Driver Injury Research Articles

Hot spot analysis of the crash locations at the roundabouts through the application of GIS

Intersection safety is a critical issue as it accounts high percent of the total fatal and injury crashes combined. One of the safe alternatives is to convert the typical intersections into roundabouts to enhance the capacity and reduce crashes. Despite the advantages in the roundabout geometric design, there is a clear gap in understanding the relationship among crash types, injury severity, and roundabout configurations. As such, this research used five years (2011 to 2015) of traffic crash data collected from Oregon’s Department of Transportation (ODOT). In this study, the Geographic information system tool (GIS) and the incident analysis tools have been used to identify hazardous location occurrence areas, hot spots, and factors that lead to roundabout accidents and decide how, when, and where accident countermeasures can be performed. The results indicated many cities with high crashes are not considered a hot spot area and the reverse. Besides, more driver injury crashes when the driver enters the roundabout at an angle, and more crashes are in four legs roundabout and three legs roundabout types. Several factors lead to roundabout crashes resulting in an injury, like drivers who did not yield right of way is the dominant crash, an improper change of traffic lanes, and followed too closely. Female drivers have more likelihood to involve in roundabout crashes than male drivers. With these findings, the current study would be useful for safety practitioners to select effective safety measures to avoid and minimize the incidence of accidents at the highest crash site.

Assessment of Commercial Truck Driver Injury Severity as a Result of Driving Actions

The disaggregate modeling approach is a new trend in the literature to analyze the injury severity of truck-involved crashes. The assessment of truck driver injury severity based on driver action is still missing in the literature. This paper presents an extensive exploratory analysis that highlights significant variability in the severity of truck drivers’ injuries based on various action types (i.e., aggressive driving, failure to keep proper lane, driving too fast, and no improper driving). Binary logistic regression with the Bayesian random intercept approach was developed to examine the factors contributing to fatal or any injuries of truck drivers using 10 years (2007–2016) of historical crash data in Wyoming. Log-likelihood ratio tests were performed to justify that separate models by various driving action types are warranted. The results demonstrated the effects of various vehicle, driver, crash, and roadway characteristics, combined with truck driver-specific action, on the corresponding severity of driver injury. The gross vehicle weight, age and gender of the driver, time of day, lighting condition, and the presence of junctions were found to have significantly different impacts on the severity of truck driver injury in various driving action-related crashes. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (27%–33%) of intra-crash correlation in driver injury severity within the same crash. Finally, based on the findings of this study, several recommendations are made.

Examining Driver Injury Severity in Single-Vehicle Road Departure Crashes Involving Large Trucks

Road departure (RD) crashes are among the most severe crashes that can result in fatal or serious injuries, especially when involving large trucks. Most previous studies neglected to incorporate both roadside and median hazards into large-truck RD crash severity analysis. The objective of this study was to identify the significant factors affecting driver injury severity in single-vehicle RD crashes involving large trucks. A random-parameters ordered probit (RPOP) model was developed using extensive crash data collected on roadways in the state of Kentucky between 2015 and 2019. The RPOP model results showed that the effect of local roadways, the natural logarithm of annual average daily traffic (AADT), the presence of median concrete barriers, cable barrier-involved collisions, and dry surfaces were found to be random across the crash observations. The results also showed that older drivers, ejected drivers, and drivers trapped in their truck were more likely to sustain severe single-vehicle RD crashes. Other variables increasing the probability of driver injury severity have included rural areas, dry road surfaces, higher speed limits, single-unit truck types, principal arterials, overturning-consequences, truck fire occurrence, segments with median concrete barriers, and roadside fixed object strikes. On the other hand, wearing seatbelt, local roads and minor collectors, higher AADT, and hitting median cable barriers were associated with lower injury severities. Potential safety countermeasures from the study findings include installing median cable barriers and flattening steep roadside embankments along those roadway stretches with high history of RD large-truck-related crashes.

Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters

Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

Potential of a Time-Triggered Crash System of a Steering Column on Driver Injuries

Potential of a Time-Triggered Crash System of a Steering Column on Driver Injuries

Field Data Study of the Effect of Knee Airbags on Lower Extremity Injury in Frontal Crashes

<div class="section abstract"><div class="htmlview paragraph">Knee airbags (KABs) are one countermeasure in newer vehicles that could influence lower extremity (LEX) injury, the most frequently injured body region in frontal crashes. To determine the effect of KABs on LEX injury for drivers in frontal crashes, the analysis examined moderate or greater LEX injury (AIS 2+) in two datasets. Logistic regression considered six main effect factors (KAB deployment, BMI, age, sex, belt status, driver compartment intrusion). Eighty-five cases with KAB deployment from the Crash Injury Research and Engineering Network (CIREN) database were supplemented with 8 cases from the International Center for Automotive Medicine (ICAM) database and compared to 289 CIREN non-KAB cases. All cases evaluated drivers in frontal impacts (11 to 1 o’clock Principal Direction of Force) with known belt use in 2004 and newer model year vehicles. Results of the CIREN/ICAM dataset were compared to analysis of a similar dataset from NASS-CDS (5441 total cases, 418 KAB-deployed). KABs were associated with a reduced rate of LEX injury in the CIREN/ICAM dataset (OR = 0.612, p=0.065), but were inconclusive in the NASS-CDS dataset (OR=0.946, p=0.761). KABs were associated with a reduced rate of knee/thigh/hip injury in CIREN/ICAM (OR = 0.555, p = 0.035) but had no measurable effect on below knee injury in CIREN/ICAM (OR = 0.928, p = 0.765) or NASS-CDS (OR=1.102, p=0.641). In conclusion, KABs were associated with reduced rates of LEX and knee/thigh/hip injury in the CIREN/ICAM dataset and had no measurable effect on below knee injury for drivers in frontal crashes in either dataset.</div></div>

Tanker Truck Rollover Avoidance Using Learning Reference Governor

<div class="section abstract"><div class="htmlview paragraph">Tanker trucks are commonly used for transporting liquid material including chemical and petroleum products. On the one hand, tanker trucks are susceptible to rollover accidents due to the high center of gravity when they are loaded and due to the liquid sloshing effects when the tank is partially filled. On the other hand, tanker truck rollover accidents are among the most dangerous vehicle crashes, frequently resulting in serious to fatal driver injuries and significant property damage, because the liquid cargo is often hazardous and flammable. Therefore, effective schemes for tanker truck rollover avoidance are highly desirable and can bring a considerable amount of societal benefit. Yet, the development of such schemes is challenging, as tanker trucks can operate in various environments and be affected by manufacturing variability, aging, degradation, etc. This paper considers the use of Learning Reference Governor (LRG) for tanker truck rollover avoidance. The LRG is an add-on scheme to a nominal closed-loop system (i.e., the tanker truck in this application). The LRG monitors the maneuver command from the driver (or from a higher-level planning algorithm in the case of an automated tanker truck) and adjusts it when necessary to enforce rollover constraints. One major novelty of this LRG scheme is that its formulation relies on learning, rather than explicit modeling of the system, where learning can be performed on a high-fidelity, black-box model of the truck system or through experimentation with the actual truck. We illustrate the effectiveness of this LRG-based rollover avoidance scheme through simulation studies corresponding to various truck operating conditions.</div></div>

Increased risk of road traffic injuries in individuals with insomnia

IntroductionFew studies have clinically assessed road traffic injuries in individuals with insomnia. This study examined the epidemiological characteristics of road traffic injuries in individuals with insomnia and identified relevant factors. MethodsIn this retrospective cohort study, we used administrative data (National Health Insurance Research Database) to identify a cohort from people admitted to hospital with insomnia and matched controls from other admitted patients. All the included data were used to determine the incidence rate of having road traffic injuries in hospital admission in individuals aged 18 and older with insomnia. A control group of individuals without insomnia with a 4:1 ratio was matched by gender, age, and index year. Cox regression models were used to evaluate the effects of insomnia on the risk of road traffic injuries (Hazard ratio, HR). The link between insomnia and repeated road traffic injury was also explored. ResultsFrom 2000 to 2015, 1,936,512 people aged 18 and older were hospitalized in Taiwan (194,035 with insomnia and 776,140 without insomnia). After other factors were controlled, the risk of road traffic injuries in individuals with insomnia was 1.23 times that of individuals without insomnia. In the 25 to 44 age group, the risk in individuals with insomnia was 1.80 times that of those without insomnia. Additionally, the risk of road traffic injuries in automobile drivers and motorcycle riders with insomnia was 1.46 and 1.29 times that of those without insomnia, respectively. The risk of repeated road traffic injuries with insomnia was 2.06 times that of those without insomnia. ConclusionsTo reduce the probability of road traffic injury, high-risk groups should seek early medical assistance to resolve insomnia.

