Cyclists
Cyclist-Car Collisions
Head Injury Tolerances of Road Cyclists in Side Impacts
Relationship to Injury Biomechanics
Methods, Tolerances, and Prevention:
Cyclist fatalities are increasing, although there is an overall decline in pedestrian and occupant fatalities, and head injuries are the most common cause of death. In Japan, head injuries account for 63% of cyclist fatalities, with the primary sources of impact being the ground at 38% and vehicle structures like the windshield frame and A-pillar, the pillar next to the windshield supporting the roof, as shown in Figure 1, at 25% [1]. Similarly, in European studies, head injuries occur at nearly the same rate as leg injuries in severe cases (AIS 3+), with common impact sources including the A-pillar, windshield, and ground.
There can be many different configurations for cyclist-to-vehicle collisions, such as the various combinations of the impact location of the vehicle and the cyclist, as well as the travel direction, as shown in Figure 2 [3]. In this literature review, the main focus will be on side effects due to the availability of information from previous research work. Specifically, we define side impacts relative to the cyclists, where the side of the cyclist is impacted by the front end of vehicles normally, instead of at an angle. This type of collision often occurs when cars turn and hit the cyclists laterally or when cyclists cross in front of moving vehicles. For example, for right-hand traffic, cyclists may perform unsafe left turns and collide with oncoming traffic. Side-impact collisions pose a significant risk of head injuries, mainly due to the enormous impact force from the difference in masses and momentum, as well as the secondary impact when the cyclists get knocked off balance or propelled sideways, causing their head to strike the pavement or the vehicle itself. The geometry/type of the car will also affect the head impact point on the vehicle [1]. High head injury criterion (HIC) with sedans usually occurs on the A-pillar. High HIC values are more prevalent in SUVs where impacts are widely distributed along the A-pillar and the bottom of the windshield/rear end of the hood. Studies have shown relatively high HIC values when the head impacts the lower end of the windshield, which is stiffer compared to other common impact points near the front of the car [1].
Importance
In our overarching research looking into numerous papers on this chosen topic, we found that most of them focused on accident reconstruction through modelling software. These more recent biomechanics studies demonstrate the ability to reconstruct various collision scenarios without having to compromise on key parameters like vehicle speed and collision type [4], thus increasing the accuracy of computational models that help us better understand the injury and impact mechanisms of cyclist-vehicle side-impact crashes.
One study conducted by L. Raslavičius et al. utilized computer simulations via MADYMO to analyze typical injuries sustained by cyclists in side-impact collisions, focusing on the tibia, pelvis, head, and neck. The researchers simulated four crash scenarios involving vehicle speeds of 40 km/h, 50 km/h, and 60 km/h, which are common in urban environments. The scenario we are interested in is the side impacts on cyclists. Figure 3 demonstrates the sequence of events in a crash.
- Accident causation and pre-accidental driving situation
- First impact (when vehicle and cyclist collide)
- Second impact: cyclist slides onto hood
- Third impact: cyclist hits the windshield
Their findings indicated that lower speed collisions typically resulted in minor leg injuries (AIS 1–2), while higher speeds (60 km/h) led to moderate injuries (AIS 3–4), which often required surgical intervention and fixation. Notably, the simulations revealed that pelvic injuries at 50 km/h or 60 km/h were unsurvivable (AIS 6) [4], highlighting the severity of such impacts. It is worth it to note that the speeds at which these tests were conducted are all city speed limits.
Most simulations showed strong agreement with medical/epidemiological data, which can be used to help inform safety policies and legislations, in addition to injury prevention measures for cyclist-related head injuries [4]. Accident reconstruction software like PC-Crash and MADYMO was also extremely important in many of the simulations we found, but has yet to reach the precision of systematic pathological autopsies [5]. Having more studies done in this field using that software can encourage the further improvement of accuracy and ease of modelling in that software. Additionally, a lot of the studies discussed how motor vehicle speeds can affect the Head Injury Criterion (HIC) and Neck Injury Criterion (Nij) [4,6], which can aid traffic control decisions on a municipal or even provincial level (ex: choosing to lower speed limits near bike lanes or installing more safety countermeasures at non signalized intersections) [6]. It is also notable that studies choose to focus on adult males as well as sedans (for ease of modelling), but this has implications for how the results can be used relative to female and child cyclists, as safety measures may not be adequate for them [6].
