Cyclist Collisions Research Articles

Retrofitting pedestrian and cyclist collision warning for improved safety of heavy trucks employed in urban areas: a case study in Florence and Pistoia, Italy

The increasing use of heavy vehicles for services such as public transport, waste collection and logistics in densely populated urban areas causes numerous collisions with serious consequences every year, particularly for pedestrians, cyclists and motorcyclists. Advanced Driver Assistance Systems (ADAS) have proven to be a solution because they can significantly reduce the occurrence and consequences of road crashes. Although many ADAS technologies will be mandatory on new trucks in 2024, it will take more than a decade before the benefits are realised due to the average fleet renewal time. To accelerate this process, the most promising solution is therefore the introduction of aftermarket ADAS (also referred to as ‘retrofit’ ADAS) on the circulating fleet. This study aims to identify a tailored solution for retrofitting ADAS capable of providing pedestrian collision warnings on heavy vehicle fleets currently circulating in urban areas. The study involves a sample of vehicles and drivers operating in the metropolitan areas of Florence and Pistoia for waste collection services. The company owner of the vehicles integrated a commercial camera-based ADAS system with three additional cameras installed on the frontal bumper and edges of the vehicle, linked with three screens positioned in the vehicle cabin. In this study, a field test campaign was performed integrating trials in a proving ground closed to traffic with testing and monitoring in urban areas. This allowed to define a set of recommended parameters depending on vehicle speed that represent a good balance between alert frequency, minimizing false positives and ensuring that the alerts indicate real potential hazards to the driver. The recommendations provided in this study can also be extended to other vehicle fleets so that rapid and substantial improvements in the safety of heavy vehicles operating in cities can be achieved.

Comparison of the number of pedestrian and cyclist injuries captured in police data compared with health service utilisation data in Toronto, Canada 2016–2021

IntroductionPedestrian and cyclist injuries represent a preventable burden to Canadians. Police-reported collision data include information on where such collisions occur but under-report the number of collisions. The primary objective of...

Integrating unsupervised and supervised learning techniques to predict traumatic brain injury: A population-based study

This work aimed to identify pre-existing health conditions of patients with traumatic brain injury (TBI) and develop predictive models for the first TBI event and its external causes by employing a combination of unsupervised and supervised learning algorithms. We acquired up to five years of pre-injury diagnoses for 488,107 patients with TBI and 488,107 matched control patients who entered the emergency department or acute care hospitals between April 1st, 2002, and March 31st, 2020. Diagnoses were obtained from the Ontario Health Insurance Plan (OHIP) database which contains province-wide claims data by physicians in Ontario, Canada for inpatient and outpatient services. A screening process was conducted on the OHIP diagnostic codes to limit the subsequent analysis to codes that were predictive of TBI, which concluded that 314 codes were significantly associated with TBI. The Latent Dirichlet Allocation (LDA) model was applied to the diagnostic codes and generated an optimal number of 19 topics that concur with published literature but also suggest other unexplored areas. Estimated word-topic probabilities from the LDA model helped us detect pre-morbid conditions among patients with TBI by uncovering the underlying patterns of diagnoses, meanwhile estimated document-topic probabilities were utilized in variable creation as form of a dimension reduction. We created 19 topic scores for each patient in the cohort which were utilized along with socio-demographic factors for Random Forest binary classifier models. Test set performances evaluated using area under the receiver operating characteristic curve (AUC) were: TBI event (AUC = 0.85), external cause of injury: falls (AUC = 0.85), struck by/against (AUC = 0.83), cyclist collision (AUC = 0.76), motor vehicle collision (AUC = 0.83). Our analysis successfully demonstrated the feasibility of using machine learning to predict TBI due to various external causes and identified the most important factors that contribute to this prediction.

Automatic topics extraction from crowdsourced cyclists near-miss and collision reports using text mining and Artificial Neural Networks

Cycling is an eco-friendly and sustainable mode of transportation. Despite its benefits, the cyclists’ risk of collision is still high when interacting with other road users. This study analyzed self-reported near-miss and collision event descriptions for the United States provided by the crowdsourcing platform, BikeMaps.org. Innovative and efficient analytic methods are needed to generate useful information from unstructured textual data sources in the transportation domain. In this study, explorative text mining, topic modeling, and machine learning are utilized to gain insights from the unstructured textual descriptions of crowdsourced near-miss and collision events. The approaches are used to unveil prevalent words and word associations for near-miss and collision events. Structural Topic Modeling (STM) is deployed to autogenerate latent themes or topics from the event descriptions. The generated topic proportions are used as input in Artificial Neural Networks (ANN) to estimate the cyclist’s propensity to a collision. It was found that cyclists had a higher propensity to a collision in topics that articulated vehicle encroachment to the bike lane, on-street parking close or into the bike lane resulting in dooring, and drivers’ violations at the crosswalk. The results and methodology used in this study can assist engineers, policymakers, and law enforcement officers to proactively reduce potential cyclist collisions, prioritizing areas where cyclist safety improvements are needed, and ultimately promoting bicycle ridership in our communities.

