A comprehensive analysis of factors influencing the injury severity of large-truck crashes

Given the importance of trucking to the economic well being of a country and the safety concerns posed by the trucks, a study of large-truck crashes is critical. This paper contributes by undertaking an extensive analysis of the empirical factors affecting injury severity of large-truck crashes. Data from a recent, nationally representative sample of large-truck crashes are examined to determine the factors affecting the overall injury severity of these crashes. The explanatory factors include the characteristics of the crash, vehicle(s), and the driver(s). The injury severity was modeled using two measures. Several similarities and some differences were observed across the two models which underscore the need for improved accuracy in the assessment of injury severity of crashes. The estimated models capture the marginal effects of a variety of explanatory factors simultaneously. In particular, the models indicate the impacts of several driver behavior variables on the severity of the crashes, after controlling for a variety of other factors. For example, driver distraction (truck drivers), alcohol use (car drivers), and emotional factors (car drivers) are found to be associated with higher severity crashes. A further interesting finding is the strong statistical significance of several dummy variables that indicate missing data – these reflect how the nature of the crash itself could affect the completeness of the data. Future efforts should seek to collect such data more comprehensively so that the true effects of these aspects on the crash severity can be determined.     

The effect of distractions on the crash types of teenage drivers

Teenage drivers are overrepresented in crashes when compared to middle-aged drivers. Driver distraction is becoming a greater concern among this group as in-vehicle devices, opportunities for distractions, and teenage drivers' willingness to engage in these activities increase. The objective of this study was to determine how different distraction factors impact the crash types that are common among teenage drivers. A multinomial logit model was developed to predict the likelihood that a driver will be involved in one of three common crash types: an angular collision with a moving vehicle, a rear-end collision with a moving lead vehicle, and a collision with a fixed object. These crashes were evaluated in terms of four driver distraction categories: cognitive, cell phone related, in-vehicle, and passenger-related distractions. Different driver distractions have varying effects on teenage drivers' crash involvement. Teenage drivers that were distracted at an intersection by passengers or cognitively were more likely to be involved in rear-end and angular collisions when compared to fixed-object collisions. In-vehicle distractions resulted in a greater likelihood of a collision with a fixed object when compared to angular collisions. Cell phone distractions resulted in a higher likelihood of rear-end collision. The results from this study need to be evaluated with caution due to the limited number of distraction related cases available in the U.S. GES crash database. Implications for identifying and improving the reporting of driver distraction related factors are therefore discussed.     

Driver inattention and driver distraction in serious casualty crashes: Data from the Australian National Crash In-depth Study

Driver inattention and driver distraction represent a major problem in road safety. Although both are believed to contribute to increased crash risk, there is currently limited reliable information on their role in crashes. The current study used in-depth data from the Australian National Crash In-depth Study to investigate the role of driver distraction and inattention in serious casualty crashes. The sample included 856 crashes from 2000 to 2011, in which at least one party was admitted to hospital due to crash-related injuries. Crashes were coded using a taxonomy of driver inattention that delineates five inattention subtypes: restricted attention, misprioritised attention, neglected attention, cursory attention, and diverted attention (distraction). Approximately 45% of crashes could not be coded due to insufficient information while in an additional 15% the participant indicated the “other driver was at fault” without specifying whether inattention was involved. Of the 340 remaining cases, most showed evidence of driver inattention (57.6%) or possible inattention (5.9%). The most common subtypes of inattention were restricted attention, primarily due to intoxication and/or fatigue, and diverted attention or distraction. The most common types of distraction involved voluntary, non-driving related distractions originating within the vehicle, such as passenger interactions. The current study indicates that a majority of serious injury crashes involve driver inattention. Most forms of inattention and distraction observed are preventable. This study demonstrates the feasibility of using in-depth crash data to investigate driver inattention in casualty crashes.     

