Incapacitating Injury Research Articles

Examining drivers injury severity for manual and automatic transmission vehicles-involved crashes: Random parameter mixed logit model with heterogeneity in means and variances

The effect of vehicle transmission type on driver injury severities have not been thoroughly studied. The study used four-year historical crash data that occurred between the year 2019 and 2022 in Ghana. The data shows 1856 and 2272 crashes for automatic and manual transmission, respectively. The study examined the factors influencing driver injury severity in crashes involving vehicles with manual and automatic transmissions, using Random Parameter Mixed Logit Model to account for heterogeneity in the dataset. It was observed that use of manual transmission is related to a higher risk of incapacitating and fatal injuries compared to automatic transmission. Specifically, for automatic transmission vehicle-involved crashes, factors related to fatal injury were overaged vehicles, public transport, morning and evening peak hours, head-on and rollover crashes. Crashes involving saloon cars and low age cars were associated with incapacitating injury whiles rainy weather condition was related to both fatal and incapacitant injuries. Regarding manual transmission, fatal injury was associated with crashes involving male and novice drivers, cars, pickup trucks, HGV, public transports, morning and evening peak hours, rainy weather conditions and curved roads. Also, buses, private cars and trip distance were related to incapacitating injury. The rollover crashes and overaged vehicles were also associated with both fatal and incapacitating injuries. Four random parameters demonstrated heterogeneity in means, with two factors influencing the variances of two parameters for automatic transmission model. For the manual transmission model, five random parameters showed heterogeneity in means, with four variables influencing the variances of three parameters. These findings are valuable for policymakers, manufacturers, and drivers in implementing targeted interventions and safety measures to promote road safety.

Identifying the latent relationships between factors associated with traffic crashes through graphical models

Traffic safety field has been oriented toward finding the relationships between crash outcomes and predictor variables to understand crash phenomena and/or predict future crashes. In the literature, the main framework established for this purpose is based on constructing a modelling equation in which crash outcome (e.g., frequencies) is examined in relation to explanatory variables chosen based on the problem at hand. Despite the importance and success of this approach, there are two issues that are generally not discussed: 1) the latent relationships between factors associated with crashes are oftentimes not the focus of analysis or not observed; and 2) there are not many tools to make informed decisions on which variables might have an impact on the crash outcome and should be included in a safety model, particularly when observations are limited. To address these issues, this paper proposes the use of graphical models, namely a Markov random field (MRF) modelling, Bayesian network modelling, and a graphical XGBoost approach, to disclose relationship topologies of explanatory variables leading to fatal and incapacitating injury pedestrian crashes. The application of graph learning models in traffic safety has a high potential because they are not only useful to understand the mechanism behind the crash occurrence but also can assist in devising accurate and reliable prevention measures by identifying the true variable structure and essential factors jointly acting towards crash occurrence, similar to a pathological examination.

Factors affecting pedestrian injury severity in pedestrian-vehicle crashes: Insights from a data mining and mixed logit model approach

There has been a concerning rise in pedestrian fatalities resulting from traffic accidents. This study conducts an in-depth analysis of the factors that contribute to the injury severity of pedestrians considering human, vehicle, roadway, and environmental characteristics of the crashes. A novel approach is proposed by combing a data mining technique and mixed logit models. First, the Apriori algorithm is employed to uncover patterns between fatal and incapacitating injury outcomes and their influencing factors. Then, mixed logit models are developed to investigate heterogeneity across all observations under two different lighting conditions. It is found that eight variables show heterogeneity affecting the injury severity of crash outcomes. Results also indicate that drivers older than 65 years old will increase the probability of pedestrian injury severity at dark-unlighted roads. Additionally, the present of signal and double yellow line has could increase the injury severity. Creating optimal lighting conditions at pedestrian crossings during nighttime hours and enhancing safety education initiatives for pedestrians are critical factors to improve pedestrian safety. The finding of this study will benefit policymakers and road safety professionals develop more effective strategies for preventing pedestrian injuries in vehicle crashes.

