A study on correlation of pedestrian head injuries with physical parameters using in-depth traffic accident data and mathematical models

A data-mining study on the prediction of head injury in traffic accidents among vulnerable road users with varying body sizes and head anatomical characteristics

Body sizes and head anatomical characteristics play the major role in the head injuries sustained by vulnerable road users (VRU) in traffic accidents. In this study, in order to study the influence mechanism of body sizes and head anatomical characteristics on head injury, we used age, gender, height, and Body Mass Index (BMI) as characteristic parameters to develop the personalized human body multi-rigid body (MB) models and head finite element (FE) models. Next, using simulation calculations, we developed the VRU head injury dataset based on the personalized models. In the dataset, the dependent variables were the degree of head injury and the brain tissue von Mises value, while the independent variables were height, BMI, age, gender, traffic participation status, and vehicle speed. The statistical results of the dataset show that the von Mises value of VRU brain tissue during collision ranges from 4.4 kPa to 46.9 kPa at speeds between 20 and 60 km/h. The effects of anatomical characteristics on head injury include: the risk of a more serious head injury of VRU rises with age; VRU with higher BMIs has less head injury in collision accidents; height has very erratic and nonlinear impacts on the von Mises values of the VRU's brain tissue; and the severity of head injury is not significantly influenced by VRU's gender. Furthermore, we developed the classification prediction models of head injury degree and the regression prediction models of head injury response parameter by applying eight different data mining algorithms to this dataset. The classification prediction models have the best accuracy of 0.89 and the best R2 value of 0.85 for the regression prediction models.

Assessment of Pedestrians' Head and Lower Limb Injuries in Tram-Pedestrian Collisions

Analysis of pedestrians' head and lower limb injuries at the tissue level is lacking in studies of tram-pedestrian collisions. The purpose of this paper therefore to investigate the impact response process and severity of pedestrians' injuries in tram-pedestrian collisions, using the Total Human Model for Safety (THUMS) pedestrian human body model together with the tram FE model. Two full-scale tram-pedestrian dummy crash tests were performed to validate the FE model, and the total correlation and analysis (CORA) score of head acceleration yielded values of 0.840 and 0.734, confirming a strong agreement between the FE-simulated head responses and the experimental head kinematics. The effects of different tram speeds and impact angles on pedestrians' impact response injuries and the differences were further analyzed. The results indicate that direct impact of the lower limb with the tram's obstacle deflector leads to lower limb bone shaft fractures and knee tissue damage. Neck fling contributed to worsened head injury. Coup contusions were the predominant type of brain contusion, surpassing contrecoup contusions, while diffuse axonal injury was mainly concentrated in the collision-side region of the brain. Pedestrians' injuries are influenced by tram velocity and impact angle: higher tram velocities increase the risk of lower limb and head injuries. The risk of head injury for pedestrians is higher when the impact angle is negative, while lower limb injuries are more significant when the impact angle is 0°. This study provides practical guidance for enhancing tram safety and protecting pedestrians.

Investigation on the Modeling and Reconstruction of Head Injury Accident Using ABAQUS/Explicit

A process of modeling and reconstructing human head injuries involved in traffic crashes based on ABAQUS/Explicit is presented in this paper. A high-fidelity finite element (FE) model previously developed by the authors is employed to simulate a real accident case that led to head injury. The most probable head impact position informed by CT images is used for the FE modeling and simulation since the head impact position is critical for accident reconstruction and future analysis of accidents that involve human head injuries. Critical von Mises stress on the skull surface of the head model is chosen as the evaluation criterion for the head injury and FE simulations on 60 cases with various human head-concrete ground impact conditions (impact speeds and angles) were run to obtain those stress values. The FE simulation results are compared with the CT images to determine the minimum speed that will cause skull fracture and the corresponding contact angle at that speed. Our study shows that the minimum speed that would cause skull fracture is 3.5 m/s when the contact angle between the occipital position of the injured head and the ground is about 30°. Effects of the impact speed and the contact angle on the maximum von Mises stress of the head model are revealed from the simulations. The method presented in this paper will help forensic pathologists to examine the head impact injuries and find out the real reasons that lead to those injuries.