Relevance
Research also helps to provide supporting evidence for helmet-use awareness and helps to suggest redesigns of passive safety devices such as airbags on the front of the car bonnet, as they can block the driver’s view and lead to more serious injuries for cyclists in low-speed collisions [6]. It is also noteworthy that there is still a large gap in data and research, and more studies are needed to help better inform improvements in the testing of safety standards, ratings, and helmet safety [7]. There remains a gap in technical literature and the effect of legislation on the safety of cyclists [8]. Thus, research remains crucial to bridging the gap between scientific discovery and policy action for improving cyclist safety and road traffic death rates [8]. Results of studies like the ones we discuss in this literature review are useful for a variety of industries, such as vehicle construction and safety equipment industries, which should seek to reduce the severity and frequency of traffic injuries, especially for cyclists [9].
Key Factors Affecting Head Injury Severity
Vehicle Speed
One key connection found was the correlation between vehicle speed and head injury severity. Data from the simulation models done in MADYMO in Raslavičius et al.’s study indicate that vehicle speed and the angle of approach are significantly associated with head injury incidence in cyclist collisions [4]. Fatal injuries among unhelmeted cyclists occurred in cases when vehicle speeds reached 50 km/h or greater [4], and e-scooter riders experienced HIC values exceeding the safety margin of 700 when vehicle speeds surpassed 40 km/h [6]. While our literature review focuses on cyclists, it should be noted that e-scooters experience higher HIC values [6], which also have implications for the speed of vehicles and injury severity. For both cyclists and e-scooter riders, it was found that HIC increased with motor vehicle speed [6].
Comparisons between bicyclists and motorcyclists found that motorcyclists experience higher head peak linear acceleration and impact speeds in vehicle collisions, but cyclists have higher peak angular speed values [10]. Overall, this point was summarized by the fact that the kinetic energy of a car before a crash and a bicycle after impact significantly influence injury severity in cyclist-vehicle crashes [11], with more transferred kinetic energy correlating with more severe injuries [11], hence speed playing a major factor in head injury severity for cyclists.
Bicycle and Car Design Factors
Another key point in the papers reviewed was that resulting injuries in cyclist-vehicle collisions are also influenced by factors such as bicycle seat height, car hood height, and car hood length [5]. Zhang et al. investigated cyclist injury mechanisms in side-impact collisions with small vehicles using PC-Crash, which comes with a simple human body, bicycle models, as well as a vehicle database with most vehicle types in the market [5]. Through collection of various case studies, autopsides, and PC-Crash's reconstruction technology, injury characteristics and biomechanics of cyclists were studied [5]. In the computation aspect specifically, the cyclist model was adjusted to match a 50th percentile Chinese adult male, as the researchers were Chinese and the motivation was based on Chinese statistics [5]. Simulations were conducted in PC-Crash, using real-world parameters derived from 31 side-impact cases to inform average seat height, hood height, and hood length. Vehicle speeds of 45 km/h and 65 km/h were tested, while bicycle speed was held constant at 10 km/h. The seat bag height/hood height ratio was varied by setting seat heights to 80, 84, and 88 cm, while hood height remained fixed [5].
Based on a subsect of the 31 case studies where the vehicle speed was less than 60 km/h, 78.5% had fatal injuries that were caused by the head colliding with the front windshield, whereas the other 21.5% did not collide with the front windshield and received skull fractures from falling [5]. Notably, the majority of fatal windshield collisions leading to skull fracture had a greater ratio of seat height to hood height plus hood length (0.55 ± 0.05) compared to those without windshield damage (0.45 ± 0.01) [5].