Short-Duration Crash Modeling to Understand the Impact of Operating Speed on Freeway Crashes During COVID-19

Gaining an understanding of speed–crash relationships is a critical issue in highway safety research. Because of the ongoing pandemic (COVID-19) there has been a reduction in traffic volume, and some early studies explain that speeding in an environment with less traffic is associated with a high number of crashes, especially fatal and serious injury crashes. This study aims to quantify the impact of operating speed on traffic crash occurrences. The study conflated several databases (speed data, roadway inventory data, and crash data) that contain data from Dallas, Texas, spanning from 2018 to 2020, to examine the speed–crash association. Using the negative binomial Lindley regression model, this study showed that the trends of crash prediction models vary over the years (2018, 2019, and 2020) by different injury severity levels (i.e., fatal crashes, fatal and incapacitating injury crashes). The 2020 models show that operating speed measures (i.e., average operating speed) have a significant impact on crash frequencies. The magnitudes of the speed measures show variations across the models at different injury severity levels.

Analyzing the Injury Severity in Overturn Crashes Involving Sport Utility Vehicles: Latent Class Clustering and Random Parameter Logit Model

The fatal or incapacitating injury caused by overturn crashes involving sport utility vehicles (SUVs) is irreparable. The purpose of this study is to identify potential factors that affect the injury severity of overturn crashes involving SUVs and develop adequate preventive strategies. Given the unobserved heterogeneity existing in the data set, crash data in North Carolina from Highway Safety Information System (HSIS) is analyzed and separated by Latent Class Clustering into six relatively homogeneous groups. To further explore the heterogeneity, random parameter logit models are developed for each cluster, and the impacts of significant factors are estimated with marginal effects. The results reveal the heterogeneity across the clusters and the homogeneity within the same cluster. Variables (including females, people over fifty years old, improper or aggressive behavior, rural areas, high-speed limit, curved roadway, rolling and mountainous terrain, adverse weather, and poor light conditions) are associated with the injury severity of the overturn crashes involving SUVs. The findings of this study can further provide decision makers with insightful countermeasures to improve transportation safety and mitigate the injuries of overturn crashes involving SUVs.

Short Duration Crash Prediction for Rural Two-Lane Roadways: Applying Explainable Artificial Intelligence

Conventional traffic crash analysis methods often use highly aggregated data, making it difficult to understand the effects of time-varying factors on crash occurrence. In this study, the combined effect of roadway geometry, speed distribution, and weather conditions on crash occurrence and severity was investigated on short duration daily level crash data. This study collected data from four different sources on rural two-lane roadways in Texas. A machine learning method, XGBoost (eXtreme Gradient Boosting), was applied to train the data. To mitigate imbalanced data problems, a synthetic minority oversampling technique (SMOTE) was applied. The XGBoost model was trained separately on all crash occurrences and severe crash occurrences. Finally, an explainable artificial intelligence (AI) technique, SHAP (SHapley Additive exPlanation), was applied to investigate the contribution of all variables to the model’s output. The results show that annual average daily traffic has a significant impact on all crash occurrences and severe crash (fatal and incapacitating injury) occurrences on rural two-lane roadways. Moreover, weather condition factors including daily precipitation, average visibility, and the standard deviation of visibility show association with high crash occurrences. The short duration crash prediction models of this study can provide more insights into the relationships between crash, geometric variables, traffic exposure, weather, and operating speed.

Does random slope hierarchical modeling always outperform random intercept counterpart? Accounting for unobserved heterogeneity in a real-time empirical analysis of critical crash occurrence

