Critical factors in fatal collisions of adult cyclists with automobiles

Injury severity prediction of cyclist crashes using random forests and random parameters logit models

Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016-2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55-64 (serious prediction). The model's McFadden Pseudo R2 is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.

Resilience engineering on the road: Using operator event sequence diagrams and system failure analysis to enhance cyclist and vehicle interactions

Future visions of transport systems include both a drive towards automated vehicles and the need for sustainable, active, modes of travel. The combination of these requirements needs careful consideration to ensure the integration of automated vehicles does not compromise vulnerable road users. Transport networks need to be resilient to automation integration, which requires foresight of possible challenges in their interaction with other road users. Focusing on a cyclist overtake scenario, the application of operator event sequence diagrams and a predictive systems failure method provide a novel way to analyse resilience. The approach offers the opportunity to review how automation can be positively integrated into road transportation to overcome the shortfalls of the current system by targeting organisational, procedural, equipment and training measures.

Influences of Urban Bikeway Design and Land Use on Bike Collision Severity: Evidence from Pohang in South Korea

Since the COVID-19 pandemic significantly impacted mobility patterns, bicycle use and bikeway infrastructure have been spotlighted worldwide. Even before the pandemic, the Korean government expanded bike networks by enacting a national policy. However, issues with bikeway quality have grown significantly due to the excessive installation of bike infrastructure without due consideration given to land use and road conditions. This study aims to investigate bike collision severity based on various kinds of bikeways and land use data from Pohang city, which has a remarkably expanded bikeway network. Bike collision data were analyzed using binary logistic regression. The results indicate that collision severity is associated with the ineffective separation of bikeways, walkway design and pavement materials on side paths, uncleared bike lanes, and land use of planned parks and open spaces. Policymakers can consider these factors to enhance the safety of bikeway networks. These findings highlight the need for bikeway policy improvements for well-designed and managed paved bikeways, particularly on the side paths and in park areas, which ensure pedestrian and cyclist safety.

Prediction of Severe Injury in Bicycle Rider Accidents: A Multicenter Observational Study

Introduction

This study aimed to establish a predictive model that includes physiological parameters and identify independent risk factors for severe injuries in bicycle rider accidents.

Methods

This was a multicenter observational study. For four years, we included patients with bicycle rider injuries in the Emergency Department-Based Injury In-depth Surveillance database. In this study, we regarded ICD admission or in-hospital mortality as parameters of severe trauma. Univariate and multivariate logistic regression analyses were performed to assess risk factors for severe trauma. A receiver operating characteristic (ROC) curve was generated to evaluate the performance of the regression model.

Results

This study included 19,842 patients, of whom 1,202 (6.05%) had severe trauma. In multivariate regression analysis, male sex, older age, alcohol use, motor vehicle opponent, load state (general and crosswalk), blood pressure, heart rate, respiratory rate, and Glasgow Coma Scale were the independent factors for predicting severe trauma. In the ROC analysis, the area under the ROC curve for predicting severe trauma was 0.848 (95% confidence interval: 0.830–0.867).

Conclusion

We identified independent risk factors for severe trauma in bicycle rider accidents and believe that physiologic parameters contribute to enhancing prediction ability.

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.

The Analysis of the Factors Influencing the Severity of Bicyclist Injury in Bicyclist-Vehicle Crashes

Transportation and technological development have for centuries strongly influenced the shaping of urbanized areas. On one hand, it undoubtedly brings many benefits to their residents. However, also has a negative impact on urban areas and their surroundings. Many transportation and technological solutions lead, for example, to increased levels of pollution, noise, excessive energy use, as well as to traffic accidents in cities. So, it is important to safe urban development and sustainability in all city aspects as well as in the area of road transport safety. Due to the long-term policy of sustainable transport development, cycling is promoted, which contributes to the increase in the number of this group of users of the transport network in road traffic for short-distance transport. On the one hand, cycling has a positive effect on bicyclists’ health and environmental conditions, however, a big problem is an increase in the number of serious injuries and fatalities among bicyclists involved in road incidents with motor vehicles. This study aims to identify factors that influence the occurrence and severity of bicyclist injury in bicyclist-vehicle crashes. It has been observed that the factors increasing the risk of serious injuries and deaths of bicyclists are: vehicle driver gender and age, driving under the influence of alcohol, exceeding the speed limit by the vehicle driver, bicyclist age, cycling under the influence of alcohol, speed of the bicyclist before the incident, vehicle type (truck), incident place (road), time of the day, incident type. The obtained results can be used for activities aimed at improving the bicyclists’ safety level in road traffic in the area of analysis.

Real-Time Nanoscopic Rider Safety System for Smart and Green Mobility Based upon Varied Infrastructure Parameters

To create a safe bicycle infrastructure system, this article develops an intelligent embedded learning system using a combination of deep neural networks. The learning system is used as a case study in the Northumbria region in England’s northeast. It is made up of three components: (a) input data unit, (b) knowledge processing unit, and (c) output unit. It is demonstrated that various infrastructure characteristics influence bikers’ safe interactions, which is used to estimate the riskiest age and gender rider groups. Two accurate prediction models are built, with a male accuracy of 88 per cent and a female accuracy of 95 per cent. The findings concluded that different infrastructures pose varying levels of risk to users of different ages and genders. Certain aspects of the infrastructure are hazardous to all bikers. However, the cyclist’s characteristics determine the level of risk that any infrastructure feature presents. Following validation, the built learning system is interoperable under various scenarios, including current heterogeneous and future semi-autonomous and autonomous transportation systems. The results contribute towards understanding the risk variation of various infrastructure types. The study’s findings will help to improve safety and lead to the construction of a sustainable integrated cycling transportation system.

