Exposure measurement in bicycle safety analysis: A review of the literature

Sex Differences in Mountain Bike Accidents in Austria from 2006 to 2018: A Retrospective Analysis

Woyke, Simon, Anja Hütter, Christopher Rugg, Willi Tröger, Bernd Wallner, Mathias Ströhle, and Peter Paal. Sex differences in mountain bike accidents in Austria from 2006 to 2018: a retrospective analysis. High Alt Med Biol. 25:89-93, 2024. Introduction: Mountain biking is becoming increasingly popular, and mountain bike (MTB) accidents are on the rise. The aim of this study was to assess sex differences in mountain biking accidents in the Austrian Alps. Methods: This retrospective study includes all MTB accidents in Austria from 2006 to 2018. Data were collected by Alpine Police officers and recorded in a national digital registry. Results: The accidents involved 5,095 mountain bikers (81% men and 19% women). The number of MTB accidents rose markedly from 208 in 2006 to 725 in 2018. Men wore a helmet more often than did women (95% vs. 92%; p = 0.001). The most common injury category was "wound/bleeding" for both sexes (men 40% and women 41%). Women were more frequently transported by helicopter or terrestrially (p > 0.001). Conclusion: In the Austrian Alps, the number of MTB accidents more than tripled between 2006 and 2018. Women were involved in only one fifth of all accidents. Sex differences in MTB accidents include (1) women wearing helmets less often, (2) women being less frequently injured, (3) women suffering fewer serious injuries, and (4) women being more frequently transported by helicopter or terrestrially, while men more often did not require transportation.

Risk Factors and Inequities in Transportation Injury and Mortality in the Canadian Census Health and Environment Cohorts (CanCHECs)

BACKGROUND

Road traffic injury contributes substantially to morbidity and mortality. Canada stands out among developed countries in not conducting a national household travel survey, leading to a dearth of national transportation mode data and risk calculations that have appropriate denominators. Since traffic injuries are specific to the mode of travel used, these risk calculations should consider travel mode.

METHODS

Census data on mode of commute is one of the few sources of these data for persons aged 15 and over. This study leveraged a national data linkage cohort, the Canadian Census Health and Environment Cohorts, that connects census sociodemographic and commute mode data with records of deaths and hospitalizations, enabling assessment of road traffic injury associations by indicators of mode of travel (commuter mode). We examined longitudinal (1996-2019) bicyclist, pedestrian, and motor vehicle occupant injury and fatality risk in the Canadian Census Health and Environment Cohorts by commuter mode and sociodemographic characteristics using Cox proportional hazards models within the working adult population.

RESULTS

We estimated positive associations between commute mode and same mode injury and fatality, particularly for bicycle commuters (hazard ratios for bicycling injury was 9.1 and for bicycling fatality was 11). Low-income populations and Indigenous people had increased injury risk across all modes.

CONCLUSIONS

This study shows inequities in transportation injury risk in Canada and underscores the importance of adjusting for mode of travel when examining differences between population groups.

Do electric bicycles cause an increased injury risk compared to conventional bicycles? The potential impact of data visualisations and corresponding conclusions

The increasing number of bicycle crashes leading to injuries in the Netherlands is frequently related (e.g., in the media) to increased use of the electric bicycle. For this reason, policy makers face the challenge of selecting and implementing the most promising countermeasures including those focused on electric bicycles. It may be questioned, however, to what extent the electric bicycle itself is a (direct) cause of crashes leading to injuries or whether other factors are important for explaining the increased number of bicycle injury crashes. On the basis of an abbreviated list of criteria by Elvik (2011), this paper illustrates the potential influence of factor inclusions, analysis selections, and data presentations on the general impression about crash causation with the electric bicycle as an example. The aim is to provide a 'best practice guide' by taking into account (1) a theoretical explanation of causal mechanisms, (2) control for confounders, and (3) a statistical association of sufficient strength and consistency in the expected direction. We conclude that an apparent increased risk of electric bicycles may be explained by factors such as age, exposure, health factors, and gender of the cyclist. A relatively simple analysis, by comparing fatality numbers of conventional and electric bicycles, showed that including or excluding these factors may lead to vastly different interpretations of fatality causes and the relative risk of electric bicycles compared to conventional bicycles.

Exploring temporal instability effects on bicyclist injury severities determinants for intersection and non-intersection-related crashes

Cycling is a sustainable and healthy mode of transportation with direct links to reducing traffic congestion, lowering greenhouse gas emissions, and improving air quality. However, from a safety perspective, bicyclists represent a risky road user group with a higher likelihood of sustaining severe injuries when involved in vehicle crashes. With various determinants known to affect bicyclist injury severity and vary across locations, this study investigates the factors affecting bicyclist injury severity and temporal instability, considering the location of crashes. More specifically, the objective of this study is to understand differences in injury severities of intersection and non-intersection-related single-bicycle-vehicle crashes using four year crash data from the state of Florida. Random parameters logit models with heterogeneity in the means and variances are developed to model bicyclist injury severity outcomes (no injury, minor injury, and severe injury) for intersection and non-intersection crashes. Several variables affecting injury severities are considered in model estimation, including weather, roadway, vehicle, driver, and bicyclist characteristics. The temporal stability of the model parameters is assessed for different locations and years using a series of likelihood ratio tests. Results indicate that the determinants of bicyclist injury severities change over time and location, resulting in different injury severities of bicyclists, with non-intersection crashes consistently resulting in more severe bicyclist injuries. Using a simulation-based out-of-sample approach, predictions are made to understand the benefits of replicating driving behaviour and facilities similar to intersections for non-intersection locations, which could benefit in reducing bicyclist injury severity probabilities.

[The severely injured older cyclist-Evaluation of the TraumaRegister DGU® : Retrospective, multicenter cross-sectional study based on the TraumaRegister DGU®]

BACKGROUND

Contrary to the trend of decreasing traffic fatalities, the number of cyclists killed in Germany has been steadily increasing in recent years. With the increasing popularity of cycling in all age groups, the number of accidents with sometimes serious injuries is rising. In the course of this, the question arises what influence age has on the type and severity of injuries, the probability of survival and the length of hospital stay in seriously injured cyclists.

METHODS

A retrospective analysis of data from the TraumaRegister DGU® (TR-DGU) from 2010 to 2019 was performed. All severely injured cyclists with a maximum abbreviated injury scale (MAIS) of 3+ (n = 14,651) in the TR-DGU were included in this study and the available parameters were evaluated. A subdivision into three age groups (60-69, 70-79, and ≥ 80 years) and a control group (20-59 years) was carried out.

RESULTS

Injuries to the head were by far the most common, accounting for 64.2%. There was a marked increase in severe head injuries in the 60-plus years age group. Furthermore, with increasing age, the probability of prehospital intubation, catecholamine requirement, intensive care and hospital length of stay, and mortality increased.

CONCLUSION

Head injuries represent the most common serious injury, especially among older cyclists. As helmet wearing was not recorded in the TraumaRegister DGU® during the evaluation period, no conclusion can be drawn about its effect. Furthermore, a higher age correlates with a longer hospital stay and a higher mortality, but does not represent an independent risk factor for death in severely injured patients.

The interactions between accessibility and crash risk from a social equity perspective: A case study at the Rotterdam-The Hague metropolitan region

PROBLEM

Transport policies generally prioritize improving safety and accessibility levels, as they are regarded as the most important indicators of the quality of the transport system serving the public. However, inequalities associated with safety and accessibility issues are generally overlooked in these policies. Despite the importance and necessity of transport policies to address equity issues, there is still scarce knowledge on the interactions between equity, safety, and accessibility. This research aims to address this gap in the literature by creating a better understanding of the relationships between accessibility levels and traffic safety with a focus on social equity perspectives.

METHOD

A crash risk evaluation method and a Gravity model are utilized to analyze cycling safety and accessibility to jobs by bicycle. Two linear regression models (LM) were conducted to investigate the statistical correlations between cycling crash risk and accessibility. Moreover, the Bivariate local Moran's I method was employed to assess the spatial inequalities of distribution of crash risk and job accessibility over different income-level populations.

RESULTS

The analyses showed that low-income people are not only disadvantaged in terms of job accessibility by bicycle but are also exposed to higher cycling crash risks, compared to high-income groups. Furthermore, most disadvantaged zones that have the highest need for road safety and accessibility improvements are identified as areas where low-income populations are exposed to higher crash risk and/or have lower access to jobs by bicycle.

