Cyclist's travel distances and risk of falls in snowy and icy conditions in German cities.

INTRODUCTION: So far, no studies are known that estimate distance-based risks for cyclist falls in snowy/icy conditions compared to other conditions to account for differences in cycling levels in the different weather situations. METHOD: The number of cyclist falls was gathered from retrospective surveys in Germany. Cycling distances were obtained from the German Household Travel Survey "Mobility in cities - SrV," assigned to meteorological data, and validated against counts and own surveys. The number of falls per distance cycled and Risk Ratios for snowy/icy versus other weather conditions were estimated. RESULTS: An average decrease of 53% in the distance travelled per person and day is estimated for snowy/icy days versus other days. This decrease is lower in regions with higher general cycling mode shares. We find average risks of falls from 9.5 to 16 (field surveys) up to 76.5 falls per 10,000 km (online survey) and average Risk Ratios for cycling in snowy/icy conditions of 20 (field survey conducted in times of other weather) to 36 (field survey conducted in times of snow/ice) and 38 (online survey conducted in times of snow/ice). The risk of suffering an injury in the event of a fall is lower in snowy/icy compared to other weather conditions. CONCLUSIONS: Seeing the current trend of growing general cycling levels in Germany, we expect more cycling in winter and, in case of unchanged winter weather and maintenance, a substantial increase of cyclist falls. The reduced risk of being injured in the event of a fall in snowy/icy conditions does not outweigh the higher risk of falling in the first place. PRACTICAL APPLICATIONS: Improved winter maintenance on cycling facilities can help increase winter cycling and reduce the risk of falls at the same time.

A Demand-Centric Repositioning Strategy for Bike-Sharing Systems.

Transport-sharing systems are eco-friendly and the most promising services in smart urban environments, where the booming Internet of things (IoT) technologies play an important role in the smart infrastructure. Due to the imbalanced bike distribution, bikes and stalls in the docking stations could be unavailable when needed, leading to bad customer experiences. We develop a dynamic repositioning strategy for the management of bikes in this paper, which supports dispatchers to keep stations in service. Two open datasets are examined, and the exploratory data analysis presents that there is a significant difference of travel patterns between working and non-working days, where the former has an excess demand at rush hours and the latter is usually at a low demand. To evaluate the effect when the demand outstrips a station's capacity, we propose a non-linear scaling technique to transform demand patterns and perform the clustering analysis for each of five categories obtained from the sophisticated analysis of the dataset. Our repositioning strategy is developed according to the transformed demands. Compared with the previous work, numerical simulations reveal that our strategy has a better performance for high-demand stations, and thus can substantially reduce the repositioning cost, which brings benefit to bike-sharing operators for managing the city bike system.

Can passive pedaling improve sexual function in patients under hemodialysis? A randomized clinical trial.

To investigate the effect of passive pedaling with mini bike on sexual function in patients under hemodialysis. This study was a randomized clinical trial. Thirty-seven patients undergoing hemodialysis were assigned to the intervention (n = 20) and control (n = 17) groups by the stratified block randomization method. The intervention group exercised with a mini bike that was automatic and tuned for patients during the first 2 h of dialysis, twice a week for 20 min each time, for 3 months. The International Index of Erectile Function and Female Sexual Function Index were used to assess the sexual function in the first, second, and third months during the intervention and one month after the intervention. A higher score indicates a better sexual function. Repeated measure ANOVA, Chi-square and Fisher exact tests, independent t, and Mann-Whitney U tests were used for data analysis. The SPSS software version 22 was used for data analysis. Sexual function scores of the intervention group were 35.9 at the beginning of the study, 34.1 in the first month, 37.4 in the second month, 34.8 in the third month, and 31.7 one month after the study. There was no significant difference in the scores of sexual function in the intervention group during the study. The mean scores of sexual function in the control group were 34.5, 34.4, 34.9, 33.8, and 33.9 at the beginning of the study, in the first month, in the second month, in the third month, and one month after the study, respectively (p > 0.05). There was no significant difference between the two groups in terms of sexual function scores during and after the intervention (p > 0.05). Passive pedaling with mini-bike had no effect on sexual function of hemodialysis patients.