An in-depth analysis of self-reported cycling injuries in single and multiparty bicycle crashes in Denmark

INTRODUCTION

Cycling is one of the main forms of transportation in Denmark. However, while the number of traffic crash fatalities in the country has decreased over the past decade, the frequency of cyclists killed or seriously injured has increased. The high rate of serious injuries and fatalities associated with cycling emphasizes the increasing need for mitigating the severity of such crashes.

METHOD

This study conducted an in-depth analysis of cyclist injury severity resulting from single and multiparty bicycle-involved crashes. Detailed information was collected using self-reporting data undertaken in Denmark for a 12-month period between 1 November 2012 and 31 October 2013. Separate multilevel logistic (MLL) regression models were applied to estimate cyclist injury severity for single and multiparty crashes. The goodness-of-fit measures favored the MLL models over the standard logistic models, capturing the intercorrelation among bicycle crashes that occurred in the same geographical area.

RESULTS

The results also showed that single bicycle-involved crashes resulted in more serious outcomes when compared to multiparty crashes. For both single and multiparty bicycle crash categories, non-urban areas were associated with more serious injury outcomes. For the single crashes, wet surface condition, autumn and summer seasons, evening and night periods, non-adverse weather conditions, cyclists aged between 45 and 64 years, male sex, riding for the purpose of work or educational activities, and bicycles with light turned-off were associated with severe injuries. For the multiparty crashes, intersections, bicycle paths, non-winter season, not being employed or retired, lower personal car ownership, and race bicycles were directly related to severe injury consequences. Practical Applications: The findings of this study demonstrated that the best way to promote cycling safety is the combination of improving the design and maintenance of cycling facilities, encouraging safe cycling behavior, and intensifying enforcement efforts.

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

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

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.

Cycling as a Sustainable Transport Alternative in Polish Cittaslow Towns

It is well known that growing motor traffic in urban areas causes air pollution and noise which affects the environment and public health. It is hardly surprising then that cycling should be used as an alternative mode of transport, not just in major cities but also in smaller ones including those that are members of the Cittaslow network. Their approach is based on sustainable development, care for the environment and transport solutions which will support a healthy lifestyle, reduced energy consumption and fewer emissions. The objective of the article is to analyse how well cycling is used as a means of transport in Polish Cittaslow towns. For this purpose, an analysis was conducted to understand how towns use their transport space to ensure accessibility and road safety. Reference is made to revitalisation programmes of Cittaslow towns with focus on what has been done to improve and build cycle paths in each town and outside of it. The work uses the following research methods: analysis of the literature, analysis of documents, including analysis of road incidents and traffic count. It has been demonstrated that cycling infrastructure in the towns under analysis has been marginalised. As a result, recommendations and suggestions are given which may inform decisions on how to build and transform cycling infrastructure in Cittaslow towns and in similar towns in Poland and abroad.

Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment

This study analyses factors associated with cyclist injury severity, focusing on vehicle type, route environment, and interactions between them. Data analysed was collected by Spanish police during 2016 and includes records relating to 12,318 drivers and cyclist involving in collisions with at least one injured cyclist, of whom 7230 were injured cyclists. Bayesian methods were used to model relationships between cyclist injury severity and circumstances related to the crash, with the outcome variable being whether a cyclist was killed or seriously injured (KSI) rather than slightly injured. Factors in the model included those relating to the injured cyclist, the route environment, and involved motorists. Injury severity among cyclists was likely to be higher where an Heavy Goods Vehicle (HGV) was involved, and certain route conditions (bicycle infrastructure, 30 kph zones, and urban zones) were associated with lower injury severity. Interactions exist between the two: collisions involving large vehicles in lower-risk environments are less likely to lead to KSIs than collisions involving large vehicles in higher-risk environments. Finally, motorists involved in a collision were more likely than the injured cyclists to have committed an error or infraction. The study supports the creation of infrastructure that separates cyclists from motor traffic. Also, action needs to be taken to address motorist behaviour, given the imbalance between responsibility and risk.

Well-being, behavioral patterns and cycling crashes of different age groups in Latin America: Are aging adults the safest cyclists?

Objectives

This study aimed at analyzing the cycling safety-related factors and the mental health indicators of elderly cyclists in comparison with other age groups.

Methods

For this cross-sectional study, we analyzed the data of 911 bicyclists from two Latin American countries that have been experiencing a substantial growth of urban cycling during the last few years: Colombia and Argentina. Participants responded to an e-questionnaire on bicycling behaviors, mental health and cycling safety.

Results

Aging adults reported lower rates of risky behaviors and traffic crashes (around .38 in five years), and, on the other hand, more cycling protective behaviors, a higher risk perception and a better knowledge of traffic norms than both other adults (26–50 years old) and young cyclists (<26). Cycling behaviors and crashes were found to be significantly related to mental health indicators, the latter being higher in aging cyclists. However, this population remains more prone to distractions experienced while cycling than other age groups.