SUMMARY

This study contributes to the transport literature by investigating the interactions between safety and accessibility and the impacts on transport equity. The findings of the statistical and spatial analysis are beneficial for the decision-makers, considering the probable mutual implications of land-use and transport developments and projects aiming to improve safety, accessibility, or both for different population groups.

Assessing bicycle crash risks controlling for detailed exposure: A Copenhagen case study

A better understanding of factors associated with bicycle crashes can inform future efforts to limit crash risks. Many previous studies have analysed crash risk based on crash databases. However, these can only provide conditional information on crash risks. A few recent studies have included aggregate flow measures in their crash risk analyses. This study incorporates detailed bicycle flow to investigate factors related to bicycle crashes. Specifically, the study assesses the relative crash risk given various conditions by applying Palm distributions to control for exposure. The study specifically investigates the relationship between weather and time conditions and the relative risk of bicycle crashes at a disaggregate level. The study uses bicycle crash data from police reports of bicycle crashes from 2017-2020 in the greater Copenhagen area (N = 4877). The relations between the bicycle crash risk and the air temperature and wind speeds are found to be highly non-linear. The relative risk of bicycle crashes is elevated at low and high temperatures (0 °C ¿ x, x ¿ 21 °C). The results also show how decreasing visibility relates to increasing bicycle crash risk. Meanwhile, cycling during the early morning peak (7-8) and afternoon peak hours (15-18) is related to an increased risk of bicycle crashes. While some of the effects are likely spurious, they highlight specific conditions associated with higher relative risk. Finally, the results illustrate the increased risk at weekend night times when cyclists are likely to bike under the influence of alcohol. In conclusion, the analysis confirms that visibility, slippery surfaces, and intoxication are all factors associated with a higher risk of bicycle crashes. Hence, it is relevant to consider how infrastructure planning and preventive measures can modify the bicycle environment to minimise these risks.

Bicycle infrastructure and the incidence rate of crashes with cars: A case-control study with Strava data in Atlanta

Introduction

Bicycling has individual and collective health benefits. Safety concerns are a deterrent to bicycling. Incomplete data on bicycling volumes has limited epidemiologic research investigating safety impacts of bicycle infrastructure, such as protected bike lanes.

Methods

In this case-control study, set in Atlanta, Georgia, USA between 2016-10-01 and 2018-08-31, we estimated the incidence rate of police-reported crashes between bicyclists and motor vehicles (n = 124) on several types of infrastructure (off-street paved trails, protected bike lanes, buffered bike lanes, conventional bike lanes, and sharrows) per distance ridden and per intersection entered. To estimate underlying bicycling (the control series), we used a sample of high-resolution bicycling data from Strava, an app, combined with data from 15 on-the-ground bicycle counters to adjust for possible selection bias in the Strava data. We used model-based standardization to estimate effects of treatment on the treated.

Results

After adjustment for selection bias and confounding, estimated ratio effects on segments (excluding intersections) with protected bike lanes (incidence rate ratio [IRR] = 0.5 [95% confidence interval: 0.0, 2.5]) and buffered bike lanes (IRR = 0 [0,0]) were below 1, but were above 1 on conventional bike lanes (IRR = 2.8 [1.2, 6.0]) and near null on sharrows (IRR = 1.1 [0.2, 2.9]). Per intersection entry, estimated ratio effects were above 1 for entries originating from protected bike lanes (incidence proportion ratio [IPR] = 3.0 [0.0, 10.8]), buffered bike lanes (IPR = 16.2 [0.0, 53.1]), and conventional bike lanes (IPR = 3.2 [1.8, 6.0]), and were near 1 and below 1, respectively, for those originating from sharrows (IPR = 0.9 [0.2, 2.1]) and off-street paved trails (IPR = 0.7 [0.0, 2.9]).

Conclusions

Protected bike lanes and buffered bike lanes had estimated protective effects on segments between intersections but estimated harmful effects at intersections. Conventional bike lanes had estimated harmful effects along segments and at intersections.

Assessing bicycle-vehicle conflicts at urban intersections utilizing a VR integrated simulation approach

Animosity between drivers and cyclists has existed on urban road networks for many years. Conflicts between these two groups of road users are exceptionally high in the shared right-of-way environments. Benchmarking methods of conflict assessments are mostly based on statistical analysis with limited data sources. The actual crash data would be valuable to understand the features of bike-car collisions, however the available data are spatially and temporally sparse. To this end, this paper proposes a simulation-based bicycle-vehicle conflict data generation and assessment approach. The proposed approach uses a three-dimensional visualization and virtual reality platform, integrating traffic microsimulation to reproduce a naturalistic driving/cycling-enabled experimental environment. The simulation platform is validated to reflect the human-resembled driving/cycling behaviors under different infrastructure designs. Comparative experiments are carried out on bicycle-vehicle interactions under different conditions, with data collected from a total of 960 scenarios. Based on the results of the surrogate safety assessment model (SSAM), the obtained key insights include: (1) scenarios of a high conflict probability do not lead to actual crashes, which suggests that the classic SSM-based measurements such as TTC or PET values may not sufficiently reflect real cyclist-driver interactions; (2) the major cause of conflicts is variation in vehicle acceleration, which suggests that drivers are considered to be the main party responsible for bicycle-vehicle conflict/crash occurrence; (3) the proposed approach is able to generate near-miss events and reproduce interaction patterns between cyclists and drivers, facilitating experiments and data collections which would be typically unavailable for this type of study.

Adolescent Aggressive Riding Behavior: An Application of the Theory of Planned Behavior and the Prototype Willingness Model

Cycling has always been popular in China, especially during the years when the government encouraged green travel. Many people participate in rides to ease traffic congestion and increase transfer convenience. Due to the disorganized and tidal nature of cycling, cyclists create many conflicts with other groups. Adolescents are vulnerable road users with a strong curiosity and risk-taking mindset. Identifying the factors influencing adolescents’ aggressive riding behavior can assist in developing strategies to prevent this behavior. An online questionnaire was used to collect data on bicycling among students in a middle school in Guangzhou, China. The theory of planned behavior (TPB) and prototype willingness model (PWM) have been applied to study travel behavior and adolescent risk behavior. To investigate the impact of psychological variables on adolescent aggressive behavior, we used TPB, PWM, TPB + PWM, and an integrated model. Behavioral intentions are greatly influenced by attitudes, subjective norms, and perceived behavioral control. Both descriptive and moral norms played a role in behavioral willingness. The integrated model explained 18.3% more behavioral variance than the TPB model. The social reactive pathway explained more variance in behavior than the rational path.

Major trauma among E-Scooter and bicycle users: a nationwide cohort study

OBJECTIVES

To determine the incidence, demographics and injury patterns involved in E-Scooter-related hospital admissions due to significant trauma compared with bicycle-related trauma within England and Wales. To compare morbidity and mortality between groups.

DESIGN

A retrospective cohort study based on data which has been prospectively collected and submitted to the UK Trauma Audit and Research Network (TARN) registry.

SETTING

Major trauma centres and trauma units within England and Wales.

PARTICIPANTS

Patients of any age who were admitted to hospitals in England and Wales with injuries following E-Scooter or bicycle incidents between the dates 1 January 2021-31 December 2021. All patients must have met TARN database inclusion criteria.

OUTCOMES

In-hospital mortality, critical care admission and length of stay (LoS), hospital LoS and discharge destination.

RESULTS

There were 293 E-Scooter trauma incidents compared with 2538 bicycle incidents. E-Scooter users were more likely to be admitted to a major trauma centre (p=0.019) or a critical care unit (p<0.001). Serious head and limb trauma (Abbreviated Injury Scale >2) occurred more frequently among the E-Scooter cohort (35.2% vs 19.7%, p<0.001 and 39.9% vs 27.2%, p<0.001, respectively) while serious chest and pelvic trauma were greater among bicycle users (p<0.001 and p=0.003, respectively). Over one-third of E-Scooter injuries were incurred outside the current legislation by patients who were intoxicated by alcohol and drugs (26%, 75/293) or under the age of 17 (14%, 41/293).

CONCLUSIONS

These early results suggest a greater relative incidence of serious trauma and an alternative pattern of injury among E-Scooter users compared with bicycles.

TRIAL REGISTRATION NUMBER

TARN210101.

Characteristics of older cyclists with self-perceived needs for improvement in cycling competence: SiFAr trial

BACKGROUND

Older cyclists are in great risk of being injured or killed in a traffic-related accident and their needs should find more consideration in safety guidelines, urban planning, and future intervention programs.