Caffeine increases performance and leads to a cardioprotective effect during intense exercise in cyclists.

The present study was designed to investigate the effects of different caffeine dietary strategies to compare the impact on athletic performance and cardiac autonomic response. The order of the supplementation was randomly assigned: placebo(4-day)-placebo(acute)/PP, placebo(4-day)-caffeine(acute)/PC and caffeine(4-day)-caffeine(acute)/CC. Fourteen male recreationally-trained cyclists ingested capsules containing either placebo or caffeine (6 mg kg-1) for 4 days. On day 5 (acute), capsules containing placebo or caffeine (6 mg kg-1) were ingested 60 min before completing a 16 km time-trial (simulated cycling). CC and PC showed improvements in time (CC vs PP, Δ - 39.3 s and PC vs PP, Δ - 43.4 s; P = 0.00; ƞ2 = 0.33) and in output power (CC vs PP, Δ 5.55 w and PC vs PP, Δ 6.17 w; P = 0.00; ƞ2 = 0.30). At the final of the time-trial, CC and PC exhibited greater parasympathetic modulation (vagal tone) when compared to the PP condition (P < 0.00; ƞ2 = 0.92). Our study provided evidence that acute caffeine intake (6 mg∙kg-1) increased performance (time-trial) and demonstrated a relevant cardioprotective effect, through increased vagal tone.

Effect of the COVID-19 pandemic on bike-sharing demand and hire time: Evidence from Santander Cycles in London.

The COVID-19 pandemic has been influencing travel behaviour in many urban areas around the world since the beginning of 2020. As a consequence, bike-sharing schemes have been affected-partly due to the change in travel demand and behaviour as well as a shift from public transit. This study estimates the varying effect of the COVID-19 pandemic on the London bike-sharing system (Santander Cycles) over the period March-December 2020. We employed a Bayesian second-order random walk time-series model to account for temporal correlation in the data. We compared the observed number of cycle hires and hire time with their respective counterfactuals (what would have been if the pandemic had not happened) to estimate the magnitude of the change caused by the pandemic. The results indicated that following a reduction in cycle hires in March and April 2020, the demand rebounded from May 2020, remaining in the expected range of what would have been if the pandemic had not occurred. This could indicate the resiliency of Santander Cycles. With respect to hire time, an important increase occurred in April, May, and June 2020, indicating that bikes were hired for longer trips, perhaps partly due to a shift from public transit.

Understanding the intention to use bike-sharing system: A case study in Xi'an, China.

Bike-sharing is widely recognized as an eco-friendly mode of transportation and seen as one of the solutions to the problem of air pollution and congestion. With the emphasis on sustainability in transportation, bike-sharing systems is an emerging topic of urban transport and sustainable mobility related research. Existing studies mainly explored the factors affecting individuals' initial intentions to start using a shared bicycle, but few looked at the likelihood that a user would continue using one This study proposed a structural equation model with bike-sharing purchase decision involvement as independent variable, bike-sharing willingness to use as dependent variable, traveler participation and traveler perceived value as intermediary variables by introducing the concepts of purchase decision involvement, customer participation and perceived value in consumer psychology and behavior. A survey on bike-sharing users in Xi'an was conducted online and offline, and 622 effective responses were collected. The research model was tested by Amos 24.0 and the empirical results showed that All influencing factors including bike-sharing's purchase decision-making involvement, traveler participation and traveler's perceived value are found to be significantly and positively associated with usage intention; traveler perceived value play a chain-mediating role between bike-sharing purchase decision involvement and usage intention; bike-sharing purchase decision involvement have indirect effects on traveler perceived value through traveler participation. The results of this study enrich the current research's in the field of sharing economy, and it is certain guiding significant for how to obtain and maintain stable customers in bicycle-sharing industry.

Sociodemographic inequities and active transportation in adults from Latin America: an eight-country observational study.