Conclusions

Although the behavioral features of aging adults were comparatively “safer” than the ones displayed by other age groups, factors such as cycling distractions and this population’s over-representation in traffic crashes suggest the need of enforcing policymaking for a better integration of this age segment in alternative transportation dynamics.

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.

Identifying high-risk built environments for severe bicycling injuries

INTRODUCTION

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

METHODS

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

RESULTS

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

Driver braking behavior analysis to improve autonomous emergency braking systems in typical Chinese vehicle-bicycle conflicts

Bicycling is one of the fundamental modes of transportation especially in developing countries. Because of the lack of effective protection for bicyclists, vehicle-bicycle (V-B) accident has become a primary contributor to traffic fatalities. Although AEB (Autonomous Emergency Braking) systems have been developed to avoid or mitigate collisions, they need to be further adapted in various conflict situations. This paper analyzes the driver's braking behavior in typical V-B conflicts of China to improve the performance of Bicyclist-AEB systems. Naturalistic driving data were collected, from which the top three scenarios of V-B accidents in China were extracted, including SCR (a bicycle crossing the road from right while a car is driving straight), SCL (a bicycle crossing the road from left while a car is driving straight) and SSR (a bicycle swerving in front of the car from right while a car is driving straight). For safety and data reliability, a driving simulator was employed to reconstruct these three scenarios and some 25 licensed drivers were recruited for braking behavior analysis. Results revealed that driver's braking behavior was significantly influenced by V-B conflict types. Pre-decelerating behaviors were found in SCL and SSR conflicts, whereas in SCR the subjects were less vigilant. The brake reaction time and brake severity in lateral V-B conflicts (SCR and SCL) was shorter and higher than that in longitudinal conflicts (SSR). The findings improve their applications in the Bicyclist-AEB and test protocol enactment to enhance the performance of Bicyclist-AEB systems in mixed traffic situations especially for developing countries.

Crash risk: How cycling flow can help explain crash data

Crash databases are commonly queried to infer crash causation, prioritize countermeasures to prevent crashes, and evaluate safety systems. However, crash databases, which may be compiled from police and hospital records, alone cannot provide estimates of crash risk. Moreover, they fail to capture road user behavior before the crash. In Sweden, as in many other countries, crash databases are particularly sterile when it comes to bicycle crashes. In fact, not only are bicycle crashes underreported in police reports, they are also poorly documented in hospital reports. Nevertheless, these reports are irreplaceable sources of information, clearly highlighting the surprising prevalence of single-bicycle crashes and hinting at some cyclist behaviors, such as alcohol consumption, that may increase crash risk. In this study, we used exposure data from 11 roadside stations measuring cyclist flow in Gothenburg to help explain crash data and estimate risk. For instance, our results show that crash risk is greatest at night on weekends, and that this risk is larger for single-bicycle crashes than for crashes between a cyclist and another motorist. This result suggests that the population of night-cyclists on weekend nights is particularly prone to specific crash types, which may be influenced by specific contributing factors (such as alcohol), and may require specific countermeasures. Most importantly, our results demonstrate that detailed exposure data can help select, filter, aggregate, highlight, and normalize crash data to obtain a sharper view of the cycling safety problem, to achieve a more fine-tuned intervention.

Reading cyclist intentions: Can a lead cyclist's behaviour be predicted?

As a cyclist, it is essential to make inferences about the intentions of other road users in order to anticipate their behaviour. There are official ways for cyclists to communicate their intentions to other road users, such as using their arms to point in the intended direction of travel. However, in everyday traffic cyclists often do not use such active forms of communication. Therefore, other visual cues have to be used to anticipate (critical) encounters or events. During this study, 108 participants completed a video internet survey in which they predicted the intentions of a lead cyclist based on visible behaviour preceding a turning manoeuvre. When the lead cyclist approached the intersection, each video was stopped just before the cyclist initiated turning. Based on visual cues, the participants had to select which direction they thought the cyclist would go. After entering their prediction, they were asked how certain they were about their prediction and on which visible behaviour(s) each prediction was based. The results show that it is very hard to predict the direction of a turning cyclist based on visual cues before the turning manoeuvre is initiated. Exploratory regression analyses revealed that observable behaviours such as head movements and cycling speed were related to prediction accuracy. These results may be used to support cyclists in traffic interactions.

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.