Analysis of driver injury severity in single-vehicle crashes on rural and urban roadways

This study analyzes driver injury severities for single-vehicle crashes occurring in rural and urban areas using data collected in New Mexico from 2010 to 2011. Nested logit models and mixed logit models are developed in order to account for the correlation between severity categories (No injury, Possible injury, Visible injury, Incapacitating injury and fatality) and individual heterogeneity among drivers. Various factors, such as crash and environment characteristics, geometric features, and driver behavior are examined in this study. Nested logit model and mixed logit model reveal similar results in terms of identifying contributing factors for driver injury severities. In the analysis of urban crashes, only the nested logit model is presented since no random parameter is found in the mixed logit model. The results indicate that significant differences exist between factors contributing to driver injury severity in single-vehicle crashes in rural and urban areas. There are 5 variables found only significant in the rural model and six significant variables identified only in the urban crash model. These findings can help transportation agencies develop effective policies or appropriate strategies to reduce injury severity resulting from single-vehicle crashes.     

Investigating driver injury severity patterns in rollover crashes using support vector machine models

Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns.     

The influence of driver distraction on the severity of injuries sustained by teenage drivers and their passengers

Studies show that teenage drivers are at a higher risk for crashes. Opportunities to engage in technology and non-technology based distractions appear to be a particular concern among this age group. An ordered logit model was developed to predict the likelihood of a severe injury for these drivers and their passenger using a national crash database (the 2003, U.S. DOT-General Estimate System [GES]). As one would expect, speeding substantially increases the likelihood of severe injuries for teenage drivers and their passengers. The results of the analysis also reveal that teenage drivers have an increased likelihood of more severe injuries if distracted by a cell phone or by passengers than if the source of distraction was related to in-vehicle devices or if the driver was inattentive. Additionally, passengers of teenage drivers are more likely to sustain severe injuries when their driver is distracted by devices or passengers than with a non-distracted or inattentive driver. This supports the previous literature on teenage drivers and extends our understanding of injuries for this age group related to distraction-related crashes.     

Factors influential in making an injury severity difference to older drivers involved in fixed object-passenger car crashes

To identify factors influencing severity of injury to older drivers in fixed object-passenger car crashes, two sets of sequential binary logistic regression models were developed. The dependent variable in one set of models was driver injury severity, whereas for the other it was the crash severity (most severe injury in the crash). For each set of models, crash or injury severity was varied from the least severity level (no injury) to the highest severity level (fatality) and vice versa. The source of data was police crash reports from the state of Florida. The model with the best fitting and highest predictive capability was used to identify the influence of roadway, environmental, vehicle, and driver related factors on severity. Travel speed, restraint device usage, point of impact, use of alcohol and drugs, personal condition, gender, whether the driver is at fault, urban/rural nature and grade/curve existence at the crash location were identified as the important factors for making an injury severity difference to older drivers involved in single vehicle crashes.     

Mixed logit model-based driver injury severity investigations in single- and multi-vehicle crashes on rural two-lane highways

Crashes occurring on rural two-lane highways are more likely to result in severe driver incapacitating injuries and fatalities. In this study, mixed logit models are developed to analyze driver injury severities in single-vehicle (SV) and multi-vehicle (MV) crashes on rural two-lane highways in New Mexico from 2010 to 2011. A series of significant contributing factors in terms of driver behavior, weather conditions, environmental characteristics, roadway geometric features and traffic compositions, are identified and their impacts on injury severities are quantified for these two types of crashes, respectively. Elasticity analyses and transferability tests were conducted to better understand the models’ specification and generality. The research findings indicate that there are significant differences in causal attributes determining driver injury severities between SV and MV crashes. For example, more severe driver injuries and fatalities can be observed in MV crashes when motorcycles or trucks are involved. Dark lighting conditions and dusty weather conditions are found to significantly increase MV crash injury severities. However, SV crashes demonstrate different characteristics influencing driver injury severities. For example, the probability of having severe injury outcomes is higher when vans are identified in SV crashes. Drivers’ overtaking actions will significantly increase SV crash injury severities. Although some common attributes, such as alcohol impaired driving, are significant in both SV and MV crash severity models, their effects on different injury outcomes vary substantially. This study provides a better understanding of similarities and differences in significant contributing factors and their impacts on driver injury severities between SV and MV crashes on rural two-lane highways. It is also helpful to develop cost-effective solutions or appropriate injury prevention strategies for rural SV and MV crashes.     

