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.
This study focuses on fully investigating the effects of bicycle facilities and other contributing features on bicycle crashes at intersections. Although multiple studies have been conducted to analyze bicycle crashes and volumes, no study to date has attempted to integrate bicycle crashes and volumes at the modeling stage. Thus, a novel methodological framework for simultaneously analyzing bicycle crashes and volumes is proposed to better identify the effects of bicycle facilities and other contributing features. Kruskal–Wallis test is adopted to classify different bicycle lanes and shared paths for the modeling analysis. The modeling results reveal the significant effects of bicycle facility-related factors such as different types of bicycle lanes, the width of shared path, and the distance between the shared path and the edge of roadway on bicycle crashes. Other factors such as vehicle volume, bicycle volume, number of lanes, percentage of government and institutional land use, and population density are also found to significantly affect bicycle crashes at intersections. Based on the estimated model, the safety-in-numbers effect was identified when bicycle crash rates decrease as bicycle volumes increase. Findings in this study could assist traffic engineers to select appropriate designs to improve bicycle safety at intersections.
Road safety research in low- and middle-income countries is limited, even though ninety percent of global road traffic fatalities are concentrated in these locations. In Colombia, road traffic injuries are the second leading source of mortality by external causes and constitute a significant public health concern in the city of Bogotá. Bogotá is among the top 10 most bike-friendly cities in the world. However, bicyclists are one of the most vulnerable road-users in the city. Therefore, assessing the pattern of mortality and understanding the variables affecting the outcome of bicyclists’ collisions in Bogotá is crucial to guide policies aimed at improving safety conditions. This study aims to determine the spatiotemporal trends in fatal and nonfatal collision rates and to identify the individual and contextual factors associated with fatal outcomes. We use confidence intervals, geo-statistics, and generalized additive mixed models (GAMM) corrected for spatial correlation. The collisions’ records were taken from Bogotá’s Secretariat of Mobility, complemented with records provided by non-governmental organizations (NGO). Our findings indicate that from 2011 to 2017, the fatal bicycling collision rates per million kilometers traveled have decreased by 50 % for females and 54 % for males. Additionally, we identified high-risk areas located in the west, southwest, and southeast of the city, where the rate of occurrence of fatal events is higher than what occurs in other parts of the city. Finally, our results show associated risk factors that differ by sex. Overall, we find that fatal collisions are positively associated with factors including collisions with large vehicles, the absence of dedicated infrastructure, steep terrain, and nighttime occurrence. Our findings support policy-making and planning efforts to monitor, prioritize, and implement targeted interventions aimed at improving bicycling safety conditions while accounting for gender differences.
Between 1994 and 2008, 66 716 hospital admissions were for cycling related injuries in Canada. Between 1994 and 2003, the rate of head injuries among young people decreased by 54.0% (95% confidence interval 48.2% to 59.8%) in provinces with helmet legislation compared with 33.1% (23.3% to 42.9%) in provinces and territories without legislation. Among adults, the rate of head injuries decreased by 26.0% (16.0% to 36.3%) in provinces with legislation but remained constant in provinces and territories without legislation. After taking baseline trends into consideration, however, we were unable to detect an independent effect of legislation on the rate of hospital admissions for cycling related head injuries.
Reductions in the rates of admissions to hospital for cycling related head injuries were greater in provinces with helmet legislation, but injury rates were already decreasing before the implementation of legislation and the rate of decline was not appreciably altered on introduction of legislation. While helmets reduce the risk of head injuries and we encourage their use, in the Canadian context of existing safety campaigns, improvements to the cycling infrastructure, and the passive uptake of helmets, the incremental contribution of provincial helmet legislation to reduce hospital admissions for head injuries seems to have been minimal.
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.
Cycling is a popular activity. However there are risks associated with cycling, including facial injury. Helmets are often worn to prevent head injury. Evidence for their protection against facial injury is limited. This meta-analysis investigated the effect of bicycle helmets on the incidence of facial injury. The PubMed/MEDLINE, Google Scholar, and Cochrane Library databases were searched. Studies included were observational and involved adult participants. Paediatric studies, studies on helmet legislation, and those combining facial injuries with other injury types were excluded. The studies were evaluated by two reviewers. Risk of bias was assessed using the RevMan bias assessment tool. Odds ratios (OR) were extracted for facial injuries and facial fractures. Two meta-analyses were performed using these categories. Nine of the 102 studies identified were included. Helmets were protective against facial injury (OR 0.69, 95% confidence interval 0.63–0.75, P < 0.0001). Five studies reported facial fracture rates; helmets were protective against these also (OR 0.79 95% confidence interval 0.70–0.90, P = 0.0003). There are no randomized controlled trials on this topic and the number of studies available is small. Bicycle helmets offer protection against facial injuries and this should be considered by cyclists when deciding whether or not to use one.