BACKGROUND: Active transportation is a crucial sort of physical activity for developing sustainable environments and provides essential health benefits. This is particularly important in Latin American countries because they present the highest burden of non-communicable diseases relative to other worldwide regions. This study aimed to examine the patterns of active transportation and its association with sociodemographic inequities in Latin American countries. METHODS: This cross-sectional study was conducted in eight countries. Participants (n = 8547, 18-65 years) self-reported their active transportation (walking, cycling, and total) using the International Physical Activity Questionnaire. Sex, age, ethnicity, socioeconomic level, education level, public and private transport use, and transport mode were used as sociodemographic inequities. RESULTS: Participants spent a total of 19.9, 3.1, and 23.3 min/day with walking, cycling, and total active transportation, respectively. Mixed and other ethnicity (Asian, Indigenous, Gypsy, and other), high socioeconomic level as well as middle and high education level presented higher walking than Caucasian, low socioeconomic and education level. Private transport mode and use of ≥ 6 days/week of private transport showed lower walking than public transport mode and ≤ 2 days/week of private transport. Use of ≥ 3 days/week of public transport use presented higher walking than ≤ 2 days/week of public transport. Men had higher cycling for active transportation than women. Use of ≥ 3 days/week of public transport use presented higher cycling than ≤ 2 days/week of public transport. ≥6 days/week showed lower cycling than ≤ 2 days/week of private transport use. Men (b: 5.57: 95 %CI: 3.89;7.26), black (3.77: 0.23;7.31), mixed (3.20: 1.39;5.00) and other ethnicity (7.30: 2.55;12.04), had higher total active transportation than women and Caucasian. Private transport mode (-7.03: -11.65;-2.41) and ≥ 6 days/week of private transport use (-4.80: -6.91;-0.31) showed lower total active transportation than public transport mode and ≤ 2 days/week of private transport use. Use of 3-5 (5.10: 1.35;8.85) and ≥ 6 days/week (8.90: 3.07;14.73) of public transport use presented higher total active transportation than ≤ 2 days/week of public transport use. Differences among countries were observed. CONCLUSIONS: Sociodemographic inequities are associated differently with active transportation across Latin American countries. Interventions and policies that target the promotion of active policies transportation essential to consider sociodemographic inequities. TRIAL REGISTRATION: ClinicalTrials.Gov NCT02226627. Retrospectively registered on August 27, 2014.

Quantifying the impact of COVID-19 on e-bike safety in China via multi-output and clustering-based regression models.

The impacts of COVID-19 on travel demand, traffic congestion, and traffic safety are attracting heated attention. However, the influence of the pandemic on electric bike (e-bike) safety has not been investigated. This paper fills the research gap by analyzing how COVID-19 affects China's e-bike safety based on a province-level dataset containing e-bike safety metrics, socioeconomic information, and COVID-19 cases from 2017 to 2020. Multi-output regression models are adopted to investigate the overall impact of COVID-19 on e-bike safety in China. Clustering-based regression models are used to examine the heterogeneous effects of COVID-19 and the other explanatory variables in different provinces/municipalities. This paper confirms the high relevance between COVID-19 and the e-bike safety condition in China. The number of COVID-19 cases has a significant negative effect on the number of e-bike fatalities/injuries at the country level. Moreover, two clusters of provinces/municipalities are identified: one (cluster 1) with lower and the other (cluster 2 that includes Hubei province) higher number of e-bike fatalities/injuries. In the clustering-based regressions, the absolute coefficients of the COVID-19 feature for cluster 2 are much larger than those for cluster 1, indicating that the pandemic could significantly reduce e-bike safety issues in provinces with more e-bike fatalities/injuries.

Effect of COVID-19 on epidemiological characteristics of road traffic injuries in Suzhou: a retrospective study.

BACKGROUND: To present the new trends in epidemiology of road traffic injuries (RTIs) during the Coronavirus disease 2019 (COVID-19) pandemic in Suzhou. METHODS: Pre-hospital records of RTIs from January to May in 2020 and the same period in 2019 were obtained from the database of Suzhou pre-hospital emergency center, Jiangsu, China. Data were extracted for analysis, including demographic characteristics, pre-hospital vital signs, transport, shock index, consciousness, pre-hospital death. A retrospective study comparing epidemiological characteristics of RTIs in Suzhou during the 5-month period in 2020 to the parallel period in 2019 was performed. RESULTS: A total of 7288 RTIs in 2020 and 8869 in 2019 met inclusion criteria. The overall volume of RTIs has statistical difference between the 2 years (p < 0.001), with fewer RTIs in 2020 compared with 2019. Electric bicycle related RTIs increased during the pandemic (2641, 36.24% vs 2380, 26.84%, p < 0.001), with a higher incidence of RTIs with disorder of consciousness (DOC) (7.22% vs 6.13%, p = 0.006). CONCLUSIONS: Under the impact of COVID-19, the total number of RTIs in Suzhou from January to May 2020 decreased. This observation was coupled with a rise in electric bicycle related injuries and an increase in the incidence of RTIs with DOC.