Research on the Circumstances of a Car-Cyclist Collision, Based on the Trajectory of the Cyclist's Movement after the Collision.

This article examines a simulated collision between a car and a cyclist, assessing the trajectory of the cyclist’s movement after the impact, namely the throwing distances and angles of the cyclist and bicycle. Information about the car and cyclist models used for the study is provided. Special software PC CRASH 8.1 for the analysis and reconstruction of traffic accidents was used to simulate a car–cyclist collision. Simulations of car–cyclist collisions were carried out, with different speeds for the car and the cyclist, and locations at the time of the impact. The movement of a bicycle after a crash tends to be irregular and is dependent on various parameters that are usually not possible to evaluate. Therefore, the parameters of the movement of the bicycle after the collision (the throwing angle and the distance) usually do not allow determination of the speed of the car before the accident. The movement of the cyclist after impact was more informative for determining the speed of the car before the accident. For example, when there was an angle of 30°, 60°, or 90° between the longitudinal axes of the car and the cyclist, there was a clear dependence between the speed of the car and the cyclist’s throwing distance, and usually also between the speed of the car and the cyclist’s throwing angle. Thus, in such cases, it is possible to determine approximately the initial speed of the car before the collision, based on the trajectory of the cyclist’s movement after the impact, namely his throwing distance and angle. In cases of real traffic accidents, with knowledge of the location of the car–cyclist collision and the position of the cyclist after the traffic accident, the speed of the car before the accident can be determined according to the abovementioned dependencies. Thus, the proposed methodology could be used in the reconstruction and examination of traffic accidents.

Spatiotemporal Patterns of Cyclist Collisions in Germany: Variations in Frequency, Severity of Injury, and Type of Collision in 2019

Cycling has gained increasing interest in Germany in recent years due to its manifold environmental, societal, and economic benefits. However, the number of cyclist collisions resulting in injury or death remains high and little is known about regional variations in frequency, severity of injury, and type of collision. This study investigates spatial and temporal patterns and characteristics of cyclist collisions across Germany in 2019. Using a detailed cyclist collision dataset for most German federal states, we identified statistically significant regional differences in frequency, severity of injury, and type of collision. To facilitate this and future cyclist collision surveillance studies in Germany, we developed and published a custom R package to download and combine the collision data with geographical data. Our analysis reveals that densely populated regions exhibit higher collision rates and a higher share of collisions involving turns, but lower severity of injuries and a lower share of collisions whilst driving in a straight line, a higher collision frequency during the work week compared to weekends, and a higher collision frequency peak during morning rush hour. We also observed a markedly high share of fatal bicycle-truck collisions in densely populated regions. In contrast, rural regions show lower collision rates, but a higher share of severe collisions, a higher share of collisions whilst driving in a straight line, as well as higher collision frequencies during weekends and summer months. Our findings underscore the complex and multifaceted geographical variations in collisions involving cyclists. The results of this study suggest that a one-size-fits-all approach to collision prevention infrastructure and policy may be insufficient for addressing variations in risk, and that future efforts to improve cyclist safety should be tailored to the local geographical context.

Configurations of underreported cyclist-motorised vehicle and single cyclist collisions: Analysis of a self-reported survey

Lower severity cycling collisions, and single cyclist collisions (or single bicycle crashes) are significantly underreported in police statistics, introducing biases into the types of collisions that are available for analysis. Furthermore, many lower severity collisions do not appear in other collision data sources (e.g. hospital and insurance data). This in turn affects priorities for cyclist safety and puts an underemphasis on certain collision types. Due to an absence of data, little is known of the configurations of unreported collisions. In this paper, data from a recent self-reporting survey of cycling collisions in Ireland is used to provide details of cyclist collisions with motorised vehicles and single cyclist collisions, with the inclusion of unreported collision types. Pre-crash scenarios and impact configurations for cyclist collisions with bonnet-type vehicles, and collision factors and fall types for single cyclist collisions are coded. Injury patterns and police underreporting levels are compared, and representative collision scenarios are identified.This study highlights the relative importance of collisions resulting from the cyclist and vehicle travelling in the same direction, specifically, nearside-hook, vehicle lane changing, and overtaking manoeuvres are emphasised. Furthermore, cases involving the cyclist struck from the side by vehicle fronts comprise a smaller share than previous studies. Specifically, side to side impacts, impacts between the front of the cyclist/bicycle and the side of the vehicle, and impacts with open(ing) doors emerge as important impact configurations with the inclusion of self-reported cases. For single cyclist collisions, the importance of loss of traction of the tyres due to slippery road conditions and interactions with tram tracks and kerbs are emphasised. Fall types differ between single cyclist collision scenarios and are related to differences in injury severity.These findings add to existing knowledge for fatal and higher severity collisions, demonstrating that cyclist safety priorities change with inclusion of underreported, and lower severity collisions. The findings are particularly relevant to road infrastructural planners, as well as in the fields of injury biomechanics, and automated vehicle safety (ADAS). Representative scenarios for collisions with bonnet-type vehicles and single cyclist collisions have been identified, allowing for their future inclusion in development of collision and injury prevention strategies. The dataset generated in this study is available from the authors on reasonable request.