In a separate study by Fanta et al., it was found similarly that longer front hoods and greater hood heights on sedans contribute to more severe head injuries than shorter hoods [9]. Using MADYMO simulation software, a validated 50th percentile male pedestrial model was used for the cyclist model [9]. Different bicycle types (mountain, road, and trekking) were used and five various car types were used, including a standard sedan, SUVs, and MPVs [9]. Vehicle speeds were varied (35, 40, and 65 km/h were used for testing), while the cyclists were kept consistent at 15 km/h [9]. For the impact type, it was a side impact where the car collided with the cyclist's side [9].
After running these simulations, it was found that HIC36 was greatest for SUVs, which had the greatest hood length and tallest hood height [9]. When looking at bicycle seat height, it was found that HIC36 values are higher for cyclists with a lower center of gravity due to cyclist posture (trekking posture is upright, whereas road bike posture is the most reclined), highlighting differences in injury risk between trekking and road bike riders [9].
Impact Location
Another point of common exploration was the discussion of impact location: was the cyclist impacting the pavement or the windshield/vehicle, and what caused more serious injury? At lower speeds, head injuries, while less serious, resulted primarily from pavement impacts [12], whereas more serious injuries occurred when striking the vehicle [5]. In a study by Zou et al. injury sources were studied through reconstructing 57 car-electric bicycle accidents in PC-Crash, in addition to using data from injury information of the riders for analysis [12]. It was found that at lower vehicle speeds (less than 30 km/h), more maximum head striking forces were caused by the ground, whereas at greater speeds (greater than 30 km/h), more maximum head striking forces were caused by the vehicle [12]. Additionally, using HIC15 as a means of identifying head injury, it was found that at higher vehicle speeds (greater than 40 km/h), the most common source was from the vehicle, while it was approximately equally from the vehicle and ground at lower vehicle speeds [12].
While road impacts are more frequent, vehicle impacts were correlated with more severe injuries (AIS 2+ or 3+ head injury severity) [5]. Vehicle-bicycle side-impact crashes were also found to produce two head acceleration peaks, whereas e-scooter crashes generate only one [6].
Windshield Impacts
Windshield impacts and how they cause severe head injury were also a common point amongst papers. Nearly 80% of head impacts were found to occur against flat surfaces [7]. Also, windshield impacts were found to pose high risks of skull fracture and diffuse axonal injury [1][13]. Other studies have also found that HIC values increased when the head impacted the lower end of the windshield, which happens to have greater stiffness [1]. However, Katsuhara et al. reproduced car-bicycle collisions using LS-DYNA as a finite element solver and THUMS Version 4 AM50 for the Occupant Model, assuming a midsize male with a height of 1.75 m and a weight of 77.0 kg to further study car-bicycle collisions [1]. FE models were developed to represent a midsize SUV and midsize sedan to compare how car body types would affect cyclist kinematics [1].
They found that head contacts were concentrated around the center of the hood and the windshield glass [1], with 40% of head-vehicle contacts occurring at the windshield glass, regardless of vehicle type. Additionally, it was found that fatal head injuries and skull fractures were more likely to occur due to windshield frame impact rather than windshield glass impact [1], based on HIC15 levels indicating a high risk of skull fracture [1]. This was further validated by data from the accident database used [1]. Furthermore, other studies have also shown windshields as a frequent impact location in cyclist-vehicle collisions, further emphasizing the commonality and severity of head injury caused by windshield impacts [8].
