Crash reconstruction and crash modification factors

This paper addresses the following question: Under what conditions can reconstructed road crashes be used to estimate the effect of a safety-related countermeasure? Results developed by Pearl and his associates are used to draw two main conclusions. First, when one can (1) identify a structural equation describing a type of crash, (2) identify an additional structural equation describing the countermeasure's impact, and (3) estimate the initiating conditions for a set of reconstructed crashes, then a lower bound for a crash modification factor can be estimated by simulating whether or not each of the reconstructed crashes would still have occurred had the countermeasure been present. If the countermeasure's effect is monotonic this bound becomes tight. Second, in situations where it is not possible to reliably identify the structural equations needed for simulation, but where one can (1) identify a set of crash inputs which, when given, make the crash outcome conditionally independent of the countermeasure, and (2) predict how the distribution of these inputs will change in response to the countermeasure, then nonparametric estimation of the countermeasure's crash modification factor is possible. When it is not possible to predict the countermeasure's effect on the conditioning variables it may still be possible to identify constraints or specifications which the countermeasure should satisfy in order to realize a target crash modification.     

Crash costs by body part injured, fracture involvement, and threat-to-life severity. United States, 2000

This paper presents costs per US motor vehicle crash victim differentiated into many more diagnostic categories than prior estimates. These unit costs, which include the first keyed to the 1990 edition of Abbreviated Injury Scale (AIS) threat-to-life severity scores, are reported by body part, whether a fracture/dislocation was involved, and the maximum AIS score among the victim's injuries. This level of detail allows for a more accurate estimation of the social costs of motor vehicle crashes. It also allows for reliable analyses of interventions targeting narrow ranges of injuries. The paper updates the medical care data underlying the US crash costs from 1979 to 1986 to the mid 1990s and improves on prior productivity cost estimates. In addition to presenting the latest generation of crash victim costs, this paper analyzes the effects of applying injury costs classified by AIS code from the 1985 edition to injury incidence data coded with the 1990 edition of AIS. This long-standing practice results in inaccurate cost-benefit analyses that typically overestimate benefits. This problem is more acute when old published costs adjusted for inflation are used rather than the recent costs.     

Costs of large truck-involved crashes in the United States

This study provides the estimates of the costs of highway crashes involving large trucks by type of truck involved. These costs represent the present value of all costs over the victims' expected life span that result from a crash. They include medically related costs, emergency services costs, property damage costs, lost productivity, and the monetized value of the pain, suffering, and lost quality of life that a family experiences because of death or injury. Based on the latest data available, the estimated cost of police-reported crashes involving trucks with a gross weight rating of more than 10,000 pounds averaged US$ 59,153 (in 2000 dollars). Multiple combination trucks had the highest cost per crash (US$ 88,483). The crash costs per 1000 truck miles however, were US$ 157 for single unit trucks, US$ 131 for single combination trucks, and US$ 63 for multiple combinations.     

Trends in older driver crash involvement rates and survivability in the United States: An update

Objective

Previous research has shown that fatal crash involvement rates per licensed driver aged 70 and older declined significantly more per year in the United States than rates for middle-aged drivers aged 35–54 during 1997–2008, and per vehicle mile traveled from 1995–1996 to 2001–2002. Analyses of police-reported crash data during 1997–2005 indicated that the greater declines for older drivers were due to decreases in crash involvement and in the risk of dying in the crashes that occurred. The current study examined if trends in crash rates, crash involvements, and survivability persisted into more recent years.

Methods

Trends for drivers 70 and older were compared with trends for drivers aged 35–54 for U.S. national fatal passenger vehicle crash involvements per 100,000 licensed drivers during 1997–2012 and for U.S. national fatal passenger vehicle crash involvements per vehicle miles traveled in 1995–1996, 2001–2002, and 2008. Using police-reported crash data during 1997–2008 from 20 U.S. states, trends in involvement rates in non-fatal crashes of various severities per 100,000 licensed drivers and changes in the odds of death and the odds of death or serious injury in a crash were compared between older and middle-aged drivers.

