Modelling of safety glass for crash simulation

In this paper, we present a numerical technique to simulate the crash behaviour of laminated safety glass via finite elements. As the main aspect of this work, we consider two coincided elements: a shell element for glass and a membrane element for the hyperelastic PVB-interlayer. We give an overview of hyperelastic models, which are used in crash simulations and investigate the material laws by Blatz–Ko, Mooney–Rivlin and Ogden. The obtained stress–strain curves are fitted by experimental results of the interlayer. For a comparison, we use an one-material-model with piecewise linear plasticity and a laminated glass model assigning material properties to the integration points. As practical applications, we simulate an impact of a sphere into a glass plate and investigate the behaviour of a windscreen during a roof crash.     

Quasi-induced exposure: The choice of exposure metrics

The quasi-induced exposure method is widely used to estimate exposure and risks of different groups of drivers and vehicles. Essentially, this method assumes that non-at-fault or passive parties in two-vehicle collisions represent a random sample of the populations on the road. Most previous works have used the whole sample of collisions to estimate exposure.There has been some concern about possible biases in quasi-induced estimates. In this paper, we argue that (1) biases are mainly due to differences in accident avoidance abilities, speeds and injury risks, and (2) because the influence of these three factors on the probability of being non-at-fault is not the same for every crash type, differences may arise among non-at-fault populations, in which case some crash types would provide a more accurate estimate of exposure than others.We explore the direction of biases due to speed, accident avoidance ability and injury risk in four accident types: accidents between vehicles travelling on different lanes in two-way, two-lane undivided roads; accidents between vehicles travelling on different lanes on multilane roads; intersection accidents; and accidents between vehicles travelling on the same lane. Our analysis shows that more research would be needed concerning the effect of speed on head-on crashes on undivided roads, and crashes on multilane roads.     

The role of exposure on differences in driver death rates by gender and age: Results of a quasi-induced method on crash data in Spain

AimPart of the differences by age and gender in driver death rates from traffic injuries depends on the amount of exposure (km/year travelled). Unfortunately, direct indicators of exposure are not available in many countries. Our aim was to compare the age and gender differences in death rates with and without adjustment by exposure using a quasi-induced exposure approach in Spain, during 2004–2012.MethodsCrude and adjusted death rate ratios (CDRR and ADRR, respectively) were calculated for each age and gender group. To obtain the latter estimates, in accordance with quasi-exposure reasoning, the number of registered drivers was replaced by the number of non-infractor drivers, passively involved in collisions with another vehicle whose driver committed an infraction. 18–29 years and female drivers were chosen as the reference categories for age and gender.ResultsStriking differences were found between CDRR and ADRR estimates. When CDRR were estimated, we found the highest traffic mortality among the youngest drivers, except for females in non-urban roads. ADRR however showed the highest mortality among the oldest groups, especially in females, peaking among drivers >74 years in all types of roads. Regarding differences by gender, both estimates revealed higher traffic mortality in males, although the differences were much smaller when using ADRR. CDRR and ADRR for males tended to converge as age increased.ConclusionsDeath risk from traffic injuries among drivers is clearly influenced by the amount of exposure. These findings further emphasize the need to obtain direct traffic exposure estimates by subgroups of drivers.     

Difficulties with quasi-induced exposure when speed varies systematically by vehicle type

The attractiveness of quasi-induced exposure lies in the simplistic nature of its theory and application. As opposed to vehicle miles of travel, quasi-induced exposure is developed solely from the accident data themselves. Involvement ratios (IRs) are used to describe the relative over- or under-involvement of different driver-vehicle combinations in traffic accidents. The issue of systematic bias in the involvement ratios is explored, and it is shown both theoretically and empirically that the variation in average speeds between vehicle types can affect relative accident involvement ratios. For vehicle types that routinely travel faster, the IRs will likely be underestimated; while for slower-moving vehicle types, the IRs will be overestimated. In the case of speed, the magnitude of the effect on the IRs is dependent both on the magnitude of the speed differential and the percentage of slower vehicles in the traffic stream. Conclusions can be extended to whenever there are speed or other behavior disparities associated with the driver-vehicle combinations in the traffic stream. Other examples include the speed discrepancy caused by different drivers (e.g., younger versus older drivers). Caution must be used in interpreting results from applications of the quasi-induced technique whenever such biases might be encountered.     