Using data mining techniques to predict the severity of bicycle crashes

To investigate the factors predicting severity of bicycle crashes in Italy, we used an observational study of official statistics. We applied two of the most widely used data mining techniques, CHAID decision tree technique and Bayesian network analysis. We used data provided by the Italian National Institute of Statistics on road crashes that occurred on the Italian road network during the period ranging from 2011 to 2013. In the present study, the dataset contains information about road crashes occurred on the Italian road network during the period ranging from 2011 to 2013. We extracted 49,621 road accidents where at least one cyclist was injured or killed from the original database that comprised a total of 575,093 road accidents. CHAID decision tree technique was employed to establish the relationship between severity of bicycle crashes and factors related to crash characteristics (type of collision and opponent vehicle), infrastructure characteristics (type of carriageway, road type, road signage, pavement type, and type of road segment), cyclists (gender and age), and environmental factors (time of the day, day of the week, month, pavement condition, and weather). CHAID analysis revealed that the most important predictors were, in decreasing order of importance, road type (0.30), crash type (0.24), age of cyclist (0.19), road signage (0.08), gender of cyclist (0.07), type of opponent vehicle (0.05), month (0.04), and type of road segment (0.02). These eight most important predictors of the severity of bicycle crashes were included as predictors of the target (i.e., severity of bicycle crashes) in Bayesian network analysis. Bayesian network analysis identified crash type (0.31), road type (0.19), and type of opponent vehicle (0.18) as the most important predictors of severity of bicycle crashes.

Characteristics of cyclist crashes in Italy using latent class analysis and association rule mining

The factors associated with severity of the bicycle crashes may differ across different bicycle crash patterns. Therefore, it is important to identify distinct bicycle crash patterns with homogeneous attributes. The current study aimed at identifying subgroups of bicycle crashes in Italy and analyzing separately the different bicycle crash types. The present study focused on bicycle crashes that occurred in Italy during the period between 2011 and 2013. We analyzed categorical indicators corresponding to the characteristics of infrastructure (road type, road signage, and location type), road user (i.e., opponent vehicle and cyclist's maneuver, type of collision, age and gender of the cyclist), vehicle (type of opponent vehicle), and the environmental and time period variables (time of the day, day of the week, season, pavement condition, and weather). To identify homogenous subgroups of bicycle crashes, we used latent class analysis. Using latent class analysis, the bicycle crash data set was segmented into 19 classes, which represents 19 different bicycle crash types. Logistic regression analysis was used to identify the association between class membership and severity of the bicycle crashes. Finally, association rules were conducted for each of the latent classes to uncover the factors associated with an increased likelihood of severity. Association rules highlighted different crash characteristics associated with an increased likelihood of severity for each of the 19 bicycle crash types.

Injury severity of pedestrians, bicyclists and motorcyclists resulting from crashes with reversing cars

OBJECTIVE

Pedestrians, bicyclists and motorcyclists can suffer serious injury in road traffic crashes. To date, no studies examine the injury severity within this vulnerable cohort following collisions with reversing cars.

MATERIAL AND METHODS

Our institution prospectively maintains a database including medical and technical information regarding traffic accidents in our area, including urban and suburban regions. In a retrospective review of this database, the authors describe the injury severity of pedestrians, bicyclists and motorcyclists following traffic crashes involving reversing cars. Injury severity was described using the abbreviated injury scale (AIS) as well as the maximum abbreviated injury scale (MAIS).

RESULTS

This study included 234 crashes occurring between 1999 and 2012. The lower extremity was injured most often while also suffering more severe injuries with a median AIS of 1 compared to 0 in all other documented body regions. The upper extremity was injured second most often. AIS ranging from 4 to 6 were infrequent. AIS 3 however, was documented for the legs in 4.3% of patients. MAIS 0, 1, 2, 3, 5 and 9 were found in 1, 164, 46, 14, 1, and 8 patients in the study cohort, respectively. Pedestrians and motorcyclists were seriously injured in 9.1% and 9.6% of cases, respectively. In contrast, no bicyclists suffered serious injuries. As to the zone of impact, most collisions occurred at the rear center of the vehicle (35%) followed by rear left (26%), rear right (20%), side rear (11%), side center (4%) and side front (3%). 204 (87.2%) collisions occurred during the day, 19 (8.1%) at night and 11 (4.7%) at twilight. Speed was similar in crashes involving pedestrians, bicyclists and motorcyclists, being as high as 7.0±3.6, 7.0±4.0 and 7.9±4.2km/h respectively.

CONCLUSIONS

This is the first study that analyzes injury severity among these vulnerable road users following collisions with reversing vehicles. The majority of collisions occur at low impact speed during the day. Most injuries resulting from these collisions are not serious, however pedestrians are at greatest risk of severe injury to any body region. The lower extremities suffer the most serious and frequent injuries within this cohort.

Circumstances and causes of fatal cycling crashes in the Czech Republic

OBJECTIVES

The circumstances and causes of death of 129 cyclists registered in the Olomouc and the Zlín regions, the Czech Republic, between 2005 and 2013 were the subject of this study.

METHODS

We analyzed the autopsy reports, where the principal cause of death was stated, and obtained a detailed description of the circumstances recorded by the police officers.

RESULTS

Eighty-three cases (64.3% of the set) were collisions involving a motor vehicle. The driver was the guilty party in 57 cases (68.7%) and the cyclist in the remaining 26 cases (31.3%). The most frequent cause of the crash was connected with right of way (29 cases). Cars were involved in 52 cases; heavy vehicles, including buses, in 26 cases; and motorcycles in 5 cases. Single-vehicle crashes consisted of 43 (33.3%) cases. We divided this group into 3 subgroups based on whether the particular case could be attributed to a cyclist having lost control of the bicycle (31 cases) or to other particular causes. Sixty-eight cases (52.7%) of fatal outcomes were directly linked to intracranial injuries. Multiple injuries were the principal cause of death in 19 cases (14.7%), followed by hemorrhagic traumatic shock (12 cases, 9.3%). Seventy-two (55.8%) cyclists died immediately after the crash and 23 (17.8%) cyclists died within a day of the accident.