Speed limit reductions from 40 km/h to 30 km/h were associated with a 28% decrease in the PMVC incidence rate in the City of Toronto (IRR = 0.72, 95% CI: 0.58–0.89). A non-significant 7% decrease in PMVC incidence rates were observed on comparator streets that remained at 40 km/h speed limits (IRR = 0.93, 95% CI: 0.70–1.25). Speed limit reduction also influenced injury severity, with a significant 67% decrease in major and fatal injuries in the post intervention period on streets with speed limit reductions (IRR = 0.33, 95% CI: 0.13–0.85) compared with a 31% not statistically significant decrease in major and fatal injuries on comparator streets (IRR = 0.69, 95% CI: 0.37–1.31). The interaction term for group and pre-post comparisons was not statistically significant (p = 0.14) indicating that there was no evidence to suggest a pre-post difference in IRRs between the intervention and comparator streets.
Investigation of pedestrian-cyclist interactions is important for understanding both objective risks and traveler comfort. There is a lack of clear understanding of conflicts between pedestrians and cyclists, as they result in few reported incidents or injuries. More research is needed on the frequency and causes of pedestrian-cyclist incidents, and how those incidents affect the comfort and perceived safety of travelers by each mode. The objectives of this research were 1) to investigate the relationship between expressed safety concerns and experienced incidents for travelers in a high-volume non-motorized shared space, and 2) to examine primary factors in pedestrian-cyclist incidents. Data were acquired from an intercept survey of 337 travelers conducted on a large university campus. Results reveal a high frequency of incidents with physical contact between people walking and cycling, consistently described by each, which led to few injuries but verified the expressed concerns of survey participants and anecdotal reporting to the campus transportation agency. Prior experience of an incident was a significant factor influencing perceptions of safety. Cyclists were at least as concerned about intermodal conflicts and safety as pedestrians and preferred to avoid pedestrian-dominated areas, but that preference was weighed against travel time, ease of wayfinding, and avoidance of motor vehicles. Both pedestrians and cyclists identified crowding and pedestrian inattention as major contributing factors to incidents, but they disagreed on whether cyclist speed was a factor. Potential strategies to reduce intermodal conflicts in shared spaces include reducing crowding, separating modes, and reducing cycling speeds.
Objectives. To quantify the impact of a citywide bicycle share program on rates of motor vehicle collisions involving a bicycle.
Methods. We conducted an interrupted time series analysis, using crash records from the Pennsylvania Department of Transportation for Philadelphia County from 2010 through 2018. We also calculated summary statistics to illustrate annual and monthly trends in rates of motor vehicle crashes involving a bicycle.
Results. The baseline rate of bike events was 106% greater (95% confidence interval [CI] = 1.25, 3.38) at the time bicycle share was implemented compared with January 2010. Before bicycle share implementation, the rate of bicycle events decreased 1% (95% CI = 0.95, 1.03) annually. After the bicycle share program started, the rate of bicycle events decreased 13% (95% CI = 0.82, 0.94) annually.
Conclusions. In the long term, programs that increase the number of bicycles on the road, such as bike share, may reduce rates of motor vehicle crashes involving a bicycle.
Objective: The primary purpose of this investigation was to identify safety-oriented bicycling practices commonly used by adult riders in an urban setting (Brooklyn, New York), and to explore whether there are any differences between the safety-oriented practices of men and women riders. Methods: 24 adult riders (14 men, 10 women) in Brooklyn were interviewed concerning their perceptions of bicycling hazards and their safety-oriented practices. Interviews were recorded, transcribed, and analyzed through thematic analysis. Fisher’s Exact Test was employed to test for gender differences. Results: Participants identified a variety of hazards, mainly due to motor vehicles but also pedestrians and roadway conditions. The analysis distilled twentyone bicycling practices to summarize prevalent views of the participants about safe riding practices. Related items were grouped under broader categories, generating seven safety-oriented bicycling strategies. Few differences based on gender were found in the analysis; however, women in this study were more likely than men to say that they felt disrespected by other road users. Conclusion: Seven strategies may be important for safe urban bicycling: minimizing exposure to other road users (especially motor vehicles) while riding, being vigilant and anticipating what others might do, riding in a predictable fashion, making one’s presence known to other road users, making sure it is safe before proceeding, obeying traffic rules, and riding at a safe speed. Future studies could develop these concepts further and test whether they are associated with involvement in traffic crashes.