A Comparative Analysis of using Various Machine learning Techniques based on Drowsy Driver Detection

In image processing or computer vision, image segmentation is a vital issue for applications such as scene understanding, medical image evaluation, robotic perception, video surveillance, increased reality or compression, etc. Every year in road accidents caused because of human mistake, the numbers of dead and injured are rising. Drowsiness and driving are particularly risky and difficult to recognize. The second leading cause of road crashes in drowsiness after alcohol. Detecting driver drowsiness is a technology of safety for vehicles that helps placed an end to driver injuries that are dozy. One of the main causes of road accidents is driver drowsiness. It is a very serious issue for road safety. We have presented various methods for detecting the drowsiness of the driverin this paper and the comparisons among such methods are extremely challenging. For this purpose, we have compared machine learning methods based on facial expression, especially on eye state. Apart from eye detection, it performed experiments on mouth detection and face detection as well. This paper explores several methods for machine learning, like SVM, CNN, or HMM. From the analysis, we have found that the HMM model achieved more accurate results in comparisonto others.

The role of gender and temporal instability in driver-injury severities in crashes caused by speeds too fast for conditions

The effect of inappropriate speed adjustment to adverse conditions on crash-injury severities, and how this effect might vary across male and female drivers, and over time, is not well understood. To study this, single-vehicle crashes occurring in rainy weather, where speed too fast for conditions is a driver action identified as a contributing factor to the crash, were considered. The differences between the resulting crash-injury severities of male and female drivers (and how these differences change over time) is then studied utilizing three years of Florida crash data and estimating random parameters multinomial logit models of driver injury severity while considering potential heterogeneity in the means and variances of parameter estimates. Model estimation results show that there were significant differences in the driver-injury severities of male and female drivers, and that the effect of factors that determine injury severities varied significantly over time (statistically significant temporal instability). This suggests that male and female drivers generally perceive and react to rainy weather conditions in fundamentally different ways, and that their responses, as reflected by the effect that explanatory variables have on injury severity probabilities, change over time. However, there were two explanatory variables that had relatively stable effects on injury-severity probabilities over time and across genders: an indicator variable for crashes involving non-collision factors (including overturn/rollover crashes) and an indicator variable for restraint usage. Policies that target these two variables could produce long-term reductions in crash-injury severities under adverse conditions.

Injury severity influence factors and collision prediction - A case study on Kuwait highways

Traffic collisions are considered a serious problem worldwide that cause severe losses. Gulf Cooperation Council (GCC) Countries have high rates of collisions, which requires urgent proactive strategies and attention to solve such problem. This study aimed to enhance traffic safety conditions and reduce collisions' severity levels at several locations in Kuwait City, Kuwait. Consequently, the study importance raised in four folds; reducing the total number of collisions on Kuwait highways, predicting the future numbers of collisions, identifying and managing risk factors contributing to collision's severity, and developing new strategies to enhance traffic safety condition in Kuwait. Three-year crash dataset from 2016 to 2018 including 4028 road collisions in Kuwait City were used to analyse the driver injury severity influence factors and to predict the future collision counts. Statistical indices were used to evaluate the mixed logit model performance, which are the MCFadden Pseudo R-Squared statistics and the two-log likelihood. Eight covariates were tested for significance in the mixed logit model estimation. Results showed that female drivers, driving during night-time, driving outside the city, and not using seatbelt produced the highest possibility of having incapacitating injuries and fatalities by 46.6% and 31.6%, respectively. Contrastingly, male drivers, driving during daytime, driving inside the city, and using seatbelt had resulted in the lowest probability of getting incapacitating injuries and fatalities by 4.7% and 0.2%, respectively. Furthermore, Bayesian hierarchical model was used to investigate the future road collision counts and to identify the collision blackspots (CBS) on Kuwait City highways. Six covariates were considered in the model. Log-linear regression model (CPM) was used to estimate the vector of means μj(t). By applying regression to mean (RTM) and predicting the trend in the mean collision rate λj (t), results showed that the average model accuracy for year 2018 was 45.76% (for a 95% confidence interval estimation using the collision dataset of previous years). It was also found that the highest number of collisions had occurred on the second ring road. Basing the results on year 2018 helped in predicting more accurate future collision counts, and in justifying collision rates. Additionally, further covariates can be added to the models for any additional recent crash data to increase the model accuracy. This work is anticipated to help Kuwaiti decision makers in designing accurate countermeasures to improve traffic safety condition.