CONCLUSIONS

Trucks were more dangerous to cyclists than cars at intersections, whereas cars were more dangerous on straight sections. The most important pattern was identified as a motor vehicle hitting a cyclist from behind on a straight road section. We identified a strong underestimation of natural death as a cause of cycling fatalities in the official police reports.

How do drivers overtake cyclists?

In Europe, the number of road crashes is steadily decreasing every year. However, the incidence of bicycle crashes is not declining as fast as that of car crashes. In Sweden, cyclists are the most frequently injured road users. Collisions between bicycles and motorized vehicles are of particular concern because the high speed and large mass of motorized vehicles create a high risk of serious injury to cyclists. In Sweden's urban areas, bicycle lanes keep bicycles separated from motorized vehicles, but on rural roads bicycle lanes are often absent, requiring drivers to interact with cyclists-usually by overtaking them. During this maneuver, drivers regulate speed and lateral position, negotiating with potential oncoming traffic to stay within their comfort zones while approaching and passing cyclists. In this study an instrumented bicycle recorded 145 overtaking maneuvers performed by car and truck drivers on public rural roads in Sweden. The bicycle was equipped with a LIDAR and two cameras to assess how drivers approached and circumvented the bicycle. The collected data allowed us to identify four overtaking phases and quantify the corresponding driver comfort zones. The presence of an oncoming vehicle was the factor that most influenced the maneuver, whereas neither vehicle speed, lane width, shoulder width nor posted speed limit significantly affected the driver comfort zone or the overtaking dynamics.

Built environment effects on cyclist injury severity in automobile-involved bicycle crashes

This analysis uses a generalized ordered logit model and a generalized additive model to estimate the effects of built environment factors on cyclist injury severity in automobile-involved bicycle crashes, as well as to accommodate possible spatial dependence among crash locations. The sample is drawn from the Seattle Department of Transportation bicycle collision profiles. This study classifies the cyclist injury types as property damage only, possible injury, evident injury, and severe injury or fatality. Our modeling outcomes show that: (1) injury severity is negatively associated with employment density; (2) severe injury or fatality is negatively associated with land use mixture; (3) lower likelihood of injuries is observed for bicyclists wearing reflective clothing; (4) improving street lighting can decrease the likelihood of cyclist injuries; (5) posted speed limit is positively associated with the probability of evident injury and severe injury or fatality; (6) older cyclists appear to be more vulnerable to severe injury or fatality; and (7) cyclists are more likely to be severely injured when large vehicles are involved in crashes. One implication drawn from this study is that cities should increase land use mixture and development density, optimally lower posted speed limits on streets with both bikes and motor vehicles, and improve street lighting to promote bicycle safety. In addition, cyclists should be encouraged to wear reflective clothing.

An exposure based study of crash and injury rates in a cohort of transport and recreational cyclists in New South Wales, Australia

This paper examines self-reported prospectively collected data from 2038 adult transport and recreational cyclists from New South Wales (Australia) to determine exposure-based incident crash and injury rates. During 25,971 days of cycling, 198 crashes were reported, comprising approximately equal numbers of falls and collisions. The overall crash rate was 0.290 (95% CI, 0.264-0.319) per 1000km or 6.06 (95% CI, 5.52-6.65) per 1000h of travel. The rate of crashes causing any injury (self-treated, or medically attended without overnight hospital stay) was 0.148 (95% CI, 0.133-0.164) per 1000km or 3.09 (95% CI, 2.79-3.43) per 1000h of travel. The rate of crashes causing a medically attended injury (without overnight hospital stay) was 0.023 (95% CI, 0.020-0.027) per 1000km or 0.49 (95% CI, 0.43-0.56) per 1000h of travel. No injuries requiring an overnight stay in hospital were reported on days meeting the inclusion criteria. After adjustment for exposure in hours, or for the risks associated with different infrastructure utilisation, the rates of crashes and medically attended injuries were found to be greater for females than males, less experienced than more experienced cyclists, and for those who rode mainly for transport rather than mainly for recreation. Comparison of estimated crash and injury rates on different infrastructure types were limited by the small number of events, however findings suggest that the separation of cyclists from motorised traffic is by itself not sufficient to ensure safe cycling.

The relation between speed environment, age and injury outcome for bicyclists struck by a motorized vehicle - a comparison with pedestrians

This study analyzes (a) the relation between injury severities, the age of the bicyclist and the speed environment at accident locations (mean travel speed of the traffic flow involved in the accident) where a bicyclist was struck by a motorized vehicle and (b) how these relations differ from those for struck pedestrians. Accident data from Sweden for the years 2004-2008 was used to identify accident locations to analyze the relations between speed environment, age and injury outcome. Seventy-seven accident sites were used for field measurements and further analysis. The results show that both speed environment and age have considerable correlation with injury severity. There was a statistically significant relation between injury severity and the speed environment, and large proportion of the serious bicycle accidents occur at locations with speeds below 30km/h. Also, the risk of serious injuries or fatalities seems to increase after the age of 45. To our knowledge this is the first study that uses the mean travel speed in this manner for analyzing injury severity of struck bicyclists.