Aggregate crash prediction models: Introducing crash generation concept

Safety conscious planning is a new proactive approach towards understanding crashes. It requires a planning-level decision-support tool to facilitate proactive approach to assessing safety effects of alternative urban planning scenarios. The objective of this research study is to develop a series of aggregate crash prediction models (ACPM) that are consistent with the trip generation step of the conventional four-step demand models. The concept of crash generation models (CGMs) is introduced utilizing trip generation data in a generalized linear regression with the assumption of a negative binomial error structure. The relationship of crash frequencies in traffic analysis zones (TAZ) and number of trips generated by purpose is investigated. This translates into immediate checking of the impact of future trip generations on crash frequencies in comprehensive transportation-planning studies (i.e. ability to forecast crashes at each time-step trips are being forecasted). A good relation was seen between crash frequency and number of trips produced/attracted by purpose per TAZ.     

Quasi-induced exposure method: Evaluation of not-at-fault assumption

Crash rates are used to establish the relative safety of various variables of concern such as driver classes, vehicle types and roadway components. Appropriate exposure data for estimating crash rates is critical but crash databases do not contain information on driver or vehicle exposure. The quasi-induced exposure method, which uses not-at-fault driver/vehicle data as an exposure metric, is a technique used in order to overcome this problem. The basic assumption made here is that not-at-fault drivers represent the total population in question. This paper examines the validity of this assumption using the Kentucky crash database to define two samples of not-at-fault drivers. One sample included only not-at-fault drivers selected from the first two vehicles in a multi-vehicle crash (two or more vehicles involved) while the other included the not-at-fault drivers from multi-vehicle crashes with more than two vehicles involved and excluding the first two drivers. The assumption is that the randomness of the involvement of drivers in the second sample is more reasonable than the drivers in the first two vehicles involved in crashes. The results indicate that these two samples are similar; there is no statistical evidence demonstrating that both samples represent two different populations in the maneuvers and other variables/factors examined here; and they are representative simple random samples of the driver population with respect to the distribution of the driver age when there is no reasonable doubt about investigating officers’ judgments. Thus, estimating relative crash propensities for any given driver type by using the quasi-induced exposure approach will yield reasonable estimates of exposure.     

Identifying repeat DUI crash factors using state crash records

The objective of this study is to identify high risk factors that are closely related to repeat DUI crashes using readily available information from the state crash records. Survival analysis was used and a Cox proportional hazards model was developed using the police-reported crash records in the state of Louisiana. A variety of variables were found to be significant in predicting repeat DUI crashes. The factors included the characteristics of the drivers (gender, race, and age), the types of the vehicle (light truck/pick up truck or other), the characteristics of the crash (hit-and-run, driver violations, and whether the driver is arrested), the type of location (residential area or other), and the characteristics of the roadway (highway type and roadway type). This study provides a comprehensive picture of the repeat DUI crashes. The model can quantitatively predict the relative hazards of repeat DUI crashes. It can be used to identify the characteristics of the crash-involved DUI drivers who are at greatest risk of being involved in a subsequent DUI crash, allowing to apply appropriate remedial measures to reduce the risk.     

Comparison of two methods to assess the effect of age and sex on the risk of car crashes

Background

This study was designed to compare two methods (direct measurement of exposure and quasi-induced exposure) for assessing the effect of age and sex on the risk of being involved in a car crash in Spain.

Methods

Spanish crash rates (per 10,000,000 driver-km) for age and sex groups of drivers aged 18–64 years old were obtained for 2004–2007, using information from the Spanish General Traffic Office (census of reported car crashes) and the Spanish Household Survey on Alcohol and Drugs (estimate of the mean km driven for each car driver). The rate ratios estimated by direct exposure estimates were compared to those obtained with the quasi-induced exposure method, which compares the age and sex of responsible and non-responsible drivers involved in the same clean collision (in which only one of the drivers committed a driving infraction).

Results

Both methods detected an increased risk of involvement in a crash for the youngest (18–20 years) and the oldest drivers (60–64 years), compared to middle-aged drivers (45–49 years). However, the rate ratios obtained with the quasi-induced method for the youngest group (2.0 for men, 1.6 for women) were much lower than those obtained with crash rates (13.4 for men, 5.7 for women). Both methods detected a similar increase in the risk of involvement of male drivers compared to women in the youngest age group. This excess risk for men was maintained with increasing age up to 45–49 years when the quasi-induced method was used. However, direct comparisons of crash rates revealed an increased risk of involvement in women compared to men of the same age from 25–29 years onward.