Helmet Use
A study by Bahrololoom et al. focused on exploring statistical analysis and Newtonian Mechanics to understand the effect of speed, mass, and crash angle on cyclist injury severity [11]. In a two-step approach, the Newtonian Mechanics were first applied to develop numerical models estimating the impact force applied to the cyclist. After identifying variables affecting the impact force, a mixed binary logistic regression model was developed to estimate injury severity, taking into account the mass of the car and bicycle, the speed of the car and bicycle prior to and after the collision, restraint use, and the cyclist's age [11]. Validated crash data from the Transport Accident Commision (TAC) was used for the development of the model [11]. While the key results found that the kinetic energy of the car pre-crash and the kinetic energy of the bicycle post-crash were statistically significant in their effect on injury severity of the cyclist, it was shown that helmet use reduced the injury severity of the cyclist [11]. This paper and others emphasized the fact that helmet use effectively reduces injury severity [11], despite some concerns regarding the validity of this claim, which will be explored in the Controversy section.
Strengths
Thanks to the ability to simulate various scenarios with methods such as MADYMO [4] and finite element (FE) simulation, comprehensive accident reconstruction and advanced multibody simulations are possible, and this will only continue to improve.
Literature on side-impact collisions certainly contributes additional understanding to cyclist kinematics, and they are found to be reliable for accident reconstruction [1][4][12]. In particular, Zou et al. use accident reconstruction to contribute a unique insight by analyzing injury sources in car-electric bicycle accidents [12]. To gather more information on the correlation and source of injuries, they reconstructed 57 car-electric bicycle accidents with PC-Crash [12]. They demonstrated that as long as the traces of the accident were explained in the process of the simulations, then these reconstructions were deemed reliable to their standard [12]. Ultimately, they concluded such as injuries in the left leg, femur, and right knee would be from automobiles [12]. Such a result indicates that side-impact collision research is well documented and will continue to collect more knowledge, based on the foundation set by reliable methods like accident reconstruction.
One particular strength of research regarding side impacts is that it can build upon findings in the literature that came before. This establishes a strong purpose and opportunity to reach new information or conclusions. For instance, while many studies provided useful information on the impact response of the cyclist and their skull fracture risks, little information was known about brain injury risk [1]. To measure the risk of DAI, Katsuhara et al. [1] utilized a strain index called the cumulative strain damage measure (CDSM), which measures the cumulative volume of elements with maximum strain over a certain percentage. Simultaneously, they also utilized HIC15 to measure skull fracture risk. A major conclusion they collected was that with higher CDSM values found at certain speeds, the results suggested that high kinetic energy is a necessity for DAI. This research found a new avenue of research where the concept of kinetic energy is important to potential brain injury, along with physical loads for skull injuries.
Side-impact collision literature can then account for nearly any variation in a scenario, via the customizability of simulation methods such as MADYMO and FE. Katsuhara et al. [1] tested various parameters to determine what would contribute to DAI. In their paper, where they simulated FE car-to-bicycle collisions, they not only tested two car body types (sedan and SUV), but also varied parameters such as car speed, bicycle speed, impact direction, and impact position [1]. They noted that the difference in kinematics resulted in the difference in head velocity change during the two car body type tests [1]. Such a direct comparison based on various values of parameters helps to validate those conclusions. Moreover, another strength is that models that reproduce the dynamics of the vehicle and cyclist before the accident can characterize changes in injury-related parameters; while they may not be able to replace physical tests, the testing can inform decisions and policies for protective measures regarding cyclists [8]. Simulation results from a HIII 50th percentile male model, for instance, can be extended to other age and gender groups [8].
Limitations
While car speeds are determined based on the top speed limits for a given situation (such as 50 km/h in Vancouver, BC, as well as the same speed in the European union (EU) along city streets [4]), the cycling speeds of the cyclists themselves are not as well documented. In a lecture by Dr. Peter Cripton regarding vehicle accident reconstruction, the concept of PDOF (Principal Direction of Force) [14] is relevant as the impact on a subject (which in this case is a cyclist) can be a determinant of potential head injury, among other factors.
