Bicycle crash frequency modeling across different crash severities using a random-forest-based Shapley Additive explanations approach

Statistical modeling and data-driven studies on bicycle accidents are widespread, however, explanations of the underlying mechanisms remain limited, particularly regarding the impact of key risk factors on the bicycle crash frequency across different crash severities. This study aims to examine the effects of various risk factors on the frequency of bicycle crashes using Random Forest and Shapley Additive Explanations (RF-SHAP), taking into account the different crash severity levels. Data from three years of London crash data (2017 to 2019) is utilized. Population demographics, land use, road infrastructure, and traffic flows, are collected in Greater London. In addition to providing superior predictive accuracy, our proposed method identified critical risk factors at different levels of severity associated with bicycle crashes. The distinct contribution of this study is the identification of the primary factors influencing the severity of bicycle collisions in London through the use of RF-SHAP. The study quantifies both the main and interactive effects of various severity risk factors on bicycle collisions. Results suggest that the proportion of building areas and population density are most critical to bicycle crash numbers in different severity levels. Also, the interaction effects of the risk factors on bicycle crashes are revealed. Specifically, results reveal a negative correlation between traffic flow and overall bicycle crash frequency when the average road network connectivity is below 2.25. After controlling the population density, the proportion of residential areas shows a three-stage pattern of influence on the slight injury crash frequency. Furthermore, a boundary value of 6.3 is identified for the safety impact of road density on fatal and severely-injured bicycle crashes. Study findings should provide insights into cost-effective safety countermeasures for bicycle infrastructures, traffic controls, and safety education. Bicycle safety can be improved through these measures over the long term.

Bicycle-related accidents in Rome: Investigating clinical patterns, demographics, injury contexts, and health outcomes for enhanced public safety

INTRODUCTION

This study aims to analyze the clinical characteristics, demographic features, and injury circumstances of patients admitted to the Emergency Department (ED) at Fondazione Policlinico Universitario A. Gemelli (IRCCS) in Rome, Italy, due to bicycle accidents.

METHODS

Data on clinical characteristics, accident timing, injury circumstances, and helmet use were collected for ED patients involved in bicycle accidents from January 2019 to December 2022. Subsequently, Abbreviated Injury Scale codes of all diagnoses were recorded and the Injury Severity Score was calculated.

RESULTS

Over the study period, 763 patients were admitted to the ED following bicycle accidents, with a 0.3 % fatality rate and a 30.4 % frequency of multitrauma. Multivariate analysis revealed that collisions with other vehicles increased trauma severity and the risk of ICU admission. Conversely, helmet use was associated with reduced severity of head trauma and a lower likelihood of ICU admission. Notably, toxicological investigations were not conducted for any ED-admitted patients.

CONCLUSIONS

Although a low mortality rate and a low incidence of multi-trauma have been shown in comparison to other nations, it is necessary to adopt prevention strategies like safety devices, more cycle paths, and better infrastructures on the one hand, and stricter laws on the other. It is essential to require toxicological testing in Italy for all accidents involving this means of transport, and to make helmet use compulsory for all ages.

Estimated prevalence of helmet use while bicycling, rollerblading, and skateboarding among middle school students in selected U.S. States- Youth Behavior Risk Survey, 2013-2019

BACKGROUND

Helmet use helps prevent severe and fatal head and brain injuries from bicycle, rollerblade, and skateboard crashes. This study explores the prevalence of self-reported helmet use among middle school students while bicycling, skateboarding, and rollerblading.

METHODS

Data from the Middle School Youth Risk Behavior Survey (YRBS) for selected states were analyzed. Self-reported prevalence (frequency) of helmet use while bicycling, rollerblading, or skateboarding and other variables (sex, grade level, and race/ethnicity) are reported.

RESULTS

The overall prevalence of rarely or never wearing a helmet while bicycling among middle school students in selected states was 68.6%; decreasing from 71.7% in 2013 to 67.1% in 2019. The overall prevalence of rarely or never wearing a helmet while rollerblading or skateboarding in middle school students in selected states was 74.6%; decreasing from 76.4% in 2013 to 73.5% in 2019. Students in 7th and 8th grade and students of non-Hispanic race/ethnicity had significantly higher odds of rarely or never wearing a helmet while bicycling or while rollerblading and skateboarding than students in 6th grade and non-Hispanic White students.