Traffic crashes impose tremendous socio-economic losses on societies. To alleviate these concerns, countless traffic safety researches have shed light on the cognition of observable crash/crash severity contributing factors. Nonetheless, some influential factors might not be observable or measurable, referred to as unobserved heterogeneity, that could be accounted for by structuring random intercepts and slopes in hierarchical models. With this respect, although it is known random slopes can capture more unobserved heterogeneity, most previous studies utilized random intercepts to simplify result interpretations, indicating an inconsistency in the literature considering the hierarchical modeling specification. This study delves into the mentioned confusion within an empirical real-time clustering critical crashes, involving fatal or incapacitating injuries, versus non-critical crashes throughout 402-miles of Interstate-80 in Wyoming. The crash dataset was conflated with real-time traffic-related and environmental contributing factors. Regarding the inclusion of random intercepts and slopes, eleven Logistic regressions were conducted. As a data-dependent matter, results depicted random slopes, compared to random intercepts, do not necessarily enhance models’ out-of-sample predictive performance because they impose much more complexity on the models’ structure. Besides, considering the type of unobserved heterogeneity, if random slopes are required, random intercepts should be accompanied to allow data showing their true patterns.

Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas

Pedestrian safety is becoming a global concern and an understanding of the contributing factors to severe pedestrian crashes is crucial. This study analyzed crash data for San Antonio, TX, over a six-year period to understand the effects of pedestrian–vehicle crash-related variables on pedestrian injury severity based on the party at fault and to identify high-risk locations. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian crashes. High-risk locations were identified through heat maps and hotspot analysis. A failure to yield the right of way and driver inattention were the primary contributing factors to pedestrian–vehicle crashes. Fatal and incapacitating injury risk increased substantially when the pedestrian was at fault. The strongest predictors of severe pedestrian injury include the lighting condition, the road class, the speed limit, traffic control, collision type, the age of the pedestrian, and the gender of the pedestrian. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, raised medians, and the use of leading pedestrian interval and hybrid beacons are recommended.

Coping with endogeneity and unobserved heterogeneity in real-time clustering critical crash occurrences nested within weather and road surface conditions

The knot of endogeneity and unobserved heterogeneity are causes of rendering parameter estimates inconsistent in real-time crash prediction. This study intends to alleviate the potential sources of these issues in detecting critical crashes, involving fatal or incapacitating injuries, versus non-critical crashes through a 402-mile Interstate-80 in Wyoming. Among different types of endogeneity, the problem of errors-in-variables and simultaneity was respectively mitigated by conflating disaggregated real-time traffic observations with aggregated environmental conditions and removing secondary crashes from the dataset. The possibility of omitted variables and unobserved heterogeneity were dealt by using random intercepts in hierarchical modeling under Bayesian inference. Three models were calibrated. Model-1 treated all predictors as fixed parameters. Model-2 and Model-3, respectively, considered weather and road surface conditions as random intercepts. Model-2 outperformed the others where the Intraclass Correlation Coefficients confirmed that the crash dataset is more nested within weather conditions. Results indicated that critical crashes require more interaction between vehicles, and shaping backward shockwave reduces their risk on Interstate-80 with a comparatively less traffic volume. Furthermore, considering different categories of weather and road surface conditions, critical crashes are more likely to occur on dry pavement and cloudy conditions compared to the wet surface and rainy days.

Man Down Situation Detection Using an in-Ear Inertial Platform.

Man down situations (MDS) are a health or life threatening situations occurring largely in high-risk industrial workplaces. MDS automatic detection is crucial for workers safety especially in isolated working conditions where workers could be unable to call for help on their own, either due to loss of consciousness or an incapacitating injury. These solution must be reliable, robust, easy to use, but also have a low false-alarm rate, short response time and good ergonomics. This project aims to improve this technology by providing a global MDS definition according to a combination of three observable critical states based on characterization of body movement and orientation data from inertial measurements (accelerometer and gyroscope): the worker falls (F), worker immobility (I), the worker is down on the ground (D). The MDS detection strategy was established based on the detection of at least two distinct states, such as F-I, F-D or I-D, over a certain period of time. This strategy was tested using a large public database, revealing a significant reduction of the false alarms rate to 1.1%, reaching up to 99% accuracy. The proposed detection strategy was also incorporated into a digital earpiece, designed to address hearing protection issues, and validated according to an in vivo test procedure based on simulations of industrial workers normal activities and critical states.