A Spatial Analysis of Land Use and Network Effects on Frequency and Severity of Cyclist-Motorist Crashes in the Copenhagen Region

OBJECTIVE

Urban and transport planners worldwide have recently designed and implemented policies for increasing the number of cyclists. Although cycling is on the rise even in car-oriented cities and regions, the fear of being involved in a crash is still the main obstacle to further increases in cycling market shares. The current study proposes the first joint model of frequency and severity of cyclist-motorist collisions with the aim of unraveling the factors contributing to both the probability of being involved in a crash and, conditional on the crash occurrence, experiencing a severe injury outcome.

METHOD

A multivariate Poisson-lognormal model with correlated autoregressive priors was estimated on a sample of 5,349 cyclist-motorist crashes that occurred in the Copenhagen region between 2009 and 2013. The model considered the links of the road network in the region as the unit of observation, controlled for traffic exposure of nonmotorized and motorized transport modes, evaluated the effect of infrastructure and land use, and accounted for heterogeneity and spatial correlation across links.

RESULTS

Results confirmed the existence of the phenomenon of safety in numbers and added to the narrative by emphasizing that the most severe crashes are the ones most benefiting from an increase in the number of cyclists. In addition, results argued that the construction of Copenhagen-style bicycle paths would significantly contribute to increasing safety, especially in suburban areas where the speed differential between cyclists and motorists is greater. Last, results illustrated a need for thinking about cycling safety in intersection design and reflecting on the importance of spatial and aspatial correlation both within and between injury categories.

CONCLUSIONS

The findings from this study illustrated how encouraging cycling would increase safety in relation to the phenomenon of safety in numbers and how, in turn, increasing safety would convince more people to cycle. In addition, they suggested how the design of bicycle infrastructure should not only consider bicycle lanes but in particular focus on bicycle paths where the number of conflicts and the stress for sharing the road are highly reduced and how thinking about road design should extend to the general level and include a discourse about safer intersections. Last, attention should be given to the road design in the city center and to traffic management, because clearly safer traffic implies more cyclists and, in turn, more cyclists imply fewer cars and less congestion.

Aggravating and mitigating factors associated with cyclist injury severity in Denmark

INTRODUCTION

Denmark is one of the leading cycling nations, where cycling trips constitute a large share of the total trips, and cycling safety assumes a top priority position in the agenda of policy makers. The current study sheds light on the aggravating and mitigating factors associated with cyclist injury severity on Danish roads by examining a comprehensive set of accidents involving a cyclist and a collision partner between 2007 and 2011.

METHOD

This study estimates a generalized ordered logit model of the severity of cyclist injuries because of its ability to accommodate the ordered-response nature of severity while relaxing the proportional odds assumption.

RESULTS

Model estimates show that cyclist fragility (children under 10 years old and elderly cyclists over 60 years of age) and cyclist intoxication are aggravating individual factors, while helmet use is a mitigating factor. Speed limits above 70-80 km/h, slippery road surface, and location of the crash on road sections are aggravating infrastructure factors, while the availability of cycling paths and dense urban development are mitigating factors. Heavy vehicle involvement and conflicts between cyclists going straight or turning left and other vehicles going straight are aggravating vehicle involvement factors. Practical applications: The results are discussed in the context of applied policies, engineering, and traffic management solutions for bicycle safety in Denmark.

The relationship between visibility aid use and motor vehicle related injuries among bicyclists presenting to emergency departments

BACKGROUND

Little is known about the effectiveness of visibility aids (VAs; e.g., reflectors, lights, fluorescent clothing) in reducing the risk of a bicyclist-motor-vehicle (MV) collision.

PURPOSE

To determine if VAs reduce the risk of a bicyclist-MV collision.

METHODS

Cases were bicyclists struck by a MV and assessed at Calgary and Edmonton, Alberta, Canada, emergency departments (EDs) from May 2008 to October 2010. Controls were bicyclists with non-MV injuries. Participants were interviewed about their personal and injury characteristics, including use of VAs. Injury information was collected from charts. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated for VAs during daylight and dark conditions, and adjusted for confounders using logistic regression. Missing values were imputed using chained equations and adjusted OR estimates from the imputed data were calculated.

RESULTS

There were 2403 injured bicyclists including 278 cases. After adjusting for age, sex, type of bicycling (commuting vs. recreational) and bicyclist speed, white compared with black (OR 0.52; 95% CI 0.28, 0.95), and bicyclist self-reported light compared with dark coloured (OR 0.67; 95% CI 0.49, 0.92) upper body clothing reduced the odds of a MV collision during daylight. After imputing missing values, white compared with black (OR 0.57; 95% CI: 0.32, 0.99) and bicyclist self-reported light compared with dark coloured (OR 0.71; 95% CI 0.52, 0.97) upper body clothing remained protective against MV collision in daylight conditions. During dark conditions, crude estimates indicated that reflective clothing or other items, red/orange/yellow front upper body clothing compared with black, fluorescent clothing, headlights and tail lights were estimated to increase the odds of a MV collision. An imputed adjusted analysis revealed that red/orange/yellow front upper body clothing colour (OR 4.11; 95% CI 1.06, 15.99) and tail lights (OR 2.54; 95% CI: 1.06, 6.07) remained the only significant risk factors for MV collisions. One or more visibility aids reduced the odds of a bicyclist MV collision resulting in hospitalization.