Results

During 2007–2012, declines in national fatal crash involvement rates per licensed driver were similar for drivers 70 and older and middle-aged drivers (18 percent each). However, when considering the entire study period, fatal crash involvement rates continued to reflect a substantially larger decline for drivers 70 and older than for middle-aged drivers (42 vs. 30 percent per licensed driver during 1997–2012, 39 vs. 26 percent per vehicle mile traveled from 1995–2006 to 2008). When analyses of police-reported crash data were extended through 2008, non-fatal injury crash involvement rates per licensed driver declined more for older than for middle-aged drivers (39 vs. 30 percent), and unlike in prior research, average annual declines were significantly larger for drivers 80 and older. Property damage-only crash involvement rates similarly declined significantly more for older than for middle-aged drivers (15 vs. 3 percent). Drivers 70 and older in 1997 were 3.5 times more likely than middle-aged drivers to die in a crash, and this ratio declined to 3.2 by 2008.

Conclusions

Although declines in fatal crash involvement rates in recent years have not differed between older and middle-aged drivers, this did not undo earlier gains for older drivers. The recent slowing in the relative magnitude of the decline for older drivers may be related to the differential effect of the U.S. recession on fatal crash involvements of drivers in these age groups. The decreased likelihood of being involved in a crash of any severity and increased survivability when a crash occurred held when examining data through 2008, and for drivers 80 and older, significant declines in crash involvement relative to middle-aged drivers extended to non-fatal injury crashes.     

Development of crash modification factors for changing lane width on roadway segments using generalized nonlinear models

This study evaluates the effectiveness of changing lane width in reducing crashes on roadway segments. To consider nonlinear relationships between crash rate and lane width, the study develops generalized nonlinear models (GNMs) using 3-years crash records and road geometry data collected for all roadway segments in Florida. The study also estimates various crash modification factors (CMFs) for different ranges of lane width based on the results of the GNMs. It was found that the crash rate was highest for 12-ft lane and lower for the lane width less than or greater than 12 ft. GNMs can extrapolate this nonlinear continuous effect of lane width and estimate the CMFs for any lane width, not only selected lane widths, unlike generalized linear models (GLMs) with categorical variables. The CMFs estimated using GNMs reflect that crashes are less likely to occur for narrower lanes if the lane width is less than 12 ft whereas crashes are less likely to occur for wider lanes if the lane width is greater than 12 ft. However, these effects varied with the posted speed limits as the effect of interaction between lane width and speed limit was significant. The estimated CMFs show that crashes are less likely to occur for lane widths less than 12 ft than the lane widths greater than 12 ft if the speed limit is higher than or equal to 40 mph. It was also found from the CMFs that crashes at higher severity levels (KABC and KAB) are less likely to occur for lane widths greater or less than 12 ft compared to 12-ft lane. The study demonstrates that the CMFs estimated using GNMs clearly reflect variations in crashes with lane width, which cannot be captured by the CMFs estimated using GLMs. Thus, it is recommended that if the relationship between crash rate and lane width is nonlinear, the CMFs are estimated using GNMs.     

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.     

On the significance of omitted variables in intersection crash modeling

Advances in safety research – trying to improve the collective understanding of motor vehicle crash causes and contributing factors – rest upon the pursuit of numerous lines of research inquiry. The research community has focused considerable attention on analytical methods development (negative binomial models, simultaneous equations, etc.), on better experimental designs (before–after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.).     

Analysis of left-turn crash injury severity by conflicting pattern using partial proportional odds models

The purpose of this study is to examine left-turn crash injury severity. Left-turning traffic colliding with opposing through traffic and with near-side through traffic are the two most frequently occurring conflicting patterns among left-turn crashes (Patterns 5 and 8 in the paper, respectively), and they are prone to be severe. Ordered probability models with either logit or probit function is commonly applied in crash injury severity analyses; however, its critical assumption that the slope coefficients do not vary over different alternatives except the cut-off points is usually too restrictive. Partial proportional odds models are generalizations of ordered probability models, for which some of the beta coefficients can differ across alternatives, were applied to investigate Patterns 5 and 8, and the total left-turn crash injuries. The results show that partial proportional odds models consistently perform better than ordered probability models. By focusing on specific conflicting patterns, locating crashes to the exact crash sites and relating approach variables to crash injury in the analysis, researchers are able to investigate how these variables affect left-turn crash injuries. For example, opposing through traffic and near-side crossing through traffic in the hour of collision were identified significant for Patterns 5 and 8 crash injuries, respectively. Protected left-turn phasing is significantly correlated with Pattern 5 crash injury. Many other variables in driver attributes, vehicular characteristics, roadway geometry design, environmental factors, and crash characteristics were identified. Specifically, the use of the partial proportional formulation allows a much better identification of the increasing effect of alcohol and/or drug use on crash injury severity, which previously was masked using the conventional ordered probability models.     