OBJECTIVE

The aim for this cross-sectional analysis was to comprehensively explore characteristics of community-dwelling cyclists aged 65 years and older with a self-perceived need to increase cycle competence.

METHODS

118 older adults (mean age 73.3 ± 5.2 years, 61% women) performed a standardized cycle course representing specific cycling skills. Additionally, health and functional assessments were carried out and characteristics regarding demographic, health, falls, bicycle equipment/type and cycling biography/behavior were obtained.

RESULTS

The majority (67.8%) of this community-dwelling adults reported being unsafe when cycling and 41.3% had a bicycle fall in the past year. More than half of the participants showed at least one limitation in each of the measured cycling skills. Women significantly had more frequent limitations in four of the cycling skills measured (p ≤ 0.001) compared to men. While no significant differences were found for falls, health and functional characteristics, women and men differed significantly in terms of bicycle type, equipment, and perceived safety (p < 0.001).

CONCLUSION

Limitations in cycling should be compensated by preventive bicycle training and a safe cycling infrastructure. Bicycle fit, the wearing of bicycle helmets and promotion of a sense of security while cycling can further reduce accident risk and must find recognition in safety guidelines. In addition, educational initiatives have to dismantle gender-related bicycle stereotypes.

Odds of self-reported minor cycle crashes with conventional and electric assisted cycles adjusted for cycling frequency in Dutch and Belgian adults a retrospective study

BACKGROUND

Cycling for transportation and recreation is gaining in popularity, especially in older age groups. The rise in electric assisted cycles (EAC) may also have a role to play in this. With an increase in the number of cyclists comes an increase in the prevalence of cycle crashes. However, there is a lack of knowledge on EAC crashes and crash studies including cycle use data. An important question is also whether the high number of serious road injuries among older cyclists, is due to increased risk or more serious consequences in the event of a crash.

STUDY AIM

To compare the odds of reporting a cycle crash on a conventional (CC) against electrically assisted cycle (EAC), while controlling for age, gender, BMI, impairments while cycling, cycling frequency and region of residence.

METHODS

A 12-month retrospective cross-sectional survey-based study, including male and female cyclists aged 40+ years, was conducted in Belgium and the Netherlands. Socio-demographics, physical and mental impairments while cycling (such as lower reaction time), crash details and cycling frequency data were collected. Cyclists were grouped into CC, EAC or both (CC + EAC) based on the type of cycle they used during the study period. Logistic regression models were used to calculate the odds of reporting a cycle crash. Main and interaction effects were studied.

RESULTS

1,919 cyclists were included in the data analysis (63.2 ± 11.1 years; 50% women). 319 (17% of the total sample) cyclists reported a crash in the previous 12 months, of which 36% were EAC crashes. Those reporting a crash were significantly younger compared to those not reporting a crash. The following significant main effects were observed: those cycling on an EAC had a higher odds of reporting a cycle crash compared to those cycling on a CC (OR = 1.41, 95% CI = 1.01-1.97); cyclists in the category average and high on mental impairments while cycling had a higher odds of reporting a cycle crash compared to those in the category low (OR = 1.72, 95% CI = 1.23-2.40 and OR = 3.49, 95% CI = 2.51-4.90, respectively); higher cycling frequency is related to higher odds of reporting a cycle crash (OR = 3.25, 95% CI = 2.25-4.90). A significant interaction effect was observed between age category and gender (OR = 1.93, 95% CI = 1.15-3.26). Post-hoc tests revealed that men in the younger age category (40-64 years) had the highest probability (18.95%) of reporting a cycle crash, whereas men in the oldest age category (65+ years) had the lowest probability (9.99%) of reporting a cycle crash. No significant difference between age categories in women was observed.

CONCLUSION

This study indicates that within a cohort of middle aged and older adults living in regions with high to low cycling modal shares, cycle type, mental impairments while cycling, cycling frequency and region of residence play a significant role in the odds of reporting a (minor) cycle crash. Men in the age category 40-64 years have a significantly higher probability of reporting a cycle crash compared to men of 65+ years. Safety campaigns and instructions should pay particular attention to men in the age category 40-64 years and those with a mental impairment while cycling.

Contraflows and cycling safety: Evidence from 22 years of data involving 508 one-way streets

Contraflow cycling on one-way streets is a low cost intervention that research shows can improve the cycling experience and increase participation. Evidence from several studies suggest that cyclists on contraflows have a lower crash risk. However, implementing contraflow cycling is often controversial, including in the United Kingdom (UK). In this paper we examine whether contraflow cycling on one-way streets alters crash or casualty rates for pedal cyclists. Focusing on inner London boroughs between 1998 and 2019, we identified 508 road segments where contraflow cycling was introduced on one-way streets. We identified road traffic crashes occurring within 10 m of these segments and labelled them as pre-contraflow, contraflow or contraflow removed crashes. We calculated rates using the number of crashes or casualties divided by the time exposed and generated 95 % confidence intervals using bootstrap resampling. We adjusted the rates for changes in cordon cycling volume and injury severity reporting. There were 1498 crashes involving pedal cyclists: 788 pre-contraflow, 703 contraflow and 7 following contraflow removal. There was no change in adjusted overall pedal cyclist crash or casualty rates when contraflow cycling was introduced. Proximity to a junction doubled the crash rate. The crash rate when pedal cyclists were travelling contraflow was the same as those travelling with flow. We have found no evidence that introducing contraflow cycling increases the crash or casualty rate for pedal cyclists. It is possible that such rates may indeed fall when contraflow cycling is introduced if more accurate spatio-temporal cycling volume data was available. We recommend all one-way streets are evaluated for contraflow cycling but encourage judicious junction design and recommend UK legislative change for mandatory-two-way cycling on one-way streets unless exceptional circumstances exist.

Assessment of inequity in bicyclist crashes using bivariate Bayesian copulas

INTRODUCTION

Physical activity associated with active transport modes such as bicycling has major health benefits and can help to reduce health concerns related to sedentary lifestyles, such as cardiovascular disease, Type II diabetes, and obesity, as well as risks of colon and breast cancer, high blood pressure, lipid disorders, osteoporosis, depression, and anxiety. However, as a vulnerable user group, bicyclists experience negative health impacts of transportation policies and infrastructure, such as traffic crashes and exposure to air and noise pollution that is disproportionately distributed within low-income and underserved areas.

METHOD

This study used aggregated (block-group) bicyclist crash data from Harris County, Texas, to analyze how various equity measures are associated with both fatal and injury (FI) and no injury (property damage only) bicyclist crashes that occurred from 2010 to 2017. We used Bayesian bivariate copula-based random effects regression analysis to evaluate these associations. In contrast to more traditional univariate analysis, this novel methodology can consider the effects of factors of interest across different severity levels or crash types to fully understand their effects and how they may differ across categories.

RESULTS

The analysis results indicate that the bicyclist exposure, vehicle exposure, population demographics, population density, the percentage of African-Americans, and households below the poverty level are associated with both FI and PDO bicyclist crashes.

CONCLUSIONS

Although more location and context-specific analyses are required, this study's overall results once again conform with the findings and assumptions in bicycling safety literature that the low-income and racially diverse communities are prone to experience more bicyclist crashes.

PRACTICAL APPLICATIONS

The findings of this study may have implications for future transportation and planning policies. These findings can be used to guide the policies and strategies targeting the elimination of inequity in transportation-related health concerns.

Have Paved Trails and Protected Bike Lanes Led to More Bicycling in Atlanta?: A Generalized Synthetic-Control Analysis

BACKGROUND

Bicycling is an important form of physical activity in populations. Research assessing the effect of infrastructure on bicycling with high-resolution smartphone data is emerging in several places, but it remains limited in low-bicycling US settings, including the Southeastern US. The Atlanta area has been expanding its bicycle infrastructure, including off-street paved trails such as the Atlanta BeltLine and some protected bike lanes.

METHODS

Using the generalized synthetic-control method, we estimated effects of five groups of off-street paved trails and protected bike lanes on bicycle ridership in their corresponding areas. To measure bicycling, we used 2 years (October 1, 2016 to September 30, 2018) of monthly Strava data in Atlanta's urban core along with data from 15 on-the-ground counters to adjust for spatiotemporal variation in app use.

RESULTS

Considering all infrastructure as one joint intervention, an estimated 1.10 (95% confidence interval [CI]: 0.99, 1.18) times more bicycle-distance was ridden than would have been expected in the same areas had the infrastructure not been built, when defining treatment areas by the narrower of two definitions (defined in text). The Atlanta BeltLine Westside Trail and Proctor Creek Greenway had especially strong effect estimates, e.g., ratios of 1.45 (95% CI: 1.12, 1.86) and 1.55 (1.10, 2.14) under each treatment-area definition, respectively. We estimated that other infrastructure had weaker positive or no effects on bicycle-distance ridden.

