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

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

Currently, research on the development of crash models in terms of crash frequency on road segments and crash severity applies the principles of spatial analysis and heterogeneity due to the methods’ suitability compared with traditional models. This study focuses on crash severity and frequency in Thailand. Moreover, this study aims to understand crash frequency and fatality. The result of the intra-class correlation coefficient found that the spatial approach should analyze the data. The crash frequency model’s best fit is a spatial zero-inflated negative binomial model (SZINB). The results of the random parameters of SZINB are insignificant, except for the intercept. The crash frequency model’s significant variables include the length of the segment and average annual traffic volume for the fixed parameters. Conversely, the study finds that the best fit model of crash severity is a logistic regression with spatial correlations. The variances of random effect are significant such as the intersection, sideswipe crash, and head-on crash. Meanwhile, the fixed-effect variables significant to fatality risk include motorcycles, gender, non-use of safety equipment, and nighttime collision. The paper proposes a policy applicable to agencies responsible for driver training, law enforcement, and those involved in crash-reduction campaigns.

A systematic assessment of the use of opponent variables, data subsetting and hierarchical specification in two-party crash severity analysis

Road crashes impose an important burden on health and the economy. Numerous efforts have been undertaken to understand the factors that affect road collisions in general, and the severity of crashes in particular. In this literature several strategies have been proposed to model interactions between parties in a crash, including the use of variables regarding the other party (or parties) in the collision, data subsetting, and estimating models with hierarchical components. Since no systematic assessment has been conducted of the performance of these strategies, they appear to be used in an ad-hoc fashion in the literature. The objective of this paper is to empirically evaluate ways to model party interactions in the context of crashes involving two parties. To this end, a series of models are estimated using data from Canada's National Collision Database. Three levels of crash severity (no injury/injury/fatality) are analyzed using ordered probit models and covariates for the parties in the crash and the conditions of the crash. The models are assessed using predicted shares and classes of outcomes, and the results highlight the importance of considering opponent effects in crash severity analysis. The study also suggests that hierarchical (i.e., multi-level) specifications and subsetting do not necessarily perform better than a relatively simple single-level model with opponent-related factors. The results of this study provide insights regarding the performance of different modelling strategies, and should be informative to researchers in the field of crash severity.

Applying hierarchical logistic models to compare urban and rural roadway modeling of severity of rear-end vehicular crashes

A rear-end crash is a widely studied type of road accident. The road area at the crash scene is a factor that significantly affects the crash severity from rear-end collisions. These road areas may be classified as urban or rural and evince obvious differences such as speed limits, number of intersections, vehicle types, etc. However, no study comparing rear-end crashes occurring in urban and rural areas has yet been conducted. Therefore, the present investigation focused on the comparison of diverse factors affecting the likelihood of rear-end crash severities in the two types of roadways. Additionally, hierarchical logistic models grounded in a spatial basis concept were applied by determining varying parameter estimations with regard to road segments. Additionally, the study compared coefficients with multilevel correlation model and those without multilevel correlation. Four models were established as a result. The data used for the study pertained to rear-end crashes occurring on Thai highways between 2011 and 2015. The results of the data analysis revealed that the model parameters for both urban and rural areas are in the same direction with the larger number of significant parameter values present in the rural rear-end crash model. The significant variables in both the urban and rural road segment models are the seat belt use, and the time of the incident. To conclude, the present study is useful because it provides another perspective of rear-end crashes to encourage policy makers to apply decisions that favor rules that assure safety.

Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors

Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus–pedestrian crashes. However, bus–pedestrian crashes that are affected by a company’s and regional characteristics have a cross-classified hierarchical structure, which is difficult to address properly using a single-level model or even a two-level multi-level model. In this study, we used a cross-classified, multi-level model to consider simultaneously the unobserved heterogeneities at these two distinct levels. Using bus–pedestrian crash data in South Korea from 2011 through to 2015, in this study, we investigated the factors related to the injury severity of the crashes, including crash level, regional and company level factors. The results indicate that the company and regional effects are 16.8% and 5.1%, respectively, which justified the use of a multi-level model. We confirm that type I errors may arise when the effects of upper-level groups are ignored. We also identified the factors that are statistically significant, including three regional-level factors, i.e., the elderly ratio, the ratio of the transportation infrastructure budget, and the number of doctors, and 13 crash-level factors. This study provides useful insights concerning bus–pedestrian crashes, and a safety policy is suggested to enhance bus–pedestrian safety.

Multilevel analysis of the role of human factors in regional disparities in crash outcomes

A growing body of research has examined the disparities in road traffic safety among population groups and geographic regions. These studies reveal disparities in crash outcomes between people and regions with different socioeconomic characteristics. A critical aspect of the road traffic crash epidemic that has received limited attention is the influence of local characteristics on human elements that increase the risk of getting into a crash. This paper applies multilevel logistic regression modeling techniques to investigate the influence of driver residential factors on driver behaviors in an attempt to explain the area-based differences in the severity of road crashes across the State of Alabama. Specifically, the paper reports the effects of characteristics attributable to drivers and the geographic regions they reside on the likelihood of a crash resulting in serious injuries. Model estimation revealed that driver residence (postal code or region) accounted for about 7.3% of the variability in the probability of a driver getting into a serious injury crash, regardless of driver characteristics. The results also reveal disparities in serious injury crash rate as well as significant proportions of serious injury crashes involving no seatbelt usage, driving under influence (DUI), unemployed drivers, young drivers, distracted driving, and African American drivers among some regions. The average credit scores, average commute times, and populations of driver postal codes are shown to be significant predictors for risk of severe injury crashes. This approach to traffic crash analysis presented can serve as the foundation for evidence-based policies and also guide the implementation of targeted countermeasures.

Cross-classified multilevel models for severity of commercial motor vehicle crashes considering heterogeneity among companies and regions

This study analyzes 86,622 commercial motor vehicle (CMV) crashes (large truck, bus and taxi crashes) in South Korea from 2010 to 2014. The analysis recognizes the hierarchical structure of the factors affecting CMV crashes by examining eight factors related to individual crashes and six additional upper level factors organized in two non-nested groups (company level and regional level factors). The study considers four different crash severities (fatal, major, minor, and no injury). The company level factors reflect selected characteristics of 1,875 CMV companies, and the regional level factors reflect selected characteristics of 230 municipalities. The study develops a single-level ordinary ordered logit model, two conventional multilevel ordered logit models, and a cross-classified multilevel ordered logit model (CCMM). As the study develops each of these four models for large trucks, buses and taxis, 12 different statistical models are analyzed. The CCMM outperforms the other models in two important ways: 1) the CCMM avoids the type I statistical errors that tend to occur when analyzing hierarchical data with single-level models; and 2) the CCMM can analyze two non-nested groups simultaneously. Statistically significant factors include taxi company's type of vehicle ownership and municipality's level of transportation infrastructure budget. An improved understanding of CMV related crashes should contribute to the development of safety countermeasures to reduce the number and severity of CMV related crashes.

An Integrated and Interdisciplinary Model for Predicting the Risk of Injury and Death in Future Earthquakes

BACKGROUND

A comprehensive technique for earthquake-related casualty estimation remains an unmet challenge. This study aims to integrate risk factors related to characteristics of the exposed population and to the built environment in order to improve communities' preparedness and response capabilities and to mitigate future consequences.

METHODS

An innovative model was formulated based on a widely used loss estimation model (HAZUS) by integrating four human-related risk factors (age, gender, physical disability and socioeconomic status) that were identified through a systematic review and meta-analysis of epidemiological data. The common effect measures of these factors were calculated and entered to the existing model's algorithm using logistic regression equations. Sensitivity analysis was performed by conducting a casualty estimation simulation in a high-vulnerability risk area in Israel.