CONCLUSIONS

Bicyclist clothing choice may be important in reducing the risk of MV collision. The protective effect of visibility aids varies based on light conditions, and non-bicyclist risk factors also need to be considered.

Association of cyclists' age and sex with risk of involvement in a crash before and after adjustment for cycling exposure

This study aimed to estimate the association of cyclists' age and sex with the risk of being involved in a crash with and without adjustment for their amount of exposure. We used the distribution of the entire population and cyclists (total and non-responsible) involved in road crashes in Spain between 1993 and 2009 held by the Spanish National Institute of Statistics and the Spanish General Traffic Directorate to calculate rates of exposure and involvement in a crash. Males aged 45-49 years were used as the reference category to obtain exposure rate ratios (ERR) and unadjusted crash rate ratios (URR). We then used these values in decomposition analysis to calculate crash rate ratios adjusted for exposure (ARR). The pattern of ARR was substantially different from URR. In both sexes the highest values were observed in the youngest age groups, and the values decreased as age increased except for a slight increase in the oldest age groups. In males, a slight increase in the lowest and highest age categories was observed for crashes resulting in severe injury or death, and a decrease was observed for the youngest cyclists who were wearing a helmet. The large differences between age and sex groups in the risk of involvement in a cycling crash are strongly dependent on differences in their exposure rates. Taking exposure rates into account, cyclists younger than 30 years and older than 65 years of age had the highest risk of being involved in a crash.

How similar are two-unit bicycle and motorcycle crashes?

This paper explores the similarities and differences between bicycle and motorcycle crashes with other motor vehicles. If similar treatments can be effective for both bicycle and motorcycle crashes, then greater benefits in terms of crash costs saved may be possible for the same investment in treatments. To reduce the biases associated with under-reporting of these crashes to police, property damage and minor injury crashes were excluded. The most common crash type for both bicycles (31.1%) and motorcycles (24.5%) was intersection from adjacent approaches. Drivers of other vehicles were coded most at fault in the majority of two-unit bicycle (57.0%) and motorcycle crashes (62.7%). The crash types, patterns of fault and factors affecting fault were generally similar for bicycle and motorcycle crashes. This confirms the need to combat the factors contributing to failure of other drivers to yield right of way to two-wheelers, and suggest that some of these actions should prove beneficial to the safety of both motorized and non-motorized two-wheelers. In contrast, child bicyclists were more often at fault, particularly in crashes involving a vehicle leaving the driveway or footpath. The greater reporting of violations by riders and drivers in motorcycle crashes also deserves further investigation.

Risk factors for causing road crashes involving cyclists: An application of a quasi-induced exposure method

A quasi-induced exposure approach was applied to the Spanish Register of Traffic Crashes to identify driver- and vehicle-related factors associated with the risk of causing a road crash involving a cyclist in Spain from 1993 to 2009. We analyzed 19,007 collisions between a bicycle and another vehicle in which only one of the drivers committed an infraction, and 13,540 records that included the group of non-infractor cyclists in the above collisions plus cyclists involved in single-bicycle crashes. Adjusted odds ratios were calculated for being responsible for each type of crash for each factor considered. Age from 10 to 19 years, male sex, alcohol or drug consumption and non-helmet use were cyclist-related variables associated with a higher risk of crash, whereas cycling more than 1h increased only the risk of single crashes. Bicycles with brake defects and ridden by two occupants were also at higher risk of involvement in a crash, whereas light defects were associated only with collisions with another vehicle. For drivers of the other vehicle, age more than 60 years, alcohol, not using safety devices and nonprofessional drivers were at higher risk. The risk of colliding with a bicycle was higher for mopeds than for passenger cars.

Perception of safety of cyclists in Dublin City

In recent years, cycling has been recognized and is being promoted as a sustainable mode of travel. The perception of cycling as an unsafe mode of travel is a significant obstacle in increasing the mode share of bicycles in a city. Hence, it is important to identify and analyze the factors which influence the safety experiences of the cyclists in an urban signalized multi-modal transportation network. Previous researches in the area of perceived safety of cyclists primarily considered the influence of network infrastructure and operation specific variables and are often limited to specific locations within the network. This study explores the factors that are expected to be important in influencing the perception of safety among cyclists but were never studied in the past. These factors include the safety behavior of existing cyclists, the users of other travel modes and their attitude toward cyclists, facilities and network infrastructures applicable to cycling as well as to other modes in all parts of an urban transportation network. A survey of existing cyclists in Dublin City was conducted to gain an insight into the different aspects related to the safety experience of cyclists. Ordered Logistic Regression (OLR) and Principal Component Analysis (PCA) were used in the analysis of survey responses. This study has revealed that respondents perceive cycling as less safe than driving in Dublin City. The new findings have shown that the compliance of cyclists with the rules of the road increase their safety experience, while the reckless and careless attitudes of drivers are exceptionally detrimental to their perceived safety. The policy implications of the results of analysis are discussed with the intention of building on the reputation of cycling as a viable mode of transportation among all network users.

