first time, findings from this study shed light on the direction and magnitude of the impact of the truck configurations coupled with curve features that contribute to truck rollover safety margins. Practical Applications: This study revealed the impact of truck configurations on the roll stability of trucks and pointed out critical cases that should be treated very cautiously by drivers. This assists transportation agencies in assigning more appropriate speed limits of curved roadways according to truck conditions.

Investigating factors influencing rollover crash risk on mountainous interstates.

INTRODUCTION: The risk of rollover crashes on mountainous roads is a major concern for transportation authorities due to adverse weather conditions and complex topography. Such crashes incur hazardous consequences on road users' lives. Therefore, it is crucial to identify the contributing factors that give rise to these severe crashes in order to identify preventive measures. Furthermore, exploring the potential sources of heterogeneity of rollover crash contributing factors is equally important. METHOD: By having a dataset of single-vehicle crashes that occurred on mountainous curved sections in Wyoming, we applied a random parameters, otherwise known as mixed, logit model to identify the factors contributing to the increased risk of rollovers. Vehicle, driver, roadway, environmental, and crash attributes variables were considered as potential predictors in the model. Then, random parameters were identified to uncover the unobserved effects. RESULTS: Weather, road surface conditions, and speeding were found to have a significant impact on rollover crash risk. These factors were also found to exhibit unobserved heterogeneity effects, which could be attributed to the drivers' responses and conditions. Furthermore, it was found that the propensity of rollovers was higher for sports utility vehicles (SUVs) and pick-up trucks among other vehicle types. CONCLUSIONS: The results indicated that investigating the impact of these factors on the risk of rollovers while taking into account unobserved heterogeneity effects is an essential step for implementing countermeasures to reduce the frequency and severity of rollover crashes. Practical applications: This study uncovered insights into the factors that lead vehicles to overturn. This aids in suggesting appropriate safety countermeasures that mitigate the occurrences of rollover crashes to transportation agencies.

Assessment of commercial truck driver injury severity based on truck configuration along a mountainous roadway using hierarchical Bayesian random intercept approach.

For the last decade, disaggregate modeling approach has been frequently practiced to analyze truck-involved crash injury severity. This included truck-involved crashes based on single and multi-vehicles, rural and urban locations, time of day variations, roadway classification, lighting, and weather conditions. However, analyzing commercial truck driver injury severity based on truck configuration is still missing. This paper aims to fill this knowledge gap by undertaking an extensive assessment of truck driver injury severity in truck-involved crashes based on various truck configurations (i.e. single-unit truck with two or more axles, single-unit truck pulling a trailer, semi-trailer/tractor, and double trailer/tractor) using ten years (2007-2016) of Wyoming crash data through hierarchical Bayesian random intercept approach. The log-likelihood ratio tests were conducted to justify that separate models by various truck configurations are warranted. The results obtained from the individual models demonstrate considerable differences among the four truck configuration models. The age, gender, and residency of the truck driver, multi-vehicles involvement, license restriction, runoff road, work zones, presence of junctions, and median type were found to have significantly different impacts on the driver injury severity. These differences in both the combination and the magnitude of the impact of variables justified the importance of examining truck driver injury severity for different truck configuration types. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (24%-42%) of intra-crash correlation (effects of the common crash-specific unobserved factors) in driver injury severity within the same crash. Finally, based on the findings of this study, several potential countermeasures are suggested.

Modeling crashes involving children, finite mixture cumulative link mixed model.