Reconstruction of a real-world car-to-pedestrian collision using geomatics techniques and numerical simulations

The aim of this study is to provide an improved method for traffic accident reconstruction based on geomatics techniques and numerical simulations. A combination of various techniques was used. First, an unmanned aerial vehicle (UAV), laser scanner and structured-light scanner were used to obtain information on the accident scene, vehicle and victim. The collected traces provided detailed initial impact conditions for subsequent numerical simulations. Then, multi-body system (MBS) simulations were conducted to reconstruct the kinematics of the car-to-pedestrian collision. Finally, a finite element (FE) simulation using the THUMS model was performed to predict injuries. A real-life vehicle-pedestrian collision was used to verify the feasibility and effectiveness of this method. The reconstruction result revealed that the kinematic and injury predictions of the numerical simulations effectively conformed to the surveillance video and investigation of the actual accident. UAV photogrammetry was demonstrated to be more efficient in accident data collection than hand sketch and measurement, and 3D laser scanning enabled an easier and more accurate modeling process of vehicle. The present study shows the feasibility of this method for use in traffic accident reconstruction.

Numerical investigation of the rider's head injury in typical single-electric self-balancing scooter accident scenarios

As the use of electric self-balancing scooters (ESSs) increases, so does the number of related traffic accidents. Because of the special control method, mechanical structure and driving posture, ESSs are prone to various single-vehicle accidents, such as collisions with fixed obstacles and falls due to mechanical failures. In various ESS accident scenarios, the rider's head injury is the most frequent injury type. In this study, several typical single-ESS accident scenarios are reconstructed via computational methods, and the risk of riders' head/brain injury is assessed in depth using various injury criteria. Results showed that two types of ESSs (solo- and two-wheeler) do not have clear differences in head kinematics and head injury risks; the head kinematics (or falling posture) and ESS accident scenario exhibit a distinct effect on the head injury responses; half of the analysed ESS riders have a 50% probability of skull fracture, a few riders have a 50% risk of abbreviated injury scale (AIS) 4+ brain injury, and none has a diffuse axonal injury; the ESS speed plays an important role in producing the head/brain injury in ESS riders, and generally, higher ESS speed generates higher level of predicted head injury parameters. These findings will provide theoretical support for preventing head injury among ESS riders and data support for developing and legislating ESSs.

Should anthropometric differences between the commonly used pedestrian computational biomechanics models and Chinese population be taken into account when predicting pedestrian head kinematics and injury in vehicle collisions in China?

Computational biomechanics models play a key role in predicting/evaluating pedestrian head kinematics and injury risk in car-to-pedestrian collisions. The human multibody models most commonly used in car-to-pedestrian collision reconstruction, such as pedestrian model by The Netherlands Organisation for Applied Scientific Research TNO, are built using the anthropometry of Western European population as defined in TNO (2013) human multibody model database. In this study, we investigate the effects of the anthropometric differences between the Western European and Chinese populations on the pedestrian head kinematics and injury responses predicted using multibody models. The comparison was conducted through car-to-pedestrian collision simulations using pedestrian multibody models representing anthropometric characteristics of Western European and Chinese populations, three typical vehicle shapes (sedan, SUV and minivan), five initial vehicle impact speeds (30, 35, 40, 45, 50 km/h), and six pedestrian walking postures. The results indicate that the change of pedestrian model anthropometry (from Western European to Chinese) exerts appreciable effects on both the predicted initial boundary conditions of the head-to-windscreen impact (in particular the head-to-windscreen impact angle) and the head injury indices in the impact with the road surface (secondary impact).

Assessment of standing passenger traumatic brain injury caused by ground impact in subway collisions

Human head is the most vulnerable region in subway collisions. To design a safer subway, the head impact biomechanical response should be studied first. This paper aims to investigate the standing passenger head-ground impact dynamic response and traumatic brain injury (TBI) in subway collisions. A standing passenger-subway interior dynamic model was numerically developed by using our previous validated finite element (FE)-multibody (MB) coupled human body model, which was integrated by the Total Human Model for Safety (THUMS) head-neck FE model and the extracted remaining body segments pedestrian MB model of TNO. A parametric study considering the handrail type, standing angle, and friction coefficient between the shoes and ground was performed. Results show that the passenger dynamic response could be divided into two categories according to whether the passenger hit handrails. Passenger TBIs severity could be efficiently alleviated by the passenger body (excluding the head) hitting the handrail first before head-ground impact. The probabilities of DAI in the cerebellum and brain stem were low. A statistical analysis of TBIs demonstrated that the risks of TBIs were sensitive to the handrail type in subway collisions, but did not to the standing angle and friction coefficient. This study provides practical help for improving the interior crashworthiness performance of subways.