CONCLUSIONS

While helmet use among middle school students improved over time, overall helmet use during bicycling, rollerblading, and skateboarding remained low. These estimates illustrate the continued call for universal implementation of helmet use efforts among kids using established strategies.

PRACTICAL APPLICATIONS

Future research on helmet use among youth who rollerblade and skateboard, as well as multi-pronged efforts to promote helmet use among middle schoolers who bicycle, skateboard, and rollerblade (inclusive of education, helmet distribution, and social marketing techniques, as well as the provision of helmets at no-cost) may be beneficial for addressing perceived risks for injury and other barriers.

Pedestrian- and bicyclist-involved crashes: Associations with spatial factors, pedestrian infrastructure, and equity impacts

INTRODUCTION

We analyze and compare the factors that influence the fatality of pedestrian and bicyclist involved crashes in New Jersey using available police-reported crash data between 2016 and 2020. Under three percent of crashes involve non-motorists statewide, but these account for about one third of all traffic fatalities in the state.

METHODS

Our analysis is broken down into five parts: we (1) analyze the relationship between minority and low-income communities and non-motorist involved crashes; (2) identify spatial differences between non-motorist involved crashes and non-motorist involved fatal crashes; (3) compare the factors affecting fatal pedestrian crashes in New Jersey and in four counties in southern New Jersey for which we have data on pedestrian infrastructure; (4) compare the factors affecting fatal pedestrian crashes and fatal cyclist crashes in New Jersey; and, (5) discuss priority areas for improving safety.

RESULTS

Crashes occur disproportionately more often in low-income communities. Moreover, we find that crashes are less likely to be geocoded if they take place in low-income and minority areas, a concerning finding considering that geocoded crashes are of paramount importance in identifying specific corridors for improvement. Light conditions, non-motorist age, posted speed, and vehicle type are significant factors influencing the fatality of non-motorist involved crashes. The proximity to a crosswalk or sidewalk is associated with decreased risk of a fatal crash for pedestrians. Cyclist crashes in low-income neighborhoods were more likely to be fatal - a finding that we attribute to lower access to bicycle facilities in low-income areas.

CONCLUSIONS

We conclude with countermeasures, including a call for better data collection.

The human impact of commercial delivery cycling injuries: a pilot retrospective cohort study

BACKGROUND

Commercial delivery cyclists represent a uniquely vulnerable and poorly understood road user. The primary aim of this study was to pilot whether cycling injuries could be categorised as either commercial or non-commercial using documentation entered into routine hospital medical records, in order to determine the feasibility of conducting a large cohort study of commercial cycling injuries in the future. A secondary aim was to determine which key demographic, incident and injury characteristics were associated with commercial versus non-commercial cycling injuries in emergency.

METHODS

Pilot retrospective cohort study of adults presenting to an acute public hospital emergency department between May 2019 and April 2020 after sustaining a cycling-related injury. Multinomial logistic regression was used to examine the demographic, incident and injury characteristics associated with commercial compared to non-commercial cycling.

RESULTS

Of the 368 people presenting to the emergency department with a cycling-related injury, we were able to categorise 43 (11.7%) as commercial delivery cyclists, 153 (41.6%) as non-commercial cyclists and the working status of 172 (46.7%) was unable to be confirmed. Both commercial and unconfirmed cyclists were more likely to be younger than non-commercial cyclists. Compared to non-commercial cyclists, commercial cyclists were 11 times more likely to speak a language other than English (AOR 11.3; 95% CI 4.07-31.30; p<0.001), less likely to be injured from non-collision incidents than vehicle collisions (AOR 0.36; 95% CI 0.15-0.91; p=0.030) and were over 13 times more likely to present to the emergency department between 8.00pm and 12.00am compared to the early morning hours (12.00 to 8.00am) (AOR 13.43; 95% CI 2.20-82.10; p=0.005).

CONCLUSIONS

The growth of commercial cycling, particularly through online food delivery services, has raised concern regarding commercial cyclist safety. Improvements in the recording of cycling injury commercial status is required to enable ongoing surveillance of commercial cyclist injuries and establish the extent and risk factors associated with commercial cycling.

