A study on crashes related to visibility obstruction due to fog and smoke

Research on weather effects has focused on snow- or rain-related crashes. However, there is a lack of understanding of crashes that occur during fog or smoke (FS). This study presents a comprehensive examination of FS-related crashes using crash data from Florida between 2003 and 2007. A two-stage research strategy was implemented (1) to examine FS-related crash characteristics with respect to temporal distribution, influential factors and crash types and (2) to estimate the effects of various factors on injury severity given that a FS-related crash has occurred. The morning hours from December to February are the prevalent times for FS-related crashes. Compared to crashes under clear-visibility conditions, FS-related crashes tend to result in more severe injuries and involve more vehicles. Head-on and rear-end crashes are the two most common crash types in terms of crash risk and severity. These crashes were more prevalent on high-speed roads, undivided roads, roads with no sidewalks and two-lane rural roads. Moreover, FS-related crashes were more likely to occur at night without street lighting, leading to more severe injuries.     

Safety modeling of suburban arterials in Shanghai,China

As urbanization accelerates in Shanghai, land continues to develop along suburban arterials which results in more access points along the roadways and more congested suburban arterials; all these changes have led to deterioration in traffic safety. In-depth safety analysis is needed to understand the relationship between roadway geometric design, access features, traffic characteristics, and safety. This study examined 161 road segments (each between two adjacent signalized intersections) of eight suburban arterials in Shanghai. Information on signal spacing, geometric design, access features, traffic characteristics, and surrounding area types were collected. The effect of these factors on total crash occurrence was investigated. To account for the hierarchical data structure, hierarchical Bayesian models were developed for total crashes. To identify diverse effects on different crash injury severity, the total crashes were separated into minor injury and severe injury crashes. Bivariate hierarchical Bayesian models were developed for minor injury and severe injury to account for the correlation among different severity levels. The modeling results show that the density of signal spacing along arterials has a significant influence on minor injury, severe injury, and total crash frequencies. The non-uniform signal spacing has a significant impact on the occurrence of minor injury crashes. At the segment-level, higher frequencies of minor injury, severe injury, and total crashes tend to occur for the segments with curves, those with a higher density of access points, those with a higher percentage of heavy vehicles, and those in inner suburban areas. This study is useful for applications such as related engineering safety improvements and making access management policy.     

Assessing the relative risk of severe injury in automotive crashes for older female occupants

A logistic regression model was used in the prediction of injury severity for individuals who are involved in a vehicular crash. The model identified females and older occupants (segmented by age 55-74, and 75 and older) as having a significantly higher risk of severe injuries in a crash. Further, interactions of older females with other factors, such as occupant seat position, crash type, and environmental factors were also shown to significantly impact the relative risk of a severe injury. This study revealed that females 75 years and older had the lowest odds of injury among all female occupants studied (OR=1.16) while females between 55 and 74 years old have higher risk of severe injuries (OR=1.74). All older females (55 and older) were at greater risk for head-on, side-impact and rear-end collisions. Seatbelt use reduced severe injuries for females in this age group, but not to the same extent as the rest of the population studied. Additionally, crashes in severe weather, which were less likely to result in severe injuries for the general population, increased the risk of severe injuries to females that were 55 and older. Among occupants of light trucks, sport utility vehicles and vans, older females were less likely than others to be severely injured. In this case, older females appear better off in vehicles which are larger and protect better in severe crashes. This research demonstrates that circumstances surrounding a crash greatly impact the severity of injuries sustained by older female occupants.     

Exploring the overall and specific crash severity levels at signalized intersections

Many studies have shown that intersections are among the most dangerous locations of a roadway network. Therefore, there is a need to understand the factors that contribute to injuries at such locations. This paper addresses the different factors that affect crash injury severity at signalized intersections. It also looks into the quality and completeness of the crash data and the effect that incomplete data has on the final results. Data from multiple sources have been cross-checked to ensure the completeness of all crashes including minor crashes that are usually unreported or not coded into crash databases. The ordered probit modeling technique has been adopted in this study to account for the fact that injury levels are naturally ordered variables. The tree-based regression methodology has also been adopted in this study to explore the factors that affect each severity level. The probit model results showed that a combination of crash-specific information and intersection characteristics result in the highest prediction rate of injury level. More specifically, having a divided minor roadway or a higher speed limit on the minor roadway decreased the level of injury while crashes involving a pedestrian/bicyclist and left turn crashes had the highest probability of a more severe crash. Several regression tree models showed a difference in the significant factors that affect the different severity types. Completing the data with minor non injury crashes improved the modeling results and depicted differences when modeling the no injury crashes.     