Realistic Reference for Evaluation of Vehicle Safety Focusing on Pedestrian Head Protection Observed From Kinematic Reconstruction of Real-World Collisions

Head-to-vehicle contact boundary condition and criteria and corresponding thresholds of head injuries are crucial in evaluation of vehicle safety performance for pedestrian protection, which need a constantly updated understanding of pedestrian head kinematic response and injury risk in real-world collisions. Thus, the purpose of the current study is to investigate the characteristics of pedestrian head-to-vehicle contact boundary condition and pedestrian AIS3+ (Abbreviated Injury Scale) head injury risk as functions of kinematic-based criteria, including HIC (Head Injury Criterion), HIP (Head Impact Power), GAMBIT (Generalized Acceleration Model for Brain Injury Threshold), RIC (Rotational Injury Criterion), and BrIC (Brain Injury Criteria), in real-world collisions. To achieve this, 57 vehicle-to-pedestrian collision cases were employed, and a multi-body modeling approach was applied to reconstruct pedestrian kinematics in these real-world collisions. The results show that head-to-windscreen contacts are dominant in pedestrian collisions of the analysis sample and that head WAD (Wrap Around Distance) floats from 1.5 to 2.3 m, with a mean value of 1.84 m; 80% of cases have a head linear contact velocity below 45 km/h or an angular contact velocity less than 40 rad/s; pedestrian head linear contact velocity is on average 83 ± 23% of the vehicle impact velocity, while the head angular contact velocity (in rad/s) is on average 75 ± 25% of the vehicle impact velocity in km/h; 77% of cases have a head contact time in the range 50–140 ms, and negative and positive linear correlations are observed for the relationships between pedestrian head contact time and WAD/height ratio and vehicle impact velocity, respectively; 70% of cases have a head contact angle floating from 40° to 70°, with an average value of 53°; the pedestrian head contact angles on windscreens (average = 48°) are significantly lower than those on bonnets (average = 60°); the predicted thresholds of HIC, HIP, GAMBIT, RIC, BrIC2011, and BrIC2013 for a 50% probability of AIS3+ head injury risk are 1,300, 60 kW, 0.74, 1,470 × 10⁴, 0.56, and 0.57, respectively. The findings of the current work could provide realistic reference for evaluation of vehicle safety performance focusing on pedestrian protection.

Relationship between head and neck injuries and helmet use in fatal motorcycle and moped crashes in Denmark

Motorcycle- and moped crashes are prevalent in motorised societies and carry a significant risk of serious injury. Whereas helmet use has reduced the frequency and severity of head injuries, the association between helmet use and neck injury risk is less clear. In the present retrospective study, we examined the relationship between helmet use and various types of head and neck injuries resulting from fatal motorcycle and moped crashes during a 20-year period. Eighty-three cases were included of whom 56 were analysed in detail based on their confirmed use/non-use of helmet. Intracranial haemorrhage was the most common finding, followed by CNS disruption and skull fracture. There was a significantly lower prevalence of skull vault fractures and epidural haemorrhage in the helmeted cases. Injuries to the brainstem and cervical spine fracture/dislocation were more common in the helmeted cases, although this was likely a function of higher speeds among motorcycle riders rather than an effect of helmet use per se. Further investigation of these findings require additional detailed information regarding the nature and severity of the crash, as well as helmet use and type, in order to assess non-confounded associations with the anatomical distribution, type and severity of observed head and neck injuries.

Variations in User Implementation of the CORA Rating Metric

The CORA rating metric is frequently used in the field of injury biomechanics to compare the similarity of response time histories. However, subjectivity exists within the CORA metric in the form of user-customizable parameters that give the metric the flexibility to be used for a variety of applications. How these parameters are customized is not always reported in the literature, and it is unknown how these customizations affect the CORA scores. Therefore, the purpose of this study was to evaluate how variations in the CORA parameters affect the resulting similarity scores. A literature review was conducted to determine how the CORA parameters are commonly customized within the literature. Then, CORA scores for two datasets were calculated using the most common parameter customizations and the default parameters. Differences between the CORA scores using customized and default parameters were statistically significant for all customizations. Furthermore, most customizations produced score increases relative to the default settings. The use of standard deviation corridors and exclusion of the corridor component were found to produce the largest score differences. The observed differences demonstrated the need for researchers to exercise transparency when using customized parameters in CORA analyses.