Fusion convolutional neural network-based interpretation of unobserved heterogeneous factors in driver injury severity outcomes in single-vehicle crashes

In this study, a fusion convolutional neural network with random term (FCNN-R) model is proposed for driver injury severity analysis. The proposed model consists of a set of sub-neural networks (sub-NNs) and a multi-layer convolutional neural network (CNN). More specifically, the sub-NN structure is designed to deal with categorical variables in crash records; multi-layer CNN structure captures the potential nonlinear relationship between impact factors and driver injury severity outcomes. Seven-year (2010–2016) single-vehicle crash data is applied. Models with different CNN layers are tested using the validation set, as well as various model layouts with and without a dropout layer or regularization term in the objective function. It is found that different model layouts provide consistent predictive performance. With the limited training data, more CNN layers result in the prematurity of the training procedure. The dropout layer and the regularization technique help improve the stability of the effects of different variables. The proposed model outperformed other five typical approaches in the predictability comparison. Moreover, a marginal effect analysis was conducted to the proposed FCNN-R model, the FCNN model and the mixed multinomial logit model. It shows that the proposed FCNN-R model can be used to uncover the underlying correlations similar to the traditional statistical models. Additionally, the temporal stability of the proposed FCNN-R approach is discussed based on the model performance in different years. Future research is recommended to include more information for improving the universality of the proposed approach.

Driver injury severity analysis of crashes in a western China's rural mountainous county: Taking crash compatibility difference into consideration

China's traffic safety attracts increasing research interest. Official data show that crashes in the western region of China are more severe than those in the eastern region. However, research on crash severity in western China is scarce. This study applied a hierarchical Bayesian logistic model to examine the significant factors related to crash and vehicle/driver levels and their heterogeneous impacts on the severity of drivers' injury. Crash data were collected from Lintao, a rural mountainous county in western China. A variable was proposed to measure the relative difference between the crashworthiness of one vehicle and the aggressivity of the other vehicle in the mixed traffic flow. Results indicated that the majority of the total variance was induced by between-crash variance, showing the suitability of the utilized hierarchical modeling approach. One crash-level variable and six vehicle/driver-level variables, namely, road type, compatibility difference, age, vehicle type, drunk driving, driving unregistered vehicle, and driving years, significantly affected modeling drivers' injury severities. Among these variables, road type (national and provincial), age (young and senior drivers), driving unregistered vehicle, and drunk driving tended to increase the odds of crash-related mortality. Driving years (new drivers with less than six years of driving experience) and vehicle type (heavy vehicle) were likely to decrease the probability of fatal outcomes. Compatibility difference was relatively significant, and the possibilities of mortality in single vehicle crashes were higher than those in multivehicle and pedestrian-involved crashes. The developed methodology and estimation results provided insights into the internal mechanism of rural crashes and effective countermeasures to prevent rural crashes. • A hierarchical Bayesian logistic model was established. • Data were collected from a rural county in western China with highly mixed traffic. • A variable measuring crash compatibility difference within a crash was employed. • The effects of seven types of violation on driver injury outcome were examined. • Seven variables were significant, while gender was not.

Retrospective evaluation of vehicle whiplash-reducing head restraint systems to prevent whiplash injury in Victoria, Australia

Head restraint systems specifically engineered to reduce the impact of whiplash injury in the event of a rear-end collision were introduced in the late 1990s with the aim of reducing whiplash injury risk that went 'beyond simple geometric improvements’ to head restraints. Whilst studies have shown that whiplash-reducing head restraint systems are highly effective in reducing whiplash injury, these were based on a limited range of systems including Toyota’s Whiplash Injury Lessening (WIL) seating system, Volvo’s Whiplash Injury Prevention System (WHIPS) and the Saab Active Head Restraint (SAHR) and have generally focussed on Swedish crash and insurance data. However, there has been no broad real-world crash-based evaluation of the effectiveness of whiplash-reducing head restraint systems currently present in the vehicle fleet that validates the results of these studies in other populations. The objective of this study was to undertake a retrospective evaluation of vehicle whiplash-reducing head restraint systems to prevent whiplash injury using real-world crash data linked to insurance claims data in Victoria, Australia. It was found that whiplash-reducing head restraint systems are associated with a statistically significant reduction in the odds of driver and front seat passenger whiplash injury in a vehicle struck in a rear-end collision of 11.6 % (95 % CI 0.20 %, 21.6 %). The results indicate that whiplash-reducing head restraint systems are an effective technology for reducing the risk of whiplash injury to drivers and front seat passengers in a vehicle struck in a rear-end collision. Considering that around a quarter of all casualty crashes involving passenger and light commercial vehicles are rear-end, the fitment of whiplash-reducing head restraint systems to all vehicles as a standard safety feature would likely see a significant reduction in the incidence of whiplash injury.