Comparison of motor vehicle-involved e-scooter and bicycle crashes using standardized crash typology.

INTRODUCTION: The market share of e-scooters in the United States has proliferated in cities: 86 million trips were made on shared e-scooters in 2019, a more than 100% increase compared to 2018. However, the interaction of e-scooters with other road users and infrastructure remains uncertain. METHOD: This study scrutinized 52 e-scooter and 79 bicycle police-reported crashes in Nashville, Tennessee, from April 2018 to April 2020 from the Tennessee Integrated Traffic Analysis Network (TITAN) database. We used descriptive analysis and a recent prototype version of the Pedestrian and Bicycle Crash Analysis Tool (PBCAT) to classify crashes based on the locations of the crashes relative to roadway segments or intersections, as well as the maneuver of the motor vehicle and e-scooter/bicycle relative to the motor vehicle. RESULTS: Two crash typologies can explain the majority of e-scooter crashes, while bicycle crashes are distributed over several crash typologies. Additionally, 1 in 10 e-scooter- and bicycle-motor vehicle crashes leads to the injury or fatality of the e-scooter rider or bicyclist. Furthermore, we noted statistically significant differences in spatial and temporal distribution, demographics, lighting conditions, and crash distance from home for e-scooter and bicycle crashes. CONCLUSIONS: The police crash report provides a comprehensive picture of e-scooter safety complementing existing literature. We found that e-scooter crash characteristics do not fully overlap with features of bicycle crashes. PRACTICAL IMPLICATIONS: A generalized engineering, education, and enforcement treatment to reduce and prevent e-scooter and bicycle crashes, injuries, and fatalities might not result in equal outcomes for each mode. More rigorous enforcement could be implemented to deter e-scooters riders under the age of 18 years and e-scooter safety campaigns could target female riders.

Behavioral pathways in bicycle-motor vehicle crashes: From contributing factors, pre-crash actions, to injury severities.

INTRODUCTION: This study performed a path analysis to uncover the behavioral pathways (from contributing factors, pre-crash actions to injury severities) in bicycle-motor vehicle crashes. METHOD: The analysis investigated more than 7,000 bicycle-motor vehicle crashes in North Carolina between 2007 and 2014. Pre-crash actions discussed in this study are actions of cyclists and motorists prior to the event of a crash, including "bicyclist failed to yield," "motorist failed to yield," "bicyclist overtaking motorist," and "motorist overtaking bicyclist." RESULTS: Model results show significant correlates of pre-crash actions and bicyclist injury severity. For example, young bicyclists (18 years old or younger) are 23.5% more likely to fail to yield to motor traffic prior to the event of a crash than elder bicyclists. The "bicyclist failed to yield" action is associated with increased bicyclist injury severity than other actions, as this behavior is associated with an increase of 5.88 percentage points in probability of a bicyclist being at least evidently injured. The path analysis can highlight contributing factors related to risky pre-crash actions that lead to severe injuries. For example, bicyclists traveling on regular vehicle travel lanes are found to be more likely to involve the "bicyclist failed to yield" action, which resulted in a total 44.38% (7.04% direct effect + 37.34% indirect effect) higher likelihood of evident or severe injuries. The path analysis can also identify factors (e.g., intersection) that are not directly but indirectly correlated with injury severity through pre-crash actions. Practical Applications: This study offers a methodological framework to quantify the behavioral pathways in bicycle-motor vehicle crashes. The findings are useful for cycling safety improvements from the perspective of bicyclist behavior, such as the educational program for cyclists.

The National Interscholastic Cycling Association Mountain Biking Injury Surveillance System: 40,000 Student-Athlete-Years of Data.