Additionally, the car, bicycle, and bicyclist models for collision simulation vary across the literature, are simple [8], and there do not seem to be standard models to use. One article analyzed bicycle-car collisions with a greatly simplified car bumper and roof and a Hybrid III dummy MADYMO model [4]. Meanwhile, another article utilized a car-bicycle finite element model for their simulation [5]. There are many methods of simulating car-bicycle collisions beyond those previously mentioned. Along with difficulty in building complex geometric models and the reproduction of accurate damage patterns, these computer models have their limitations for collision modeling and in forensic research [5].
Another factor is that isolating for head injury tolerance is simply not possible due to the contribution of loads from other body parts, as well as the distribution of loads to them. While HIC values are present in the literature [1][4][6] and can, therefore, provide insight into injury tolerances, they cannot be isolated and must be considered with other injury modes in regions other than the head. Moreover, while CDSM was used to estimate the risk of developing DAI, the validity of CDSM has yet to be fully proven [1]. Hence, research that goes into newer and less-explored effects of side-impact collisions on brain injury requires more study and development.
An important consideration is that the majority of the car-to-bicycle vehicle model simulations were performed with the cyclist not wearing a helmet [1][4][6]. While there are articles detailing the use of a helmet to reduce crash severity outcomes for bicyclists [11], the distinction is still important to emphasize, as this section is specific to head injury tolerances and yet is closely related to helmet use.
Simulation models suggest that vehicle speed is a significant factor in the incidence of head injuries during cycling crashes [9]. Cyclists involved in high-impact collisions can sustain various forms of brain injury, including traumatic brain injury (TBI), hematoma (subdural and epidural), concussion, contusion, and diffuse axonal injury [15]. The severity and type of injury depend on impact force and head acceleration, with contusions being among the most common injuries observed in cycling crashes [16]. Through research, concussions frequently occur when impact forces exceed biomechanical thresholds, with 75% of cases resulting from linear accelerations above 96 g’s and rotational accelerations exceeding 7,235 rad/s² [14]. Depending on the severity of the TBI, the cyclist can experience temporary and short-term memory loss to permanent disability. Cyclists who sustain a mild traumatic brain injury (TBI) may experience symptoms such as headaches, dizziness, and short-term memory loss. These symptoms might not appear immediately after a crash, making them a secondary injury—one that develops gradually over time. In more severe cases, a TBI can lead to significant brain damage and permanent disability [17].
These findings emphasize the crucial role of impact mechanics in brain injury risk, emphasizing the need for enhanced protective measures, such as improved helmet design [19,20] and cycling infrastructure to mitigate injury severity. Moreover, head injuries can lead to lower quality of life and are at greatest risk of death from seizures, change in mental/behavioral, or nervous system conditions, and other external causes of injury and poisoning, compared with individuals in the general population of similar age, gender, and race [20]. Simply put, a head injury from a cycling collision can greatly affect one’s life.
Controversy
One main controversy that is relevant to the topic of head injuries in road cyclists is the debate over the effectiveness of helmets in preventing head injury. Helmets, to summarize, work by reducing the potential injury caused by a linear or direct impact to the head by dissipating energy with their foam and shell components [18]. In the argument for helmets, one popular paper by Thompson et al. from 1999 concludes that helmets are effective in preventing head and facial injury for cyclists; additionally, helmets were concluded to be effective for people of all ages, including those involved in motor vehicle crashes [18]. Furthermore, helmets are more cost-effective when it comes to healthcare costs; those wearing helmets had significantly lower median hospitalization costs compared to those not wearing helmets [19].
With recent advances in helmet technologies, such as MIPS (Multi-direction Impact Protection System) and AIM (Angular Impact Mitigation), there is a clear shift in focus towards angled impacts, which are more representative of common, real-world crashes [21]. Unlike traditional helmets designed primarily to mitigate linear forces, MIPS integrates a low-friction layer against the head inside the helmet, allowing the helmet to move during oblique impacts [21]. This reduces the sudden rotational forces that contribute to brain injuries [21]. Similarly, AIM technology utilizes an engineered shell structure and internal padding to dissipate rotational energy and implement crush-style measures [22] similar to what you would see in a car crash. This addresses the limitations of conventional helmet designs that do not adequately protect against angular acceleration [21, 22]. These advancements reflect a growing recognition of rotational acceleration as a significant contributor to traumatic brain injury [22] and that helmet standards and technology should evolve accordingly. Nonetheless, it is still supported that helmets are effective in the prevention of head injuries [19], despite many older or commonplace helmet designs lacking the consideration of angular impact and acceleration [22].