Injury severity influence factors and collision prediction - A case study on Kuwait highways

Traffic collisions are considered a serious problem worldwide that cause severe losses. Gulf Cooperation Council (GCC) Countries have high rates of collisions, which requires urgent proactive strategies and attention to solve such problem. This study aimed to enhance traffic safety conditions and reduce collisions' severity levels at several locations in Kuwait City, Kuwait. Consequently, the study importance raised in four folds; reducing the total number of collisions on Kuwait highways, predicting the future numbers of collisions, identifying and managing risk factors contributing to collision's severity, and developing new strategies to enhance traffic safety condition in Kuwait. Three-year crash dataset from 2016 to 2018 including 4028 road collisions in Kuwait City were used to analyse the driver injury severity influence factors and to predict the future collision counts. Statistical indices were used to evaluate the mixed logit model performance, which are the MCFadden Pseudo R-Squared statistics and the two-log likelihood. Eight covariates were tested for significance in the mixed logit model estimation. Results showed that female drivers, driving during night-time, driving outside the city, and not using seatbelt produced the highest possibility of having incapacitating injuries and fatalities by 46.6% and 31.6%, respectively. Contrastingly, male drivers, driving during daytime, driving inside the city, and using seatbelt had resulted in the lowest probability of getting incapacitating injuries and fatalities by 4.7% and 0.2%, respectively. Furthermore, Bayesian hierarchical model was used to investigate the future road collision counts and to identify the collision blackspots (CBS) on Kuwait City highways. Six covariates were considered in the model. Log-linear regression model (CPM) was used to estimate the vector of means μj(t). By applying regression to mean (RTM) and predicting the trend in the mean collision rate λj (t), results showed that the average model accuracy for year 2018 was 45.76% (for a 95% confidence interval estimation using the collision dataset of previous years). It was also found that the highest number of collisions had occurred on the second ring road. Basing the results on year 2018 helped in predicting more accurate future collision counts, and in justifying collision rates. Additionally, further covariates can be added to the models for any additional recent crash data to increase the model accuracy. This work is anticipated to help Kuwaiti decision makers in designing accurate countermeasures to improve traffic safety condition.

Practical advantage of crossed random intercepts under Bayesian hierarchical modeling to tackle unobserved heterogeneity in clustering critical versus non-critical crashes

Traditional hierarchical modeling has been proposed to account for unobserved heterogeneity in the crash analysis. Previous studies investigated the grouping of individual observations between different clusters by considering a single random factor at level-2 of data structure. This approach, however, hinders exploring the possible crossed effects of additional random factors at the level-2 of data hierarchy on the response variable. The current study aims to expand the previous attempts by introducing the concept of Cross-Classified Random Effects Modeling (CCREM) and utilizing crossed random intercepts to account for the crossed effects of two random factors.Aligned with the Connected Vehicle Pilot Deployment Program on Interstate-80 (I-80), this paper intends to cluster critical crashes, involving fatal or incapacitating injuries, versus non-critical crashes through a 402-mile I-80 in Wyoming during the first five months of 2017. Aggregated environmental conditions were conflated with disaggregated real-time traffic observations. Concerning road surface conditions and longitudinal grade categories, four Logistic Regression models were calibrated under Bayesian Inference. Model-1 considered these two factors as fixed parameters; however, in each of Model-2 and Model-3, one of these factors was treated as a random intercept. Model-4 considered both factors as random intercepts and investigated their crossed effect on the critical crash probability.Model-4 outperformed the others and showed that the maximum probability of critical crashes arises on dry pavements and steep downgrades. In contrast, the combined effect of wet pavements and less steep downgrades is associated with the minimum risk of critical crashes. It was revealed that the probability of critical crashes varies at any given value of real-time traffic-related predictors according to different combinations of longitudinal grade and road surface conditions. This finding indicates an essential need for Active Traffic Management to timely apply interventions not only based on real-time traffic-related predictors but also according to various combinations of environmental conditions.