Exploring factors contributing to injury severity at freeway merging and diverging locations in Ohio

Identifying factors that affect crash injury severity and understanding how these factors affect injury severity is critical in planning and implementing highway safety improvement programs. Factors such as driver-related, traffic-related, environment-related and geometric design-related were considered when developing statistical models to predict the effects of these factors on the severity of injuries sustained from motor vehicle crashes at merging and diverging locations. Police-reported crash data at selected freeway merging and diverging areas in the state of Ohio were used for the development of the models. A generalized ordinal logit model also known as partial proportional odds model was applied to identify significant factors increasing the likelihood of one of the five KABCO scale of injury severity: no injuries, possible/invisible injuries, non-incapacitating injuries, incapacitating injuries, or fatal injuries. The results of this study show that semi-truck related crashes, higher number of lanes on freeways, higher number of lanes on ramps, speeding related crashes, and alcohol related crashes tend to increase the likelihood of sustaining severe injuries at freeway merging locations. In addition, females and older persons are more likely to sustain severe injuries especially at freeway merge locations. Alcohol related crashes, speeding related crashes, angle-type collisions, and lane-ramp configuration type D significantly increase the likelihood of severe injury crashes at diverging areas. Poor lighting condition tends to increase non-incapacitating injuries at diverging areas only. Moreover, adverse weather condition increases the likelihood of no-injury and fatal injuries at merging areas only and adverse road conditions tend to increase a range of injury severity levels from possible/invisible injuries to incapacitating injuries at merging areas only.     

Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention.     

Examining driver injury severity outcomes in rural non-interstate roadway crashes using a hierarchical ordered logit model

Rural non-interstate crashes induce a significant amount of severe injuries and fatalities. Examination of such injury patterns and the associated contributing factors is of practical importance. Taking into account the ordinal nature of injury severity levels and the hierarchical feature of crash data, this study employs a hierarchical ordered logit model to examine the significant factors in predicting driver injury severities in rural non-interstate crashes based on two-year New Mexico crash records. Bayesian inference is utilized in model estimation procedure and 95% Bayesian Credible Interval (BCI) is applied to testing variable significance. An ordinary ordered logit model omitting the between-crash variance effect is evaluated as well for model performance comparison. Results indicate that the model employed in this study outperforms ordinary ordered logit model in model fit and parameter estimation. Variables regarding crash features, environment conditions, and driver and vehicle characteristics are found to have significant influence on the predictions of driver injury severities in rural non-interstate crashes. Factors such as road segments far from intersection, wet road surface condition, collision with animals, heavy vehicle drivers, male drivers and driver seatbelt used tend to induce less severe driver injury outcomes than the factors such as multiple-vehicle crashes, severe vehicle damage in a crash, motorcyclists, females, senior drivers, driver with alcohol or drug impairment, and other major collision types. Research limitations regarding crash data and model assumptions are also discussed. Overall, this research provides reasonable results and insight in developing effective road safety measures for crash injury severity reduction and prevention.     

Driver injury severity: an application of ordered probit models

This paper describes the use of ordered probit models to examine the risk of different injury levels sustained under all crash types, two-vehicle crashes, and single-vehicle crashes. The results suggest that pickups and sport utility vehicles are less safe than passenger cars under single-vehicle crash conditions. In two-vehicle crashes, however, these vehicle types are associated with less severe injuries for their drivers and more severe injuries for occupants of their collision partners. Other conclusions also are presented; for example. the results indicate that males and younger drivers in newer vehicles at lower speeds sustain less severe injuries.     

