Differences in male and female injury severities in sport-utility vehicle, minivan, pickup and passenger car accidents

This research explores differences in injury severity between male and female drivers in single and two-vehicle accidents involving passenger cars, pickups, sport-utility vehicles (SUVs), and minivans. Separate multivariate multinomial logit models of injury severity are estimated for male and female drivers. The models predict the probability of four injury severity outcomes: no injury (property damage only), possible injury, evident injury, and fatal/disabling injury. The models are conditioned on driver gender and the number and type of vehicles involved in the accident. The conditional structure avoids bias caused by men and women's different reporting rates, choices of vehicle type, and their different rates of participation as drivers, which would affect a joint model of all crashes. We found variables that have opposite effects for the genders, such as striking a barrier or a guardrail, and crashing while starting a vehicle. The results suggest there are important behavioral and physiological differences between male and female drivers that must be explored further and addressed in vehicle and roadway design.     

A time of day analysis of crashes involving large trucks in urban areas

Previous studies have looked at different factors that contribute to large truck-involved crashes, however a detailed analysis considering the specific effects of time of day is lacking. Using the Crash Records Information System (CRIS) database in Texas, large truck-involved crashes occurring on urban freeways between 2006 and 2010 were separated into five time periods (i.e., early morning, morning, mid-day, afternoon and evening). A series of log likelihood ratio tests were conducted to validate that five separate random parameters logit models by time of day were warranted. The outcomes of each time of day model show major differences in both the combination of variables included in each model and the magnitude of impact of those variables. These differences show that the different time periods do in fact have different contributing factors to each injury severity further highlighting the importance of examining crashes based on time of day. Traffic flow, light conditions, surface conditions, time of year and percentage of trucks on the road were found as key differences between the time periods.     

Injury severities of truck drivers in single- and multi-vehicle accidents on rural highways

In adverse driving conditions, such as inclement weather and/or complex terrain, trucks are often involved in single-vehicle (SV) accidents in addition to multi-vehicle (MV) accidents. Ten-year accident data involving trucks on rural highway from the Highway Safety Information System (HSIS) is studied to investigate the difference in driver-injury severity between SV and MV accidents by using mixed logit models. Injury severity from SV and MV accidents involving trucks on rural highways is modeled separately and their respective critical risk factors such as driver, vehicle, temporal, roadway, environmental and accident characteristics are evaluated. It is found that there exists substantial difference between the impacts from a variety of variables on the driver-injury severity in MV and SV accidents. By conducting the injury severity study for MV and SV accidents involving trucks separately, some new or more comprehensive observations, which have not been covered in the existing studies can be made. Estimation findings indicate that the snow road surface and light traffic indicators will be better modeled as random parameters in SV and MV models respectively. As a result, the complex interactions of various variables and the nature of truck-driver injury are able to be disclosed in a better way. Based on the improved understanding on the injury severity of truck drivers from truck-involved accidents, it is expected that more rational and effective injury prevention strategy may be developed for truck drivers under different driving conditions in the future.     

Highway accident severities and the mixed logit model: an exploratory empirical analysis

Many transportation agencies use accident frequencies, and statistical models of accidents frequencies, as a basis for prioritizing highway safety improvements. However, the use of accident severities in safety programming has been often been limited to the locational assessment of accident fatalities, with little or no emphasis being placed on the full severity distribution of accidents (property damage only, possible injury, injury)-which is needed to fully assess the benefits of competing safety-improvement projects. In this paper we demonstrate a modeling approach that can be used to better understand the injury-severity distributions of accidents on highway segments, and the effect that traffic, highway and weather characteristics have on these distributions. The approach we use allows for the possibility that estimated model parameters can vary randomly across roadway segments to account for unobserved effects potentially relating to roadway characteristics, environmental factors, and driver behavior. Using highway-injury data from Washington State, a mixed (random parameters) logit model is estimated. Estimation findings indicate that volume-related variables such as average daily traffic per lane, average daily truck traffic, truck percentage, interchanges per mile and weather effects such as snowfall are best modeled as random-parameters-while roadway characteristics such as the number of horizontal curves, number of grade breaks per mile and pavement friction are best modeled as fixed parameters. Our results show that the mixed logit model has considerable promise as a methodological tool in highway safety programming.     