CONCLUSIONS

This study advances research on the topic because of its setting in the US Southeast, simultaneous assessment of several infrastructure groups, and data-driven approach to estimating effects. See video abstract at, http://links.lww.com/EDE/B936.

CYCLANDS: Cycling geo-located accidents, their details and severities

Several cities and national authorities across the globe publish records on road accidents and crashes. This data is vital for road safety analysis, enabling researchers to develop models to understand how different factors impact the frequency and severity of accidents. However, researchers studying cycling safety face additional challenges as datasets containing solely cycling accidents are scarce, may contain errors, among others. Thus, we publish CYCLANDS: CYCling geo-Located AccideNts, their Details and Severities. CYCLANDS is a curated collection of 30 datasets on cycling crashes to lower the barrier in objective cycling research comprising nearly 1.6 M cycling accidents. All observations include the severity and location of the accident. This collection fosters the worldwide study of cycling safety by providing a testbed for researchers to develop tools and models for cycling safety analysis, ultimately improving the safety of those who cycle.

Measurement(s)	Cycling Accident
Technology Type(s)	Reports
Sample Characteristic - Location	World

Bicyclists injury severities: An empirical assessment of temporal stability

Cyclists are among the most vulnerable participants in road traffic, making their safety a top priority. Riding behavior of bicyclists could shift over time, affecting the level of injuries sustained in bicyclist-involved crashes. Many studies have been done to identify the factors influencing bicyclist injury severity, but the temporal stability of these variables over time needs further study. The temporal instability of components that affect the cyclist injury levels in bicycle collisions is explored in this paper. To obtain potential unobserved heterogeneity, yearly models of cyclist-injury levels (including potential consequences of no, minor, and severe injury) were measured separately applying a random parameters logit model that allows for potential heterogeneity in estimated parameters' means and variances. Employing a data source on bicycle collisions in Los Angeles, California, over the course of six years (January 1, 2012 to December 31, 2017), several variables which may impact the injury level of cyclists were explored. This paper has also employed a set of likelihood ratio tests assessing the temporal instability of the models. The temporal instability of the explanatory parameters has been evaluated with marginal effects. The results of the model assessment indicate that several factors may raise the chances of severe bicyclist injuries in collisions, including cyclists older than 55 years old, cyclists who were identified to be at-fault in crashes, rear-end collisions, cyclists who crossed into opposing lane before the collision, crashes occurring early mornings (i.e., 00:00 to 06:00) and so on. The results also showed that the details and estimated parameters of the model do not remain stable over the years, however the source of this instability is unclear. In addition, the findings of model estimation demonstrate that considering the heterogeneity in the random parameter means and variances will enhance the overall model fit. This study also emphasizes the significance of accounting for the transferability of estimated models and the temporal instability of parameters influencing the injury severity outcomes in order to dynamically examine the collected data and adjust safety regulations according to new observations.

Visual Acuity and the Risk of Cycling Injuries: Register-Based Cohort Study From Adolescence to Middle-age

BACKGROUND

Cycling is increasingly encouraged in many countries as an inexpensive and healthy choice of transportation. Operating any vehicle on the road requires high visual acuity, but few studies to our knowledge have examined the association between vision and cycling injuries.

METHODS

We examined whether poorer visual acuity is associated with increased risk of fatal and nonfatal cycling injuries. We used prospectively recorded register data for 691,402 men born between 1970 and 1992 in Sweden. We followed these men from an average age of 18 years, when visual acuity was assessed during the conscription assessment, to age 45 at the latest. We identified fatal and nonfatal cycling and car injuries using Patient and Cause of Death registers. Cox regression models were used to estimate hazard ratios and 95% confidence intervals.

RESULTS

Based on visual acuity for the eye with the best vision, moderately impaired acuity 0.9 to 0.6 when wearing refractive correction was associated with increased risk for cycling injuries (hazard ratio = 1.44 [95% confidence interval = 1.16, 1.79]) compared with unimpaired vision (uncorrected visual acuity 1.0) and after adjustment for a wide range of potential confounders. This association remained consistent across various sensitivity analyses. Visual acuity was not associated with car injury risk.

CONCLUSIONS

In this cohort study, poorer vision was specifically associated with a higher rate of cycling injuries.

Uncertainty matters: Bayesian modeling of bicycle crashes with incomplete exposure data

BACKGROUND

One major challenge faced by neighborhood-level bicycle safety analysis is the lack of complete and reliable exposure data for the entire area under investigation. Although the conventional travel-diary surveys, together with the emerging smartphone fitness applications and bike-sharing systems, provide straightforward and valuable opportunities to estimate territory-wide bicycle activities, the obtained ridership suffers inherently from underreporting.

METHODS

We introduced the Bayesian simultaneous-equation model as a sound methodological alternative here to address the uncertainty arising from incomplete exposure data when modeling bicycle crashes. The proposed method was successfully fitted to a crowdsourced dataset of 792 bicycle-motor vehicle (BMV) crashes aggregated from 209 neighborhoods over a 3-year period in Hong Kong.

RESULTS

Our analysis empirically demonstrated the bias due to omission of activity-based exposure measures or to the direct use of cycling distance extracted from the travel-diary survey without correcting for incompleteness. By modeling bicycle activities and the frequency of BMV crashes simultaneously, we also provided new evidence that an expansion of bicycle infrastructure was likely associated with a significant increase in cycling levels and a substantial reduction in the risk of BMV crashes, despite a slight increase in the absolute number of BMV crashes.

CONCLUSIONS

Our approach is promising in adjusting for the uncertainty in raw exposure data, extrapolating the missing exposure values, and untangling the linkage among built environment, bicycle activities, and the frequency of BMV crashes within a unified framework. To promote safer cycling, designated facilities should be provided to consecutively separate cyclists from motor vehicles.

Association of human mobility with road crashes for pandemic-ready safer mobility: A New York City case study

BACKGROUND

The COVID-19 pandemic has reshaped our cities in many ways. The number of motor vehicles on the road has plummeted during lockdowns, and an increasing number of people are turning to walking and biking. From a road safety perspective, the overall question is what effects the human behavior shift brings on the crash occurrence and, more importantly, how to support decision-makers on safer mobility policies?

METHOD

Based on anonymous mobile phone location and crash report data in New York City, this study attempts to provide some new insights by using survival analysis (the hazard function approach) to explore the effects of human mobility changes due to the pandemic on crashes that involve injuries and fatalities (of pedestrian, cyclist or motorist).

RESULTS

(1) the increased percentage of people staying at home improves pedestrian and cyclist safety, which adds evidence for making walking and cycling more appealing; (2) the increased percentage of people staying at home raises the likelihood of injuries for motor vehicle drivers, suggesting that it will be critical to monitor the driving behavior and establish new speed limits during the future pandemic waves and in the post-pandemic era as well; (3) non-work trips (e.g., shopping, recreation, personal business, etc.) are positively associated with crash injuries for motor vehicle drivers as well as pedestrian and cyclist; (4) human mobility factors were found not related to crash fatalities; (5) control NPIs implemented increased the motor vehicle drivers' crash risk.

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.

Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle-bicycle crashes

INTRODUCTION

In recent years, Australia is seeing an increase in the total number of cyclists. However, the rise of serious injuries and fatalities to cyclists has been a major concern. Understanding the factors affecting the fatalities and injuries of bicyclists in crashes with motor vehicles is important to develop effective policy measures aimed at improving the safety of bicyclists. This study aims to identify the factors affecting motor vehicle-bicycle (MVB) crashes in Victoria, Australia and introducing effective countermeasures for the identified risk factors.

METHOD

A data set of 14,759 MVB crash records from Victoria, Australia between 2006 and 2019 was analyzed using the binary logit model and latent class clustering.

RESULTS

It was observed that the factors that increase the risk of fatalities and serious injuries of bicyclists (FSI) in all clusters are: elderly bicyclist, not using a helmet, and darkness condition. Likewise, in areas with no traffic control, clear weather, and dry surface condition (cluster 1), high speed limits increase the risk of FSI, but the occurrence of MVB crashes in cross intersection and T-intersection has been significantly associated with a reduction in the risk of FSI. In areas with traffic control and unfavorable weather conditions (cluster 2), wet road surface increases the risk of FSI, but the areas with give way sign and pedestrian crossing signs reduce the risk of FSI. Practical Applications: Recommendations to reduce the risk of fatalities or serious injury to bicyclists are: improvement of road lighting and more exposure of bicyclists using reflective clothing and reflectors, separation of the bicycle and vehicle path in mid blocks especially in high-speed areas, using a more stable bicycle for the older people, monitoring helmet use, improving autonomous emergency braking, and using bicyclist detection technology for vehicles.