Characteristics of Cyclist Crashes Using Polytomous Latent Class Analysis and Bias-Reduced Logistic Regression

Although the number of cyclist crashes is decreasing in Japan, the fatality rate is not. Thus, reducing their severity is a major challenge. We used a polytomous latent class analysis to understand their characteristics and bias-reduced logistic regression to analyze their severity. Specifically, 90,696 combinations and 139,955 cyclist accidents were divided into 17 classes. The variable contributing the most to the classification was the crash location. Common fatality risks included older age groups and rural areas, whereas other factors differed among crash locations. Median strips, stop signs, and boundaries between the sidewalk and roadway affected the severity of crashes at intersections. Moreover, the existence of a median strip, collision partner, and time period affected the severity of crashes between intersections. On the sidewalks, the fatality risk was higher when the front part of the bicycle was subjected to the collision.

Severe and Fatal Cycling Crash Injury in Britain: Time to Make Urban Cycling Safer

Pedal cycling is advocated for increasing physical activity and promoting health and wellbeing. However, whilst some countries have achieved zero cyclist deaths on their roads, this is not the case for Great Britain (GB). A retrospective cross-sectional analysis was conducted of STATS19 cyclist crash data, a dataset of all police-reported traffic crashes in GB. Information about crash location, casualty, driver and vehicles involved were included as predictors of casualty severity (fatal or severe vs. slight). Sixteen thousand one hundred seventy pedal cycle crashes were reported during 2018. Severe or fatal cyclist crash injury was associated with increasing age of the cyclist (35-39 years, OR 1.38, 95% CI 1.11 to 1.73; 55-59 years, OR 1.73, 95% CI 1.35 to 2.2; 70 years and over, OR 2.87, 95% CI 2.12 to 3.87), higher road speed limits (50 MPH OR 2.10, 95% CI 1.43 to 3.07; 70 MPH OR 4.12, 95% CI 2.12 to 8.03), the involvement of goods vehicles (OR 2.08, 95% CI 1.30 to 3.33) and the months of May and June (OR 1.34 to 1.36, 95% CI 1.06 to 1.73). Urban planning that includes physical separation of pedal cyclists from other road users, raising awareness around the risks from goods vehicles and reducing road speed should be the urgent focus of interventions to increase the benefits and safety of cycling.

Comparing the cycling behaviours of Australian, Chinese and Colombian cyclists using a behavioural questionnaire paradigm

Cyclists are vulnerable road users and face disproportionately high rates of road trauma, especially in low- and middle-income countries. Behaviour of road users is a system outcome, and thus studying cyclist behaviours can identify problems in the whole road transport system and assist in generating long-lasting, cost-effective solutions to promote cyclist safety. This study aims to investigate the similarities and disparities of cycling behaviour among countries with different income levels and cycling prevalence, and the relationships among cyclist demographic characteristics, behaviours and crash involvement. It applies the Cycling Behaviour Questionnaire (CBQ) to measure the behaviour of cyclists in three countries: Australia (high-income, emerging cycling country), China (mid-income, traditional cycling country) and Colombia (low-income, emerging cycling country). A total of 1094 cyclists (Australia 347, China 368, and Colombia 379) with a mean age of 31.8 years completed the online questionnaire. Confirmatory Factor Analysis was used to examine the factorial structure of the CBQ. The results showed that the three-factor CBQ (i.e., Traffic Violations, Errors and Positive Behaviours) had a stable and uniform factorial structure across all three countries. Cyclists reported more frequent positive behaviours than risky behaviours across all three countries. The Australian cyclists reported more positive behaviours and fewer violations than cyclists from the other countries, which is likely to reflect the road environment, policy and enforcement differences. Male cyclists tended to engage in more risky riding than females and reported more crash involvement. Older cyclists reported less risky riding and less involvement in crashes than younger cyclists. Cyclists who rode more often reported more frequent risky riding. The study helps understand the prevalence of risky/positive cyclist behaviours among countries and provides insights for developing systematic countermeasures to improve cycling environment and cyclist safety.