A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey

A retrospective cross-sectional study is conducted analysing 11,771 traffic accidents reported by the police between January 2008 and December 2013 which are classified into three injury severity categories: fatal, injury, and no injury. Based on this classification, a multinomial logit analysis is performed to determine the risk factors affecting the severity of traffic injuries. The estimation results reveal that the following factors increase the probability of fatal injuries: drivers over the age of 65; primary-educated drivers; single-vehicle accidents; accidents occurring on state routes, highways or provincial roads; and the presence of pedestrian crosswalks. The results also indicate that accidents involving cars or private vehicles or those occurring during the evening peak, under clear weather conditions, on local city streets or in the presence of traffic lights decrease the probability of fatal injuries. This study comprises the most comprehensive database ever created for a Turkish sample. This study is also the first attempt to use an unordered response model to determine risk factors influencing the severity of traffic injuries in Turkey.     

Analysis of driver injury severity in rural single-vehicle crashes

Rural roads carry less than fifty percent of the traffic in the United States. However, more than half of the traffic accident fatalities occurred on rural roads. This research focuses on analyzing injury severities involving single-vehicle crashes on rural roads, utilizing a latent class logit (LCL) model. Similar to multinomial logit (MNL) models, the LCL model has the advantage of not restricting the coefficients of each explanatory variable in different severity functions to be the same, making it possible to identify the impacts of the same explanatory variable on different injury outcomes. In addition, its unique model structure allows the LCL model to better address issues pertinent to the independence from irrelevant alternatives (IIA) property. A MNL model is also included as the benchmark simply because of its popularity in injury severity modeling. The model fitting results of the MNL and LCL models are presented and discussed. Key injury severity impact factors are identified for rural single-vehicle crashes. Also, a comparison of the model fitting, analysis marginal effects, and prediction performance of the MNL and LCL models are conducted, suggesting that the LCL model may be another viable modeling alternative for crash-severity analysis.     

An empirical analysis of farm vehicle crash injury severities on Iowa's public road system

Farm vehicle crashes are a major safety concern for farmers as well as all other users of the public road system in agricultural states. Using data on farm vehicle crashes that occurred on Iowa's public roads between 2004 and 2006, we estimate a multinomial logit model to identify crash-, farm vehicle-, and driver-specific factors that determine farm vehicle crash injury severity outcomes. Estimation findings indicate that there are crash patterns (rear-end manner of collision; single-vehicle crash; farm vehicle crossed the centerline or median) and conditions (obstructed vision and crash in rural area; dry road, dark lighting, speed limit 55 mph or higher, and harvesting season), as well as farm vehicle and driver-contributing characteristics (old farm vehicle, young farm vehicle driver), where targeted intervention can help reduce the severity of crash outcomes. Determining these contributing factors and their effect is the first step to identifying countermeasures and safety strategies in a bid to improve transportation safety for all users on the public road system in Iowa as well as other agricultural states.     

A mixed logit analysis of two-vehicle crash severities involving a motorcycle

Using motorcycle crash data for Iowa from 2001 to 2008, this paper estimates a mixed logit model to investigate the factors that affect crash severity outcomes in a collision between a motorcycle and another vehicle. These include crash-specific factors (such as manner of collision, motorcycle rider and non-motorcycle driver and vehicle actions), roadway and environmental conditions, location and time, motorcycle rider and non-motorcycle driver and vehicle attributes. The methodological approach allows the parameters to vary across observations as opposed to a single parameter representing all observations. Our results showed non-uniform effects of rear-end collisions on minor injury crashes, as well as of the roadway speed limit greater or equal to 55 mph, the type of area (urban), the riding season (summer) and motorcyclist's gender on low severity crashes. We also found significant effects of the roadway surface condition, clear vision (not obscured by moving vehicles, trees, buildings, or other), light conditions, speed limit, and helmet use on severe injury outcomes.     