Conclusions

Both direct measurement of driving exposure and the quasi-induced exposure method detected some well-known patterns of risk associated with driver's age and sex. However, factors that could explain important differences between the two methods deserve attention, especially those related with the excess risk for the youngest drivers as well as sex-related risk.     

A comprehensive review on the quasi-induced exposure technique

Objective

The goal is to comprehensively examine the state-of-the-art applications and methodological development of quasi-induced exposure and consequently pinpoint the future research directions in terms of implementation guidelines, limitations, and validity tests.

Methods

The paper conducts a comprehensive review on approximately 45 published papers relevant to quasi-induced exposure regarding four key topics of interest: applications, responsibility assignment, validation of assumptions, and methodological development.

Results

Specific findings include that: (1) there is no systematic data screening procedure in place and how the eliminated crash data will impact the responsibility assignment is generally unknown; (2) there is a lack of necessary efforts to assess the validity of assumptions prior to its application and the validation efforts are mostly restricted to the aggregated levels due to the limited availability of exposure truth; and (3) there is a deficiency of quantitative analyses to evaluate the magnitude and directions of bias as a result of injury risks and crash avoidance ability.

Conclusions

The paper points out the future research directions and insights in terms of the validity tests and implementation guidelines.     

Factors influencing premature graduation from the use of child restraints in Japan

This study examined the association of child passenger restraint use by younger and older children taking into account situational factors and driver/child passenger characteristics. The Japanese national traffic accident data pertaining to children injured in rear-end collisions where the drivers were not-at-fault was analyzed, while applying the quasi-induced exposure method. Multivariate logistic regression analyses were conducted to examine the adjusted effects of predictors for proper restraint use by 0-5, 6-9, and 10-12-year-old children. Unbelted drivers, child's seating position, the number of total occupants, and the child's age were significantly associated with restraint use by both younger and older children. Riding in the rear seats was strongly associated with older SB-age children not being properly restrained, suggesting a link between the lack of booster seat-use requirements and the generally low restraint use rate in rear seats as well as the premature graduation from CRS use in general. The results were discussed in light of other international findings in this field.     

A review of the validity of the underlying assumptions of quasi-induced exposure

In recent years, the quasi-induced exposure technique has been widely implemented in a variety of traffic safety-related settings. One of the primary concerns associated with the applications of quasi-induced exposure is that the underlying assumptions are not explicitly verified or validated before the exposure measurement is adopted. Of principal interest is the assumption that the non-responsible driver/vehicles in two-vehicle crashes are representative of the general driving population on the road at the time of crash occurrence. The objective here is to provide an alternative to test the not-at-fault assumptions with the use of three-or-more-vehicle crashes, which are readily available in many crash databases. With the use of Michigan and Maine crash data as examples, the examination of the validity is developed at two levels: (1) at all locations where crashes took place; (2) at locations where three-or-more-vehicle crashes are prone to occur. Non-responsible drivers are disaggregated by three basic driver-vehicle characteristics (age, gender, and vehicle type) and compared at these two levels for statistical and operational (practical) differences. Examination of the results demonstrates that all of the examined non-responsible driver distributions are consistently similar from both operational and statistical points of view. Compared to other approaches to validation, such as using “exposure truth” (e.g., actual vehicle miles traveled or a trip diary), the proposed validation is much more simplistic, straightforward, and cost-effective.     

Analysis of fatal motorcycle crashes: crash typing

There were 2074 crashes fatal to a motorcycle driver in the United States during 1992. A computer program was developed to convert Fatal Accident Reporting System (FARS) data for these crashes into standard format English language “crash reports.” The computer generated reports were analyzed and crash type categories were defined. Five defined crash type categories accounted for 1785 (86%) of the 2074 crash events: Ran off-road (41%); ran traffic control (18%); oncoming or head-on (11%); left-turn oncoming (8%); and motorcyclist down (7%). Alcohol and excessive speed were common factors associated with motorcyclist crash involvement. Left turns and failure to yield were common factors associated with the involvement of other motorists. Suggested countermeasures include helmet use and enforcement of speed and impaired driving laws.     