Identify sequence of events likely to result in severe crash outcomes

The current practice of crash characterization in highway engineering reduces multiple dimensions of crash contributing factors and their relative sequential connections, crash sequences, into broad definitions, resulting in crash categories such as head-on, sideswipe, rear-end, angle, and fixed-object. As a result, crashes that are classified in the same category may contain many different crash sequences. This makes it difficult to develop effective countermeasures because these crash categorizations are based on the outcomes rather than the preceding events. Consequently, the efficacy of a countermeasure designed for a specific type of crash may not be appropriate due to different pre-crash sequences. This research seeks to explore the use of event sequence to characterize crashes. Additionally, this research seeks to identify crash sequences that are likely to result in severe crash outcomes so that researchers can develop effective countermeasures to reduce severe crashes. This study utilizes the sequence of events from roadway departure crashes in the Fatality Analysis Reporting System (FARS), and converts the information to form a new categorization called “crash sequences.” The similarity distance between each pair of crash sequences were calculated using the Optimal Matching approach. Cluster analysis was applied to group crash sequences that are etiologically similar in terms of the similarity distance. A hybrid model was constructed to mitigate the potential sample selection bias of FARS data, which is biased toward more severe crashes. The major findings include: (1) in terms of a roadway departure crash, the crash sequences that are most likely to result in high crash severity include a vehicle that first crosses the median or centerline, runs-off-road on the left, and then collides with a roadside fixed-object; (2) seat-belt and airbag usage reduces the probability of dying in a roadway departure crash by 90%; and (3) occupants who are seated on the side of the vehicle that experience a direct impact are 2.6 times more likely to die in a roadway departure crash than those not seated on the same side of the vehicle where the impact occurs.     

Stop versus yield on pedestrian-involved fatal crashes in the United States

In an effort to improve pedestrian safety, several states in the United States changed their pedestrian laws by changing the requirement that drivers yield to pedestrians in crosswalks to a requirement that drivers stop for pedestrians in crosswalks. This study examined whether this change had an effect on pedestrian safety in the United States, with its focus on low-speed roads. To examine the association between changes in pedestrian laws and changes in pedestrian-involved fatal crashes, three approaches were employed: before–after analysis, time-series analysis, and cross-sectional analysis. Pedestrian-involved fatal traffic crashes on low-speed roads were extracted from the U.S. national fatal crash database, the Fatality Analysis Reporting System (FARS), from 1980 through 2005. This study found no statistically significant reduction in pedestrian-involved fatal crashes attributable to changes in the laws, yet this finding is not definitive because of study limitations such as the omission of relevant exposure data.     

Young drivers--reduced crash risk with passengers in the vehicle

Studies have shown that the effect of passengers on accident propensity among young drivers is ambiguous--in some cases positive and some negative. In Sweden, various kinds of information are compiled in registers, including a national accident database and exposure data collected in a national investigation of the driving habits of license holders. Access to such data offers a good opportunity to study crash risks related to driving with and without passengers. This was done for drivers in three different age groups (18-24, 25-64 and >65 years) accompanied by one, two or three or more passengers. Differences in crash risk were estimated using incidence density ratios (IDRs) and 95% confidence intervals. The results show that passengers had an overall protective effect, that is, the crash risk was higher for those who drove alone, regardless of their age or gender. This protective effect increased with every extra passenger (up to eight), indicating that the more passengers in the vehicle, the safer the driving. The influence of passengers was weakest (albeit still positive) among the youngest drivers (ages 18-24 years), especially the males in that group. The protective impact showed the same pattern on all days of the week, but was most marked from Friday to Sunday for most of the drivers, regardless of age.     