RESULTS

the integrated model outcomes indicated an increase in the total number of casualties compared with the prediction of the traditional model; with regard to specific injury levels an increase was demonstrated in the number of expected fatalities and in the severely and moderately injured, and a decrease was noted in the lightly injured. Urban areas with higher populations at risk rates were found more vulnerable in this regard.

CONCLUSION

The proposed model offers a novel approach that allows quantification of the combined impact of human-related and structural factors on the results of earthquake casualty modelling. Investing efforts in reducing human vulnerability and increasing resilience prior to an occurrence of an earthquake could lead to a possible decrease in the expected number of casualties.

Factors associated with single-vehicle and multi-vehicle road traffic collision injuries in Ireland

Generalised linear regression models were used to identify factors associated with fatal/serious road traffic collision injuries for single- and multi-vehicle collisions. Single-vehicle collisions and multi-vehicle collisions occurring during the hours of darkness or on a wet road surface had reduced likelihood of a fatal/serious injury. Single-vehicle 'driver with passengers' collisions occurring at junctions or on a hill/gradient were less likely to result in a fatal/serious injury. Multi-vehicle rear-end/angle collisions had reduced likelihood of a fatal/serious injury. Single-vehicle 'driver only' collisions and multi-vehicle collisions occurring on a public/bank holiday or on a hill/gradient were more likely to result in a fatal/serious injury. Single-vehicle collisions involving male drivers had increased likelihood of a fatal/serious injury and single-vehicle 'driver with passengers' collisions involving drivers under the age of 25 years also had increased likelihood of a fatal/serious injury. Findings can enlighten decision-makers to circumstances leading to fatal/serious injuries.

Safety analysis of urban signalized intersections under mixed traffic

INTRODUCTION

This study examined the crash causative factors of signalized intersections under mixed traffic using advanced statistical models.

METHOD

Hierarchical Poisson regression and logistic regression models were developed to predict the crash frequency and severity of signalized intersection approaches. The prediction models helped to develop general safety countermeasures for signalized intersections.

RESULTS

The study shows that exclusive left turn lanes and countdown timers are beneficial for improving the safety of signalized intersections. Safety is also influenced by the presence of a surveillance camera, green time, median width, traffic volume, and proportion of two wheelers in the traffic stream. The factors that influence the severity of crashes were also identified in this study.

PRACTICAL APPLICATION

As a practical application, the safe values of deviation of green time provided from design green time, with varying traffic volume, is presented in this study. This is a useful tool for setting the appropriate green time for a signalized intersection approach with variations in the traffic volume.

Wrong-way driving crashes on French divided roads

CONTEXT

The objective of divided roads is to increase users' safety by posting unidirectional traffic flows. It happens however that drivers proceed in the wrong direction, endangering themselves as well as other users. The crashes caused by wrong-way drivers are generally spotlighted by the media and call for public intervention.

OBJECTIVES

This paper proposes a characterization of wrong-way driving crashes occurring on French divided road on the 2008-2012 period. The objective is to identify the factors that delineate between wrong-way driving crashes and other crashes.

METHOD

Building on the national injury road crash database, 266 crashes involving a wrong-way driver were identified. Their characteristics (related to timing, location, vehicle and driver) are compared to those of the 22,120 other crashes that occurred on the same roads over the same period. The comparison relies on descriptive statistics, completed by a logistic regression.

RESULTS

Wrong-way driving crashes are rare but severe. They are more likely to occur during night hours and on non-freeway roads than other crashes. Wrong-way drivers are older, more likely to be intoxicated, to be locals, to drive older vehicles, mainly passenger cars without passengers, than other drivers.

PERSPECTIVES

The differences observed across networks can help prioritizing public intervention. Most of the identified WW-driving factors deal with cognitive impairment. Therefore, the specific countermeasures such as alternative road signs should be designed for and tested on cognitively impaired drivers. Nevertheless, WW-driving factors are also risk factors for other types of crashes (e.g. elderly driving, drunk driving and age of the vehicle). This suggests that, instead of (or in addition to) developing WW-driving specific countermeasures, managing these risk factors would help reducing a larger number of crashes.