Hamiltonian Monte Carlo with Random Effect for Analyzing Cyclist Crash Severity

Vulnerable traffic users, such as bikers and pedestrians, account for a significant number of fatalities on the roadways. Extensive research has been conducted in the literature review to identify factors to those crashes. Studying factors to those crashes is especially important in the Western state in the US, due to one of the highest fatality rates in the nation and its unique geographic conditions. The first step in identifying factors to the severity of cyclist crashes is to find the underlying factors to that type of crash, while accounting for the heterogeneity in the dataset. Various techniques such as mixed parameter or mixed effect models have been employed in the literature to account for the heterogeneity of the dataset. In the mixed effect model, often the random effect parameter has been assigned subjectively, and based on some attributes and engineering intuitions. Those assignments are expected to account for the heterogeneity in the dataset and enhancement of the model fit. However, a question might arise whether those factors could account for an optimum amount of the heterogeneity in the dataset. A more reasonable way might be to let the algorithm such as the finite mixture model (FMM) to identify those clusters based on parameters of the Gaussian model, means and covariance matrices of the dataset, and allocate each observation to the related clusters. Thus, in this study, to capture optimum amount of heterogeneity, first we implemented the finite mixture model in the context of maximum likelihood, due the label switching issue of the method in the context of the Bayesian method. After assignment of the parameters to the observation, the main method of Hamiltonian Monte Carlo (HMC) with random effect was implemented. The results highlighted a significant improvement in the model fit, in terms of Widely Applicable Information Criterion (WAIC). The results of this study highlighted factors such as older biker age, increased number of lanes, nighttime travelling, increased posted speed limit and driving while under emotional conditions are some factors contributing to an increased severity of bikers’ crash severity. Extensive discussion has been made regarding the methodological algorithms and model parameters estimations.

Individual and contextual factors associated with bicyclist injury severity in traffic incidents between bicyclists and motorists in Chile

Bicyclists are vulnerable road users who risk incurring severe injuries from traffic incidents involving motorists. However, the prevalence of severe bicycle injuries varies across countries and is not well-documented in Latin American countries. Studies from developed countries outside of Latin America have shown that individual and contextual factors are associated with severe injuries incurred by bicyclists in road traffic incidents with motorists, but it is not clear whether these factors are the same as those incurred by Latin American bicyclists. Moreover, most studies on bicyclist-motorist traffic incidents have treated injury severity as a binary variable for analysis although injuries range widely in severity. The aims of this study were to determine the prevalence of bicycle injuries from incidents between motorists and bicyclists in Chile and examine the associations between individual and contextual factors and bicyclist injury severity, treated as an ordinal outcome variable, in these incidents. Data on road traffic incidents between bicyclists and motorists from the 2016 Traffic Accident of Bicycle Riders and Consequences database of the Chilean Transport Ministry were analysed. Multilevel mixed-effects ordinal regression models were used to examine associations. In total, 81.2 % of 4093 traffic incidents between bicyclists and motorists resulted in nonfatal injuries to bicyclists and another 2.3 % resulted in fatalities. Most incidents involved collisions (84.3 %), and most were due to a motorist being distracted while driving (50.4 %). Severe bicyclist injuries were more likely when the incident involved a stationary cyclist who was struck, a collision between a moving bicycle and a moving motor vehicle, or an overturning motor vehicle striking a bicyclist (p < 0.001). Other factors included the motorist driving under the influence of alcohol (p = 0.05), the incident taking place in a mid-size community (p = 0.04), the incident occurring between 7:00 pm and 4:59 am (p < 0.01), and the injured bicyclist being under 18 years or 45+ years of age (p < 0.05). These findings suggest the need for educational programs that promote safe driving behaviour in the presence of bicyclists, better enforcement of laws and higher penalties for distracted or drunk driving, and provision of high-quality exclusive bicyclist infrastructure to address the vulnerability of the youngest and oldest bicyclists on shared roads, particularly in mid-size communities, and to provide better lighting on bikeways for evening bicycling, to reduce the high incidence of severe bicyclist injuries in motorist-bicyclist incidents.

Enhancing trauma registries by integrating traffic records and geospatial analysis to improve bicyclist safety

BACKGROUND

Trauma registries are used to identify modifiable injury risk factors for trauma prevention efforts. However, these may miss factors useful for prevention of bicycle-automobile collisions, such as vehicle speeds, driver intoxication, street conditions, and neighborhood characteristics. We hypothesize that (GIS) analysis of trauma registry data matched with a traffic accident database could identify risk factors for bicycle-automobile injuries and better inform injury prevention efforts.

METHODS

The trauma registry of a US Level I trauma center was used retrospectively to identify bicycle-motor vehicle collision admissions from January 1, 2010, to December 31, 2018. Data collected included demographics, vitals, injury severity scores, toxicology, helmet use, and mortality.Matching with the Statewide Integrated Traffic Records System was done to provide collision, victim and GIS information. The GIS mapping of collisions was done with census tract data including poverty level scoring. Incident hot spot analysis to identify statistically significant incident clusters was done using the Getis Ord Gi* statistic.