Analysis of injury severity of large truck crashes in work zones

Work zones are critical parts of the transportation infrastructure renewal process consisting of rehabilitation of roadways, maintenance, and utility work. Given the specific nature of a work zone (complex arrangements of traffic control devices and signs, narrow lanes, duration) a number of crashes occur with varying severities involving different vehicle sizes. In this paper we attempt to investigate the causal factors contributing to injury severity of large truck crashes in work zones. Considering the discrete nature of injury severity categories, a number of comparable econometric models were developed including multinomial logit (MNL), nested logit (NL), ordered logit (ORL), and generalized ordered logit (GORL) models. The MNL and NL models belong to the class of unordered discrete choice models and do not recognize the intrinsic ordinal nature of the injury severity data. The ORL and GORL models, on the other hand, belong to the ordered response framework that was specifically developed for handling ordinal dependent variables. Past literature did not find conclusive evidence in support of either framework. This study compared these alternate modeling frameworks for analyzing injury severity of crashes involving large trucks in work zones. The model estimation was undertaken by compiling a database of crashes that (1) involved large trucks and (2) occurred in work zones in the past 10 years in Minnesota. Empirical findings indicate that the GORL model provided superior data fit as compared to all the other models. Also, elasticity analysis was undertaken to quantify the magnitude of impact of different factors on work zone safety and the results of this analysis suggest the factors that increase the risk propensity of sustaining severe crashes in a work zone include crashes in the daytime, no control of access, higher speed limits, and crashes occurring on rural principal arterials.     

Analysis of injury severity of drivers involved in single- and two-vehicle crashes on highways in Ontario

This study analyzes driver's injury severity in single- and two-vehicle crashes and compares the effects of explanatory variables among various types of crashes. The study identified factors affecting injury severity and their effects on severity levels using 5-year crash records for provincial highways in Ontario, Canada. Considering heteroscedasticity in the effects of explanatory variables on injury severity, the heteroscedastic ordered logit (HOL) models were developed for single- and two-vehicle crashes separately. The results of the models show that there exists heteroscedasticity for young drivers (≤30), safety equipment and ejection in the single-vehicle crash model, and female drivers, safety equipment and head-on collision in the two-vehicle crash models. The results also show that young car drivers have opposite effects between single-car and car–car crashes, and sideswipe crashes have opposite effects between car–car and truck–truck crashes. The study demonstrates that separate HOL models for single-vehicle and different types of two-vehicle crashes can identify differential effects of factors on driver's injury severity.     

The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives

Reducing the severity of injuries resulting from motor-vehicle crashes has long been a primary emphasis of highway agencies and motor-vehicle manufacturers. While progress can be simply measured by the reduction in injury levels over time, insights into the effectiveness of injury-reduction technologies, policies, and regulations require a more detailed empirical assessment of the complex interactions that vehicle, roadway, and human factors have on resulting crash-injury severities. Over the years, researchers have used a wide range of methodological tools to assess the impact of such factors on disaggregate-level injury-severity data, and recent methodological advances have enabled the development of sophisticated models capable of more precisely determining the influence of these factors. This paper summarizes the evolution of research and current thinking as it relates to the statistical analysis of motor-vehicle injury severities, and provides a discussion of future methodological directions.     

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.     