Hierarchical Bayesian random intercept model-based cross-level interaction decomposition for truck driver injury severity investigations

Traffic crashes occurring on rural roadways induce more severe injuries and fatalities than those in urban areas, especially when there are trucks involved. Truck drivers are found to suffer higher potential of crash injuries compared with other occupational labors. Besides, unobserved heterogeneity in crash data analysis is a critical issue that needs to be carefully addressed. In this study, a hierarchical Bayesian random intercept model decomposing cross-level interaction effects as unobserved heterogeneity is developed to examine the posterior probabilities of truck driver injuries in rural truck-involved crashes. The interaction effects contributing to truck driver injury outcomes are investigated based on two-year rural truck-involved crashes in New Mexico from 2010 to 2011. The analysis results indicate that the cross-level interaction effects play an important role in predicting truck driver injury severities, and the proposed model produces comparable performance with the traditional random intercept model and the mixed logit model even after penalization by high model complexity. It is revealed that factors including road grade, number of vehicles involved in a crash, maximum vehicle damage in a crash, vehicle actions, driver age, seatbelt use, and driver under alcohol or drug influence, as well as a portion of their cross-level interaction effects with other variables are significantly associated with truck driver incapacitating injuries and fatalities. These findings are helpful to understand the respective or joint impacts of these attributes on truck driver injury patterns in rural truck-involved crashes.     

Road safety engineering: an effective tool in the fight against whiplash injuries

Road safety engineering can play an integral part in the prevention of whiplash injuries. While improvements to vehicle design can reduce the severity of whiplash injuries when a crash occurs, improvements to road safety can prevent whiplash-inducing crashes from occurring in the first place. Whiplash injuries are most commonly associated with rear end crashes. Unfortunately, rear end crashes are also the most common type of crash at urban signalized intersections, where the majority of crashes occur in British Columbia, Canada. The Insurance Corporation of British Columbia (ICBC), through the road improvement program, has been funding road improvements in order to reduce the frequency of collisions at high crash locations in British Columbia. Several road safety engineering countermeasures specifically targeted at rear end collisions have been researched and deployed. These countermeasures include simple and affordable solutions such as signal visibility enhancements, as well as complex and expensive solutions such as intersection geometric upgrades. When appropriately used, these countermeasures have proven to be extremely cost-effective in reducing the frequency of rear end collisions. Widespread application of signal visibility enhancements is now being pursued to further decrease the risk of rear end collisions and whiplash injuries. Costs are the direct cost of the ICBC portion of the investment and benefits are only those associated with reduced insurance claims over a 2-year period.     

Comparison of moped,scooter and motorcycle crash risk and crash severity

The increased popularity of mopeds and motor scooters in Australia and elsewhere in the last decade has contributed substantially to the greater use of powered two-wheelers (PTWs) as a whole. As the exposure of mopeds and scooters has increased, so too has the number of reported crashes involving those PTW types, but there is currently little research comparing the safety of mopeds and, particularly, larger scooters with motorcycles. This study compared the crash risk and crash severity of motorcycles, mopeds and larger scooters in Queensland, Australia. Comprehensive data cleansing was undertaken to separate motorcycles, mopeds and larger scooters in police-reported crash data covering the five years to 30 June 2008. The crash rates of motorcycles (including larger scooters) and mopeds in terms of registered vehicles were similar over this period, although the moped crash rate showed a stronger downward trend. However, the crash rates in terms of distance travelled were nearly four times higher for mopeds than for motorcycles (including larger scooters). More comprehensive distance travelled data is needed to confirm these findings. The overall severity of moped and scooter crashes was significantly lower than motorcycle crashes but an ordered probit regression model showed that crash severity outcomes related to differences in crash characteristics and circumstances, rather than differences between PTW types per se. Greater motorcycle crash severity was associated with higher (>80 km/h) speed zones, horizontal curves, weekend, single vehicle and nighttime crashes. Moped crashes were more severe at night and in speed zones of 90 km/h or more. Larger scooter crashes were more severe in 70 km/h zones (than 60 km/h zones) but not in higher speed zones, and less severe on weekends than on weekdays. The findings can be used to inform potential crash and injury countermeasures tailored to users of different PTW types.     

Impact of pavement conditions on crash severity

Pavement condition has been known as a key factor related to ride quality, but it is less clear how exactly pavement conditions are related to traffic crashes. The researchers used Geographic Information System (GIS) to link Texas Department of Transportation (TxDOT) Crash Record Information System (CRIS) data and Pavement Management Information System (PMIS) data, which provided an opportunity to examine the impact of pavement conditions on traffic crashes in depth. The study analyzed the correlation between several key pavement condition ratings or scores and crash severity based on a large number of crashes in Texas between 2008 and 2009. The results in general suggested that poor pavement condition scores and ratings were associated with proportionally more severe crashes, but very poor pavement conditions were actually associated with less severe crashes. Very good pavement conditions might induce speeding behaviors and therefore could have caused more severe crashes, especially on non-freeway arterials and during favorable driving conditions. In addition, the results showed that the effects of pavement conditions on crash severity were more evident for passenger vehicles than for commercial vehicles. These results provide insights on how pavement conditions may have contributed to crashes, which may be valuable for safety improvement during pavement design and maintenance. Readers should notice that, although the study found statistically significant effects of pavement variables on crash severity, the effects were rather minor in reality as suggested by frequency analyses.     