Despite the efforts in the literature review on the traffic safety of children, the majority of past studies mainly focused only on the child's seatbelt status, or its position while ignoring other underlying factors that might contribute to the severity of those crashes. Inclusion of ther factors is especially important for a mountainous state like Wyoming with one of the highest rates of children's traffic fatality in the country. Thus, this study is conducted to fill the gap by identifying important factors contributing to the severity of crashes involving children. Here child is defined as any passengers under 9 years old. A first step in identifying factors to the severity of crashes involving children is implementing a reliable statistical method that could account for heterogeneity across various observations. So, in this study, to account for the heterogeneity in the dataset, the standard cumulative link model (CLM) was extended to the random effect model, while instead of assigning the subjective attribute for random effect, an objective hierarchy through the finite mixture modeling (FMM) was used. The FMM was employed in the context of the CLM to prevent the loss of information due to disaggregation of the dataset into the homogeneous datasets. The comparison results highlighted that the random effect model by the objective hierarchy would result in a significant improvement in the model fit compared with the standard cumulative link model. The results highlighted factors such as safety equipment in use, type of collision, and various drivers' characteristics and actions such as belting condition, alcohol and drug involvement are some of the factors contributing to the severity of child crashes. As expected, the main findings of our results highlighted that various drivers' actions and behaviors are the main causes that children would undergo a higher severity level in crashes. An extensive discussion regarding the implications of the results and the implemented statistical method were given in the context of the manuscript.

Application of machine learning technique for optimizing roadside design to decrease barrier crash costs, a quantile regression model approach.

INTRODUCTION: In-transport vehicles often leave the travel lane and encroach onto natural objects on the roadsides. These types of crashes are called run-off the road crashes (ROR). Such crashes accounts for a significant proportion of fatalities and severe crashes. Roadside barrier installation would be warranted if they could reduce the severity of these types of crashes. However, roadside barriers still account for a significant proportion of severe crashes in Wyoming. The impact of the crash severity would be higher if barriers are poorly designed, which could result in override or underride barrier crashes. Several studies have been conducted to identify optimum values of barrier height. However, limited studies have investigated the monetary benefit associated with adjusting the barrier heights to the optimal values. In addition, few studies have been conducted to model barrier crash cost. This is because the crash cost is a heavily skewed distribution, and well-known distributions such as linear or poison models are incapable of capturing the distribution. A semi-parametric distribution such as asymmetric Laplace distribution can be used to account for this type of sparse distribution. METHOD: Interaction between different predictors were considered in the analysis. Also, to account for exposure effects across various barriers, barrier lengths and traffic volumes were incorporated in the models. This study is conducted by using a novel machine-learning-based cost-benefit optimization to provide an efficient guideline for decision makers. This method was used for predicting barrier crash costs without barrier enhancement. Subsequently the benefit was obtained by optimizing traffic barrier height and recalculating the benefit and cost. The trained model was used for crash cost prediction on barriers with and without crashes. RESULTS: The results of optimization clearly demonstrated the benefit of optimizing the heights of road barriers around the state. Practical Applications: The findings can be utilized by the Wyoming Department of Transportation (WYDOT) to determine the heights of which barriers should be optimized first. Other states can follow the procedure described in this paper to upgrade their roadside barriers.

A correlated random parameters approach to investigate large truck rollover crashes on mountainous interstates.

Rollover risk on mountainous interstates is a major concern for transportation agencies due to the combined mixed effects of adverse weather conditions and complex topography. Such crashes incur hazardous consequences on road users' lives. Therefore, a correlated random parameters logit modeling framework was employed to investigate the influences of crash precursors on rollover risk to identify effective safety countermeasures. This approach was selected to account for both the crash contributing factors' unobserved heterogeneity effects and the correlations among those effects. The data, used in this study, were those of single-truck crashes on Wyoming's interstate curved sections. The traditional logit and uncorrelated random parameters, or mixed, logit models were attempted as well. With that, the analysis results indicated that the fit of the correlated random parameters logit model was superior to those of the others. It also revealed insights regarding correlations among random parameters that were obscure in the other models. According to its results, on average, veering off the road, overcorrections and severe winds raised the risk of single-truck rollover crashes. On the other hand, median barriers, roadside guardrails, tight horizontal curves, icy road surfaces, wet surfaces and surfaces covered by loose material, in general, reduced the likelihood of rollovers. Correlations, such as those between severe winds and icy surfaces and those between roadside guardrails and icy surfaces, were inferred as well. This study's results will assist transportation officials in efficiently identifying appropriate countermeasures to mitigate the impact of truck rollovers particularly during adverse weather conditions.

