Driver Injury Severity Research Articles

A multivariate approach for modeling driver injury severity by body region

Road traffic crashes remain a major concern globally resulting in loss of life and worsening the quality of life and productivity of the crash survivors. The current study contributes to road safety literature by focusing on developing high resolution crash severity models based on driver injury severity reported using Abbreviated Injury Scale (AIS) by body region. For this purpose, the research develops a joint random parameters multivariate model structure with as many dimensions as severity by body location. The proposed model system is developed using Crash Injury Research Engineering Network (CIREN) data, which includes patients admitted to trauma centers due to a crash from 2005 to 2015. The dataset contained information about a comprehensive set of exogenous variables including driver characteristics, vehicle characteristics, crash characteristics, roadway characteristics, and environmental characteristics. The empirical analysis involves the estimation of Random Parameters Multivariate Generalized Ordered Probit Model that allows for the influence of common unobserved factors affecting the vehicle occupant severity across body locations. The model estimation results are further augmented by conducting elasticity analysis to highlight the differential impact of various factors on severity across body regions.

Temporal stability of the factors related to the severity of drivers’ injuries in rear-end collisions

Most of the previous studies that investigated the factors increasing the severity of rear-end collisions were based on analyzing collision reports from multiple years and combining them into a single dataset for analysis. Analyzing pooled data from multiple years carries the risk of introducing aggregation bias in the analysis. Those aggregated models might be structurally unstable, and the significance of the risk factors identified using those aggregated models might change over time due to the ongoing changes in vehicle technologies, law-enforcement technologies, and drivers’ attitudes. This study demonstrates the importance of testing the temporal stability of pooled data by utilizing logistic regression modeling to analyze all rear-end collisions that occurred in North Carolina for the period from January 1, 2004 to December 31, 2015. Separate models were developed for each year to model injury severity of striking and struck drivers. The year-wise models were compared together to identify the most temporally stable factors, and it was found that older and female drivers are usually more severely injured, but they do not increase injury severity of the drivers they collide with. It was also found that compared to other light-duty vehicles, passenger cars are usually associated with increased injury severity to their drivers and reduced injury severity to the drivers of the vehicles they collide with. The increased age of a vehicle was found to increase the injury severity of its driver as well as the driver of the vehicle it collides with. Dark conditions were found to increase drivers’ injury severity, but adverse weather conditions have no similar effect. For comparison, aggregated models were also developed by pooling data from all analysis years (from 2004 to 2015) and were found to return significant factors that were found by the year-wise models to be temporally unstable. Chow tests were performed on the data, and it was found that pooling data for four years or more generally returned structurally unstable models.

A temporal analysis of driver-injury severities in crashes involving aggressive and non-aggressive driving

Aggressive driving has become a national traffic-safety concern, with increasing congestion and other stress-inducing factors making it more likely drivers take out their frustrations by driving aggressively. Looking at single-vehicle crashes, this study investigates differences between resulting crash-injury severities when aggressive and non-aggressive driving behavior is observed, and how these differences changed over time. Using three years of crash data from 2015 to 2017 (inclusive), random parameters multinomial logit models with unobserved heterogeneity in means and variances were estimated. The available crash data included a wide variety of factors known to influence driver-injury severity including data related to the crash, vehicle, driver, spatial and temporal characteristics, roadway attributes, and traffic volume. Model estimates show that there were significant differences in driver-injury severities resulting from aggressive and non-aggressive driving, and that the effect of factors that determine injury severities changed significantly over time (statistically significant temporal instability). However, it is noteworthy that crashes involving non-aggressive drivers had many explanatory variables that produced temporally stable marginal effects, whereas crashes involving aggressive drivers had only one such variable (restraint belt usage). This suggests the possibility that temporal instability found in many recent safety studies may be driven by a subset of the crash population, and that there may be temporal stability in many crashes. Exploring this possibility is a promising direction for future empirical investigation.

Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective

Globally, road traffic accidents are an important public health concern which needs to be tackled. A multidisciplinary approach is required to understand what causes them and to provide the evidence for policy support. In Spain, one of the roads with the highest fatality rate is the crosstown road, a particular type of rural road in which urban and interurban traffic meet, producing conflicts and interference with the population. This paper contributes to the previous existing research on the Spanish crosstown roads, providing a new vision that had not been analyzed so far: the driver’s perspective. The main purpose of the investigation is to identify the contributing factors that increment the likelihood of a fatal outcome based on single-vehicle crashes, which occurred on Spanish crosstown roads in the period 2006-2016. In order to achieve this aim, 1064 accidents have been analyzed, applying a latent cluster analysis as an initial tool for the fragmentation of crashes. Next, a multinomial logit (MNL) model was applied to find the most important factors involved in driver injury severity. The statistical analysis reveals that factors such as lateral crosstown roads, low traffic volumes, higher percentages of heavy vehicles, wider lanes, the non-existence of road markings, and finally, infractions, increase the severity of the drivers’ injuries.

Analysis of the effects of visibility and warning devices on driver injury severity at highway-rail grade crossings considering temporal transferability of data

This study explores the joint effect of visibility and warning devices on driver injury severity at the highway-rail grade crossings (HRGCs), while also considering other contributing factors. For this purpose, four mixed logit models are developed to estimate the determinants of driver injury severity considering the combinations of visibility conditions (daylight vs. no daylight) and type of warning devices (active vs. passive warning). The models were calibrated using the data obtained from the USDOT Federal Railroad Administration for HRGC crashes that occurred over a ten-year period 2008–2017 across the United States. A temporal transferability test was conducted and confirmed the stability of model specifications considering a ten-year span of collected data. The pseudo-elasticity analysis was conducted to ascertain marginal impact of the contributing factors on driver injury severity in each model. While the vehicle speed, train speed, time of day and driver age are found to be common significant factors among the four models, there are marked differences between parameters associated with various crash factors. The study provides new insight into the driver injury severity in train-vehicle collisions considering visibility and type of warning devices, which can help in setting up proper policies to improve safety at HRGCs.

Comparative risk factor analyses on bi-level injury severity of taxi and private car crashes in Seoul, South Korea

Objectives: Taxis, one of the main transportation modes that occupy the roadways in Seoul, are semipublic transportation modes for transporting passengers safely and promptly. Considering that one fifth of passenger vehicles on the roads in Seoul are taxis and the crash rate of taxis is double the exposure to traffic, it is important to identify risk factors of taxis from that of private cars. In this paper, crash causes and characteristics in both taxi crashes and private car crashes are investigated to identify the risk factors in accordance with the injury severity.Methods: An eight-year light-vehicle crash dataset was utilized, in which injury levels were defined as severe vs. non-severe. Three binary logit models that estimate the severity of crashes, the injury severity for at-fault drivers, and the injury severity for victims were modeled for taxi crashes and private car crashes. Independent variables were extracted and included in the models to evaluate the odds ratio of each predictor variable.Results: The results indicated that violation of traffic signals and signs was the highest contributor among all violation types for taxi crashes and parties involved (at-fault driver and victims), while driving on the wrong side of the road resulted in the highest increase in the odds ratio for private cars. Head-on collision and nighttime driving increased the likelihood of severe injury risk for all models, while age was the most prominent factor for the injury level of victims. Use of seatbelts had a major impact on the at-fault drivers, especially for taxis.Conclusions: This study identified the risk factors that affect the crash- and party-related severity level when casualties involved taxis and private cars. By employing both crash- and party-level models, the study not only identifies the risk factors among taxis and private car crashes but also provides a comprehensive picture of the injury profile of all vehicular occupants, which helps to devise safety measures that enhance the safety and reduce the injury severity for parties involved in crashes.

Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

The study of road accidents and the adoption of measures to reduce them is one of the most important targets of the Sustainable Development Goals for 2030. To further progress in the improvement of road safety, it is necessary to focus studies on specific groups, such as light trucks and vans. Since 2013 in Spain, there has been an upturn in accidents in these two categories of vehicles and a renewed interest to deepen our understanding of the causes that encourage this behavior. This paper focuses on using machine learning methods to explain driver-injury severity in run-off-roadway and rollover types of accidents. A Random Forest (RF)-classification tree (CART) approach is used to select the relevant categorical variables (driver, vehicle, infrastructure, and environmental factors) to obtain models that classify, explain, and predict the severity of such accidents with good accuracy. A support vector machine and binomial logit models were applied in order to contrast the variable importance ranking and the performance analysis, and the results are convergent with the RF+CART approach (more than 70% accuracy). The resulting models highlight the importance of using safety belts, as well as psychophysical conditions (alcohol, drugs, or sleep deprivation) and injury localization for the two accident types.

Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.

Temporal stability of driver injury severities in animal-vehicle collisions: A random parameters with heterogeneity in means (and variances) approach

This study investigates the determinants of driver injury severity in animal-vehicle collisions while systematically accounting for unobserved heterogeneity in the data by using three methodological approaches: mixed logit model, mixed logit model with heterogeneity in means, and mixed logit model with heterogeneity in means and variances. Using the data from Washington state from January 1, 2012 to December 31, 2016, a wide range of factors that could potentially affect the injury severity of drivers were examined. Moreover, the temporal stability and transferability of the models were investigated through a series of likelihood ratio tests. Marginal effects were also used to study the temporal stability of the explanatory variables. Model estimation results show that many parameters can potentially increase the likelihood of severe injuries in Animal-vehicle crashes including freeways/expressways, daylight crashes, early morning crashes, dry road surface and clear weather condition. Moreover, the model estimation results show that accounting for the heterogeneity in the means (and variances) of the random parameters can improve the overall fit of the model. Some variables showed relatively similar marginal effects among different methodological approaches while some others showed different marginal effects upon the application of different methods. With regard to temporal stability of explanatory variables, the findings of this study show how underestimating the temporal stability concept may lead to inaccurate and unreliable conclusions.

Comparison of contributing factors for injury severity of large truck drivers in run-off-road crashes on rural and urban roadways: Accounting for unobserved heterogeneity

Abstract In spite of numerous efforts to quantitatively identify the factors contributing to the injury severity of different crash types in rural and urban settings, the distinction between rural and urban areas regarding the injury severity of run-off-road (ROR) crashes involving large trucks is still not clearly understood. As such, the objective of this study is to investigate the effect of area type (i.e., urban vs. rural) on injury severity outcomes sustained by drivers in ROR crashes involving large trucks while accounting for unobserved heterogeneity. To do this, the latent class ordered probit models with two classes are developed. The crash data pertaining to ROR crashes involving large trucks in Oregon between 2007 and 2014 were utilized. The estimation results reveal that the developed latent class ordered probit models (for urban and rural areas) are substantially distinct in terms of the contributing factors affect urban and rural ROR crash severities. The results indicate that female drivers and speed limit of 55 mph were associated with moderate injuries (non-incapacitating) in rural roadway ROR crashes while no injury outcome is most likely for crashes occurred in urban roadways with raised medians and on areas with a population density between 10,001 and 25,000. Also, the findings show that some factors increase the risk propensity of sustaining higher injury levels regardless of the land use setting such as crashes on horizontal curves, not wearing seatbelt, and driver fatigue. The findings of this study could benefit trucking industry, transportation agencies, and safety practitioners to prevent or alleviate the injury severity of ROR crashes involving large trucks by developing appropriate and cost-effective countermeasures.

A Comprehensive Analysis for the Heterogeneous Effects on Driver Injury Severity in Single-Vehicle Passenger Car and SUV Rollover Crashes

In road traffic crashes, although rollover crashes account for a relatively low proportion, those result in a high fatality rate. The present study performed random parameters ordered logit models to examine risk factors as well as their heterogeneous effects on driver injury severity in single-vehicle passenger car and SUV rollover crashes. Crash data for the empirical analysis were extracted from Texas Crash Record Information System (CRIS) database during the year 2016. Model estimation results show that six variables (male drivers, drivers’ age, airbag deployment, failure to drive in single lane, speed limit, and rural area) were found to produce normally distributed parameters in passenger car model, while nine parameters (male drivers, safety belt use, airbag deployment, drug or alcohol use, failure to drive in single lane, improper evasive action, vehicle model year, friday, and rural area) in SUV model were found to be normally distributed. Several other factors with fixed parameters were found to be associated with driver injury severity in single-vehicle passenger car or SUV rollover crashes, most notably: ejection or partial ejection, turning left, intersection, August, adverse weather conditions, and night with light. These variables were significant in both models; most variables have stronger effects on nonincapacitating injury and serious injury outcomes in SUV than in passenger car rollover crashes. These findings provide a deep insight into causality nature and factor involved in driver injury severity in single-vehicle passenger car and SUV rollover crashes and are also helpful for transport agencies to determine appropriate countermeasures aimed at mitigating injuries sustained by drivers in single-vehicle rollover crashes.