The aim of the present study was to investigate the relationship between bicycle helmet use and safety behavior at signalized intersections. Two hypotheses were investigated: The first states that bicycle helmet use leads to risker behavior because of the increased sense of protection (risk compensation), the other states that helmeted cyclists have a general inclination toward safer behavior (safety package) and that helmet use is one of several behaviors for improving safety. Based on video recordings of 1031 cyclists at 12 signalized intersections in Denmark, two indicators of risky behavior were compared between helmeted and unhelmeted cyclists: Speed and time after the onset of yellow at which the cyclists crossed the stop line. Linear regression models were developed with gender, type of bicycle, and intersection characteristics as predictor variables, in addition to helmet use.Helmeted and unhelmeted cyclists do not differ in how many seconds after the onset of yellow they cross the stopping line. This is consistent with the absence of both risk compensation and safety package, alternatively with a general inclination of helmeted cyclists toward safer behavior which is about offset by risk compensation. Helmeted cyclists had higher speed on average, which indicates that risk compensation may occur. However, the higher speed may also be due to the generally better fitness of helmeted cyclists which is likely to result from larger cycling volumes. Moreover, the effect of helmet use on speed may be overestimated because of a lack of control for potential confounding variables. The results show further that, regardless of helmet use, before-red (lights on a separate bicycle signal shift to red before the main signal) is related to later crossings of the stop lane after the onset of yellow and that cyclists stop earlier on average at intersections with right-turn signals. The results do not provide support for the position that promoting or mandating bicycle helmet use will have adverse safety effects because of more risky behavior among helmeted cyclists.
The present study has investigated relationships between cyclists’ safety equipment use, crash involvement, and other safety relevant behavior. The main focus is on relationships that indicate either behavioral adaptation (safety equipment use leads to riskier behavior) or precautionary behavior (safety equipment is used for cycling in risky situations). Three consecutive surveys were conducted in 2015, 2016, and 2017 years among 650 Norwegian cyclists. Most items were dichotomized and analyzed with logistic regression models. In contrast to the behavioral adaptation hypothesis, regular use of safety equipment (bicycle lights, high-visibility clothing, and helmets) was found to be negatively related to some types of high-risk behavior (listening to music and taking chances while cycling). Regular use of bicycle lights and high-visibility clothing is also negatively related to collision involvement. Safety equipment use was found to be positively related to regular winter cycling and cycling in mixed traffic (not on sidewalks), and it is most likely used as a precautionary measure in such situations. Some cyclists learn from crash involvement by starting to use safety equipment after a crash, but the results do not indicate that crash involvement deters from cycling. The main conclusion from the study is that recommending, promoting or even mandating safety equipment for cyclists can be expected to improve safety and that behavioral adaptation is not likely to occur, at least not to an extent that will outweigh the positive safety effects. The results do not support reservations against the use of “sporty” (well-equipped) models in campaigns for promoting cycling.
An increased modal share of bicycle traffic is a key mechanism to reduce emissions and solve traffic-related problems. However, a lack of (perceived) safety keeps people from using their bikes more frequently. To improve safety in bicycle traffic, city planners need an overview of accidents, near miss incidents, and bike routes. Such information, however, is currently not available. In this paper, we describe SimRa, a platform for collecting data on bicycle routes and near miss incidents using smartphone-based crowdsourcing. We also describe how we identify dangerous near miss hotspots based on the collected data and propose a scoring model.