Analysis of pedestrian-vehicle crash injury severity factors in Colorado 2006–2016

This paper investigates factors associated with the severity of pedestrian outcomes from motor vehicle crashes by analyzing a database of all 13,856 reported pedestrian crashes in Colorado over an 11-year period from 2006 to 2016. A total of 14,391 pedestrians were involved in these crashes, resulting in 612 (4.3%) pedestrian fatalities, 11,576 (80.4%) pedestrian injuries, and 2203 (15.3%) property damage only outcomes. The objective is to analyze crash records, as similarly compiled by other states, to show how lives potentially saved by improved factor levels can be estimated as needed for benefit-cost comparisons of alternative countermeasures.Odds ratios of fatal versus non-fatal pedestrian outcomes are computed both independently (unadjusted) and from logistic regression (adjusted) for each factor level accounting for possible correlations between factors. Also computed are odds ratios for fatal plus incapacitating injuries and odds ratios for just 2011−2016 versus all years. This study found that intersection proximity, lighting condition, vehicle type and speed, pedestrian age, pedestrian impairment, and driver impairment by drugs or alcohol were all significant factors associated with the severity of pedestrian outcomes from motor vehicle crashes. Risk ratios from these odds ratios are used to estimate lives potentially saved by having better factor levels present at the time of these crashes. These estimates reflect the relative magnitudes of benefits that might be achieved by potential countermeasures taking into account the number of cases affected.

293 The Dangers of Off-Road Vehicles to Youths: Not Something to Kid Around About

Children are not allowed to operate motor vehicles such as cars until they are 16 years old due to the risks associated with the operation of such powerful machines. Yet, the operation of off-road vehicles (ORVs) by youths under 16 years old has been largely normalized in both rural and urban communities, despite the significant safety risks involved. To better understand this issue, the goal of this study was to characterize roadway ORV crashes with youth operators, including riding behaviors and crash outcomes, in a Midwest state. This study examined ORV roadway crashes involving operators <16 years of age (115 crashes) that were documented in Iowa Department of Transportation (DOT) records from 2002-2017. Descriptive and comparative analyses was performed using the Statistical Package for the Social Sciences (SPSS). In Iowa’s pediatric roadway crashes from 2002-2017, 63% of victims were male and 81% were 12-15 years old. Females were more commonly passengers than were males (60% vs. 24%), as were younger (<12 years) as compared to older riders (56% vs. 26%). Only a small percentage of victims were helmeted, 24% of operators and 14% of passengers. Additionally, 73% of all roadway crashes involved multiple riders on the ATV, around 40% occurred on paved roads, and 82% occurred in rural areas. Collisions with an object or another vehicle were the mechanism in 65% of crashes. Roads with speed limits over 50 mph were the location of around half (52%) of all crashes. Among pediatric victims, 3.5% were killed in the crash and 46% of the pediatric roadway crashes resulted in either major injury or death. Moreover, a higher proportion of the fatal or incapacitating injuries were observed in adolescents (12-15 years) than in younger children (35% vs. 23%). Our results illustrate that multiple risky behaviors are common among youth in roadway ORV crashes, including riding on high speed roads, with passengers, and/or without helmets. Adolescents (12-15 years old) are the large majority of pediatric victims, and serious injury or death resulted in almost half of all crashes. Based on these results, it is clear that multiple targeted approaches are needed for youth under 16 years old in order to prevent pediatric fatalities and severe injuries, particularly on the road.

Hypercarnivorous teeth and healed injuries to Canis chihliensis from Early Pleistocene Nihewan beds, China, support social hunting for ancestral wolves.