Conversely, some believe helmets do not have enough evidence to support the claim that they are effective at head injury prevention. Thompson et al. only used case-control studies of cyclists who were already in the hospital with serious brain injuries, leading to critics accusing the paper of being biased [19]. Additionally, reviewers criticized the lack of consideration of confounding factors such as differences in risk-taking behaviour [23]. Although people would expect those who wear helmets to take safety more seriously, it has been suggested (with no supporting evidence) that helmets cause people to take more risks due to a false sense of security [23]. Observational studies regarding time trends of head injury show that there is a positive trend with increasing helmet use and declining rates of injury [23]. While randomized controlled trials would provide stronger evidence to support the use of helmets, it would be unethical and impractical to conduct such a study due to the large population of participants needed and the ethics of providing some participants with helmets and others without, as the risk of injury is not ethical [23]. It is also believed by some that for children specifically, helmets increase the chance of cervical spine injuries due to the head-neck-helmet biomechanics from the helmet weight that could potentially exert forces on the neck [19]. However, this has been disproven by two meta-analyses that reported no increased risk of cervical spine injury due to helmet use [19].
This controversy is important to our topic at hand, as the number of preventable injuries for cyclists has increased over the past ten years [24]. Rates of helmet use remain low despite substantial evidence supporting helmet effectiveness [19]. It is crucial to acknowledge that accessibility may be an issue in achieving safety and injury prevention for cyclists. A multivariable analysis in 2016 found that helmet use decreased among lower-income, non-white, Hispanic populations among children 10-14 years of age [19]. Additionally, it was found that Black children and children with public insurance were less likely to be helmeted at the time of injury [19]. This discrepancy should be noted for future consideration in helmet legislation and education for those in these marginalized groups to ensure that safety and injury prevention are accessible for everyone. Legislation is effective in increasing helmet use [19], and ensuring that the population remains educated on this topic and that misinformation is cleared up is crucial to ensuring community safety.
Future Research Priorities
Despite incredible advances in simulations of car-bicycle collisions, results have not explored diverse populations, with the 50th percentile male being a baseline due to its tenure as a reliable and widely used dummy [4]. A future research avenue can be the extension of simulation results from the 50th percentile male model to different age and gender groups [8], which can include a special emphasis on injuries to women, children, and the elderly [4]. Additionally, while there has been research documented already for other impact scenarios, another future focus can be the modelling of these situations [1][11]. Other such scenarios can include collisions to the back of the cyclist [8] or even head-on collisions. It is feasible that future work can include comparing injury differences among cyclists against other regions of the body with the development of more advanced models and software [10].
The information from the literature can inform organizations such as NHTSA and levels of government to implement road safety laws to improve the safety of cyclists and cars alike. Depending on the results, which may be proven to be true or false, some inferences can be made with corresponding law changes. It is also possible for the literature to inform changes made to the design of the bicycles themselves if the changes render the cyclists safer on the road. Of course, the literature will continue to inform the design and importance of wearing helmets, which is an ongoing controversy among everyday citizens [19].
For more physical testing, the use of ATDs could be a potential direction to validate model simulations. Given how expensive ATDs can be, the literature will require more robust results to warrant the development of ATDs for cycling tests. A workaround to this could be the use of non-instrumented test dummies with wearable technologies, such as those along the wrist or ankle, for collecting data in components like an accelerometer. This could potentially be useful if the physical test is looking at one aspect of a potential region for injury, such as the neck.
References
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