Assessment pedestrian crossing safety using vehicle-pedestrian interaction data through two different approaches: Fixed videography (FV) vs In-Motion Videography (IMV)

A significant portion of pedestrian accidents occurs in the outskirts areas due to the high vehicle speed and lack of safety facilities for pedestrians. Behavioral study on drivers and pedestrians is the key to better understand the causes of pedestrian accidents in order to develop safety models. Despite numerous studies on pedestrian safety based on various roads, outskirt areas have not been considered. Hence, the present study focuses on evaluating the safety of pedestrian crossing in urban and outskirt areas and to determine the differences of drivers and pedestrians' behaviors between these areas through data based on fixed videography (FV) and in-motion videography (IMV). These approaches may lead to an exact analysis of the behavioral differences of road users behaviors from the perspective of pedestrians (FV data) and drivers (IMV data) in urban and outskirts roads. Accordingly, behavioral studies were conducted at urban and outskirts sites through FV as well as IMV using the behavior of 29 participants in the same roads in Babol city, Iran. The gap acceptance model using linear regression and pedestrian crossing probability model using logistic regression for both approaches showed similarity on results in both urban and outskirts roads. Furthermore, behaviors of pedestrians crossing and drivers' yielding on urban and outskirts roads were very similar. Vehicle speed, the distance of vehicle to pedestrian at the possible collision point, size of pedestrian groups, and waiting time before crossing were the most important behavioral differences of pedestrian for choosing a gap acceptance and probability of crossing on various sites through two different approaches. The inference of the models obtained in this study will lead to a better understanding of the behavior of road users for studies on advanced driving assistance systems (ADAS).

A 6-year survey of road traffic accidents in Southwest China: Emphasis on traumatic brain injury

BACKGROUND

The objective of this study is to provide an up-to-date overview of the patterns of injuries, especially traumatic brain injury (TBI) caused by RTAs and to discuss some of the public health consequences.

METHODS

A scientific team was established to collect road traffic accidents occurring between 2013 and 2018 in Chongqing, Southwest China. For each accident, the environment-, vehicle-, and person- variables were analyzed and determined. The overall injury distribution and TBI patterns of four types of road users (driver, passenger, motorcyclist and pedestrian) were compared. The environmental and time distribution of accidents with TBI were shown by bar and pie chart. The risks of severe brain injury whether motorcyclist wearing helmets or not were compared and the risk factors of severe TBI in pedestrian were determined by odds ratio analysis.

RESULTS

This study enrolled 2131 accidents with 2741 persons of all kind of traffic participants, 1149 of them suffered AIS1+ head injury and 1598(58%) died in 7 days. The most common cause of deaths is due to head injury with 714(85%) and 1266(79%) persons died within 2 hours. Among 423 persons suffered both skull fracture and intracranial injury, 102 (24.1%) have an intracranial injury but no skull fractures, while none of the skull fractures without intracranial injury was found. Besides, motorcyclists without a helmet were at higher risks for all the brain injury categories. The risk of pedestrian suffering severe TBI at an impact speed of more than 70 km/h is 100 times higher than that with an impact speed of less than 40 km/h.

CONCLUSION

It is urgently needed to develop a more reliable brain injury evaluation criterion for better protection of the road users. We believe that strengthening the emergency care to head injury at the scene is the most effective way to reduce traffic fatality.

A new approach for uncertainty analysis of ETW Rider Head Injury Reconstruction via Coupling Response Surface and Monte Carlo methodologies