Comparing Factors Affecting Injury Severity of Passenger Car and Truck Drivers

This study aims to explore factors affecting passenger car and truck driver injury severity in passenger car-truck crashes. Police-reported crash data from 2007 to 2017 in Canada are collected. Two-vehicle crashes involving one truck and one passenger car are extracted for modeling. Different injury severities are not equally represented. To address the data imbalance issue, this study applies four different data imbalance treatment approaches, including over-sampling, under-sampling, a hybrid method, and a cost-sensitive learning method. To test the performances of different classifiers, five classification models are used, including multinomial logistic regression, Naive Bayes, Classification and Regression Tree, support vector machine, and eXtreme Gradient Boosting (XGBoost). In both the passenger car driver and truck driver injury severity analysis, XGBoost combined with cost-sensitive learning generates the best results in terms of G-mean, area under the curve, and overall accuracy. Additionally, the Shapley Additive Explanations (SHAP) approach is adopted to interpret the result of the best-performing model. Most of the explanatory variables have similar effects on passenger car and truck driver fatality risks. Nevertheless, six variables exhibit opposite effects, including the age of the passenger car driver, crash hour, the passenger car age, road surface condition, weather condition and the truck age. Results of this study could provide some valuable insights for improving truck traffic safety. For instance, properly installing traffic control devices could be an effective way to reduce fatality risks in passenger car-truck crashes. Besides, passenger car drivers should be extremely cautious when driving between midnight to 6 am on truck corridors.

Analysis of the impacts of risk factors on teenage and older driver injury severity using random-parameter ordered probit

The study analyzed injury severity of teenage and older drivers using 2015–2016 crash data from New Mexico. The fitness of the random-parameter ordered probit models developed for each age group was tested using likelihood ratio, comparing them to a unified model that combines both age groups, as well as comparing the random-parameter to fixed-parameter ordered probit for each age group. In both cases separate random-parameter ordered probit provided better results. It was found that vehicle type and age, lighting condition, alcohol or drug use, speeding, and seatbelt use were significant both for the teenage and older driver injury severity. The weather condition and gender were significant only in the teenage driver model, while driver inattention was significant for older drivers. The impacts of crash factors on injury severity was analyzed using marginal effects. The results indicate notable differences in the effects of contributing factors on driver injury severity between teenage and older drivers, including the sensitivity to changes in the mutual predictor parameter values.

Modelling Driver Injury Severity at Signalized Intersections in Malaysia

Modelling Driver Injury Severity at Signalized Intersections in Malaysia

A latent class approach for driver injury severity analysis in highway single vehicle crash considering unobserved heterogeneity and temporal influence

Temporal variation has been recognized as one of the major sources of unobserved heterogeneity in traffic safety research that has not been completely addressed. Overlooking temporal variation may result to biased estimates of effects of impact factors. This paper develops a latent class mixed logit model with temporal indicators to investigate highway single-vehicle crashes and the effects of significant contributing factors to driver injury severity. Crash data from 2010 to 2016 in Washington State is collected with a total of 31,115 single-vehicle crashes. The developed model is able to interpret both within- and across- class unobserved heterogeneity and temporal variation. After a careful comparison, a two-class model is selected as the final model. Estimation results show that: two temporal indicators show significant influence on latent class probability functions; urban indicator and principle type are found to be random parameters and have significant heterogeneity in the mean as a function of male indicator and driver’s age indicators. Variables with fixed effects, including animal collision, overturn collision, off-road collision, winter, minor arterial, interstate, wet, snow, ice, speed limit, vehicle age, turning movement, out control movement, lane-change movement, no airbag, deployed airbag, partial and total ejection, seatbelt, and no liability, show significant impacts on different levels of injury severity outcomes in each class. This study provided an insightful understanding of the time-varying effects of the significant factors on driver injury severity using marginal effect analysis, and the temporal indicators in the proposed model were found to enhance the model capability of temporal variation identification.