Bayesian hierarchical modelling of traffic barrier crash severity.

Severe vehicle crashes have resulted in a large-scale social and economic loss. As a result, the reduction of those crashes has become one of the key objectives of policy makers. Although traffic barriers have been utilized to reduce the run-of-road crash severity, still those crashes account for a high number of severe crashes. Previous studies of traffic barrier crashes often either ignore the heterogeneity across different traffic barrier types, or just focus on specific types of traffic barriers. Thus, this study developed a Bayesian Hierarchical model (BHM) to identify the contributory factors impacting the severity of traffic barrier crashes while accounting for that heterogeneity. The assessment of model fit, inter-class correlation (ICC) coefficient, and deviance information criterion (DIC) all favoured the use of the BHM. Besides accounting for the heterogeneity between barrier types, the interaction across variables shoulder width and traffic barrier height were incorporated into the analysis. Due to the differences in traffic barrier design and vehicle performance across different roadway classifications, only Wyoming interstate traffic barrier were considered. Results indicated that there is an important interaction term between traffic barrier height and shoulder width so that the impact of these two predictors should not be separated. In addition, having a citation record, negotiating a curve, being a female driver, non-speed compliance, alcohol involvement, and showing emotional signs at the time of crash were factors increasing the severity of traffic barrier crashes. On the other hand, having a turn before hitting a traffic barrier, being a younger driver, and driving in adverse weather conditions were factors that significantly decrease the severity of traffic barrier crashes.

The impact of traffic barrier geometric features on crash frequency and injury severity of non-interstate highways.

INTRODUCTION: The main objective of this research is to investigate the effect of traffic barrier geometric characteristics on crashes that occurred on non-interstate roads. METHOD: For this purpose, height, side-slope rate, post-spacing, and lateral offset of about 137 miles of traffic barriers were collected on non-interstate (state, federal aid primary, federal aid secondary, and federal aid urban) highways in Wyoming. In addition, crash reports recorded between 2008 and 2017 were added to the traffic barrier dataset. The safety performance of traffic barriers with regards to their geometric features was analyzed in terms of crash frequency and crash severity using random-parameters negative binomial, and random-parameters ordered logit models, respectively. RESULTS: From the results, box beam barriers with a height of 27-29 inches were less likely to be associated with injury and fatal injury crashes compared to other barrier types. On the other hand, the likelihood of a severe injury crash was found to be higher for box beam barriers with a height taller than 31 inches. Both W-beam and box beam barriers with a post-spacing between 6.1 and 6.3 inches reduced the probability of severe injury crashes. In terms of the crash frequency, flare traffic barriers had a lower crash frequency compared to parallel traffic barriers. Non-interstate roads without longitudinal rumble strips were associated with a higher rate of traffic barrier crashes.

Application of Quantile Mixed Model for modeling Traffic Barrier Crash Cost.