ABSTRACT: A web-based injury surveillance system was implemented through a collaboration between University of Utah researchers and the National Interscholastic Cycling Association (NICA) to better understand injury characteristics in mountain biking. Data were collected from NICA leagues during the 2018 and 2019 seasons. Injuries were tracked in 41,327 student-athlete-years, identifying 1750 unique injuries during 1155 injury events. Rider-dependent and rider-independent variables were analyzed. The most commonly reported injuries were concussion (23.6%), injuries to the wrist/hand (22.3%), and shoulder (15.6%). Half of all injury events occurred on downhills. Men and women reported similar yet significantly different injury rates (2.69% and 3.21%, respectively; P = 0.009). Women sustained more lower-limb injuries (37.8% vs 28.3%; P = 0.003). Nearly 50% of crashes resulted in an emergency room visit. Youth mountain bike racing is a rapidly growing sport. Acute traumatic injuries are common. Injury surveillance system data are now being used to inform injury prevention strategies and direct future research.

Injuries related to electric scooter and bicycle use in a Washington, DC, emergency department.

OBJECTIVE: E-scooter use has grown rapidly in the United States. Its rise in popularity has coincided with the promotion of cycling in many cities, but more needs to be known about how these transportation modes compare to determine if cycling should serve as an appropriate benchmark for policy decisions and safety expectations regarding e-scooters. METHODS: We examined characteristics of adults seeking treatment in a Washington, DC, emergency department (ED) for injuries associated with riding e-scooters during 2019 (n = 99) or bicycles during 2015-2017 (n = 337). RESULTS: E-scooter incidents less frequently involved moving vehicles (13.1% vs. 37.7%) or occurred on roads (24.5% vs. 50.7%) than cycling incidents. A smaller proportion of injured e-scooter riders were ages 30-49 (32.3% vs. 48.4%) and a larger proportion were 50 and older (34.3% vs. 22.6%) or female (45.5% vs. 29.1%). Distal lower extremity injuries were more common among e-scooter riders (13.1% vs. 3.0%; RR, 2.76; 95% CI, 1.79-3.54), and injuries to the proximal upper extremity (9.1% vs. 20.5%; RR, 0.49; 95% CI, 0.24-0.92) or chest, abdomen, and spine (3.0% vs. 14.0%; RR, 0.24; 95% CI, 0.07-0.70) were less common. Head injury rates were similar, but e-scooter riders more often experienced concussion with loss of consciousness (4.0% vs. 0.6%; RR, 3.03; 95% CI, 1.20-4.09) and were far less likely to wear helmets (2.0% vs. 66.4%). Estimated ED presentation rates per million miles traveled citywide were higher among e-scooter riders than cyclists (RR, 3.76; 95% CI, 3.08-4.59). CONCLUSIONS: E-scooters and bicycles are both popular forms of micromobility, but the characteristics of riders injured on them, the ways in which they become injured, and the types of injuries they sustain differ substantially. E-scooter rider injury rates, though currently high, may decrease as they gain experience; however, if the number of new users continues to climb, they will persist in using the ED more often than cyclists per mile that they travel.

Provisional COVID-19 infrastructure induces large, rapid increases in cycling.

The bicycle is a low-cost means of transport linked to low risk of transmission of infectious disease. During the COVID-19 crisis, governments have therefore incentivized cycling by provisionally redistributing street space. We evaluate the impact of this new bicycle infrastructure on cycling traffic using a generalized difference in differences design. We scrape daily bicycle counts from 736 bicycle counters in 106 European cities. We combine these with data on announced and completed pop-up bike lane road work projects. Within 4 mo, an average of 11.5 km of provisional pop-up bike lanes have been built per city and the policy has increased cycling between 11 and 48% on average. We calculate that the new infrastructure will generate between $1 and $7 billion in health benefits per year if cycling habits are sticky.

Investigation of accidents involving powered two wheelers and bicycles - A European in-depth study.