In this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to evaluate two main research topics. The first one is investigation of the effect of road infrastructure, road geometry, street, accident, atmospheric and cyclist related parameters on the classification of cyclist injury-severity similarly to other studies in the literature. The second one is examination of the performance of the new fuzzy decision approaches described in detail in this study for the classification of cyclist injury-severity. For this purpose, the data set containing bicycle-vehicle accidents in 2013–2017 was analyzed with the classic C4.5 algorithm and two different hybrid fuzzy decision mechanisms, namely DT-based converted FL (DT-CFL) and novel DT-based revised FL (DT-RFL). The model performances were compared according to their accuracy, precision, recall, and F-measure values. The results indicated that the parameters that have the greatest effect on the injury-severity in bicycle-vehicle accidents are gender, vehicle damage-extent, road-type as well as the highly effective parameters such as pavement type, accident type, and vehicle-movement. The most successful classification performance among the three models was achieved by the DT-RFL model with 72.0 % F-measure and 69.96 % Accuracy. With 59.22 % accuracy and %57.5 F-measure values, the DT-CFL model, rules of which were created according to the splitting criteria of C4.5 algorithm, gave worse results in the classification of the injury-severity in bicycle-vehicle accidents than the classical C4.5 algorithm. In light of these results, the use of fuzzy decision mechanism models presented in this study on more comprehensive datasets is recommended for further studies.
As downtown areas continue to grow, residents are encouraged to use alternative modes of transportation, inducing cycling. Therefore, cycling is experiencing a growth, mainly in urban areas. Although cycling offers significant health, cost, and time benefits, cyclist safety within motor vehicle traffic appears to be deteriorating. Traffic injuries and fatalities have been declining in general, but the number of cyclists who die or are severely injured in crashes has been increasing. This increase in cyclist injuries/fatalities is likely due to the existing vehicle, infrastructure, and policy designs failing to facilitate the increase in cycling volume. We present a review of countermeasures suggested or implemented to improve cycling safety and propose a taxonomy dividing these countermeasures into three design categories: gear/vehicle, infrastructure, and policy. The scope was restricted to cyclist-driver interactions since bicycle-bicycle and bicyclepedestrian collisions tend to be less severe. Based on the somewhat limited evidence reported in the literature, it appears that infrastructure design solutions are the most effective. We present an overview of the available evidence and also highlight countermeasures that require further evaluation.
Introduction: Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes. Method: The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes. The GWOLR modeling approach allows the relationships between injury severity and its contributing factors to vary across the spatial domain, to account for the spatial heterogeneity. This approach makes use of geo-referenced data. This study explored more than 7,000 geo-referenced bicycle--motor-vehicle crashes in North Carolina. Results: This study performed a series of non-stationarity tests to identify local relationships that vary substantially across the spatial domain. These local relationships are related to the bicyclist (bicyclist age, bicyclist behavior, bicyclist intoxication, bicycle direction, bicycle position), motorist (driver age, driver intoxication, driver behavior, vehicle speed, vehicle type) and traffic (traffic volume). Conclusions: Results from the regular OLR are in general consistent with previous findings. For example, an increased bicyclist injury severity is associated with older bicyclists, bicyclist being intoxicated, and higher motor-vehicle speeds. Results from the GWOLR show local (rather than global) relationships between contributing factors and bicyclist injury severity. Practical Applications: Researchers and practitioners may use GWOLR to prioritize cycling safety countermeasures for specific regions. For example, GWOLR modeling estimates in the study highlighted the west part (from Charlotte to Asheville) of North Carolina for increased bicyclist injury severity due to the intoxication of road users including both bicyclists and drivers. Therefore, if a countermeasure is concerned with the road user intoxication, there may be a priority for the region from Charlotte to Asheville (relative to other areas in North Carolina).
In Spain, the presence of cyclists’ groups riding on two-lane rural roads in a single file or in parallel line is growing. The number of overtaking maneuvers to them is also increasing. This is one of the most dangerous interactions between motor vehicles and bicycles. However, the risk of these maneuvers has not been analyzed in depth. This research analyzes the objective and subjective risk of overtaking maneuvers to cyclists’ groups. During this maneuver, the motorized vehicle overtakes the bicycles with a certain speed and lateral distance. These are the surrogate measures used to analyze the objective risk, whereas the subjective risk was analyzed based on the subjective risk perception that 10 cyclists riding instrumented bicycles (in different group configurations) indicated when every motor vehicle overtook them. Results show that the cyclists most exposed to the overtaking maneuver are those at the front and at the rear of the group. In relation to the configuration, the risk is higher in parallel lines, as the lateral clearance is lower compared with a single line. It is even higher when the overtaking maneuver is flying, which is usually performed at higher speeds and lower lateral clearance. The subjective risk perception increases with higher speed and lower lateral clearance, and is higher at the rear positions. Overtaking in which lateral distance is less than 1.5 m is perceived as the riskiest. These results provide scientific recommendations to enhance safety for cyclists’ groups, and to integrate cycling with vehicular traffic on two-lane rural roads.