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.

Relating crash frequency and severity: evaluating the effectiveness of shoulder rumble strips on reducing fatal and major injury crashes

To approach the goal of "Toward Zero Deaths," there is a need to develop an analysis paradigm to better understand the effects of a countermeasure on reducing the number of severe crashes. One of the goals in traffic safety research is to search for an effective treatment to reduce fatal and major injury crashes, referred to as severe crashes. To achieve this goal, the selection of promising countermeasures is of utmost importance, and relies on the effectiveness of candidate countermeasures in reducing severe crashes. Although it is important to precisely evaluate the effectiveness of candidate countermeasures in reducing the number of severe crashes at a site, the current state-of-the-practice often leads to biased estimates. While there have been a few advanced statistical models developed to mitigate the problem in practice, these models are computationally difficult to estimate because severe crashes are dispersed spatially and temporally, and cannot be integrated into the Highway Safety Manual framework, which develops a series of safety performance functions and crash modification factors to predict the number of crashes. Crash severity outcomes are generally integrated into the Highway Safety Manual using deterministic distributions rather than statistical models. Accounting for the variability in crash severity as a function geometric design, traffic flow, and other roadway and roadside features is afforded by estimating statistical models. Therefore, there is a need to develop a new analysis paradigm to resolve the limitations in the current Highway Safety Manual methods. We propose an approach which decomposes the severe crash frequency into a function of the change in the total number of crashes and the probability of a crash becoming a severe crash before and after a countermeasure is implemented. We tested this approach by evaluating the effectiveness of shoulder rumble strips on reducing the number of severe crashes. A total of 310 segments that have had shoulder rumble strips installed during 2002-2009 are included in the analysis. It was found that shoulder rumble strips reduce the total number of crashes, but have no statistically significant effect on reducing the probability of a severe crash outcome.

Multilevel analysis in road safety research

Hierarchical structures in road safety data are receiving increasing attention in the literature and multilevel (ML) models are proposed for appropriately handling the resulting dependences among the observations. However, so far no empirical synthesis exists of the actual added value of ML modelling techniques as compared to other modelling approaches. This paper summarizes the statistical and conceptual background and motivations for multilevel analyses in road safety research. It then provides a review of several ML analyses applied to aggregate and disaggregate (accident) data. In each case, the relevance of ML modelling techniques is assessed by examining whether ML model formulations (i) allow improving the fit of the model to the data, (ii) allow identifying and explaining random variation at specific levels of the hierarchy considered, and (iii) yield different (more correct) conclusions than single-level model formulations with respect to the significance of the parameter estimates. The evidence reviewed offers different conclusions depending on whether the analysis concerns aggregate data or disaggregate data. In the first case, the application of ML analysis techniques appears straightforward and relevant. The studies based on disaggregate accident data, on the other hand, offer mixed findings: computational problems can be encountered, and ML applications are not systematically necessary. The general recommendation concerning disaggregate accident data is to proceed to a preliminary investigation of the necessity of ML analyses and of the additional information to be expected from their application.

Characterizing Bicycle Collisions by Neighborhood in a Large Midwestern City

Introduction. Local environmental factors provide important contributions to bicycle safety. The purpose of this study was to characterize bicycle collisions by neighborhood in Cincinnati, Ohio. Background. The majority of prior bicycle safety research has focused on helmet use, especially among youth. Studies that have considered the neighborhood have centered on the built environment and its facilitation of bicycling (e.g., connectivity of roads and road conditions). Other broad conditions may be associated with injury beyond the use of protective equipment and the physical environment. Method. This study sought to determine spatial clustering, local patterning, temporal differences (time of day and season of year), and significant neighborhood-level predictors of bicycle collisions. Bicycle collision data were obtained from the Cincinnati, Ohio Police Department. Conclusions. This study showed that collisions occur at higher rates in the south-central and southwest neighborhoods of Cincinnati, Ohio. There were seasonal and time-of-day differences with respect to collision rates with summer and afternoon being the most common collision times. Neighborhood ethnicity, population density and presence of public transportation were all significant predictors of bicycle collisions. These findings will be disseminated to local city authorities and bicycle advocacy groups.