Estimating the costs of occupational injury in the United States

This article estimates workplace injury costs in the U.S. These costs have been studied in less detail than most injury costs. Our methods, which mostly use regularly published data, produce order-of-magnitude estimates. Overall, workplace injuries cost the U.S. an estimated $140 billion annually. This estimate includes $17 billion in medical and emergency services, $60 billion in lost productivity, $5 billion in insurance costs, and $62 billion in lost quality of life. One sixth of the societal costs result from the 3% of workplace injuries in motor vehicle crashes. Motor vehicle costs per injury are almost 6 times the workplace injury average.     

脉冲波和阶跃波输入的产品破损边界

以产品包装破损极限理论为基础,以有限上升阶跃波为例,对阶跃波和脉冲波作用下的产品破损边界曲线进行了求解,并对其进行了系统的分析和比较.     

Costs of occupational injuries in construction in the United States

This paper presents costs of fatal and nonfatal injuries for the construction industry using 2002 national incidence data from the Bureau of Labor Statistics and a comprehensive cost model that includes direct medical costs, indirect losses in wage and household productivity, as well as an estimate of the quality of life costs due to injury. Costs are presented at the three-digit industry level, by worker characteristics, and by detailed source and event of injury. The total costs of fatal and nonfatal injuries in the construction industry were estimated at $11.5 billion in 2002, 15% of the costs for all private industry. The average cost per case of fatal or nonfatal injury is $27,000 in construction, almost double the per-case cost of $15,000 for all industry in 2002. Five industries accounted for over half the industry's total fatal and nonfatal injury costs. They were miscellaneous special trade contractors (SIC 179), followed by plumbing, heating and air-conditioning (SIC 171), electrical work (SIC 173), heavy construction except highway (SIC 162), and residential building construction (SIC 152), each with over $1 billion in costs.     

The fatality and injury risk of light truck impacts with pedestrians in the United States

In the United States, passenger vehicles are shifting from a fleet populated primarily by cars to a fleet dominated by light trucks and vans (LTVs). Because light trucks are heavier, stiffer, and geometrically more blunt than passenger cars, they pose a dramatically different type of threat to pedestrians. This paper investigates the effect of striking vehicle type on pedestrian fatalities and injuries. The analysis incorporates three major sources of data, the Fatality Analysis Reporting System (FARS), the General Estimates System (GES), and the Pedestrian Crash Data Study (PCDS). The paper presents and compares pedestrian impact risk factors for sport utility vehicles, pickup trucks, vans, and cars as developed from analyses of US accident statistics. Pedestrians are found to have a two to three times greater likelihood of dying when struck by an LTV than when struck by a car. Examination of pedestrian injury distributions reveals that, given an impact speed, the probability of serious head and thoracic injury is substantially greater when the striking vehicle is an LTV rather than a car.     

A simultaneous equations model of crash frequency by severity level for freeway sections

This paper presents a simultaneous equations model of crash frequencies by severity level for freeway sections using five-year crash severity frequency data for 275 multilane freeway segments in the State of Washington. Crash severity is a subject of much interest in the context of freeway safety due to higher speeds of travel on freeways and the desire of transportation professionals to implement measures that could potentially reduce crash severity on such facilities. This paper applies a joint Poisson regression model with multivariate normal heterogeneities using the method of Maximum Simulated Likelihood Estimation (MSLE). MSLE serves as a computationally viable alternative to the Bayesian approach that has been adopted in the literature for estimating multivariate simultaneous equations models of crash frequencies. The empirical results presented in this paper suggest the presence of statistically significant error correlations across crash frequencies by severity level. The significant error correlations point to the presence of common unobserved factors related to driver behavior and roadway, traffic and environmental characteristics that influence crash frequencies of different severity levels. It is found that the joint Poisson regression model can improve the efficiency of most model coefficient estimators by reducing their standard deviations. In addition, the empirical results show that observed factors generally do not have the same impact on crash frequencies at different levels of severity.     

On the nature of over-dispersion in motor vehicle crash prediction models

Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts--variation over and above that accounted for by the Poisson density. The extra--variation--or dispersion--is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models--tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31-40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions. This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with non-informative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites.     