Introduction. Despite bicycling being considered ten times more dangerous than driving, the evidence suggests that high-bicycling-mode-share cities are not only safer for bicyclists but for all road users. We look to understand what makes these cities safer. Are the safety differences related to ‘safety-in-numbers’ of bicyclists, or can they be better explained by built environment differences or the people that inhabit them?
Methods. Based on thirteen years of data from twelve large U.S. cities, we investigated over 17,000 fatalities and 77,000 severe injuries across nearly 8700 block groups via multilevel, longitudinal, negative binomial regression models. We hypothesize three pathways towards better road safety outcomes: i) travel behavior differences (e.g. ‘safety-in-numbers’ or shifts to ‘safer’ modes); ii) built environment differences (e.g. infrastructure that helps promote safer environments); and iii) socio-demographic/socio-economic differences (e.g. some cities may be populated by those with lower road safety risk).
Results. The results suggest that more bicyclists is not the reason these cities are safer for all road users. Better safety outcomes are instead associated with a greater prevalence of bike facilities – particularly protected and separated bike facilities – at the block group level and, more strongly so, across the overall city. Higher intersection density, which typically corresponds to more compact and lower-speed built environments, was strongly associated with better road safety outcomes for all road users. The variables representing gentrification also accounted for much of our explainable variation in safety outcomes.
Conclusion. This paper provides an evidence-based approach to building safer cities. While the policy implications of this work point to protected and separated bike infrastructure as part of the solution, we need to keep in mind that these approaches are complementary and should not be considered in isolation. Moreover, our results – particularly the safety disparities associated with gentrification – suggest equity issues and the need for future research.
In the years following the introduction of legislation in Australian states mandating the wearing of helmets, there was a decline in the number of deaths. Debate has occurred, however, as to why this occurred. The Traffic Accident Reporting System database, which records data for all police-reported crashes in South Australia, was searched for all cases of deaths occurring in the state in bicycle riders aged ≤14 years from January 1982 to December 2001. The numbers of deaths were then compared over the 10-year periods before (1982–1991) and after (1992–2001) the introduction of helmet legislation, and also on a yearly basis from 1982 to 2001. Comparing the numbers of deaths in the two periods before and after helmet legislation in 1991 showed a marked decrease in cases from 36 to 12. However, in examining the numbers of deaths per year in greater detail, it appears that these were already steadily reducing from nine cases per year in 1982 (2.9/100,000) to two cases in 1991 (0.67/100,000) to a virtual plateau after 1991 (ranging from 0 to 2 cases annually). It seems that the introduction of compulsory bicycle helmet wearing in South Australia came at a time when the numbers of child cyclist deaths had been steadily declining over the preceding decade. While helmet wearing clearly protects children who are still riding bicycles, the reasons for the reduction in numbers of deaths appears more complex than legislative change and likely involves a subtle interaction with other behavioural and societal factors and preferences.
Walkability and bikeability indices are used to succinctly quantify how conducive an environment is to walking and cycling, often including factors related to comfort and perceived safety. The potential assumption that “walkable” and “bikeable” mean safe for walking and cycling (i.e., the association with objective safety or crash risk) has not yet been examined. This study investigates the association between two widely used measures (walk score and bike score) and pedestrian and cyclist crashes in Vancouver, Canada, to determine whether more walkable and bikeable areas of the city are also safer for walking and biking, after controlling for exposure. Multivariate Bayesian crash models with random and spatial effects are developed for pedestrian–motor-vehicle and cyclist–motor-vehicle crashes in 134 traffic analysis zones using 5 years of crash data with walking, cycling, and motor-vehicle traffic volume controls for exposure. Results indicate that areas of the city with higher walkability and bikeability can be potentially associated with greater pedestrian and cyclist crash risk, respectively, even after controlling for exposure. While the clear answer is that neighborhood walkability and bikeability does not indicate safety for pedestrians and cyclists, questions remain as to whether they should, and if so, how they could be modified to better incorporate objective risk.