Run-off the road crashes account for a significant proportion of severe injuries to vehicle occupants. Traffic barriers have been installed with an objective to keep vehicles on the roadway, and prevent them from hitting natural obstacles like trees or boulders. However, still injuries and fatalities of barrier crashes account for high proportion of fatalities on roadway. Due to challenging geometrics characteristics of Wyoming's roadway, a high mileage of barriers has been installed in the state. The high mileages of barriers result in a high number of barrier crashes in terms of crash frequency and severity due to high exposure. Previous studies mainly focused on crash frequency or individual crash severity. However, it has been recognized the importance of accounting for both aspects of crash severity, and crash frequency. So, in this study, crashes are aggregated across different barriers, and those crashes were converted into costs by considering the impacts of both crash severity and frequency. However, one of the main challenges of this type of dataset is highly skewness of crash data due to its sparseness nature. An improper use of model distribution of crash cost would result in biased estimations of the covariates, and erroneous results. Thus, in order to address this issue, a semi-parametric method of quantile regression technique was implemented to account for the skewness of the response by relaxing model distribution parameters. Also, to account for the heterogeneity in the dataset due to barriers' types, a random intercept model accounting for the structure of the data was implemented. In addition, interaction terms between significant predictors were considered. Understanding what factors with which magnitude contribute to the barrier crash costs is crucial for the future barriers' optimization process. Thus, contributory factors to barriers crash cost with high, medium, and low values, corresponding to 95th, 70th, and 60th percentiles were considered, and a comparison was made across these models. It was found, for instance, that although factors such as rollover, driving under the influence, and presence of heavy truck all have contributory impacts on the cost of crashes, their impacts are greater on higher quantiles, or higher barriers' costs. These models were compared from various perspectives such as intra class correlation (ICC), and standard error of coefficients. This study highlights the changes in coefficient estimates while modeling crash costs.

Assessment of tire failure related crashes and injury severity on a mountainous freeway: Bayesian binary logit approach.

Although tires maintain the only contact between the vehicle and the ground, tire failures are still underrepresented in traffic safety assessments. Vehicle stability and safety can deteriorate significantly by a sudden tire failure. The current body of literature on tire failure-related crashes is limited, and no previous study was found to extensively investigate the factors associated with tire failures and the corresponding injury severity. The contributions of this study include (i) investigating the factors affecting tire failures, (ii) assessing the impacts of tire failures on occupant injury severity, and (iii) demonstrating the necessity of statewide tire inspection regulations. An extensive exploratory analysis was performed using ten years (2007-2016) of historical crash data along I-80 in Wyoming. Binary logistic regression with the Bayesian inference approach was applied to develop two separate models: tire failure and injury severity model. The results from the tire failure model showed that vehicle speeds greater than 75 mph, commercial motor vehicles, summer season, daytime, the presence of rough surface, downgrades, and concrete pavement are all related to higher tire failure occurrences. On the other hand, the incidence of a tire failure in a crash significantly contributed to more severe injuries when combined with any of the following instances: fire or explosion, rollover, guardrail hits, runoff road, angle, rear-end, clear weather, speeding, downgrades, and curved segments. With the incorporation of the random intercept in the modeling procedure, the injury severity analysis found a strong presence (42 %) of intra-crash correlation (effects of the common crash-specific unobserved factors) in occupant injury severity within the same crash. Finally, based on the findings of the study, recommendations are provided to alleviate tire-related problems.

Estimating the effect of geometric features of side traffic barriers on crash severity of interstate roads in Wyoming.

Past roadside safety studies mostly evaluated the impact of traffic barrier geometric features using simulation tools or by conducting field crash tests. While past simulation and field crash tests could present important findings for upgrading the geometric design of traffic barriers, there is still a gap regarding conducting an actual data analysis on side traffic barriers crashes with regards to their geometric dimensions. This paper aims at filling this gap by combining a statewide dataset of side traffic barrier geometric features with historical crashes on interstate roads in Wyoming. Therefore, geometric features including system height, post-spacing, lateral offset (from the edge of pavement), and side-slope of over 150 miles of side traffic barriers were inventoried by conducting a field survey on interstate roads in Wyoming. For the statistical analysis, a random-parameters ordered logit model was utilized to investigate variables impacting crash severity of side traffic barriers. It was found that system height could significantly impact the crash severity of side box beam barriers. Box beam barriers with a system height between 25 and 31 in. were identified to be less severe in comparison to other height categories, while showing minimum risks of severe crashes in the system height of 29-31 in.. On the other hand, box beam barriers with a height taller than 31 in. may increase crash severity.