Evaluating alternate discrete outcome frameworks for modeling crash injury severity

This paper focuses on the relevance of alternate discrete outcome frameworks for modeling driver injury severity. The study empirically compares the ordered response and unordered response models in the context of driver injury severity in traffic crashes. The alternative modeling approaches considered for the comparison exercise include: for the ordered response framework-ordered logit (OL), generalized ordered logit (GOL), mixed generalized ordered logit (MGOL) and for the unordered response framework-multinomial logit (MNL), nested logit (NL), ordered generalized extreme value logit (OGEV) and mixed multinomial logit (MMNL) model. A host of comparison metrics are computed to evaluate the performance of these alternative models. The study provides a comprehensive comparison exercise of the performance of ordered and unordered response models for examining the impact of exogenous factors on driver injury severity. The research also explores the effect of potential underreporting on alternative frameworks by artificially creating an underreported data sample from the driver injury severity sample. The empirical analysis is based on the 2010 General Estimates System (GES) data base—a nationally representative sample of road crashes collected and compiled from about 60 jurisdictions across the United States. The performance of the alternative frameworks are examined in the context of model estimation and validation (at the aggregate and disaggregate level). Further, the performance of the model frameworks in the presence of underreporting is explored, with and without corrections to the estimates. The results from these extensive analyses point toward the emergence of the GOL framework (MGOL) as a strong competitor to the MMNL model in modeling driver injury severity.     

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

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

Heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes

In this study, a mixed logit model is developed to identify the heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes. The random parameter of the variables in the mixed logit model, the heterogeneous mean, is elaborated by driver gender-based linear regression models. The model is estimated using crash data in New Mexico from 2010 to 2012. The percentage changes of factors’ predicted probabilities are calculated in order to better understand the model specifications. Female drivers are found more likely to experience severe or fatal injuries in rollover crashes than male drivers. However, the probability of male drivers being severely injured is higher than female drivers when the road surface is unpaved. Two other factors with fixed parameters are also found to significantly increase driver injury severities, including Wet and Alcohol Influenced. This study provides a better understanding of contributing factors influencing driver injury severities in rollover crashes as well as their heterogeneous impacts in terms of driver gender. Those results are also helpful to develop appropriate countermeasures and policies to reduce driver injury severities in single-vehicle rollover crashes.     

An examination of the environmental,driver and vehicle factors associated with the serious and fatal crashes of older rural drivers

Motor vehicle crashes involving rural drivers aged 75 years and over are more than twice as likely to result in a serious or fatal injury as those involving their urban counterparts. The current study examined some of the reasons for this using a database of police-reported crashes (2004–2008) to identify the environmental (lighting, road and weather conditions, road layout, road surface, speed limit), driver (driver error, crash type), and vehicle (vehicle age) factors that are associated with the crashes of older rural drivers. It also determined whether these same factors are associated with an increased likelihood of serious or fatal injury in younger drivers for whom frailty does not contribute to the resulting injury severity. A number of environmental (i.e., undivided, unsealed, curved and inclined roads, and areas with a speed limit of 100 km/h or greater) and driver (i.e., collision with a fixed object and rolling over) factors were more frequent in the crashes of older rural drivers and additionally associated with increased injury severity in younger drivers. Moreover, when these environmental factors were entered into a logistic regression model to predict whether older drivers who were involved in crashes did or did not sustain a serious or fatal injury, it was found that each factor independently increased the likelihood of a serious or fatal injury. Changes, such as the provision of divided and sealed roads, greater protection from fixed roadside objects, and reduced speed limits, appear to be indicated in order to improve the safety of the rural driving environment for drivers of all ages. Additionally, older rural drivers should be encouraged to reduce their exposure to these risky circumstances.     