RESULTS

Of 25,535 registry admissions, 531 (2.1%) were bicyclists struck by automobiles, 425 (80.0%) were matched to Statewide Integrated Traffic Records System. Younger age (odds ratio [OR], 1.026; 95% confidence interval [CI], 1.013-1.040, p < 0.001), higher census tract poverty level percentage (OR, 0.976; 95% CI, 0.959-0.993, p = 0.007), and high school or less education (OR, 0.60; 95 CI, 0.381-0.968; p = 0.036) were predictive of not wearing a helmet. Higher census tract poverty level percentage (OR, 1.019; 95% CI, 1.004-1.034; p = 0.012) but not educational level was predictive of toxicology positive-bicyclists in automobile collisions. Geographic information systems analysis identified hot spots in the catchment area for toxicology-positive bicyclists and lack of helmet use.

CONCLUSION

Combining trauma registry data and matched traffic accident records data with GIS analysis identifies additional risk factors for bicyclist injury. Trauma centers should champion efforts to prospectively link public traffic accident data to their trauma registries.

LEVEL OF EVIDENCE

Prognostic and Epidemiological, level III.

Analysis of the severity of vehicle-bicycle crashes with data mining techniques

INTRODUCTION

Although cycling is increasingly being promoted for transportation, the safety concern of bicyclists is one of the major impediments to their adoption. A thorough investigation on the contributing factors to fatalities and injuries involving bicyclist.

METHOD

This paper designs an integrated data mining framework to determine the significant factors that contribute to the severity of vehicle-bicycle crashes based on the crash dataset of Victorian, Australia (2013-2018). The framework integrates imbalanced data resampling, learning-based feature extraction with gradient boosting algorithm and marginal effect analysis. The top 10 significant predictors of the severity of vehicle-bicycle crashes are extracted, which gives an area under ROC curve (AUC) value of 0.8236 and computing time as 37.8 s.

RESULTS

The findings provide insights for understanding and developing countermeasures or policy initiatives to reduce severe vehicle-bicycle crashes.

Equestrian Road Safety in the United Kingdom: Factors Associated with Collisions and Horse Fatalities

Over 60% of UK horse riders report having experienced a road-related near-miss or accident. The aim of this study was to describe horse-related road incidents (n = 4107) reported to the British Horse Society (2010-2020) and to identify factors associated with higher odds of collisions with another vehicle and horse fatalities using multivariable logistic regression modelling. Drivers passed the horse too closely in 84.2% of incidents while road rage and speeding were reported in 40.3% and 40.1% of incidents, respectively. Close passing distance alone (odds ratio [OR] 18.3, 95% confidence interval [CI] 6.5, 51.6) or in combination with speeding (OR 4.4, CI 1.7, 11.7) was associated with higher collision odds compared to speeding alone. Speeding was, however, associated with higher horse fatality odds (OR 2.3, CI 1.2, 4.6). Wearing high visibility clothing reduced odds of collision (OR 0.2, CI 0.1, 0.4). A fatal injury to a horse was almost 12 times as likely to result in severe to fatal rider/handler injury. Loose horses contribute significantly to road-related horse fatalities. Driver behaviour of how to pass horses safely on UK roads needs further improvement and will help reduce the risk of collisions and horse and human fatalities.

Pedestrians under influence (PUI) crashes: Patterns from correspondence regression analysis

INTRODUCTION

Alcohol-related impairment is a key contributing factor in traffic crashes. However, only a few studies have focused on pedestrian impairment as a crash characteristic. In Louisiana, pedestrian fatalities have been increasing. From 2010 to 2016, the number of pedestrian fatalities increased by 62%. A total of 128 pedestrians were killed in traffic crashes in 2016, and 34.4% of those fatalities involved pedestrians under the influence (PUI) of drugs or alcohol. Furthermore, alcohol-PUI fatalities have increased by 120% from 2010 to 2016. There is a vital need to examine the key contributing attributes that are associated with a high number of PUI crashes.

METHOD

In this study, the research team analyzed Louisiana's traffic crash data from 2010 to 2016 by applying correspondence regression analysis to identify the key contributing attributes and association patterns based on PUI involved injury levels.