Corridor-level signalized intersection safety analysis in Shanghai, China using Bayesian hierarchical models

Most traffic crashes in Chinese cities occur at signalized intersections. Research on the intersection safety problem in China is still in its early stage. The recent development of an advanced traffic information system in Shanghai enables in-depth intersection safety analyses using road design, traffic operation, and crash data. In Shanghai, the road network density is relatively high and the distance between signalized intersections is small, averaging about 200m. Adjacent signalized intersections located along the same corridor share similar traffic flows, and signals are usually coordinated. Therefore, when studying intersection safety in Shanghai, it is essential to account for intersection correlations within corridors. In this study, data for 195 signalized intersections along 22 corridors in the urban areas of Shanghai were collected. Mean speeds and speed variances of corridors were acquired from taxis equipped with Global Positioning Systems (GPS). Bayesian hierarchical models were applied to identify crash risk factors at both the intersection and the corridor levels. Results showed that intersections along corridors with lower mean speeds were associated with fewer crashes than those with higher speeds, and those intersections along two-way roads, under elevated roads, and in close proximity to each other, tended to have higher crash frequencies.

A disaggregate model for quantifying the safety effects of winter road maintenance activities at an operational level

This research presents a disaggregated modeling approach for investigating the link between winter road collision occurrence, weather, road surface conditions, traffic exposure, temporal trends and site-specific effects. This approach is unique as it allows for quantification of the safety effects of different winter road maintenance activities at an operational level. Different collision frequency models are calibrated using hourly data collected from 31 different highway routes across Ontario, Canada. It is found that factors such as visibility, precipitation intensity, air temperature, wind speed, exposure, month of the winter season, and storm hour have statistically significant effects on winter road safety. Most importantly, road surface conditions are identified as one of the major contributing factors, representing the first contribution showing the empirical relationship between safety and road surface conditions at such a disaggregate level. The applicability of the modeling framework is demonstrated using several examples, such as quantification of the benefits of alternative maintenance operations and evaluation of the effects of different service standards using safety as a performance measure.

Assessing causality in multivariate accident models

This paper discusses the application of operational criteria of causality to multivariate statistical models developed to identify sources of systematic variation in accident counts, in particular the effects of variables representing safety treatments. Nine criteria of causality serving as the basis for the discussion have been developed. The criteria resemble criteria that have been widely used in epidemiology. To assess whether the coefficients estimated in a multivariate accident prediction model represent causal relationships or are non-causal statistical associations, all criteria of causality are relevant, but the most important criterion is how well a model controls for potentially confounding factors. Examples are given to show how the criteria of causality can be applied to multivariate accident prediction models in order to assess the relationships included in these models. It will often be the case that some of the relationships included in a model can reasonably be treated as causal, whereas for others such an interpretation is less supported. The criteria of causality are indicative only and cannot provide a basis for stringent logical proof of causality.