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

One of the principal objectives of traffic accident analyses is to identify key factors that affect the severity of an accident. However, with the presence of heterogeneity in the raw data used, the analysis of traffic accidents becomes difficult. In this paper, Latent Class Cluster (LCC) is used as a preliminary tool for segmentation of 3229 accidents on rural highways in Granada (Spain) between 2005 and 2008. Next, Bayesian Networks (BNs) are used to identify the main factors involved in accident severity for both, the entire database (EDB) and the clusters previously obtained by LCC. The results of these cluster-based analyses are compared with the results of a full-data analysis. The results show that the combined use of both techniques is very interesting as it reveals further information that would not have been obtained without prior segmentation of the data. BN inference is used to obtain the variables that best identify accidents with killed or seriously injured. Accident type and sight distance have been identify in all the cases analysed; other variables such as time, occupant involved or age are identified in EDB and only in one cluster; whereas variables vehicles involved, number of injuries, atmospheric factors, pavement markings and pavement width are identified only in one cluster.     

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.     

Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations

Standard multinomial logit (MNL) and mixed logit (MXL) models are developed to estimate the degree of influence that bicyclist, driver, motor vehicle, geometric, environmental, and crash type characteristics have on bicyclist injury severity, classified as property damage only, possible, nonincapacitating or severe (i.e., incapacitating or fatal) injury. This study is based on 10,029 bicycleinvolved crashes that occurred in the State of Ohio from 2002 to 2008. Results of likelihood ratio tests reveal that some of the factors affecting bicyclist injury severity at intersection and non-intersection locations are substantively different and using a common model to jointly estimate impacts on severity at both types of locations may result in biased or inconsistent estimates. Consequently, separate models are developed to independently assess the impacts of various factors on the degree of bicyclist injury severity resulting from crashes at intersection and non-intersection locations.Several covariates are found to have similar impacts on injury severity at both intersection and non-intersection locations. Conversely, six variables were found to significantly influence injury severity at intersection locations but not non-intersection locations while four variables influenced bicyclist injury severity only at non-intersection locations. In crashes occurring at intersection locations, the likelihood of severe bicyclist injury increases by 14.8 percent if the bicyclist is not wearing a helmet, 82.2 percent if the motorist is under the influence of alcohol, 141.3 percent if the crash-involved motor vehicle is a van, 40.6 percent if the motor vehicle strikes the side of the bicycle, and 182.6 percent if the crash occurs on a horizontal curve with a grade. Results from non-intersection locations show the likelihood of severe injuries increases by 374.5 percent if the bicyclist is under the influence of drugs, 150.1 percent if the motorist is under the influence of alcohol, 53.5 percent if the motor vehicle strikes the side of the bicycle and 99.9 percent if the crash-involved motor vehicle is a heavy-duty truck.     

Effects of excessive speeding and falling asleep while driving on crash injury severity in Ethiopia: A generalized ordered logit model analysis

The severity of injury from vehicle crash is a result of a complex interaction of factors related to drivers’ behavior, vehicle characteristics, road geometric and environmental conditions. Knowing to what extent each factor contributes to the severity of an injury is very important. The objective of the study was to assess factors that contribute to crash injury severity in Ethiopia. Data was collected from June 2012 to July 2013 on one of the main and busiest highway of Ethiopia, which extends from the capital Addis Ababa to Hawassa. During the study period a total of 819 road crashes was recorded and investigated by trained crash detectors. A generalized ordered logit/partial proportional odds model was used to examine factors that might influence the severity of crash injury. Model estimation result suggested that, alcohol use (Coef. = 0.5565; p-value = 0.017), falling asleep while driving (Coef. = 1.3102; p-value = 0.000), driving at night time in the absence of street light (Coef. = 0.3920; p-value = 0.033), rainfall (Coef. = 0.9164; p-value = 0.000) and being a minibus or vans (Coef. = 0.5065; p-value = 0.013) were found to be increased crash injury severity. On the other hand, speeding was identified to have varying coefficients for different injury levels, its highest effects on sever and fatal crashes. In this study risky driving behaviors (speeding, alcohol use and sleep/fatigue) were a powerful predictor of crash injury severity. Therefore, better driver licensing and road safety awareness campaign complimented with strict police enforcement can play a pivotal role to improve road safety. Further effort needed as well to monitor speed control strategies like; using the radar control and physical speed restraint measures (i.e., rumble strips).     