Motor vehicle driver death and high state maximum speed limits: 1991-1993

OBJECTIVE: To measure the association between motor vehicle crash (MVC) driver death and high state maximum speed limits. METHODS: This study used a case-control design and assessed driver deaths from three major types of MVCs: non-collision; collision with motor vehicles in transit; and collision with stationary objects. The study period was 1991-1993. For each type of crash, case subject populations of fatally injured drivers were obtained from the U.S. Department of Transportation Fatality Analysis Reporting System. Four control subject populations, each associated with a different cause of death, were obtained from a U.S. national death certificate database (the causes of death were unintentional poisoning, non-Hodgkin lymphoma, drowning, and diabetes mellitus). Subjects were considered exposed if the state in which they crashed (for cases) or died (for controls) had a maximum speed limit greater than 55mph. Each of the three case subject populations was compared against each of the four control subject populations. Odds ratios (ORs) were adjusted for age and gender. RESULTS: For non-collision driver death, ORs ranged from 3.06 to 6.56, depending on the year and control group; all the ORs were significant. For collision with motor vehicles in transit driver death, ORs ranged from 1.12 to 2.22; all the ORs were significant. For collision with stationary objects driver death, ORs ranged from 0.87 to 1.83. CONCLUSIONS: There was a moderately strong and significant association between non-collision driver death and high state maximum speed limits. For collision with motor vehicles in transit driver death, the association was somewhat milder but still consistent. For collision with stationary objects driver death, the presence of an association was unclear. During 1991-1993, the effects of high state maximum speed limits may have been different for different types of MVCs.     

Modeling left-turn crash occurrence at signalized intersections by conflicting patterns

In order to better understand the underlying crash mechanisms, left-turn crashes occurring at 197 four-legged signalized intersections over 6 years were classified into nine patterns based on vehicle maneuvers and then were assigned to intersection approaches. Crash frequency of each pattern was modeled at the approach level by mainly using Generalized Estimating Equations (GEE) with the Negative Binomial as the link function to account for the correlation among the crash data. GEE with a binomial logit link function was also applied for patterns with fewer crashes. The Cumulative Residuals test shows that, for correlated left-turn crashes, GEE models usually outperformed basic Negative Binomial models. The estimation results show that there are obvious differences in the factors that cause the occurrence of different left-turn collision patterns. For example, for each pattern, the traffic flows to which the colliding vehicles belong are identified to be significant. The width of the crossing distance (represented by the number of through lanes on the opposing approach of the left-turning traffic) is associated with more left-turn traffic colliding with opposing through traffic (Pattern 5), but with less left-turning traffic colliding with near-side crossing through traffic (Pattern 8). The safety effectiveness of the left-turning signal is not consistent for different crash patterns; "protected" phasing is correlated with fewer Pattern 5 crashes, but with more Pattern 8 crashes. The study indicates that in order to develop efficient countermeasures for left-turn crashes and improve safety at signalized intersections, left-turn crashes should be considered in different patterns.     

A systems analysis of the Ladbroke Grove rail crash

On 5 October 1999, near London Paddington Station, two trains collided on a main line near Ladbroke Grove. The immediate "human error" that preceded this crash was a Signal Passed At Danger (SPAD). Thirty-one people lost their lives and many more were injured. The crash prompted an extensive multi-disciplinary investigation and hearing to identify the factors that contributed to the Signal Passed At Danger event. This included the involvement of psychologists to consider the human factors "responsible" for the crash and the broader system context, including the operational and organizational environment that may have contributed. This paper summarizes the key factors identified in relation to this crash within a system analysis framework. This framework considers multiple sources of influence upon the driver in relation to the committed Signal Passed At Danger. These influences include direct factors attributable to the driver and the immediate circumstances of the event, as well as indirect, or latent, factors within the operational procedures and the management of the organization. This systemic combination of factors, not an isolated case of human error, conspired to propagate the events that resulted in the Signal Passed At Danger event and subsequent crash. This particular case demonstrates that train crashes cannot be distilled to a single causal factor. Rather, such crashes result from a system failure in which unpredicted interactions between direct and indirect influences coincide at an inopportune instant.