Traditional vehicle accident reconstructions do not take into account all existing uncertainties and may over- or under-estimate the injury risk. The objective of this study was to introduce a new uncertainty analysis method by applying Response Surface-Monte Carlo Methods (RS-MCM) to predict head injury risk in real electric two wheelers (ETW) to vehicle accidents. Vehicle impact velocity ranges in three detailed ETWs accidents (including video records and injury reports) were estimated using direct linear transformation (DLT) or video frame (VF) methods. A response surface methodology (RSM) was used to obtain an approximate model of the each real ETW accident, and a vehicle impact velocity distribution was estimated by applying the Monte Carlo Method (MCM) to the resulting model. If the velocity distribution was in agreement with the initial estimated velocity, the reconstruction quality was deemed acceptable. The injury severity was then assessed using the initial conditions resulting from the range of potential head impact conditions identified in the reconstruction activities. The identified head linear and angular impact velocities were input to finite element analyses to the THUMS Ver4.02 pedestrian head model and resulting in head injury criteria (HIC). The HIC values were further explored using the same RSM method used earlier to establish impact conditions. The distribution of reconstructed AIS levels show good agreement with the injury results from forensic reports. The results illustrated that the RS-MCM enriches the information for head trauma injury mechanisms caused by the vehicle collisions or ground impact.

Influence of Skull Fracture on Traumatic Brain Injury Risk Induced by Blunt Impact

This study is aimed at investigating the influence of skull fractures on traumatic brain injury induced by blunt impact via numerous studies of head-ground impacts. First, finite element (FE) damage modeling was implemented in the skull of the Total HUman Model for Safety (THUMS), and the skull fracture prediction performance was validated against a head-ground impact experiment. Then, the original head model of the THUMS was assigned as the control model without skull element damage modeling. Eighteen (18) head-ground impact models were established using these two FE head models, with three head impact locations (frontal, parietal, and occipital regions) and three impact velocities (25, 35, and 45 km/h). The predicted maximum principal strain and cumulative strain damage measure of the brain tissue were employed to evaluate the effect of skull fracture on the cerebral contusion and diffuse brain injury risks, respectively. Simulation results showed that the skull fracture could reduce the risk of diffuse brain injury risk under medium and high velocities significantly, while it could increase the risk of brain contusion under high-impact velocity.

Characteristics of pedestrian head injuries observed from real world collision data

Head injury is one of the most common injury types in vehicle-to-pedestrian collisions, which leads to death and long-term disabilities. However, detailed analysis of pedestrian head injuries in real world collisions is scarce. Thus the current study used two samples of 120 cases and 184 cases extracted from 1060 pedestrian collision cases captured during 2000-2015 from the GIDAS (German In-Depth-Accident Study) database to investigate the detailed characteristics of AIS2+ pedestrian head injuries. Firstly, the interrelationship between different head injury types (skull fracture, focal brain injury, concussion and diffuse axonal injury (DAI)) was analysed using the sample of 120 cases which each had at least one AIS2+ head injury. Then the influences of impact speed, pedestrian age and car front shape parameters on the injury risk of skull fracture, focal brain injury and concussion were assessed using the logistic regression method, based on the sample of 184 AIS1+ cases where the primary head contact location was within the windscreen glass area. The results show that: skull fractures and focal brain injuries dominate for AIS3+ head injuries and are generally associated with each other; concussion is the most important injury type for AIS2 head injuries and usually occurs in isolation. Further, for head impacts to the windscreen glass area a higher bonnet leading edge helps to reduce concussion odds, and none of the selected car front shape parameters are significant for the odds of skull fracture and focal brain injury, and vehicle impact speed and pedestrian age are insignificant for concussion. These detailed characteristics of pedestrian head injuries provide a basis for future pedestrian head injury prevention strategies with skull fractures and focal brain injuries being the most important injuries to address.

A Computationally Efficient Finite Element Pedestrian Model for Head Safety: Development and Validation

Head injuries are often fatal or of sufficient severity to pedestrians in vehicle crashes. Finite element (FE) simulation provides an effective approach to understand pedestrian head injury mechanisms in vehicle crashes. However, studies of pedestrian head safety considering full human body response and a broad range of impact scenarios are still scarce due to the long computing time of the current FE human body models in expensive simulations. Therefore, the purpose of this study is to develop and validate a computationally efficient FE pedestrian model for future studies of pedestrian head safety. Firstly, a FE pedestrian model with a relatively small number of elements (432,694 elements) was developed in the current study. This pedestrian model was then validated at both segment and full body levels against cadaver test data. The simulation results suggest that the responses of the knee, pelvis, thorax, and shoulder in the pedestrian model are generally within the boundaries of cadaver test corridors under lateral impact loading. The upper body (head, T1, and T8) trajectories show good agreements with the cadaver data in vehicle-to-pedestrian impact configuration. Overall, the FE pedestrian model developed in the current study could be useful as a valuable tool for a pedestrian head safety study.