Evolution of the crashworthiness and aggressivity of the Spanish car fleet

This paper investigates the relationship between a passenger car's year of registration and its crashworthiness and aggressivity in real-world crashes. Crashworthiness is defined as the ability of a car to protect its own occupants, and has been evaluated in single and two-car crashes. Aggressivity is defined as the ability to protect users travelling in other vehicles, and has been evaluated only in two-car crashes. The dependent variable is defined as the proportion of injured drivers who are killed or seriously injured; following previous research, we refer to this magnitude as injury severity. A decrease in the injury severity of a driver is interpreted as an improvement in the crashworthiness of their car; similarly, a decrease in the injury severity of the opponent driver is regarded as an improvement in aggressivity. Data have been extracted from the Spanish Road Accident Database, which contains information on every accident registered by the police in which at least one person was injured. Two types of regression models have been used: logistic regression models in single-car crashes, and generalised estimating equations (GEE) models in two-car crashes. GEE allow to take account of the correlation between the injury severities of drivers involved in the same crash. The independent variables considered have been: year of registration of the subject car (crashworthiness component), year of registration of the opponent car (aggressivity component), and several factors related to road, driver and environment. Our models confirm that crashworthiness has largely improved in two-car crashes: when crashing into the average opponent car, drivers of cars registered before 1985 have a significantly higher probability of being killed or seriously injured than drivers of cars registered in 2000-2005 (odds ratio: 1.80; 95% confidence interval: 1.61; 2.01). In single-car crashes, the improvement in crashworthiness is very slight (odds ratio: 1.04; 95% confidence interval: 0.93; 1.16). On the other hand, we have also found a significant worsening in aggressivity in two-car crashes: the driver of the average car has a significantly lower probability of being killed or seriously injured when crashing into a car registered before 1985, than when crashing into a car registered in 2000-2005 (odds ratio: 0.52; 95% confidence interval: 0.45; 0.60). Our results are consistent with a large amount of previous research that has reported significant improvements in the protection of car occupants. They also add to some recent studies that have found a worsening in the aggressivity of modern cars. This trend may be reflecting the impact of differences in masses and travel speeds, as well as the influence of consumer choices. The precise reasons have to be investigated. Also, the causes have to be found for so large a discrepancy between crashworthiness in single and two-car crashes.

Multilevel data and bayesian analysis in traffic safety

BACKGROUND

Traditional crash prediction models, such as generalized linear regression model, are incapable of taking into account multilevel data structure. Therefore they suffer from a common underlying limitation that each observation (e.g. a crash or a vehicle involvement) in the estimation procedure corresponds to an individual situation in which the residuals exhibit independence.

PROBLEM

However, this "independence" assumption may often not hold true since multilevel data structures exist extensively because of the traffic data collection and clustering process. Disregarding the possible within-group correlations may lead to production of models with unreliable parameter estimates and statistical inferences. PROPOSED THEORY: In this paper, a 5 x ST-level hierarchy is proposed to represent the general framework of multilevel data structures in traffic safety, i.e. [Geographic region level-Traffic site level-Traffic crash level-Driver-vehicle unit level-Occupant level] x Spatiotemporal level. The involvement and emphasis for different sub-groups of these levels depend on different research purposes and also rely on the heterogeneity examination on crash data employed. To properly accommodate the potential cross-group heterogeneity and spatiotemporal correlation due to the multilevel data structure, a bayesian hierarchical approach that explicitly specifies multilevel structure and reliably yields parameter estimates is introduced and recommended.

CASE STUDIES

Using bayesian hierarchical models, the results from several case studies are highlighted to show the improvements on model fitting and predictive performance over traditional models by appropriately accounting for the multilevel data structure.

Estimation of fatality and injury risk by means of in-depth fatal accident investigation data

OBJECTIVE

In this article the factors affecting fatality and injury risk of road users involved in fatal accidents are analyzed by means of in-depth accident investigation data, with emphasis on parameters not extensively explored in previous research.

METHODS

A fatal accident investigation (FAI) database is used, which includes intermediate-level in-depth data for a harmonized representative sample of 1300 fatal accidents in 7 European countries. The FAI database offers improved potential for analysis, because it includes information on a number of variables that are seldom available, complete, or accurately recorded in road accident databases. However, the fact that only fatal accidents are examined requires for methodological adjustments, namely, the correction for two types of effects on a road user's baseline risk: "accident size" effects, and "relative vulnerability" effects. Fatality and injury risk can be then modeled through multilevel logistic regression models, which account for the hierarchical dependences of the road accident process.