Collaborative hunting by complex social groups is a hallmark of large dogs (Mammalia: Carnivora: Canidae), whose teeth also tend to be hypercarnivorous, specialized toward increased cutting edges for meat consumption and robust p4-m1 complex for cracking bone. The deep history of canid pack hunting is, however, obscure because behavioral evidence is rarely preserved in fossils. Dated to the Early Pleistocene (>1.2 Ma), Canis chihliensis from the Nihewan Basin of northern China is one of the earliest canines to feature a large body size and hypercarnivorous dentition. We present the first known record of dental infection in C. chihliensis, likely inflicted by processing hard food, such as bone. Another individual also suffered a displaced fracture of its tibia and, despite such an incapacitating injury, survived the trauma to heal. The long period required for healing the compound fracture is consistent with social hunting and family care (food-sharing) although alternative explanations exist. Comparison with abundant paleopathological records of the putatively pack-hunting Late Pleistocene dire wolf, Canis dirus, at the Rancho La Brea asphalt seeps in southern California, U.S.A., suggests similarity in feeding behavior and sociality between Chinese and American Canis across space and time. Pack hunting in Canis may be traced back to the Early Pleistocene, well before the appearance of modern wolves, but additional evidence is needed for confirmation.

A Bayesian spatial Poisson-lognormal model to examine pedestrian crash severity at signalized intersections

Reducing nonmotorized crashes requires a profound understanding of the causes and consequences of the crashes at the facility level. Generally, existing literature on bicyclists and pedestrian crash models suffers from two distinct problems: lack of exposure/volume data and inadequacy in capturing potential correlations across various crash aspects. To develop a robust framework for pedestrian crash analysis, this research employed a multivariate model across multiple pedestrian crash severities incorporating a crucial piece of information: pedestrian exposure. A multivariate spatial (conditional autoregressive) Poisson-lognormal model in a Bayesian framework was developed to examine the significant factors influencing the fatal, incapacitating injury (or suspected serious injury), and non-incapacitating injury pedestrian crashes at 409 signalized intersections in the Austin area. Various explanatory variables were used to examine the pedestrian crashes, including traffic characteristics, road geometry, built environment features, and pedestrian exposure volume at intersections, which was estimated through a direct demand model as part of the study. Model results revealed valuable insights. The superior performance of the multivariate model over the univariate model emphasized the need to jointly model multiple pedestrian crash severities. The results showed the significant positive influence of speed limit on fatal pedestrian crashes and revealed that both incapacitating and non-incapacitating injury crashes increase with increasing motorized traffic volume. Bus stop presence was found to have a negative influence on incapacitating injury crashes and a positive influence on non-incapacitating injury crashes. Moreover, the pedestrian volume at intersections positively influences non-incapacitating injury crashes. The difference in influence across crash types warrants careful and focused policy design of intersections to reduce pedestrian crashes of all severity types.

Modeling severities of motorcycle crashes using random parameters

One of the critical areas of road safety is motorcycle safety. Motorcyclists are more vulnerable to injuries than occupants of other motor vehicles when involved in crashes. Researchers have studied the relationships between motorcycle crash severity and crash contributing factors. They are crash characteristics, roadway geometric design features, traffic characteristics, socio-demographics and environmental conditions. However, few researchers considered unobserved heterogeneity effects when modeling motorcycle crash injury severities, let alone interaction effects. In this research, motorcycle crashes in Wyoming that occurred from 2008 to 2017 were analyzed. Specifically, the injury severities of single motorcycle crashes and multiple vehicle crashes involving motorcycles were modeled. The response was whether the motorcycle crash incurred an incapacitating injury or fatality or not. The binary logistic regression and mixed binary logistic regression modeling structures were implemented. The mixed models revealed effects that otherwise would have been undisclosed in the binary logistic regression models’ results. According to the results of single motorcycle crashes, the majority of motorcycle-animal crashes and of motorcycle-barrier crashes were likely to be severe relative to other single motorcycle crashes. It was also found that horizontal curves increased the risk of severe injuries. Young riders were found to be less at risk of being gravely injured in single motorcycle crashes than older riders as well. Furthermore, riding under the influence and high posted speed limits increased the odds of severe crashes regardless of whether the crashes were single motorcycle crashes or multiple vehicle crashes involving motorcycles. Additionally, the mixed models uncovered interaction effects and unobserved effects pertaining to speed limits. • Severities of motorcycle crashes in Wyoming were modeled using logistic regression. • Random parameters were incorporated in the modeling. • Latent effects were inferred regarding crash types and roadway factors. • Recommendations were made to reduce motorcycle crash severity.