Comprehensive analysis of vehicle-pedestrian crashes at intersections in Florida

This study analyzes vehicle-pedestrian crashes at intersections in Florida over 4 years, 1999-2002. The study identifies the group of drivers and pedestrians, and traffic and environmental characteristics that are correlated with high pedestrian crashes using log-linear models. The study also estimates the likelihood of pedestrian injury severity when pedestrians are involved in crashes using an ordered probit model. To better reflect pedestrian crash risk, a logical measure of exposure is developed using the information on individual walking trips in the household travel survey. Lastly, the impact of average traffic volume on pedestrian crashes is examined. As a result of the analysis, it was found that pedestrian and driver demographic factors, and road geometric, traffic and environment conditions are closely related to the frequency and injury severity of pedestrian crashes. Higher average traffic volume at intersections increases the number of pedestrian crashes; however, the rate of increase is steeper at lower values of average traffic volume. Based on the findings in the analysis, some countermeasures are recommended to improve pedestrian safety.     

Traffic accidents involving fatigue driving and their extent of casualties

The rapid progress of motorization has increased the number of traffic-related casualties. Although fatigue driving is a major cause of traffic accidents, the public remains not rather aware of its potential harmfulness. Fatigue driving has been termed as a “silent killer.” Thus, a thorough study of traffic accidents and the risk factors associated with fatigue-related casualties is of utmost importance. In this study, we analyze traffic accident data for the period 2006–2010 in Guangdong Province, China. The study data were extracted from the traffic accident database of China's Public Security Department. A logistic regression model is used to assess the effect of driver characteristics, type of vehicles, road conditions, and environmental factors on fatigue-related traffic accident occurrence and severity. On the one hand, male drivers, trucks, driving during midnight to dawn, and morning rush hours are identified as risk factors of fatigue-related crashes but do not necessarily result in severe casualties. Driving at night without street-lights contributes to fatigue-related crashes and severe casualties. On the other hand, while factors such as less experienced drivers, unsafe vehicle status, slippery roads, driving at night with street-lights, and weekends do not have significant effect on fatigue-related crashes, yet accidents associated with these factors are likely to have severe casualties. The empirical results of the present study have important policy implications on the reduction of fatigue-related crashes as well as their severity.     

Driver distraction in an unusual environment: Effects of text-messaging in tunnels

Text messaging while driving can be distracting and significantly increases the risk of being involved in a collision. Compared to freeway driving, driving in a tunnel environment introduces factors that may interact with driver attentional resources to exacerbate the effects of distraction on driving safety. With planning and design of the 18 km Stockholm Bypass tunnel ongoing, and because of the potentially devastating consequences of crashes in long tunnels, it is critical to assess the effects of driver distraction in a tunnel environment.     

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.     

The impact of driver age on lost life years for other road users in France: A population based study of crash-involved road users

BACKGROUND: One of the concerns in road safety is the threat older drivers may pose to other road users. Using the rate of lost life years, the present study provides a public health approach to quantify this potential threat. METHODS: A total of 1570686 motorised vehicle drivers or motorcycle riders and 652246 non-drivers, i.e. vehicle passengers, pedestrians and cyclists involved in injury crashes in France between 1996 and 2004, were included in a population based cross-sectional study. Fatality rates and rate of lost life years for each crash-involved driver age class were calculated for the drivers themselves and for other road users. RESULTS: The study has shown a significant reduction in the rate of lost life years for crash-involved other road users (whether passengers, pedestrians, cyclists or opposing drivers) as driver age increases. Other road users lost half as many years of life when involved in crashes with drivers aged over 85 than with drivers under 65 (1.26 and 2.32 per 100 expected remaining life years, respectively). CONCLUSION: Our findings suggest that among road users involved in injury crashes, older drivers are less dangerous for the other road users. By attributing other road users' lost life years to each driver age, this study represents a new contribution to the debate about ageing and road safety.     

Driver distraction and driver inattention: definition, relationship and taxonomy

There is accumulating evidence that driver distraction and driver inattention are leading causes of vehicle crashes and incidents. However, as applied psychological constructs, they have been inconsistently defined and the relationship between them remains unclear. In this paper, driver distraction and driver inattention are defined and a taxonomy is presented in which driver distraction is distinguished from other forms of driver inattention. The taxonomy and the definitions provided are intended (a) to provide a common framework for coding different forms of driver inattention as contributing factors in crashes and incidents, so that comparable estimates of their role as contributing factors can be made across different studies, and (b) to make it possible to more accurately interpret and compare, across studies, the research findings for a given form of driver inattention.     

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

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

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

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