Propensity scores-potential outcomes framework to incorporate severity probabilities in the Highway Safety Manual crash prediction algorithm

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.     

How crash severity in rear impacts influences short- and long-term consequences to the neck

The main public-health problem concerning WAD are injuries leading to long-term consequences. Yet epidemiological studies mostly concentrate on data based on the injury outcome occurring shortly after the crash. The purpose of this article is to study the influence of crash severity in rear impacts leading to short and long-term consequences to the neck (WAD 1-3), lasting less than or more than 1 year. The influence of change of velocity as well as the car acceleration were investigated by using data from crash pulse recorders (CPR) installed in vehicles, involved in rear impacts. The influence of the car acceleration were also investigated by studying the frequency of occurrence of a tow-bar (hinge) on the struck car. Apart from real-life data, full-scale car-to-car crashes were performed to evaluate the influence of a tow-bar on the struck car. The crash tests showed that a tow-bar may significantly affect the acceleration of the car as well as that of the occupant. According to real-life crashes, a tow-bar on the struck car increased the risk of long-term consequences by 22% but did not affect the risk of short-term consequences. Out of the 28 crash recorder-equipped struck cars involving 38 occupants, 15 sustained no injury where the peak acceleration was 6g or less, 20 sustained short-term consequences where the peak acceleration was 10g or less. Three occupants from two different crashes sustained long-term consequences. The two crashes which resulted in long-term disabling neck injuries had the highest peak acceleration (15 and 13 x g), but not the highest change of velocity.     

Modeling occupant-level injury severity: An application to large-truck crashes

Most of the injury-severity analyses to date have focused primarily on modeling the most-severe injury of any crash, although a substantial fraction of crashes involve multiple vehicles and multiple persons. In this study, we present an extensive exploratory analysis that highlights that the highest injury severity is not necessarily the comprehensive indicator of the overall severity of any crash. Subsequently, we present a panel, hetroskedastic ordered-probit model to simultaneously analyze the injury severities of all persons involved in a crash. The models are estimated in the context of large-truck crashes. The results indicate strong effects of person-, driver-, vehicle-, and crash-characteristics on the injury severities of persons involved in large-truck crashes. For example, several driver behavior characteristics (such as use of illegal drugs, DUI, and inattention) were found to be statistically significant predictors of injury severity. The availability of airbags and the use of seat-belts are also found to be associated with less-severe injuries to car-drivers and car-passengers in the event of crashes with large trucks. Car drivers’ familiarity with the vehicle and the roadway are also important for both the car drivers and passengers. Finally, the models also indicate the strong presence of intra-vehicle correlations (effect of common vehicle-specific unobserved factors) among the injury propensities of all persons within a vehicle.     

Built environment effects on cyclist injury severity in automobile-involved bicycle crashes

This analysis uses a generalized ordered logit model and a generalized additive model to estimate the effects of built environment factors on cyclist injury severity in automobile-involved bicycle crashes, as well as to accommodate possible spatial dependence among crash locations. The sample is drawn from the Seattle Department of Transportation bicycle collision profiles. This study classifies the cyclist injury types as property damage only, possible injury, evident injury, and severe injury or fatality. Our modeling outcomes show that: (1) injury severity is negatively associated with employment density; (2) severe injury or fatality is negatively associated with land use mixture; (3) lower likelihood of injuries is observed for bicyclists wearing reflective clothing; (4) improving street lighting can decrease the likelihood of cyclist injuries; (5) posted speed limit is positively associated with the probability of evident injury and severe injury or fatality; (6) older cyclists appear to be more vulnerable to severe injury or fatality; and (7) cyclists are more likely to be severely injured when large vehicles are involved in crashes. One implication drawn from this study is that cities should increase land use mixture and development density, optimally lower posted speed limits on streets with both bikes and motor vehicles, and improve street lighting to promote bicycle safety. In addition, cyclists should be encouraged to wear reflective clothing.     