Investigating occupant injury severity of truck-involved crashes based on vehicle types on a mountainous freeway: A hierarchical Bayesian random intercept approach.

Earlier research on injury severity of truck-involved crashes focused primarily on single-truck and multi-vehicle crashes with truck involvement, or investigated truck-involved injury severity based on rural and urban locations, time of day variations, lighting conditions, roadway classification, and weather conditions. However, the impact of different vehicle-truck collisions on corresponding occupant injury severity is lacking. Therefore, this paper advances the current research by undertaking an extensive assessment of the occupant injury severity in truck-involved crashes based on vehicle types (i.e., single-truck, truck-car, truck-SUV/pickup, and truck-truck), and identifies the major occupant-, crash-, and geometric-related contributing factors. A series of log-likelihood ratio tests were conducted to justify that separate model by vehicle and occupant types are warranted. Injury severity models were developed using 10 years of crash data (2007-2016) on I-80 in Wyoming through binary logistic modeling with a Bayesian inference approach. The modeling results indicated that there were significant differences between the influences of a variety of variables on the injury severities when the truck-involved crashes are broken down by vehicle types and separated by occupant types. The age and gender of occupants, truck driver occupation, driver residency, sideswipes, presence of junctions, downgrades, curves, and weather conditions were found to have significantly different impacts on the occupant injury severity in different vehicle-truck crashes. Finally, with the incorporation of the random intercept in the modeling procedure, the presence of intra-crash and intra-vehicle correlations (effects of the common crash- and vehicle-specific unobserved factors) in injury severities were identified among persons within the same crash and same vehicle.

Application of multi-group structural equation modelling for investigation of traffic barrier crash severity.

The severity of traffic barrier in the literature has been modelled considering different factors including human, environmental and road/traffic barrier characteristics. However, all these factors are interacting in a complicated way, and a real relationship between these factors is still unclear. A structural equation modelling (SEM) can be adopted to capture the intricate relationships between the contributory factors and latent (unseen) factors. This study was conducted by adopting multi-group SEM to unlock the complicated relationship between confounding factors and traffic barrier crash severity by considering differences across two important groups. Due to the possible difference across different highway systems, multi-group SEM was used instead of standard SEM to account for the differences across highway and interstate roadway system. SEM is a combination of confirmatory and path analysis, which could examine relationship between different observed and latent factors. Besides using factor analysis for identification of latent factors, item/variable cluster analysis was conducted to identify all the latent factors. Although cluster analysis often has been used in other fields, this is the first time this method has been applied in transportation problems for SEM modeling. The inclusion of the factors identified by cluster analysis show an improvement in goodness of fit. This study was conducted to evaluate the traffic barrier crash severity in terms of death, injury and severity of crashes. It examined the nature and causes of severe traffic barrier in Wyoming. The results indicated that different factors contribute to the severity size of traffic barrier crashes including different traffic barrier types, demographic characteristics, weather conditions, and indirect impact of force direction. The results indicated that collision force is a latent factor with highest impact on crash severity compared with other latent factors. Different models with different number of latent were compared based on different goodness-of-fit indices and a best model, with an acceptable model fit, was selected between them. A more discussion about the model presented in the manuscript.

Examination of the severity of two-lane highway traffic barrier crashes using the mixed logit model.