Hierarchical binary logit model to compare driver injury severity in single-vehicle crash based on age-groups

Most of the previous single-vehicle crash analysis studies ignored the effect of road-segments level at higher plan that could probably be unobserved heterogeneity and vary among crash-level factor from one road-segment to next and possibly could lead to a potential biased estimated result. This study developed a hierarchical binary logit model which have the ability to account for both unobserved heterogeneity and correlation within road-segment, to investigate and compare the impact of significant factors influencing fatal single-vehicle crash between young, mid-age and old driver model. A seven-years from 2011 to 2017 crash data, Department of Highway (DOH), Thailand were used in this study. The Intra-Class-Correlation values indicate the importance of road-segment level that 10.1%, 12.2% and 12.8% of the total variation were accounted by random effect from road-segment heterogeneity for young, mid-age and old driver model, respectively. The estimated result of this study shows that influence of alcohol and fatigue increase risk of fatal crash among young and old driver, seatbelt-usage reduce risk of being fatal among mid-age and old driver, roadside safety feature (guardrail) significantly reduce fatality risk among young and mid-age driver, and night time driving without light increase probability of fatal crash for mid-age driver. This study recommends the need to enforce the law on driver under influence of alcohol and seatbelt usage, educational campaign on driving, and installation of guardrail on curve road.

Examining driver injury severity in left-turn crashes using hierarchical ordered probit models

Objective Few existing studies in the literature devoted efforts to examine the driver injury severity in left-turn crashes. To fill this research gap, this paper aims to provide a comprehensive study of the contributing factors of left-turn crashes and the corresponding injury severities. Methods The hierarchical ordered probit (HOPIT) model is first applied to study driver injury severity in left-turn crashes. The HOPIT model can overcome the limitations of traditional ordered probit models since its thresholds are always positive and ordered. It is a function of unique explanatory parameters that do not necessarily affect the ordered probability outcomes directly. Considering the driving condition during the wintertime could be significantly different from other seasons, this study divided the overall crash dataset into “winter” and “other-season” subsets based on the temperature, snowing condition, and other factors. Results With the “other-season” dataset, results demonstrated that contributing factors, such as young drivers, male drivers, clear, light, and ramp intersection with crossroad, are associated with a decrease in injury severity. On the contrary, factors like drug, alcohol, disregard traffic control device, high-speed limit, the protected left-turn signal, etc., are related to an increase in injury severity. In winter, results revealed that only nine contributing factors are significant to the left-turn crash. Alcohol, disregard traffic control device, nighttime, high-speed limit, head-on collision, and state road are associated with an increase in injury severity. Also, two-vehicle involved, snow, ramp intersection with crossroad are related to a decrease in injury severity. Conclusions The HOPIT model is applied to examine contributing factors of left-turn crashes and the corresponding injury severity, based on left-turn crash records from 2010 to 2017 in Utah. Eighteen significant factors of left-turn crash injury severity are identified in the overall dataset. In seasons rather than winter, the significant factors are almost the same as that of the entire year. In the winter, less significant factors and different patterns are found compared with the overall crashes.

Factors associated with driver injury severity of lane changing crashes involving younger and older drivers

Lane change maneuvers can be a difficult driving task for many drivers, particularly, younger and inexperienced drivers and older drivers. Frequent and improper lane changing has a substantial effect on both traffic operations and safety. This study investigates the factors that contribute to the injury severity of crashes resulting from improper lane change maneuvers involving younger and older drivers in Alabama. Crash data for the study was obtained from the Critical Analysis Reporting Environment (CARE) system developed by the Center for Advanced Public Safety at the University of Alabama. Preliminary data analysis showed that younger drivers were responsible for about 63.1 % of the crashes. Random logit with heterogeneity in means models were developed to identify significant crash factors that were associated with the crash outcomes. The results show that younger male drivers were more likely to be severely injured in lane changing crashes whereas older male drivers were less likely to be severely injured. The results further show that younger drivers were more likely to sustain a major injury on six-lane highways whereas older drivers sustained major injuries from overtaking maneuvers on two-lane highways. It was further found that while younger drivers got into major injury crashes under daylight conditions, older drivers were more likely to be involved in major injury crashes under dark/unlit lighting conditions. Improper lane changing constitutes some form of aggressive driving which needs to be addressed considering that many lives are lost annually from the practice. The findings from this study may be used by road safety advocates and practitioners to identify what and how to target crash countermeasures to the younger and older driver population groups in the state.