RESULTS

The findings identified five risk clusters: intersection crashes at business/industrial locations, mid-block crashes on undivided roadways at residential and business/residential locations, segment related crashes associated with a pedestrian standing in the road, open country crashes with no lighting at night, and pedestrian violation related crashes on divided roadways. The association maps identified several critical attributes that are more associated with fatal and severe PUI crashes. These attributes are dark to no lighting, open country roadways, and non-intersection locations. Practical Applications: The findings of this study may be used to help design effective mitigation strategies to reduce PUI crashes.

Differences in severity of injuries between motorcyclist and bicyclist fatalities in single vehicle collisions

To understand the injury severity of bicyclists and motorcyclists in single vehicle collisions, we performed a retrospective analysis of forensic autopsy cases of 25 motorcyclists and 14 bicyclists performed from 1999 to 2018. Collision details, injury characteristics, and involvement of alcohol were examined. The injury severity between bicyclists and motorcyclists was compared. The average age of victims was 62.3 years. All motorcyclists and no bicyclists wore a helmet. Motorcyclists had more severe injuries than bicyclists (mean Injury Severity Score of 42 and 27, respectively). The motorcyclists had more severe chest injuries but fewer severe facial injuries than bicyclists, owing to the difference in collision velocity or rate of helmet use (p < 0.05). Alcohol was present in the blood of 52.0% of bicyclists but no motorcyclists. The mean blood alcohol concentration of these bicyclists was 1.59 mg/mL. The bicyclists under the influence of alcohol had more severe injuries to neck and upper extremities than non-drunken bicyclists (p < 0.05). Our results may be useful for determining the cause of death and reconstructing the mechanisms of fatal injuries in bicyclists and motorcyclists.

Identifying high-risk built environments for severe bicycling injuries

INTRODUCTION

This study is aimed at filling part of the knowledge gap on bicycling safety in the built environment by addressing two questions. First, are built environment features and bicyclist injury severity correlated; and if so, what built environment factors most significantly relate to severe bicyclist injuries? Second, are the identified associations varied substantially among cities with different levels of bicycling and different built environments?

METHODS

The generalized ordered logit model is employed to examine the relationship between built environment features and bicyclist injury severity.

RESULTS

Bicyclist injury severity is coded into four types, including no injury (NI), possible injury (PI), evident injury (EI), and severe injury and fatality (SIF). The findings include: (a) higher percentages of residential land and green space, and office or mixed use land are correlated with lower probabilities of EI and SIF; (b) land use mixture is negatively correlated with EI and SIF; (c) steep slopes are positively associated with bicyclist injury severity; (d) in areas with more transit routes, bicyclist injury is less likely to be severe; (e) a higher speed limit is more likely to correlate with SIF; and (f) wearing a helmet is negatively associated with SIF, but positively related to PI and EI. Practical applications: To improve bicycle safety, urban planners and policymakers should encourage mixed land use, promote dense street networks, place new bike lanes in residential neighborhoods and green spaces, and office districts, while avoiding steep slopes. To promote bicycling, a process of evaluating the risk of bicyclists involving severe injuries in the local environment should be implemented before encouraging bicycle activities.

Injuries related to bicycle accidents: an epidemiological study in The Netherlands

Background

This study aims to analyze the incidence and outcomes of bicycle-related injuries in hospitalized patients in The Netherlands.

Methods

Bicycle accidents resulting in hospitalization in a level-I trauma center in The Netherlands between 2007 and 2017 were retrospectively identified. We subcategorized data of patients involved in a regular bicycle, race bike, off-road bike or e-bike accident. The primary outcomes were mortality rate and incidence of multitrauma. Secondary outcomes were differences between bicycle subcategories. Independent risk factors were identified using multivariable logistic regression. All variables with a p value < 0.20 in univariable analysis were entered in multivariable analysis.

Results

We identified 1986 patients. The mortality rate after emergency room admission was 5.7%, and 41.0% were multitraumas. A higher age, multitrauma and cerebral haemorrhages were independent risk factors for in hospital mortality. Independent risk factors found for multitrauma were a higher age, two-sided trauma, e-bike accidents and cerebral haemorrhage.

Conclusion

Bicycle accidents resulting in hospitalization have a high mortality rate. Furthermore, a high incidence of multitrauma, fractures and cerebral haemorrhages were found. Considering the increasing incidence of bicycle accident victims needing hospital admission, new and more efficient prevention strategies are essential.