Safety effects of reducing the speed limit from 90 km/h to 70 km/h

Speed is one of the main risk factors in traffic safety, as it increases both the chances and the severity of a crash. In order to achieve improved traffic safety by influencing the speed of travel, road authorities may decide to lower the legally imposed speed limits. In 2001 the Flemish government decided to lower speed limits from 90 km/h to 70 km/h on a considerable number of highways. The present study examines the effectiveness of this measure using a comparison group before- and after study to account for general trend effects in road safety. Sixty-one road sections with a total length of 116 km were included. The speed limits for those locations were restricted in 2001 and 2002. The comparison group consisted of 19 road sections with a total length of 53 km and an unchanged speed limit of 90 km/h throughout the research period. Taking trend into account, the analyses showed a 5% decrease [0.88; 1.03] in the crash rates after the speed limit restriction. A greater effect was identified in the case of crashes involving serious injuries and fatalities, which showed a decrease of 33% [0.57; 0.79]. Separate analyses between crashes at intersections and at road sections showed a higher effectiveness at road sections. It can be concluded from this study that speed limit restrictions do have a favorable effect on traffic safety, especially on severe crashes. Future research should examine the cause for the difference in the effect between road sections and intersections that was identified, taking vehicle speeds into account.     

Predicting crash likelihood and severity on freeways with real-time loop detector data

Real-time crash risk prediction using traffic data collected from loop detector stations is useful in dynamic safety management systems aimed at improving traffic safety through application of proactive safety countermeasures. The major drawback of most of the existing studies is that they focus on the crash risk without consideration of crash severity. This paper presents an effort to develop a model that predicts the crash likelihood at different levels of severity with a particular focus on severe crashes. The crash data and traffic data used in this study were collected on the I-880 freeway in California, United States. This study considers three levels of crash severity: fatal/incapacitating injury crashes (KA), non-incapacitating/possible injury crashes (BC), and property-damage-only crashes (PDO). The sequential logit model was used to link the likelihood of crash occurrences at different severity levels to various traffic flow characteristics derived from detector data. The elasticity analysis was conducted to evaluate the effect of the traffic flow variables on the likelihood of crash and its severity.The results show that the traffic flow characteristics contributing to crash likelihood were quite different at different levels of severity. The PDO crashes were more likely to occur under congested traffic flow conditions with highly variable speed and frequent lane changes, while the KA and BC crashes were more likely to occur under less congested traffic flow conditions. High speed, coupled with a large speed difference between adjacent lanes under uncongested traffic conditions, was found to increase the likelihood of severe crashes (KA). This study applied the 20-fold cross-validation method to estimate the prediction performance of the developed models. The validation results show that the model's crash prediction performance at each severity level was satisfactory. The findings of this study can be used to predict the probabilities of crash at different severity levels, which is valuable knowledge in the pursuit of reducing the risk of severe crashes through the use of dynamic safety management systems on freeways.     

Occupant injury in rollover crashes – Contribution of planar impacts with objects and other vehicles

Planar impacts with objects and other vehicles may increase the risk and severity of injury in rollover crashes. The current study compares the frequency of injury measures (MAIS 2+, 3+, and 4+; fatal; AIS 2+ head and cervical spine; and AIS 3+ head and thorax) as well as vehicle type distribution (passenger car, SUV, van, and light truck), crash kinematics, and occupant demographics between single vehicle single event rollovers (SV Pure) and multiple event rollovers to determine which types of multiple event rollovers can be pooled with SV Pure to study rollover induced occupant injury. Four different types of multiple event rollovers were defined: single and multi-vehicle crashes for which the rollover is the most severe event (SV Prim and MV Prim) and single and multi-vehicle crashes for which the rollover is not the most severe event (SV Non-Prim and MV Non-Prim). Information from real world crashes was obtained from the National Automotive Sampling System – Crashworthiness Data System (NASS-CDS) for the period from 1995 through 2011. Belted, contained or partially ejected, adult occupants in vehicles that completed 1–16 lateral quarter turns were assigned to one of the five rollover categories. The results showed that the frequency of injury in non-primary rollovers (SV Non-Prim and MV Non-Prim) involving no more than one roof inversion is substantially greater than in SV Pure, but that this disparity diminishes for crashes involving multiple inversions. It can further be concluded that for a given number of roof inversions, the distribution of injuries and crash characteristics in SV Pure and SV Prim crashes are sufficiently similar for these categories to be considered collectively for purposes of understanding etiologies and developing strategies for prevention.