RESULTS

The results show that the baseline fatality risk of road users involved in fatal accidents decreases with accident size and increases with the vulnerability of the road user. On the contrary, accident size increases nonfatal injury risk of road users involved in fatal accidents. Other significant effects on fatality and injury risk in fatal accidents include road user age, vehicle type, speed limit, the chain of accident events, vehicle maneuver, and safety equipment. In particular, the presence and use of safety equipment such as seat belt, antilock braking system (ABS), and electronic stability program (ESP) are protection factors for car occupants, especially for those seated at the front seats.

CONCLUSIONS

Although ABS and ESP systems are typically associated with positive effects on accident occurrence, the results of this research revealed significant related effects on accident severity as well. Moreover, accident consequences are more severe when the most harmful event of the accident occurs later within the accident chain.

The impact of traffic violations on the estimated cost of traffic accidents with victims

We analyse accidents with victims and calculate the influence of traffic violations on the probability of having a serious or fatal accident, compared to a slight accident. Traffic violations related to speed limitations, administrative infringements or faults related to the driver are considered. Data were obtained from all available reports on accidents with victims that occurred in Spain from 2003 to 2005. A multinomial logistic regression model is specified to find the probability that an accident with victims is slight, serious or fatal, given the presence/absence of thirty different types of traffic violations. The average cost per victim and the average number of victims per accident are then used to find the estimated cost of an accident with victims, given the information on the traffic violations incurred. This demonstrates which combinations of traffic violations lead to higher estimated average costs, compared to cases in which no traffic violation occurred. We conclude with some recommendations on the severity of penalties, and suggest that regulators penalize the occurrences of some specific combinations of traffic violations more rigorously.

Risk and protection factors in fatal accidents

This paper aims at addressing the interest and appropriateness of performing accident severity analyses that are limited to fatal accident data. Two methodological issues are specifically discussed, namely the accident-size factors (the number of vehicles in the accident and their level of occupancy) and the comparability of the baseline risk. It is argued that - although these two issues are generally at play in accident severity analyses - their effects on, e.g., the estimation of survival probability, are exacerbated if the analysis is limited to fatal accident data. As a solution, it is recommended to control for these effects by (1) including accident-size indicators in the model, (2) focusing on different sub-groups of road-users while specifying the type of opponent in the model, so as to ensure that comparable baseline risks are worked with. These recommendations are applied in order to investigate risk and protection factors of car occupants involved in fatal accidents using data from a recently set up European Fatal Accident Investigation database (Reed and Morris, 2009). The results confirm that the estimated survival probability is affected by accident-size factors and by type of opponent. The car occupants' survival chances are negatively associated with their own age and that of their vehicle. The survival chances are also lower when seatbelt is not used. Front damage, as compared to other damaged car areas, appears to be associated with increased survival probability, but mostly in the case in which the accident opponent was another car. The interest of further investigating accident-size factors and opponent effects in fatal accidents is discussed.

Geographic disparities in access to the medical home among US CSHCN

OBJECTIVES

In this study we examined geographic disparities in medical home access among US children with special health care needs (CSHCN) aged 0 to 17 years.

METHODS

The 2005-2006 National Survey of Children With Special Health Care Needs was used to estimate prevalence and odds of not having a medical home and 5 component outcomes according to state. Logistic regression was used to examine individual-level and state-level determinants of access.

RESULTS

Medical home access varied substantially across geographic areas. CSHCN in Alaska, Arizona, Washington, DC, Florida, Illinois, Massachusetts, New Jersey, Nevada, and Virginia had at least 50% higher adjusted odds of not having a medical home than CSHCN in Iowa. The adjusted prevalence of CSHCN lacking a medical home varied from a low of 46% in Iowa and Ohio to a high of 59% in Alaska and 61% in New Jersey. CSHCN in several western and southwestern states experienced greater problems with access to a personal doctor/nurse, a usual source of care, specialty care referrals, care coordination, and family-centered care. Adjustment for age, gender, race/ethnicity, household socioeconomic status, language use, insurance coverage, and functional limitation reduced state disparities in access. CSHCN in states with higher immigrant and non-English-speaking populations had significantly lower medical home access. Increases in state health care expenditure and infrastructure and Medicaid/State Children's Health Insurance Program eligibility were associated with increased access to a personal doctor/nurse.