Distraction of cyclists: how does it influence their risky behaviors and traffic crashes?

Background

Undisputedly, traffic crashes constitute a public health concern whose impact and importance have been increasing during the past few decades. Specifically, road safety data have systematically shown how cyclists are highly vulnerable to suffering traffic crashes and severe injuries derived from them. Furthermore, although the empirical evidence is still very limited in this regard, in addition to other human factors involved in cycling crashes, distractions while cycling appear to be a major contributor to the road risk of cyclists.

Objectives

The main objectives of this study were, first, to explore the prevalence and trends of cycling distractions within an international sample of bike users, and second, to determine the influence of such distractions on road crashes suffered by cyclists, simultaneously considering the explanatory role of risky behaviors (errors and traffic violations) as potentially mediating variables between cycling distractions and traffic crashes.

Methods

For this cross-sectional study, we analyzed the data obtained from 1,064 cyclists—61.2% male and 38.8% female—from 20 different countries, who answered an on-line questionnaire on cycling-related features, habits, behaviors and accidents.

Results

The prevalence of different cycling distractions oscillated between 34.7% and 83.6%. The most common distractions were those related to the behavior of other users, physical elements of the road, weather conditions and phone calls. Age trends and differences were also found, thus establishing a positive association between age and distractibility during cycling. Furthermore, the effect of distractions on traffic crashes of cyclists was significant when tested together with age, risk perception and risky behaviors on the road.

Conclusion

The results of this study support the hypotheses that distractions have a major prevalence among bike users, and that they play a significant role in the prediction of the traffic crash rates of cyclists, through the mediation of risky behaviors.

Bicycle helmets - To wear or not to wear? A meta-analyses of the effects of bicycle helmets on injuries

A meta-analysis has been conducted of the effects of bicycle helmets on serious head injury and other injuries among crash involved cyclists. 179 effect estimates from 55 studies from 1989-2017 are included in the meta-analysis. The use of bicycle helmets was found to reduce head injury by 48%, serious head injury by 60%, traumatic brain injury by 53%, face injury by 23%, and the total number of killed or seriously injured cyclists by 34%. Bicycle helmets were not found to have any statistically significant effect on cervical spine injury. There is no indication that the results from bicycle helmet studies are affected by a lack of control for confounding variables, time trend bias or publication bias. The results do not indicate that bicycle helmet effects are different between adult cyclists and children. Bicycle helmet effects may be somewhat larger when bicycle helmet wearing is mandatory than otherwise; however, helmet wearing rates were not found to be related to bicycle helmet effectiveness. It is also likely that bicycle helmets have larger effects among drunk cyclists than among sober cyclists, and larger effects in single bicycle crashes than in collisions with motor vehicles. In summary, the results suggest that wearing a helmet while cycling is highly recommendable, especially in situations with an increased risk of single bicycle crashes, such as on slippery or icy roads.

Infrastructural and Human Factors Affecting Safety Outcomes of Cyclists

The increasing number of registered road crashes involving cyclists during the last decade and the high proportion of road crashes resulting in severe injuries and fatalities among cyclists constitutes a global issue for community health, urban development and sustainability. Nowadays, the incidence of many risk factors for road crashes of cyclists remains largely unexplained. Given the importance of this issue, the present study has been conducted with the aim of determining relationships between infrastructural, human factors and safety outcomes of cyclists. Objectives: This study aimed, first, to examine the relationship between key infrastructural and human factors present in cycling, bicycle-user characteristics and their self-reported experience with road crashes. And second, to determine whether a set of key infrastructural and human factors may predict their self-reported road crashes. Methods: For this cross-sectional study, a total of 1064 cyclists (38.8% women, 61.2% men; M = 32.8 years of age) from 20 different countries across Europe, South America and North America, participated in an online survey composed of four sections: demographic data and cycling-related factors, human factors, perceptions on infrastructural factors and road crashes suffered. Results: The results of this study showed significant associations between human factors, infrastructural conditions and self-reported road crashes. Also, a logistic regression model found that self-reported road crashes of cyclists could be predicted through variables such as age, riding intensity, risky behaviours and problematic user/infrastructure interactions. Conclusions: The results of this study suggest that self-reported road crashes of cyclists are influenced by features related to the user and their interaction with infrastructural characteristics of the road.