Role of exposure in bicycle safety analysis: Effect of cycle path choice

Despite the recognized environmental and health benefits of cycling, bicyclists are vulnerable to severe injuries and mortalities in the road crashes. Therefore, it is of paramount importance to identify the possible factors that may affect the bicycle crash risk. However, reliable estimates of bicycle exposure are often not available for the safety risk evaluation of different entities. The objective of this study is to advance the estimation of exposure in the bicycle safety analysis, using the detailed origin-destination data of each trip of the London public bicycle rental system. Two approaches including shortest path method (SPM) and weighted shortest path method (WSPM) are proposed to model the bicycle path choice and to estimate the bicycle distance traveled (BDT). Then, the bicycle crash frequency models that adopt BDTs as the exposure estimated using SPM and three WSPMs are developed. Three exposure measures including bicycle trips, bicycle time traveled (BTT), and BDT are assessed. Results indicate that the bicycle crash frequency models that incorporate the BDTs using WSPM have superior model fit. Moreover, the bicycle crash frequency model that incorporate the BDTs as the exposure outperforms those that incorporate the bicycle trips and BTT as the exposures. Findings of current study are indicative to the development of bicycle crash frequency model. Moreover, it should enhance the understanding on the roles of environmental, traffic and bicyclist factors in bicycle crash risk, based on appropriate estimates of bicycle exposures. Therefore, it should be useful to the transport planners and engineers for the development of bicycle infrastructures that can improve the overall bicycle safety in the long run.

“Not as Safe as I Believed”: Differences in Perceived and Self-Reported Cycling Behavior between Riders and Non-Riders

Cycling behavior remains a key issue for explaining several traffic causalities occurring every day. However, recent studies have shown how the assessment of the own safety-related behaviors on the road may substantially differ from how third parties assess them. Thus, the aim of this study was to evaluate the differences between cyclists’ self-reported behavior and the proxy-reported behavior that other (non-cyclist) road users perceive from bike riders. For this purpose, this study used data from two samples: (i) 1064 cyclists (M = 32.83 years) answering the Cycling Behavior Questionnaire—CBQ, and (ii) 1070 non-cyclists (M = 30.83 years) answering an adapted version of the CBQ for external raters—ECBQ. The results show how the self-reported and proxy-reported behaviors of cyclists greatly differ in terms of all behavioral factors composing the CBQ model, i.e., traffic violations, riding errors, and positive behaviors. Also, external raters (non-cyclists) are those targeting significantly riskier behaviors than those self-reported by cyclists. These discrepancies between perceived behaviors may give rise to conflicting viewpoints on the interaction between bicycle riders and other road users. Therefore, this study underscores the importance of behavioral awareness, providing highlights for future studies on the behavioral interaction between cyclists and other road users. Results can be used to improve the road safety of all road users by giving indications on self-and proxy-perceived safety-related behaviors and visibility of protective riding habits.

Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models

INTRODUCTION

Bicyclists are more vulnerable compared to other road users. Therefore, it is critical to investigate the contributing factors to bicyclist injury severity to help provide better biking environment and improve biking safety. According to the data provided by National Highway Traffic Safety Administration (NHTSA), a total of 8,028 bicyclists were killed in bicycle-vehicle crashes from 2007 to 2017. The number of fatal bicyclists had increased rapidly by approximately 11.70% during the past 10 years (NHTSA, 2019).

METHODS

This paper conducts a latent class clustering analysis based on the police reported bicycle-vehicle crash data collected from 2007 to 2014 in North Carolina to identify the heterogeneity inherent in the crash data. First, the most appropriate number of clusters is determined in which each cluster has been characterized by the distribution of the featured variables. Then, partial proportional odds models are developed for each cluster to further analyze the impacts on bicyclist injury severity for specific crash patterns.

RESULTS

Marginal effects are calculated and used to evaluate and interpret the effect of each significant explanatory variable. The model results reveal that variables could have different influence on the bicyclist injury severity between clusters, and that some variables only have significant impacts on particular clusters.

CONCLUSIONS

The results clearly indicate that it is essential to conduct latent class clustering analysis to investigate the impact of explanatory variables on bicyclist injury severity considering unobserved or latent features. In addition, the latent class clustering is found to be able to provide more accurate and insightful information on the bicyclist injury severity analysis. Practical Applications: In order to improve biking safety, regulations need to be established to prevent drinking and lights need to be provided since alcohol and lighting condition are significant factors in severe injuries according to the modeling results.

At-risk-measure Sampling in Case-Control Studies with Aggregated Data

Transient exposures are difficult to measure in epidemiologic studies, especially when both the status of being at risk for an outcome and the exposure change over time and space, as when measuring built-environment risk on transportation injury. Contemporary "big data" generated by mobile sensors can improve measurement of transient exposures. Exposure information generated by these devices typically only samples the experience of the target cohort, so a case-control framework may be useful. However, for anonymity, the data may not be available by individual, precluding a case-crossover approach. We present a method called at-risk-measure sampling. Its goal is to estimate the denominator of an incidence rate ratio (exposed to unexposed measure of the at-risk experience) given an aggregated summary of the at-risk measure from a cohort. Rather than sampling individuals or locations, the method samples the measure of the at-risk experience. Specifically, the method as presented samples person-distance and person-events summarized by location. It is illustrated with data from a mobile app used to record bicycling. The method extends an established case-control sampling principle: sample the at-risk experience of a cohort study such that the sampled exposure distribution approximates that of the cohort. It is distinct from density sampling in that the sample remains in the form of the at-risk measure, which may be continuous, such as person-time or person-distance. This aspect may be both logistically and statistically efficient if such a sample is already available, for example from big-data sources like aggregated mobile-sensor data.

Methodological considerations in MVC epidemiological research

Background

The global burden of MVC injuries and deaths among vulnerable road users, has led to the implementation of prevention programmes and policies at the local and national level. MVC epidemiological research is key to quantifying MVC burden, identifying risk factors and evaluating interventions. There are, however, several methodological considerations in MVC epidemiological research.

Methods

This manuscript collates and describes methodological considerations in MVC epidemiological research, using examples drawn from published studies, with a focus on the vulnerable road user population of children and adolescents.

Results

Methodological considerations in MVC epidemiological research include the availability and quality of data to measure counts and calculate event rates and challenges in evaluation related to study design, measurement and statistical analysis. Recommendations include innovative data collection (eg, naturalistic design, stepped-wedge clinical trials), combining data sources for a more comprehensive representation of collision events, and the use of machine learning/artificial intelligence for large data sets.

Conclusions

MVC epidemiological research can be challenging at all levels: data capture and quality, study design, measurement and analysis. Addressing these challenges using innovative data collection and analysis methods is required.

The unknown denominator problem in population studies of disease frequency

Problems related to unknown or imprecisely measured populations at risk are common in epidemiologic studies of disease frequency. The size of the population at risk is typically conceptualized as a denominator to be used in combination with a count of disease cases (a numerator) to calculate incidence or prevalence. However, the size of the population at risk can take other epidemiologic properties in relation to an exposure of interest and the count outcome, including confounding, modification, and mediation. Using spatial ecological studies of injury incidence as an example, we identify and evaluate five approaches that researchers have used to address "unknown denominator problems": ignoring, controlling for a proxy, approximating, controlling by study design, and measuring the population at risk. We present a case example and recommendations for selecting a solution given the data and the hypothesized relationship between an exposure of interest, a count outcome, and the population at risk.