Limits of the quasi-induced exposure method when compared with the standard case-control design. Application to the estimation of risks associated with driving under the influence of cannabis or alcohol

The disparities between the quasi-induced exposure (QIE) method and a standard case–control approach with crash responsibility as disease of interest are studied. The 10,748 drivers who had been given compulsory cannabis and alcohol tests subsequent to involvement in a fatal crash in France between 2001 and 2003 were used to compare the two approaches. Odds ratios were assessed using conditional and unconditional logistic regressions. While both approaches found that drivers under the influence of alcohol or cannabis increased the risk of causing a fatal crash, the two approaches are not equivalent. They differ mainly with regards to the driver sample selected. The QIE method results in splitting the overall road safety issue into two sub-studies: a matched case–control study dealing with two-vehicle crashes and a case–control study dealing with single-vehicle crashes but with a specific control group. Using a specific generic term such as “QIE method” should not hide the real underlying epidemiological design. On the contrary, the standard case–control approach studies drivers involved in all type of crashes whatever the distribution of the responsibility in each crash. This method also known as “responsibility analysis” is the most relevant for assessing the overall road safety implications of a driver characteristic.     

Exploration and comparison of crash modification factors for multiple treatments on rural multilane roadways

As multiple treatments (or countermeasures) are simultaneously applied to roadways, there is a need to assess their combined safety effects. Due to a lack of empirical crash modification factors (CMFs) for multiple treatments, the Highway Safety Manual (HSM) and other related studies developed various methods of combining multiple CMFs for single treatments. However, the literature did not evaluate the accuracy of these methods using CMFs obtained from the same study area. Thus, the main objectives of this research are: (1) develop CMFs for two single treatments (shoulder rumble strips, widening shoulder width) and one combined treatment (shoulder rumble strips + widening shoulder width) using before–after and cross-sectional methods and (2) evaluate the accuracy of the combined CMFs for multiple treatments estimated by the existing methods based on actual evaluated combined CMFs. Data was collected for rural multi-lane highways in Florida and four safety performance functions (SPFs) were estimated using 360 reference sites for two crash types (All crashes and Single Vehicle Run-off Roadway (SVROR) crashes) and two severity levels (all severity (KABCO) and injury (KABC)).     

Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model

Multi-vehicle rear-end accidents constitute a substantial portion of the accidents occurring at signalized intersections. To examine the accident characteristics, this study utilized the 2001 Florida traffic accident data to investigate the accident propensity for different vehicle roles (striking or struck) that are involved in the accidents and identify the significant risk factors related to the traffic environment, the driver characteristics, and the vehicle types. The Quasi-induced exposure concept and the multiple logistic regression technique are used to perform this analysis. The results showed that seven road environment factors (number of lanes, divided/undivided highway, accident time, road surface condition, highway character, urban/rural, and speed limit), five factors related to striking role (vehicle type, driver age, alcohol/drug use, driver residence, and gender), and four factors related to struck role (vehicle type, driver age, driver residence, and gender) are significantly associated with the risk of rear-end accidents. Furthermore, the logistic regression technique confirmed several significant interaction effects between those risk factors.     

Modelling the hierarchical structure of road crash data--application to severity analysis

Road crashes have an unquestionably hierarchical crash-car-occupant structure. Multilevel models are used with correlated data, but their application to crash data can be difficult. The number of sub-clusters per cluster is small, with less than two cars per crash and less than two occupants per car, whereas the number of clusters can be high, with several hundred/thousand crashes. Application of the Monte-Carlo method on observed and simulated French road crash data between 1996 and 2000 allows comparing estimations produced by multilevel logistic models (MLM), Generalized Estimating Equation models (GEE) and logistic models (LM). On the strength of a bias study, MLM is the most efficient model while both GEE and LM underestimate parameters and confidence intervals. MLM is used as a marginal model and not as a random-effect model, i.e. only fixed effects are taken into account. Random effects allow adjusting risks on the hierarchical structure, conferring an interpretative advantage to MLM over GEE. Nevertheless, great care is needed for data coding and quite a high number of crashes are necessary in order to avoid problems and errors with estimates and estimate processes. On balance, MLM must be used when the number of vehicles per crash or the number of occupants per vehicle is high, when the LM results are questionable because they are not in line with the literature or finally when the p-values associated to risk measures are close to 5%. In other cases, LM remains a practical analytical tool for modelling crash data.     