CONCLUSIONS

Although individual-level sociodemographic and state-level health policy variables are important predictors of access, substantial geographic disparities remain, with CSHCN in several western and northeastern states at high risk of not having a medical home.

A population based estimation of the driver protection provided by passenger cars: France 1996-2005

The technology used in cars to protect occupants is constantly developing. Demonstrating the beneficial effects in the field is complex as the most recent vehicles are generally used by drivers who differ from other drivers and who drive in different traffic conditions. This paper presents an overall estimation of the consequences of changes in the secondary safety of cars, taking account of most of these factors. The data come from information collected about injury road traffic crashes by the police in France between 1996 and 2005. The risk of the driver being killed has been evaluated for the 144,034 drivers involved in two-car crashes and for the 63,621 drivers involved in single-car crashes. The study shows that when a recent car is in collision with an older car the driver of the former is better protected than the driver of the latter. These improvements in secondary safety are not observed in the case of single-car crashes, very probably because of higher impact speeds. Our findings also confirm the need for protection systems to be better adapted to the specific characteristics of users and for an improvement in the crash compatibility of vehicles, in particular to overcome the consequences of differences between the masses of vehicles.

Diseases, consumption of medicines and responsibility for a road crash: a case-control study

The role of medical conditions in crashes is a topic of public debate. Some studies suggest that there has been a reduction in road traffic crashes subsequent to the medical restrictions introduced on drivers with medical deficiencies. As in today's society the car is an important factor for independence and socialization, it seems important to consider whether diseases or consumption of drugs increase the risk of causing a road crash in comparison to well-known major crash risk factors. A case-control study was conducted (733 injured drivers). The cases were subjects who were partly or totally responsible for their crash. The 304 controls were the non-responsible drivers. Diseases and medicine consumption were analyzed using logistic regression models. Cases were characterized by a higher percentage of young men. They were more frequently affected by fatigue, as were subjects who had consumed alcohol. A higher risk in subjects suffering from hypertension is observed (adjusted odds ratio [adjOR]=3.82; 95%CI=[1.42-10.24]). An association between antidepressant consumption and responsibility appeared (adjOR=3.61; 95%CI=[1.30-10.03]).

CONCLUSION

Medical factors associated with responsibility were arterial hypertension and antidepressant consumption. Other medical conditions do not seem to play a preponderant role comparing to individual behaviours.

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.

Severity of driver injury and vehicle damage in traffic crashes at intersections: a Bayesian hierarchical analysis

Most crash severity studies ignored severity correlations between driver-vehicle units involved in the same crashes. Models without accounting for these within-crash correlations will result in biased estimates in the factor effects. This study developed a Bayesian hierarchical binomial logistic model to identify the significant factors affecting the severity level of driver injury and vehicle damage in traffic crashes at signalized intersections. Crash data in Singapore were employed to calibrate the model. Model fitness assessment and comparison using intra-class correlation coefficient (ICC) and deviance information criterion (DIC) ensured the suitability of introducing the crash-level random effects. Crashes occurring in peak time and in good street-lighting condition as well as those involving pedestrian injuries tend to be less severe. But crashes that occur in night time, at T/Y type intersections, and on right-most lane, as well as those that occur in intersections where red light cameras are installed tend to be more severe. Moreover, heavy vehicles have a better resistance on severe crash and thus induce less severe injuries, while crashes involving two-wheel vehicles, young or aged drivers, and the involvement of offending party are more likely to result in severe injuries.