INTRODUCTION: The number of road fatalities have been falling throughout the European Union (EU) over the past 20 years and most Member States have achieved an overall reduction. Research has mainly focused on protecting car occupants, with car occupant fatalities reducing significantly. However, recently there has been a plateauing in fatalities amongst 'Vulnerable Road Users' (VRUs), and in 2016 accidents involving VRUs accounted for nearly half of all EU road deaths. METHOD: The SaferWheels study collected in-depth data on 500 accidents involving Powered Two-Wheelers (PTWs) and bicycles across six European countries. A standard in-depth accident investigation methodology was used by each team. The Driver Reliability and Error Analysis Method (DREAM) was used to systematically classify accident causation factors. RESULTS: The most common causal factors related to errors in observation by the PTW/bicycle rider or the driver of the other vehicle, typically called 'looked but failed to see' accidents. Common scenarios involved the other vehicle turning or crossing in front of the PTW/bicycle. A quarter of serious or fatal injuries to PTW riders occurred in accidents where the rider lost control with no other vehicle involvement. CONCLUSIONS: Highly detailed data have been collected for 500 accidents involving PTWs or bicycles in the EU. These data can be further analyzed by researchers on a case-study basis to gain detailed insights on such accidents. Preliminary analysis suggests that 'looked but failed to see' remains a common cause, and in many cases the actions of the other vehicle were the critical factor, though PTW rider speed or inexperience played a role in some cases. Practical Applications: The collected data can be analyzed to better understand the characteristics and causes of accidents involving PTWs and bicycles in the EU. The results can be used to develop policies aimed at reducing road deaths and injuries to VRUs.

Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model.

INTRODUCTION: As a convenient and affordable means of transportation, the e-bike is widely used by different age rider groups and for different travel purposes. The underlying reasons for e-bike riders suffering from severe injury may be different in each case. METHOD: This study aims to examine the underlying risk factors of severe injury for different groups of e-bike riders by using a combined method, integration of a classification tree and a logistic regression model. Three-year of e-bike crashes occurring in Hunan province are extracted, and risk factor including rider's attribute, opponent vehicle and driver's attribute, improper behaviors of riders and drivers, road, and environment characteristics are considered for this analysis. RESULTS: E-bike riders are segmented into five groups based on the classification tree analysis, and the group of non-occupational riders aged over 55 in urban regions is associated with the highest likelihood of severe injury among the five groups. The logistics analysis for each group shows that several risk factors such as high-speed roads have commonly significant effects on injury severity for different groups; while major factors only have significant effects for specific groups. PRACTICAL APPLICATION: Based on model results, policy implications to alleviate the crash injury for different e-bike riders groups are recommended, which mainly include enhanced education and enforcement for e-bike risky behaviors, and traffic engineering to regulate the use of e-bikes on high speed roads.

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.

Improving knowledge of cyclist crashes based on hospital data including crash descriptions from open text fields.

INTRODUCTION: In this study we explore the added value of bicycle crash descriptions from open text fields in hospital records from the Aarhus municipality in Denmark. We also explore how bicycle crash data from the hospital complements crash data registered by the police in the same area and time period. METHOD: The study includes 5,313 Danish bicycle crashes, of which 4,205 were registered at the hospital and 1,078 by the police. All crashes occurred from 2010 to 2015. We performed an in-depth analysis of the open text fields on hospital records to identify factors associated with each crash using four categories: bicyclist, road, bicycle, and the other party. We employed the chi-squared test to compare the distribution of variables between crashes registered at the hospital and by the police. A binary logit model was used to estimate the probability that a crash factor is identified, and that each crash factor is associated with a single-bicycle crash. RESULTS: The open-ended text fields in hospital records provide detailed information about crash factors not available in police records, including riding speed, inattention, clothing, specific road conditions, and bicycle defects. The factors alcohol and curb had the highest odds of being identified in relation to a single-bicycle crash. Crash data registered at the hospital included a larger number of bicycle crashes, particularly single-bicycle crashes and crashes with slight injuries only. CONCLUSION: Crash information registered at the hospital in Aarhus Municipality contributes to a better understanding of bicycle crashes due to detailed information about crash-associated factors as well as information about a larger number of bicycle crashes, particularly single-bicycle crashes. Practical implication: Efforts to improve access to detailed information about bicycle crashes are needed to provide a better basis for bicycle crash prevention.