Cyclists are often passed by motor vehicles in low-cycling countries where most riding occurs on roads. When passing events occur, the lateral passing distance (LPD) between motor vehicles and bicycles is critically important for objective and subjective safety. This systematic review identified 42 papers reporting 36 independent studies of LPD. Seven papers reported sufficient information to be included in meta-analyses for four different variables; road width, speed limit, on-road bicycle lanes compared to no bicycle lanes and bus versus car. The meta-analyses showed significant positive relationships between LPD and road width, and between LPD and speed limit; and smaller LPDs when cyclists were passed by buses rather than cars. The effect of on-road bicycle infrastructure was inconsistent. Studies that considered cyclist factors such as gender or type of cyclist showed mixed results. Seven out of eight studies found that the closer the cyclist rode to the kerb, the larger the LPD. Lastly, the passing strategy chosen by drivers (accelerative or flying) also showed mixed results. The results of the review are consistent with the predictions of the Multiple Comfort Zone model which predicts that the distance a driver leaves between their vehicle and other road users will be based on attempting to maintain their own comfort zone. Other motor vehicles pose a greater risk to drivers than bicycles, therefore the distance a driver chooses between their vehicle and a bicycle may be compromised if there are other vehicles present or if the available travel space is restricted. As governments around the world grapple with increasing health costs caused by physical inactivity, bicycle riding is being encouraged. Given the importance of the passing manoeuvre for cyclist safety, when designing infrastructure to foster cycling, factors that influence LPD should be considered.
This study explores the contributing factors that influence bicyclist injury severity at three types of intersection: roundabouts, crossroads, and T-junctions. Using bicycle-involved crash data in the UK over nine years (from 2009 to 2017), the bicyclist injury severity (with three severity levels: fatal injury, serious injury, and slight injury) was estimated using the generalized ordered logit (GOL) model and partial proportional odds (PPO) model. The marginal effects of each explanatory variable were computed to investigate the impacts on bicyclist injury severity occurring probabilities. A wide range of variables potentially affecting injury severity was considered, including bicyclist characteristics, intersection characteristics, environmental conditions, bicyclist movement and location preceding the crash, and types of collisions. Our findings show that the PPO model outperforms the GOL model for analyzing the factors that affect the bicyclist injury severity at intersections. The factors that affect cycling safety at various intersections show enormous differences. Specifically, nine variables have significant impacts on bicyclist injury severity at those three types of intersections. And there are only two variables, four variables, and eleven variables that have significant impact on bicyclist injury severity at roundabouts, crossroads, and T-junctions, respectively. The findings of this study can help decision makers better understand the spatial heterogeneity of the factors that influence the bicyclist injury severity at various intersections.
Background
Urban planners frequently neglect the role of subjective risk perception during urban cycling. Several findings suggest a complex relationship between the risk of being involved in a crash and the subjective anticipation of this risk.
Data collection and methods
We investigate the relation of objective risks (operationalized through crashes involving cyclists) and subjective risk perception (operationalized through citizens’ reports in a crowdsourcing project) in a medium-sized German city. Using GIS methods, these datasets are linked to various infrastructure and traffic properties that have been found relevant for cycling safety.
Results
Despite a generally high alignment of objective and subjective risk, our findings highlight that the subjective risk perception at a given location can deviate significantly from the actual crash risk. For example, the subjective perception of high risk on one-way streets with bikeways in opposing direction is not matched by a high level of objective risk. Vice versa, some rather dangerous situations (e.g., tram stops) are not perceived as particularly dangerous.
Conclusions
Understanding why and where cyclists over- or underestimate the actual crash risk may provide a foundation for the design of safer cycling infrastructures, as well as for promoting cycling as a comfortable mode of transportation.
Objectives. We compared cycling injury risks of 14 route types and other route infrastructure features.
Methods. We recruited 690 city residents injured while cycling in Toronto or Vancouver, Canada. A case-crossover design compared route infrastructure at each injury site to that of a randomly selected control site from the same trip.