Effects Influencing Pedestrian–Vehicle Crash Frequency by Severity Level: A Case Study of Seoul Metropolitan City, South Korea

This study aimed to determine how built environments affect pedestrian–vehicle collisions. The study examined pedestrian–vehicular crashes that occurred between 2013 and 2015 in Seoul, Korea, by comparing and analyzing different effects of the built environment on pedestrian–vehicle crashes. Specifically, the study analyzed built environment attributes, land use environment, housing types, road environment, and traffic characteristics to determine how these factors affect the severity of pedestrian injury. The results of the statistical analysis appear to infer that the built environment attributes had dissimilar impacts on pedestrian collisions, depending on the injury severity. In general, both incapacitating and non-incapacitating injuries appear to be more likely to be caused by the built environment than fatal and possible injuries. These results highlight the need to consider injury severity when implementing more effective interventions and strategies for ensuring pedestrian safety. However, because of the small sample size, an expanded research project regarding this issue should be considered, as it would contribute to the development and implementation of effective policies and interventions for pedestrian safety in Korea. This study therefore offers practical information regarding the development of such an expanded study to inform future traffic safety policies in Seoul to establish a “safe walking city.”

Long-term crash trends at single- and double-lane roundabouts in Washington State

Introduction: Despite the proven safety benefits, crashes still occur at roundabouts. This study examined long-term trends in total crash counts, crash severity, and crashes involving common driver errors (failing to yield the right-of-way and speeding) in the period following the completion of single- and double-lane roundabouts in Washington state. Method: Crashes occurring during 2010–2016 at single- and double-lane roundabouts completed between 2009 and 2015 in Washington state were included. Poisson regression examined changes in annual total crash counts over time. Logistic regression estimated average annual changes in the odds that a crash involved an evident/incapacitating/fatal injury and that a crash involved a driver error. Regression models were estimated for single- and double-lane roundabouts separately. Results: Annual total crash counts declined significantly by 8.8% over time at double-lane roundabouts and increased nonsignificantly over time at single-lane roundabouts. The study estimated a significant 32.1% annual reduction in the odds that a crash involved an evident or incapacitating injury at double-lane roundabouts and a nonsignificant 18.9% reduction at single-lane roundabouts. There was a significant 10.6% annual decline in the odds that a crash was right-of-way related at double-lane roundabouts and a significant 19.1% annual decline in the odds that a crash was speeding-related at single-lane roundabouts. Conclusions: The current study demonstrates that safety can improve over time at double-lane roundabouts as drivers gain experience navigating them. At the same time, it is important that roundabouts include design elements that will prevent right-of-way mistakes and reduce speeds. Practical applications: Communities installing double-lane roundabouts may find that their benefits will increase the longer they are in place, even if initial changes in crashes and injuries are underwhelming.

Mapping Child Safety Seat Use in Cases of Fatal or Incapacitating Child Motor Vehicle Injury in Cook County, Illinois from 2011 to 2015.

Child safety seat use reduces the risk of fatal injury by 71% for infants and 54% for toddlers, yet more than one-third of child passengers killed in traffic crashes in the US are unrestrained. Nearly half (47%) of crash injuries occur within 5 miles of the injured person's home. Mapping the location of motor vehicle crashes resulting in serious or fatal injury to unrestrained child passengers may pinpoint high-risk neighborhoods. Illinois Department of Transportation data were used to map crashes that resulted in a fatal or incapacitating injury to a child passenger (age 0 to 8) in Cook County, IL from 2011 to 2015. Maptitude® Geographic Information System (GIS) software was used to identify hot spots of unrestrained child passenger injury on the South Side and West Side of Chicago. Of 174 zip codes in Cook County, 3 zip codes on the South Side of Chicago (60620, 60621; 60628) accounted for 11% of the total unrestrained fatalities and incapacitating injuries among children. Results of this study reveal the feasibility of detecting geographic disparities in child passenger safety at the zip code and neighborhood level and indicate the potential for more targeted allocation of resources.