Roles of infrastructure and land use in bicycle crash exposure and frequency: A case study using Greater London bike sharing data

Cycling is increasingly promoted as a sustainable transport mode. However, bicyclists are more vulnerable to fatality and severe injury in road crashes, compared to vehicle occupants. It is necessary to identify the contributory factors to crashes and injuries involving bicyclists. For the prediction of motor vehicle crashes, comprehensive traffic count data, i.e. AADT and vehicle kilometer traveled (VKT), are commonly available to proxy the exposure. However, extensive bicycle count data are usually not available. In this study, revealed bicycle trip data of a public bicycle rental system in the Greater London is used to proxy the bicycle crash exposure. Random parameter negative binomial models are developed to measure the relationship between possible risk factors and bicycle crash frequency at the zonal level, based on the crash data in the Greater London in 2012-2013. Results indicate that model taking the bicycle use time as the exposure measure is superior to the other counterparts with the lowest AIC (Akaike information criterion) and BIC (Bayesian information criterion). Bicycle crash frequency is positively correlated to road density, commercial area, proportion of elderly, male and white race, and median household income. Additionally, separate bicycle crash prediction models are developed for different seasons. Effects of the presence of Cycle Superhighway and proportion of green area on bicycle crash frequency can vary across seasons. Findings of this study are indicative to the development of bicycle infrastructures, traffic management and control, and education and enforcement strategies that can enhance the safety awareness of bicyclists and reduce their crash risk in the long run.

Cyclist crash rates and risk factors in a prospective cohort in seven European cities

Increased cycling uptake can improve population health, but barriers include real and perceived risks. Crash risk factors are important to understand in order to improve safety and increase cycling uptake. Many studies of cycling crash risk are based on combining diverse sources of crash and exposure data, such as police databases (crashes) and travel surveys (exposure), based on shared geography and time. When conflating crash and exposure data from different sources, the risk factors that can be quantified are only those variables common to both datasets, which tend to be limited to geography (e.g. countries, provinces, municipalities) and a few general road user characteristics (e.g. gender and age strata). The Physical Activity through Sustainable Transport Approaches (PASTA) project was a prospective cohort study that collected both crash and exposure data from seven European cities (Antwerp, Barcelona, London, Örebro, Rome, Vienna and Zürich). The goal of this research was to use data from the PASTA project to quantify exposure-adjusted crash rates and model adjusted crash risk factors, including detailed sociodemographic characteristics, attitudes about transportation, neighbourhood built environment features and location by city. We used negative binomial regression to model the influence of risk factors independent of exposure. Of the 4,180 cyclists, 10.2 % reported 535 crashes. We found that overall crash rates were 6.7 times higher in London, the city with the highest crash rate, relative to Örebro, the city with the lowest rate. Differences in overall crash rates between cities are driven largely by crashes that did not require medical treatment and that involved motor-vehicles. In a parsimonious crash risk model, we found higher crash risks for less frequent cyclists, men, those who perceive cycling to not be well regarded in their neighbourhood, and those who live in areas of very high building density. Longitudinal collection of crash and exposure data can provide important insights into individual differences in crash risk. Substantial differences in crash risks between cities, neighbourhoods and population groups suggest there is great potential for improvement in cycling safety.

Exploring the severity of bicycle-vehicle crashes using latent class clustering approach in India

INTRODUCTION

Bicyclists are vulnerable users in the shared asset like roadways. However, people still prefer to use bicycles for environmental, societal, and health benefits. In India, the bicycle plays a role in supporting the mobility to more people at lower cost and are often associated with the urban poor. Bicyclists represents one of the road user categories with highest risk of injuries and fatalities. According to the report by the Ministry of Road Transport and Highways (Accidents, 2017) in India, there is a sharp increase in the number of fatal victims for bicyclists in 2017 over 2016. The number of cyclists killed jumped from 2,585 in 2016 to 3,559 in 2017, a 37.7% increase.

METHOD

Few studies have only investigated the crash risk perceived by the bicyclists while interacting with other road users. The present paper investigates the injury severity of bicyclists in bicycle-vehicle crashes that occurred in the state of Tamilnadu, India during the nine year period (2009-2017). The analyses demonstrate that dividing bicycle-vehicle collision data into five clusters helps in reducing the systematic heterogeneity present in the data and identify the hidden relationship between the injury severity levels of bicyclists and cyclists demographics, vehicle, environmental, temporal cause for the crashes.

RESULTS

Latent Class Clustering (LCC) approach was used in the present study as a preliminary tool for the segmentation of 9,978 crashes. Later, logistic regression analysis was used to identify the factors that influence bicycle crash severity for the whole dataset as well as for the clusters that were obtained from the LCC model. Results of this study show that combined use of both techniques reveals further information that wouldn't be obtained without prior segmentation of the data. Few variables such as season, weather conditions, and light conditions were significant for certain clusters that were hidden in the whole dataset. This study can help domain experts or traffic safety researchers to segment traffic crashes and develop targeted countermeasures to mitigate injury severity.

Development and reproducibility of an instrument to assess behavioral and environmental aspects related to cyclist safety

Abstract In urgency and emergency services, the bicycle is the second means of transportation more widely used by the victims at the time of the accident. However, aspects associated with major and minor accidents are poorly understood. The aim was to develop an instrument and test its reproducibility, in order to evaluate behavioral and environmental aspects related to cyclist safety. The instrument was based on footage taken through a camera attached to the cyclist helmet and from a review of literature. Take part in the study academics that used the bicycle to travel at least once a week. Participants were instructed to indicate any safety-critical events on their way and situations of minor gravity based on the perception of real imminence of an accident. In order to identify aspects related to cyclist safety, the routes were divided in periods of 30 seconds. In order to test inter-rater reproducibility, two researchers received theoretical-practical training and performed the instrument in a sample of 100 periods. In order to evaluate the intra-rater reproducibility, one of the evaluators performed a second application after 07 days. The reproducibility of the categorical variables of the instrument were tested through general agreement and Kappa index. For the variables with continuous measuring range, the Intraclass Correlation Coefficient (ICC) was used. The percentage agreement varied between 88-100% and the Kappa values varied between 0.76-1.00. The ICC values ranged from 0.96-0.99. The developed instrument presents adequate reproducibility for use in research to evaluate the cyclist safety in urban contexts.

A Surrogate Video-Based Safety Methodology for Diagnosis and Evaluation of Low-Cost Pedestrian-Safety Countermeasures: The Case of Cochabamba, Bolivia

Due to a lack of reliable data collection systems, traffic fatalities and injuries are often under-reported in developing countries. Recent developments in surrogate road safety methods and video analytics tools offer an alternative approach that can be both lower cost and more time efficient when crash data is incomplete or missing. However, very few studies investigating pedestrian road safety in developing countries using these approaches exist. This research uses an automated video analytics tool to develop and analyze surrogate traffic safety measures and to evaluate the effectiveness of temporary low-cost countermeasures at selected pedestrian crossings at risky intersections in the city of Cochabamba, Bolivia. Specialized computer vision software is used to process hundreds of hours of video data and generate data on road users’ speed and trajectories. We find that motorcycles, turning movements, and roundabouts, are among the key factors related to pedestrian crash risk, and that the implemented treatments were effective at four-legged intersections but not at traditional-design roundabouts. This study demonstrates the applicability of the surrogate methodology based on automated video analytics in the Latin American context, where traditional methods are challenging to implement. The methodology could serve as a tool to rapidly evaluate temporary treatments before they are permanently implemented and replicated.

Cyclists injured in traffic crashes in Hong Kong: A call for action

BACKGROUND

Perceived as a minor transportation mode mainly for recreation, cycling and its related safety issues have not been treated as a citywide concern in Hong Kong and have thus received inadequate research efforts. Our study aimed to illuminate the safety challenges faced by cyclists in Hong Kong.

METHODS

We examined the police crash records from 1998 to 2017 and developed a Bayesian Poisson state space model to evaluate the longitudinal change in traffic injuries to cyclists. We then used quasi-induced exposure to measure the annual relative risk of crash involvement for cycling. Based on an officially published travel characteristics survey, we further measured the risk of injury for cycling per minutes cycled.

RESULTS

Between 1998 and 2017, Hong Kong witnessed a more than twofold increase in the number of cyclist injuries, with an average annual increase rate of 5.18% (95% CI: 0.53%-12.77%). By 2017, cyclists were 2.21 (1.82-2.69) times more likely to be involved in traffic crashes than in 1998. Per 10 million minutes, the injury rates for cycling were 28.64 (27.43-29.70) and 42.54 (41.07-44.02) on weekdays during 2001-2003 and 2010-2012. After adjusting for sex and age groups, cyclists were 1.95 (1.43-2.61) times more likely to be injured in 2010-2012 than in 2001-2003. Per minutes traveled, cyclists also sustained significantly higher risks of fatality and injury than pedestrians, private car drivers and passengers, taxi passengers, public bus passengers, and minibus passengers. A comparison of Hong Kong with other regions suggests that Hong Kong is among the most dangerous areas for cycling in terms of fatality rate per minutes cycled.