Results. Of 14 route types, cycle tracks had the lowest risk (adjusted odds ratio [OR] = 0.11; 95% confidence interval [CI] = 0.02, 0.54), about one ninth the risk of the reference: major streets with parked cars and no bike infrastructure. Risks on major streets were lower without parked cars (adjusted OR = 0.63; 95% CI = 0.41, 0.96) and with bike lanes (adjusted OR = 0.54; 95% CI = 0.29, 1.01). Local streets also had lower risks (adjusted OR = 0.51; 95% CI = 0.31, 0.84). Other infrastructure characteristics were associated with increased risks: streetcar or train tracks (adjusted OR = 3.0; 95% CI = 1.8, 5.1), downhill grades (adjusted OR = 2.3; 95% CI = 1.7, 3.1), and construction (adjusted OR = 1.9; 95% CI = 1.3, 2.9).
Conclusions. The lower risks on quiet streets and with bike-specific infrastructure along busy streets support the route-design approach used in many northern European countries. Transportation infrastructure with lower bicycling injury risks merits public health support to reduce injuries and promote cycling.
In order to maximize the public health benefits of cycling, the negative impacts of cycling, such as the number and types of crashes, should be identified. Single-bicycle crashes, in which other road users are not collided with, are one of the main safety concerns in cycling, but comprehensive knowledge on these crashes is not available due to poor data sources. This study aimed to identify characteristics of commuters’ single-bicycle crashes in Finland. Firstly, insurance data covering 9268 commuter bicycle crashes in 2016 and 2017 were analyzed to find single-bicycle crashes. The insurance data are based on self-reported crashes. In total, 3448 single-bicycle crashes were found with crash descriptions that were informative enough for investigation of their characteristics. According to the results, 62.9% (95% confidence interval +/− 1.6%) of the crashes were related to the infrastructure. In the majority of infrastructure-related crashes, the road surface was slippery. The slippery road surface was typically due to icy or snowy conditions. The lack of proper data complicates the recognition of single-bicycle crashes, and hence policy actions and research.
5704 (2.5%) participants reported cycling as their main form of commuter transport. Median follow-up was 8.9 years (interquartile range 8.2-9.5 years), and overall 10 241 (4.4%) participants experienced an injury. Injuries occurred in 397 (7.0%) of the commuters who cycled and 7698 (4.3%) of the commuters who used a non-active mode of transport. After adjustment for major confounding sociodemographic, health, and lifestyle factors, cycling to work was associated with a higher risk of injury compared with commuting by a non-active mode (hazard ratio 1.45, 95% confidence interval 1.30 to 1.61). Similar trends were observed for commuters who used mixed mode cycling. Walking to work was not associated with a higher risk of injury. Longer cycling distances during commuting were associated with a higher risk of injury, but commute distance was not associated with injury in non-active commuters. Cycle commuting was also associated with a higher number of injuries when the external cause was a transport related incident (incident rate ratio 3.42, 95% confidence interval 3.00 to 3.90). Commuters who cycled to work had a lower risk of cardiovascular disease, cancer, and death than those who did not. If the associations are causal, an estimated 1000 participants changing their mode of commuting to include cycling for 10 years would result in 26 additional admissions to hospital for a first injury (of which three would require a hospital stay of a week or longer), 15 fewer first cancer diagnoses, four fewer cardiovascular disease events, and three fewer deaths.
Compared with non-active commuting to work, commuting by cycling was associated with a higher risk of hospital admission for a first injury and higher risk of transport related incidents specifically. These risks should be viewed in context of the health benefits of active commuting and underscore the need for a safer infrastructure for cycling in the UK.
Evaluating the level of service (LOS) of bicycle lanes is essential in planning, designing, and maintaining bicycle infrastructure. This study presents a new concept—the cyclist domain—for characterizing interactions between cyclists and proposes a cyclist-domain-based method for evaluating the LOS of bicycle lanes. Unlike conventional bicycle lane LOS evaluation method, which counts the weighted events numbers and may generate inaccurate results and even misleading decisions, we specified four types of overlapping cyclist domains to characterize potential impacts in various riding events. The cyclist domain influence ratio (CDIR) derived from cycling trajectories is used to measure the negative impact between cyclists. We then propose an LOS evaluation method for bicycle lanes based on CDIRs. The user comfort is defined as the sum of products of CDIRs and respective durations. The bicycle lane LOS is then evaluated by the average comfort of all users. The case study shows that the proposed method can efficiently and effectively evaluate the bicycle lane LOS.