INTRODUCTION: Vehicles in transport sometimes leave the travel lane and encroach onto natural or artificial objects on the roadsides. These types of crashes are called run-off the road crashes, which account for a large proportion of fatalities and severe crashes to vehicle occupants. In the United States, there are about one million such crashes, with roadside features leading to one third of all road fatalities. Traffic barriers could be installed to keep vehicles on the roadways and to prevent vehicles from colliding with obstacles such as trees, boulder, and walls. The installation of traffic barriers would be warranted if the severity of colliding with the barrier would be less severe than colliding with other fix objects on the sides of the roadway. However, injuries and fatalities do occur when vehicle collide with traffic barriers. A comprehensive analysis of traffic barrier features is lacking due to the absence of traffic barrier features data. Previous research has focused on simulation studies or only a general evaluation of traffic barriers, without accounting for different traffic barrier features. METHOD: This study is conducted using an extensive traffic barrier features database for the purpose of investigating the impact of different environmental and traffic barrier geometry on this type of crash severity. This study only included data related to two-lane undivided roadway systems, which did not involve median barrier crashes. Crash severity is modeled using a mixed binary logistic regression model in which some parameters are fixed and some are random. RESULTS: The results indicated that the effects of traffic barrier height, traffic barrier offset, and shoulder width should not be separated, but rather considered as interactions that impact crash severity. Rollover, side slope height, alcohol involvement, road surface conditions, and posted speed limit are some factors that also impact the severity of these crashes. The effects of gender, truck traffic count, and time of a day were found to be best modeled with random parameters in this study. The effects of these risk factors are discussed in this paper. PRACTICAL APPLICATIONS: Results from this study could provide new guidelines for the design of traffic barriers based upon the identified roadway and traffic barrier characteristics.

Investigating the effect of geometric dimensions of median traffic barriers on crashes: Crash analysis of interstate roads in Wyoming using actual crash datasets.

INTRODUCTION: Despite the numerous safety studies done on traffic barriers' performance assessment, the effect of variables such as traffic barrier's height has not been identified considering a comprehensive actual crash data analysis. This study seeks to identify the impact of geometric variables (i.e., height, post-spacing, sideslope ratio, and lateral offset) on median traffic barriers' performance in crashes on interstate roads. METHOD: Geometric dimensions of over 110 miles median traffic barriers on interstate Wyoming roads were inventoried in a field survey between 2016 and 2018. Then, the traffic barrier data collected was combined with historical crash records, traffic volume data, road geometric characteristics, and weather condition data to provide a comprehensive dataset for the analysis. Finally, an ordered logit model with random-parameters was developed for the severity of traffic barrier crashes. Based on the results, traffic barrier's height was found to impact crash severity. RESULTS: Crashes involving cable barriers with a height between 30″ and 42″ were less severe than other traffic barrier types, while concrete barriers with a height shorter than 32″ were more likely involved with severe injury crashes. As another important finding, the post-spacing of 6.1-6.3 ft. was identified as the least severe range in W-beam barriers. PRACTICAL APPLICATIONS: The results show that using flare barriers should reduce the number of crashes compared to parallel barriers.

Modeling safety performance of the new super DDI design in terms of vehicular traffic and pedestrian.

Most existing interchanges in the United States were built more than 50 years ago based on old design policies. Many of these designs are not consistent with current traffic and pedestrian demands anymore. Therefore, this inconsistency has caused problems regarding operation and safety. This paper models safety performance of a new design called super diverging diamond interchange (super DDI) considering VISSIM simulation and surrogate safety assessment model (SSAM). Six other interchange designs were also considered for comparing to the new super DDI design. Overall, 252 simulation scenarios were modeled in VISSIM and then tested by SSAM. Also, the same number of tests were considered to evaluate pedestrian performance of the designs considered in this study. Based on results, super DDI showed a high potential either in terms of traffic safety and pedestrian safety. In comparison to other designs, super DDI had the minimum number of simulated conflicts as well as the lowest mean speed and time to collision (TTC) of simulated conflicts. This fact shows that super DDI could perform promising in reducing crash frequency and crash severity. Reviewing the geometry of the super DDI, lower traffic involving in each conflict point should be one of the main reasons for the promising traffic safety performance of the design. Regarding pedestrian performance, super DDI got the third rank of the lowest mean pedestrian travel times. There is no free-flowing conflict between vehicles and pedestrians in a super DDI. Therefore, pedestrian paths of the super DDI are predicted to be safer than the paths in a typical DDI design.