CONCLUSIONS

Cyclist injuries have become a substantial public health burden in Hong Kong. A range of countermeasures with proven effectiveness should be promptly implemented to improve the safety of these vulnerable road users.

Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)

The majority of bicycle crash studies aim at determining risk factors and estimating crash risks by employing statistics. Accordingly, the goal of this paper is to evaluate bicycle–motor vehicle crashes by using spatial and temporal approaches to statistical data. The spatial approach (a weighted kernel density estimation approach) preliminarily estimates crash risks at the macro level, thereby avoiding the expensive work of collecting traffic counts; meanwhile, the temporal approach (negative binomial regression approach) focuses on crash data that occurred on urban arterials and includes traffic exposure at the micro level. The crash risk and risk factors of arterial roads associated with bicycle facilities and road environments were assessed using a database built from field surveys and five government agencies. This study analysed 4120 geocoded bicycle crashes in the city of Antwerp (CA, Belgium). The data sets covered five years (2014 to 2018), including all bicycle–motorized vehicle (BMV) crashes from police reports. Urban arterials were highlighted as high-risk areas through the spatial approach. This was as expected given that, due to heavy traffic and limited road space, bicycle facilities on arterial roads face many design problems. Through spatial and temporal approaches, the environmental characteristics of bicycle crashes on arterial roads were analysed at the micro level. Finally, this paper provides an insight that can be used by both the geography and transport fields to improve cycling safety on urban arterial roads.

Using CyclePhilly data to assess wrong-way riding of cyclists in Philadelphia

PROBLEM

The increasing use of smartphones and low cost GPS have provided new sources for collecting data and using them to explain travel behavior. This study aims to use data collected from a smartphone application (CyclePhilly) to explain wrong-way riding behavior of cyclists on one-way segments to help better identify the demographic and network factors influencing the wrong-way riding decision making.

METHODS

The data used in this study consist of two different sources: (a) Route trips data downloaded from the CyclePhilly Website contained trips detailed up to segment level, collected from May 2014 to April 2016 (12,202 trips by 300 unique users); and (b) Open Street Maps (OSM). Using ArcGIS, we calculate detour routes for each wrong way segment. We then built a mixed logistic regression model to identify the trip and riders' characteristics affecting wrong-way riding behavior. Next, we explore the characteristics of road facilities associated with wrong-way riding behavior.

RESULTS AND DISCUSSION

Only 2.7% of travel distance is wrong-way, yet 42% of trips include a wrong-way segment. Commute trips have a higher chance of wrong-way riding. The longer the trips also include more wrong-way riding. Segments with higher detour ratios (ratio of distance with a detour to the wrong-way distance) are found to be associated with more wrong-way behavior. Compared to roads with no bike lane, roads with sharrow markings and buffered bike lane discourage wrong way riding.

PRACTICAL APPLICATIONS

This study proposes new methods that can be adapted to use naturalistic and probe data and analyze city-wide aberrant riders' behavior. These help planners and engineers choose between various types of bike infrastructure. Wrong-way riding is one application that can be investigated, but probe bicycle datasets provide unprecedented resolution and volume of data that will allow for more sophisticated safety and planning analyses.

Cycling injury risk in London: A case-control study exploring the impact of cycle volumes, motor vehicle volumes, and road characteristics including speed limits

Cycling injury risk is an important topic, but few studies explore cycling risk in relation to exposure. This is largely because of a lack of exposure data, in other words how much cycling is done at different locations. This paper helps to fill this gap. It reports a case-control study of cycling injuries in London in 2013-2014, using modelled cyclist flow data alongside datasets covering some characteristics of the London route network. A multilevel binary logistic regression model is used to investigate factors associated with injury risk, comparing injury sites with control sites selected using the modelled flow data. Findings provide support for 'safety in numbers': for each increase of a natural logarithmic unit (2.71828) in cycling flows, an 18% decrease in injury odds was found. Conversely, increased motor traffic volume is associated with higher odds of cycling injury, with one logarithmic unit increase associated with a 31% increase in injury odds. Twenty-mile per hour compared with 30mph speed limits were associated with 21% lower injury odds. Residential streets were associated with reduced injury odds, and junctions with substantially higher injury odds. Bus lanes do not affect injury odds once other factors are controlled for. These data suggest that speed limits of 20 mph may reduce cycling injury risk, as may motor traffic reduction. Further, building cycle routes that generate new cycle trips should generate 'safety in numbers' benefits.

Where do bike lanes work best? A Bayesian spatial model of bicycle lanes and bicycle crashes

US municipalities are increasingly introducing bicycle lanes to promote bicycle use, increase roadway safety and improve public health. The aim of this study was to identify specific locations where bicycle lanes, if created, could most effectively reduce crash rates. Previous research has found that bike lanes reduce crash incidence, but a lack of comprehensive bicycle traffic flow data has limited researchers' ability to assess relationships at high spatial resolution. We used Bayesian conditional autoregressive logit models to relate the odds that a bicycle injury crash occurred on a street segment in Philadelphia, PA (n = 37,673) between 2011 and 2014 to characteristics of the street and adjacent intersections. Statistical models included interaction terms to address the problem of unknown bicycle traffic flows, and found bicycle lanes were associated with reduced crash odds of 48% in streets segments adjacent to 4-exit intersections, of 40% in streets with one- or two-way stop intersections, and of 43% in high traffic volume streets. Presence of bicycle lanes was not associated with change in crash odds at intersections with less or more than 4 exits, at 4-way stop and signalized intersections, on one-way streets and streets with trolley tracks, and on streets with low-moderate traffic volume. The effectiveness of bicycle lanes appears to depend most on the configuration of the adjacent intersections and on the volume of vehicular traffic. Our approach can be used to predict specific street segments on which the greatest absolute reduction in bicycle crash odds could occur by installing new bicycle lanes.

Pedestrian falls: A review of the literature and future research directions

INTRODUCTION

Pedestrian falls (PFs) - falls in public spaces without collisions with other road users - are a significant cause of serious transport-related injuries, amounting to three-quarters of all pedestrians admitted to hospital.

METHODS

This scoping review examined peer-reviewed research on PFs published between 1995 and 2015. Electronic databases (Scopus, SafetyLit, and PubMed) were used to find studies identifying PFs or outdoor falls (the latter also including falls in gardens).

RESULTS

We identified only 28 studies reporting relevant information on PFs (i.e., 15 prospective, 10 retrospective, and 3 intervention studies). The results show that more walking is related to a lower risk of PFs. Older people, especially older women, have a higher risk of (injurious) PFs. Outdoor fall victims have equally good or better health characteristics and scores on balance tests compared to those who have not experienced such falls. Road factors such as uneven surfaces, busy junctions, stairs, and slippery surfaces seem to play an important role in PFs, but much of the research on these factors is of a qualitative nature.

CONCLUSIONS

PF victims are generally in good health (apart from normal age-related problems) but at risk due to road factors.

PRACTICAL APPLICATIONS

We recommend to adopt a human factors approach. The road system should be adapted to human capabilities and limitations including those of pedestrians. Measures such as preventing uneven surfaces and good winter maintenance seem to be effective. However, we advise more quantitative research on road factors to inform design guidelines and standards for public space authorities given the qualitative nature of current research on road factors.

Mechanisms underlying cognitive conspicuity in the detection of cyclists by car drivers

OBJECTIVE

The aim of this study was to evaluate the visibility of cyclists for motorists in a simulated car driving task.

BACKGROUND

In several cases involving collisions between cars and cyclists, car drivers failed to detect the latter in time to avoid collision because of their low conspicuity.

METHOD

2 groups of motorists (29.2 years old), including 12 cyclist-motorists and 13 non-cyclist-motorists, performed a vulnerable road user detection task in a car-driving simulator. They had to detect cyclists and pedestrians in an urban setting and evaluate the realism of the cyclists, the traffic, the city, the infrastructure, the car driven and the situations. Cyclists appeared in critical situations derived from previous accounts given by injured cyclists and from cyclists' observations in real-life situations. Cyclist's levels of visibility for car drivers were either high or low in these situations according to the cyclists.

RESULTS

Realism scores were similar and high in both groups. Cyclist-motorists had fewer collisions with cyclists and detected cyclists at a greater distance in all situations, irrespective of cyclist visibility. Several mechanisms underlying the cognitive conspicuity of cyclists for car drivers were considered.

CONCLUSION

The attentional selection of a cyclist in the road environment during car driving depends on top-down processing.

APPLICATION

We consider the practical implications of these results for the safety of vulnerable road users and future directions of research.