Comprehensive analysis of single- and multi-vehicle large truck at-fault crashes on rural and urban roadways in Alabama

The research described in this paper analyzed injury severities at a disaggregate level for single-vehicle (SV) and multi-vehicle (MV) large truck at-fault accidents for rural and urban locations in Alabama. Given the occurrence of a crash, four separate random parameter logit models of injury severity (with possible outcomes of major, minor, and possible or no injury) were estimated. The models identified different sets of factors that can lead to effective policy decisions aimed at reducing large truck-at-fault accidents for respective locations. The results of the study clearly indicated that there are differences between the influences of a variety of variables on the injury severities resulting from urban vs. rural SV and MV large truck at-fault accidents. The results showed that some variables were significant only in one type of accident model (SV or MV) but not in the other accident model. Again, some variables were found to be significant in one location (rural or urban) but not in other locations. The study also identified important factors that significantly impact the injury severity resulting from SV and MV large truck at-fault accidents in urban and rural locations based on the estimated values of average direct pseudo-elasticity. A careful study of the results of this study will help policy makers and transportation agencies identify location specific recommendations to increase safety awareness related to large truck involved accidents and to improve overall highway safety.     

A novel framework for improvement of road accidents considering decision-making styles of drivers in a large metropolitan area

Road accidents can be caused by different factors such as human factors. Quality of the decision-making process of drivers could have a considerable impact on preventing disasters. The main objective of this study is the analysis of factors affecting road accidents by considering the severity of accidents and decision-making styles of drivers. To this end, a novel framework is proposed based on data envelopment analysis (DEA) and statistical methods (SMs) to assess the factors affecting road accidents. In this study, for the first time, dominant decision-making styles of drivers with respect to severity of injuries are identified. To show the applicability of the proposed framework, this research employs actual data of more than 500 samples in Tehran, Iran. The empirical results indicate that the flexible decision style is the dominant style for both minor and severe levels of accident injuries.     

Bivariate ordered-response probit model of driver's and passenger's injury severities in collisions with fixed objects

A bivariate ordered-response probit model of driver's and most severely injured passenger's severity (IS) in collisions with fixed objects is developed in this study. Exact passenger's IS is not necessarily observed, especially when only most severe injury of the accident and driver's injury are recorded in the police reports. To accommodate passenger IS as well, we explicitly develop a partial observability model of passenger IS in multi-occupant vehicle (HOV). The model has consistent coefficients for the driver IS between single-occupant vehicle (SOV) and multiple-occupant vehicle accidents, and provides more efficient coefficient estimates by taking into account the common unobserved factors between driver and passenger IS. The results of the empirical analysis using 4-year statewide accident data in Washington State reveal the effects of driver's characteristics, vehicle attributes, types of objects, and environmental conditions on both driver and passenger IS, and that their IS have different elasticities to some of the risk factors.     

Corporate cost of occupational accidents: an activity-based analysis

The systematic accident cost analysis (SACA) project was carried out during 2001 by The Aarhus School of Business and PricewaterhouseCoopers Denmark with financial support from The Danish National Working Environment Authority. Its focused on developing and testing a method for evaluating occupational costs of companies for use by occupational health and safety professionals. The method was tested in nine Danish companies within three different industry sectors and the costs of 27 selected occupational accidents in these companies were calculated. One of the main conclusions is that the SACA method could be used in all of the companies without revisions. The evaluation of accident cost showed that 2/3 of the costs of occupational accidents are visible in the Danish corporate accounting systems reviewed while 1/3 is hidden from management view. The highest cost of occupational accidents for a company with 3.600 employees was estimated to approximately US$ 682.000. The paper includes an introduction regarding accident cost analysis in companies, a presentation of the SACA project methodology and the SACA method itself, a short overview of some of the results of the SACA project and a conclusion. Further information about the project is available at http://www.asb.dk/saca.