A generalized ordered logit analysis of risk factors associated with driver injury severity

Aim Road traffic crashes remain a major public health issue and have been the subject of debate in many studies due to their effect on society. This study contributes to the discussion by investigating the risk factors that significantly contribute to driver injury severity sustained in traffic crashes.

Traffic injury severity prediction along with identification of contributory factors using learning vector quantization: a case study of the city of London

This study presents learning vector quantization neural network modelling to predict injury severity of driver as well as riders, which applies to the backbone of traffic networks for London’s central business district. The potential associations between injury severity classes and crash related factors that contribute to their generation are discovered. Accordingly, the model is addressed as an identification technique for contributory factors and range of interventions for road safety. Unsurprisingly, approaching a T/staggered junction is detected as an accident hotspot. Injuries caused by going ahead on a bend and turning manoeuvres are ranked as the next most important contributory factors. Likewise, the affect of most junction actions were almost triple compared to the other indexes. All other sensitive predictors approximately were held near as equal; injuries involving a stationary or parked vehicle, factors related to junction control, crossing facilities, alcohol involvement, rush hours, and vehicle type. Following this implication, with the purpose of maximising the likelihood of injury accuracy, the model is predicted through the most sensitive predictors.

Effect of Age on Severity of Injuries in Traffic Accidents Involving Elderly Drivers

Background: The number of elderly drivers involves in traffic accidents is increasing with the aging of the population. This study aimed to investigate the relationship between the driver's age and injury severity in traffic accidents caused by elderly drivers. Methods: Data from the Emergency Department-based Injury In-depth Surveillance (EDIIS) from 2011-2016 was used to analyze traffic accidents by drivers aged 60 and older. Patients were classified into non-severe and severe injury group. The driver's age was subdivided by 5-year interval to compare the difference in age groups. Multivariate logistic regression was used to identify the factors related to the severe injury. Results: Among a total of 262,361 subjects, 6,427 patients were included, of which 890 (13.9%) were severely injured. There were more male in all age groups. The accidents occurred most in the daytime and general roads. In multivariate analysis, drivers aged 75-79 had 1.39 odds ratio (OR) of severe injury compared to drivers aged 60-64 (95% confidence interval [CI]: 1.06-1.84). Male (OR: 1.71, 95% CI: 1.40-2.09), alcohol intake (OR: 2.02, 95% CI: 1.39-2.94), and non-use of seatbelt (OR: 2.06, 95% CI: 1.71-2.48) were associated with severe injury, as well as the counterpart and road class. Conclusion: Among elderly drivers, there were significant differences in characteristics of traffic accidents according to age groups. The risk of severe injury increased in drivers aged over 75 years comparing to relatively young elderly drivers. These results provide the practical adjustment in determining the age standard when establishing traffic safety policies.

Age-Related Differences in Motor-Vehicle Crash Severity in California

With ongoing changes in the age distribution of drivers in the United States, it is important to obtain insights on how to make the roadways equally safe for drivers across different age groups. In light of this, the objective of this study is to examine various crash characteristics and make recommendations on how to potentially improve roadway safety for all age groups. Using the Highway Safety Information System (HSIS) data, this study investigates the factors influencing motor-vehicle crash injury severity for young (aged 16–25), middle-aged (aged 26–64), and older drivers (above 64) in the state of California. A multinomial logit model was used to separately model crashes involving each age group and to evaluate the weight of different predictor variables on driver injury severity. The predictor variables were classified into four—driver, roadway, accident and environmental characteristics. Results suggest that there are close relationships between severity determinants for young and middle-aged drivers. However, older drivers tend to be most cautious among all age groups under all environmental and roadway conditions. Young drivers are more likely to explore their driving skills due to newness to driving. Middle-aged drivers are familiar with driving and tend to demonstrate less cautious behaviors, especially male drivers. Another insight obtained from this study is that older driver behavior is less dynamic compared to other age groups; their driving pattern is usually regular regardless of the surrounding conditions.

Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model.

The existing studies on drivers’ injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers’ injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work.