Cyclist-motorist crash patterns in Denmark: a latent class clustering approach

OBJECTIVE

The current study aimed at uncovering patterns of cyclist-motorist crashes in Denmark and investigating their prevalence and severity. The importance of implementing clustering techniques for providing a holistic overview of vulnerable road users' crash patterns derives from the need to prioritize safety issues and to devise efficient preventive measures.

METHOD

The current study focused on cyclist-motorist crashes that occurred in Denmark during the period between 2007 and 2011. To uncover crash patterns, the current analysis applied latent class clustering, an unsupervised probabilistic clustering approach that relies on the statistical concept of likelihood and allows partial overlap across clusters.

RESULTS

The analysis yielded 13 distinguishable cyclist-motorist latent classes. Specific crash patterns for urban and rural areas were revealed. Prevalent features that allowed differentiating the latent classes were speed limit, infrastructure type, road surface conditions, number of lanes, motorized vehicle precrash maneuvers, the availability of a cycle lane, cyclist intoxication, and helmet wearing behavior. After the latent class clustering, the distribution of cyclists' injury severity within each cluster was analyzed.

CONCLUSIONS

The latent class clustering approach provided a comprehensive and clear map of cyclist-motorist crash patterns. The results are useful for prioritizing and resolving safety issues in urban areas, where there is a significant share of cyclists potentially involved in multiple hazardous situations or where extensive bicycle sharing programs are planned.

Cyclists and drivers in road interactions: A comparison of perceived crash risk

Today's increase in the number of cyclists has triggered a change in the interactions to be handled by road users. However, few studies have investigated crash risk perceived by cyclists interacting with other users, and few have compared cyclists' and drivers' perceptions of crash risk in bike-car interactions, the most dangerous situation for cyclists. Our aims here are to study perceived crash risk (no matter the seriousness of the crash) in six common road situations during which cyclist crashes are frequent and also to study cyclists' and drivers' perceived risk in bike-car interactions, in comparison to other interaction types (cyclist vs. cyclist and driver vs. driver). We predicted that perceived risk of being involved in a crash during a particular interaction would be greater when in interaction with a car than with a bike, and that drivers would perceive more risk than cyclists would. We also predicted that perceived risk would decrease with drivers' and cyclists' experience of their transportation mode and their perceived control over the interaction situation. We ran an online survey on two samples, experienced cyclists (N=336) and non-cyclist car drivers (N=92). Participants evaluated their personal risk - as cyclists or as drivers - of being involved in a road crash if they were in an interaction with a bike or a car for each of six risky road situations. Experience was measured in terms of years of vehicle driving and driven km; perceived control was measured in terms of perceived skill and responsibility for the risky behavior. The results validated our hypotheses: perceived risk was higher for car drivers than for cyclists and for interacting with a car than with a bike. The implications of these results for interventions to improve road safety for both cyclists and car drivers are discussed.

Environmental determinants of bicycling injuries in Alberta, Canada

This study examined environmental risk factors for bicycling injuries, by combining data on bicyclist injuries collected by interviews in the emergency department (ED) with street-level environmental audits of injury locations, capturing path, roadway, safety, land use, and aesthetic characteristics. Cases were bicyclists struck by a motor vehicle (MV) or with severe injuries (hospitalized). Controls were bicyclists who were not hit by a car or those seen and discharged from the ED, matched on time and day of injury. Logistic regression odds ratios (ORs) adjusted for age, sex, peak time, and bicyclist speed with 95% confidence intervals (CIs) were estimated to relate injury risk to environmental characteristics. Factors contributing to MV events included greater traffic volume (OR 5.13; 95% CI [1.44, 18.27]), intersections (OR 6.89; 95% CI [1.48, 32.14]), retail establishments (OR 5.56; 95% CI [1.72, 17.98]), and path obstructions (OR 3.83; 95% CI [1.03, 14.25]). Locations where the road was in good condition (OR 0.25; 95% CI [0.07, 0.96]) and where there was high surveillance from surrounding buildings (OR 0.32; 95% CI [0.13, 0.82]) were associated with less severe injuries. These findings could be used by bicyclists and transportation planners to improve safety.

Risk factors for severe injury in cyclists involved in traffic crashes in Victoria, Australia

This study examines the impact of cyclist, road and crash characteristics on the injury severity of cyclists involved in traffic crashes reported to the police in Victoria, Australia between 2004 and 2008. Logistic regression analysis was carried out to identify predictors of severe injury (serious injury and fatality) in cyclist crashes reported to the police. There were 6432 cyclist crashes reported to the police in Victoria between 2004 and 2008 with 2181 (33.9%) resulting in severe injury of the cyclist involved. The multivariate analysis found that factors that increase the risk of severe injury in cyclists involved in traffic crashes were age (50 years and older), not wearing a helmet, riding in the dark on unlit roads, riding on roads zoned 70 km/h or above, on curved sections of the road, in rural locations and being involved in head-on collisions as well as off path crashes, which include losing control of vehicle, and on path crashes which include striking the door of a parked vehicle. While this study did not test effectiveness of preventative measures, policy makers should consider implementation of programs that address these risk factors including helmet programs and environmental modifications such as speed reduction on roads that are frequented by cyclists.