Temporal stability of driver injury severity in single-vehicle roadway departure crashes: A random thresholds random parameters hierarchical ordered probit approach

This study examines contributing variables affecting the driver injury-severity in single-vehicle roadway departure crashes. To capture the threshold heterogeneity and unobserved heterogeneity, the random thresholds random parameters hierarchical ordered probit (HOPIT) approach is employed using the crash data collected from 2014 to 2017 in the State of North Carolina. Three injury severity levels are considered: severe injury, minor injury, and no injury. Attributes that potentially affect crash severity are examined, including driver, crash, roadway, and environmental characteristics. A series of likelihood ratio tests are conducted to examine the temporal stability of factors across different studied years. The marginal effects of factors in different injury-severity models are compared. Significant temporal instability is found among the studied year periods. The threshold value estimated using the random threshold random parameters HOPIT model is found to be random parameters and determined by specific explanatory variables. Additionally, the effects of some factors (e.g., alcohol, curved roadway, passenger car, SUV, and wet/water surface) on injury severity are relatively stable, while others (e.g., female driver, collector, and clear weather) present temporal unstable effects. Regarding the temporal instability, decision-makers need to treat the factors carefully to avoid developing incorrect countermeasures.

Motor vehicle driver injury severity analysis utilizing a random parameter binary probit model considering different types of driving licenses in 4-legs roundabouts in South Australia

A roundabout may not provide an acceptable level of control and can be confusing to inexperienced drivers. Therefore, the purpose of this study is to identify the contributing factors that lead to specific driver injury severity by utilizing a random parameter binary probit model sustained by different experiences of motor drivers at 4-legs roundabouts in South Australia. Four models were estimated based on seven years of crash data (2012–2018), considering different types of motorist-driving license: learner, provisional, full, and for all datasets, including unknown licensures. The model estimates variables have been categorized into a driver, crash, temporal, spatial, vehicle, roadway characteristics, and vehicle movements. The results showed there are differences between resulting crash-injury severities when driver experience has been observed. Besides, several parameters were found to be random and normally distributed: safety equipment, crash type (rear-end crash), number of involved vehicles, weekdays indicator, stats area (crash occurred within metropolitan), vehicle type (passenger car), and posted speed limit (more than 50 km/hr.). In addition, the log-likelihood and the transferability test indicated that the data should be separated and analyzed according to the driver's license. Findings can help authorities to improve driver safety considering the influence of the driver experience.

PECULIARITIES OF INJURIES TO THE DRIVER AND THE PASSENGER OF CARS IN CASES OF TRAFFIC ACCIDENTS DEPENDING ON THE CLASS OF THE VEHICLE AND THE ANGLE OF IMPACT

. One of the most pressing problems today is road traffic injuries. According to the WHO, road deaths have risen to 1.35 million a year, or an average of about 3,700 deaths each day. In addition, tens of millions of people receive various injuries. It should be noted that the examination of the nature and mechanisms of injuries in car accidents has always been considered a relevant and complex problem of forensic science and practice. Of particular difficulty from an forensic expert standpoint are cases of injury inside the car and establishing the location of the victims. In the forensic literature, the issue of differentiation of injuries in drivers and passengers still remains insufficiently studied, especially taking into account the increasing number and variety of cars, changes in their design features and protective equipment.Aim of the work. Review scientific publications on the specifics of injuries to the driver and passenger of in cases of traffic accidents, depending on the class of vehicle and the angle of impact.Conclusions. The analysis of scientific works shows that at this stage, the mechanism of occurrence, localization and differentiation of injuries in drivers and passengers depending on the angle of collision and class of car with different design features of the cabin and control panel remains insufficiently studied. An in-depth study of these features will allow forensic experts to conduct a differential diagnosis of injuries in victims and determine their location in the passenger compartment of the car. It should be noted that the correct identification and location of the driver and passengers at the time of the accident allows to identify who was at fault, which greatly facilitates the work of judicial and investigative bodies.