Pedestrian crash estimation models for signalized intersections

The focus of this paper is twofold: (1) to examine the non-linear relationship between pedestrian crashes and predictor variables such as demographic characteristics (population and household units), socio-economic characteristics (mean income and total employment), land use characteristics, road network characteristics (the number of lanes, speed limit, presence of median, and pedestrian and vehicular volume) and accessibility to public transit systems, and (2) to develop generalized linear pedestrian crash estimation models (based on negative binomial distribution to accommodate for over-dispersion of data) by the level of pedestrian activity and spatial proximity to extract site specific data at signalized intersections. Data for 176 randomly selected signalized intersections in the City of Charlotte, North Carolina were used to examine the non-linear relationships and develop pedestrian crash estimation models. The average number of pedestrian crashes per year within 200 feet of each intersection was considered as the dependent variable whereas the demographic characteristics, socio-economic characteristics, land use characteristics, road network characteristics and the number of transit stops were considered as the predictor variables. The Pearson correlation coefficient was used to eliminate predictor variables that were correlated to each other. Models were then developed separately for all signalized intersections, high pedestrian activity signalized intersections and low pedestrian activity signalized intersections. The use of 0.25 mile, 0.5 mile and 1 mile buffer widths to extract data and develop models was also evaluated.     

A disaggregate approach to crash rate analysis

This paper presents a disaggregate approach to crash rate analysis. Enumerating crash rates on a per trip-kilometer basis, the proposed method removes the linearity assumption inherent in the conventional quotient indicator of accidents per unit travel distance. The approach involves combining two disparate datasets on a geographic information systems (GIS) platform by matching accident records to a defined travel corridor. As an illustration of the methodology, travel information from the Victorian Activity and Travel Survey (VATS) and accident records contained in CrashStat were used to estimate the crash rates of Melbourne residents in different age-sex groups according to time of the day and day of the week. The results show a polynomial function of a cubic order when crash rates are plotted against age group, which contrasts distinctly with the U-shape curve generated by using the conventional aggregate quotient approach. Owing to the validity of the many assumptions adopted in the computation, this study does not claim that the results obtained are conclusive. The methodology, however, is seen as providing a framework upon which future crash risk measures could be based as the use of spatial tracking devises become prevalent in travel surveys.     

Computational and experimental crash analysis of the road safety barrier

The paper describes the computational analysis and experimental crash tests of a new road safety barrier. The purpose of this research was to develop and evaluate a full-scale computational model of the road safety barrier for use in crash simulations and to further compare the computational results with real crash test data. The impact severity and stiffness of the new design have been evaluated with the dynamic nonlinear elasto-plastic analysis of the three-dimensional road safety barrier within the framework of the finite element method with LS-DYNA code. Comparison of computational and experimental results proved the correctness of the computational model. The tests have also shown that the new safety barrier assures controllable crash energy absorption which in turn increases the safety of vehicle occupants.     

Driver health and crash involvement: a case-control study

The study investigates the relative crash involvement risk associated with diagnosed medical conditions, subjective symptoms and the use of some medicines based on self-report questionnaires from 4448 crash-involved drivers. Whereas many previous studies of medical conditions and crash risk have focussed exclusively on elderly drivers, this study included drivers of all ages. Relative risk for each health condition was estimated by comparing drivers with and without the condition, regarding the odds of being at fault for the crash. Statistical significance was tested by a logistic regression analysis for each condition with crash culpability as the dependent variable. Relative risks were expressed as odds ratios (OR) adjusted for age and annual driving distance. The analyses identified the following significant risk factors: non-medicated diabetes (OR=3.08), a history of myocardial infarction (OR=1.77), using glasses when driving (OR=1.26), myopia (OR=1.22), sleep onset insomnia (OR=1.87), frequent tiredness (OR=1.36), anxiety (OR=3.15), feeling depressed (OR=2.43) and taking antidepressants (OR=1.70). In addition, there was a relatively large and nearly significant relative risk for drivers who had suffered a stroke (OR=1.93). For some additional conditions the crude odds ratios were significant, but failed to reach significance after correction for age and annual driving distance.