Interaction driver-bicyclist on rural roads: Effects of cross-sections and road geometric elements

The interaction of motorists and bicyclists, particularly during passing maneuvers, is cited as one of the primary causes of bicyclist fatalities. This paper reports the results of a driving simulator study, which sought to analyze the effects that three cross-section configurations of a two-lane rural road and four geometric elements of the road have on driver behavior, during the interaction with a cyclist. A two-lane rural road, about 11km long, was designed and implemented in an advanced-interactive driving simulator. Three different cross-sections (all with same width, but with and without a bicycle lane and for different widths of bicycle lane) were tested. Forty participants carried out three driving sessions (one for each road alignment with different cross-section) and were exposed to the condition of bicycle traffic along four geometric elements of the alignment (2 tangents with different lengths, right curve and left curve). The driving simulator experiments were designed in such a way that, along the sections where the driver-cyclist interactions occurred, the oncoming traffic was absent. Overall, 468 speed profiles and 468 lateral position profiles were plotted to obtain the descriptive variables of the driver behavior during the interaction with the cyclist. The influences of cross-sections, geometric elements and bicycle traffic conditions on driver behavior were evaluated by a multivariate variance analysis. The presence of the cyclist determined different levels of influence on driver's trajectory for the three cross-sections. A wider bicycle lane ensured a higher later clearance distance between driver and cyclist, allowing safer overtaking maneuver. The interferences of the cyclist on driver's behavior depended on the geometric elements. On tangents, the lowest lateral clearances were recorded and no speed reduction was observed, compared to the cyclist absence condition. On the left curve, the higher lateral clearance was recorded, due to the concordant tendencies of the driver to move away from the cyclist and to cut the curve. This determined an excessive and risky displacement of the vehicle to the opposing lane, whose criticality was also emphasized by the high speed adopted by the driver. On the right curve, the lateral clearance was higher than that recorded on the tangents, probably due to the necessity of the driver to perform the demanding maneuver of entering in the right curve, which also determined a speed reduction compared to the cyclist absence condition. The obtained results provide suggestions for the most efficient cross-section reorganization of existing two-lane rural roads in order to improve the road safety.

Consequences of Underestimating Impalement Bicycle Handlebar Injuries in Children

Impalement bicycle handlebar trauma injuries are rare; however, on initial assessment, they have the potential of being underestimated. We reviewed our prospective trauma database of 3,894 patients for all bicycle injuries from January 2010 to May 2015. Isolated pedal bike injuries were reported in 2.6% (N = 101) of the patients who were admitted to the trauma service. Fifteen patients suffered direct handlebar trauma. Patients were grouped into blunt trauma (n = 12) and impalement trauma (n = 3). We examined gender, age, injury severity score (ISS), Glasgow Coma Scale score, use of protective devices, need for surgical intervention, need for intensive care (ICU), and hospital length of stay. Mean age was 9.6 years. All children with penetrating injuries were males. Mean ISS was less than 9 in both groups. None of the children were wearing bicycle helmets. Three patients who sustained blunt injuries required ICU care due to associated injuries. All of the children with impalement injuries required several surgical interventions. These injuries included a traumatic direct inguinal hernia, a medial groin and thigh laceration with resultant femoral hernia, and a lateral deep thigh laceration. Impalement bicycle handlebar injuries must be thoroughly evaluated, with a similar importance given to blunt injuries. A high index of suspicion must be maintained when examining children with handlebar impalement injuries, as they are at risk for missed or underestimation of their injuries.

Bicycle helmet use among persons 5years and older in the United States, 2012

INTRODUCTION

In 2013, injuries to bicyclists accounted for 925 fatalities and 493,884 nonfatal, emergency department-treated injuries in the United States. Bicyclist deaths increased by 19% from 2010 to 2013. The greatest risk of death and disability to bicyclists is head injuries. The objective of this study was to provide estimates of prevalence and associated factors of bicycle riding and helmet use among children and adults in the United States.

METHOD

CDC analyzed self-reported data from the 2012 Summer ConsumerStyles survey. Adult respondents (18+years) were asked about bicycle riding and helmet use in the last 30days for themselves and their children (5 to 17years). For bicycle riders, CDC estimated the prevalence of helmet use and conducted multivariable regression analyses to identify factors associated with helmet use.

RESULTS

Among adults, 21% rode bicycles within the past 30days and 29% always wore helmets. Respondents reported that, of the 61% of children who rode bicycles within the past 30days, 42% always wore helmets. Children were more likely to always wear helmets (90%) when their adult respondents always wore helmets than when their adult respondents did not always wear helmets (38%). Children who lived in states with a child bicycle helmet law were more likely to always wear helmets (47%) than those in states without a law (39%).

CONCLUSIONS

Despite the fact that bicycle helmets are highly effective at reducing the risk for head injuries, including severe brain injuries and death, less than half of children and adults always wore bicycle helmets while riding.

PRACTICAL APPLICATION

States and communities should consider interventions that improve the safety of riding such as policies to promote helmet use, modeling of helmet wearing by adults, and focusing on high risk groups, including Hispanic cyclists, occasional riders, adults, and children ages 10 to 14.

Characteristics of bicycle crashes in an adolescent population in Flanders (Belgium)

INTRODUCTION

In regions where transportation is mainly motorized, air pollution and traffic congestion are rife. Active transportation such as cycling might be a solution but safety is a major concern. An efficient science based safety policy is needed. The aim of this paper is to analyze in depth the bicycle crash causes and characteristics in an adolescent population (14-18 yr).

METHODS

By using questionnaires for self-reported bicycle crashes, bicycle crash data were collected from insurance companies (January 2014-June 2015) and from schools (November 2013-March 2014). Six bicycle crash causes were predefined and possible differences between schools and insurance companies were analyzed.

RESULTS

Eighty-six school and 78 insurance registered crashes were analyzed. "Distraction of the cyclist" and "third party crossing a bicycle path failing to see the cyclist" are the main causes of bicycle crashes (both 29%). Bad (maintained) infrastructure accounted for 21% of the crash causes. Bicycle crashes reported at insurance companies needed significantly more medical attention and led to high absenteeism (57% at least one day of absenteeism). Only 21% of the bicycle crashes reported at insurance companies were also reported in the official police database.

CONCLUSION

The human factor was the main cause accounting for 79% of the crashes. Bicycle crashes involving a car accounted for 42% and single bicycle crashes accounted for 31% of the total number of crashes. From the bicycle crashes registered at insurance companies 21% was also registered in official police statistics. A combination of information, education and changing the bicycle specific environment might reduce the consequences of human errors more efficiently.

Fatal and serious injuries related to vulnerable road users in Canada

INTRODUCTION

The goals of this study were to analyze possible trends of fatal and serious injuries related to vulnerable road users in Canada (pedestrians, cyclists and motorcyclists) from 1990 to 2012 and the role of alcohol and drugs in these cases. Drugs have rarely been documented with respect to vulnerable road users.

METHOD

The Traffic Injury Research Foundation's National Fatality and Serious Injury Databases and the Public Health Agency of Canada's Canadian Hospitals Injury Reporting and Prevention Program databases were used. Numbers and rates of fatalities and serious injuries among vulnerable road users were analyzed and regression models were used to assess changes over time.

RESULTS

The analyses show that while the absolute number of fatalities and the rate per 100,000 population among vulnerable road users may be decreasing, no such trends are apparent when looking at the proportions of these road user fatalities out of all motor-vehicle fatalities. The trend for the proportion of motorcyclist fatalities is significantly increasing (coef.=0.16, p<0.001). The elderly (76years or older) are overrepresented among pedestrian fatalities, and serious injuries (they represent 18.5% of all pedestrian fatalities but only 5.8% of the population), while those 15years or younger are overrepresented among cyclists (they represent 23.3% of cyclist fatalities but 19.5% of the population), and those 16 to 25years old are overrepresented among motorcyclists (27.2% of motorcyclists fatalities and 13.6% of population). Alcohol and drug use among fatally injured vulnerable road users were significant problems, especially among pedestrians. Among fatally injured pedestrians tested for alcohol and drugs, 39.7% and 43.4% tested positive, respectively.

CONCLUSIONS

With the promotion of walking and cycling as forms of exercise and the popularity of motorcycling, the safety of vulnerable road users is an important issue. The results corroborate previous research and extend our understanding about the influence of alcohol and drugs in vulnerable road user injuries.

PRACTICAL APPLICATIONS

These findings can help better inform prevention and mitigation initiatives for vulnerable road users.