Characteristics of cyclist collisions in Ireland: Analysis of a self-reported survey

As both a utility mode of transport and recreational activity, cycling has well-known health, environmental, and economic benefits. For these reasons it has been encouraged in many countries, including the Republic of Ireland. However, with increasing popularity there have been concurrent increases in road traffic related cyclist injuries. This study aims to characterise cyclist collisions, which are known to be underreported in Police statistics.For data collection, a survey addressing collisions was distributed to cyclists across the country in 2018. Univariable testing was used to identify differences in collision factors and injury outcomes for cyclist collisions with motorised vehicles, and those where a motorised vehicle is not involved as a collision partner i.e. single cyclist, cyclist-pedestrian, or cyclist-cyclist collisions. Furthermore, binary logistic regression modelling was used to clarify biasing factors for Police reporting of collisions.The largest proportion of collisions was between cyclists and motorised vehicles (56%), followed by single cyclist collisions (29%), collisions with other cyclists (8%), and pedestrians (7%). The odds of Police reporting for collisions with motorised vehicles in this study was 20 times greater than single cyclist collisions, 10 times greater than cyclist-cyclist collisions, and 4 times greater than collisions with pedestrians. The odds of Police reporting of serious injury collisions was 7 times greater than minor injury collisions. There were several differences in road, environmental, and human factors, and injury patterns between cyclist-motorised vehicle collisions and non-motorised vehicle collisions.The findings of this study indicate that greater attention should be paid to the following underreported collision types: 1) those that do not involve collisions with motorised vehicles (single cyclist collisions in particular), which have been shown to have differing collision characteristics to motorised vehicle collisions, and 2) less severe injuries, which have been shown to be a substantial contributor to the cyclist safety problem. Furthermore, surveys have been shown to be a valuable mechanism for investigation of lower severity cyclist injuries, which are largely unrecorded in Police or hospital data.

Not all protected bike lanes are the same: Infrastructure and risk of cyclist collisions and falls leading to emergency department visits in three U.S. cities

ObjectiveProtected bike lanes separated from the roadway by physical barriers are relatively new in the United States. This study examined the risk of collisions or falls leading to emergency department visits associated with bicycle facilities (e.g., protected bike lanes, conventional bike lanes demarcated by painted lines, sharrows) and other roadway characteristics in three U.S. cities. MethodsWe prospectively recruited 604 patients from emergency departments in Washington, DC; New York City; and Portland, Oregon during 2015–2017 who fell or crashed while cycling. We used a case-crossover design and conditional logistic regression to compare each fall or crash site with a randomly selected control location along the route leading to the incident. We validated the presence of site characteristics described by participants using Google Street View and city GIS inventories of bicycle facilities and other roadway features. ResultsCompared with cycling on lanes of major roads without bicycle facilities, the risk of crashing or falling was lower on conventional bike lanes (adjusted OR = 0.53; 95 % CI = 0.33, 0.86) and local roads with (adjusted OR = 0.31; 95 % CI = 0.13, 0.75) or without bicycle facilities or traffic calming (adjusted OR = 0.39; 95 % CI = 0.23, 0.65). Protected bike lanes with heavy separation (tall, continuous barriers or grade and horizontal separation) were associated with lower risk (adjusted OR = 0.10; 95 % CI = 0.01, 0.95), but those with lighter separation (e.g., parked cars, posts, low curb) had similar risk to major roads when one way (adjusted OR = 1.19; 95 % CI = 0.46, 3.10) and higher risk when they were two way (adjusted OR = 11.38; 95 % CI = 1.40, 92.57); this risk increase was primarily driven by one lane in Washington. Risk increased in the presence of streetcar or train tracks relative to their absence (adjusted OR = 26.65; 95 % CI = 3.23, 220.17), on downhill relative to flat grades (adjusted OR = 1.92; 95 % CI = 1.38, 2.66), and when temporary features like construction or parked cars blocked the cyclist’s path relative to when they did not (adjusted OR = 2.23; 95 % CI = 1.46, 3.39). ConclusionsCertain bicycle facilities are safer for cyclists than riding on major roads. Protected bike lanes vary in how well they shield riders from crashes and falls. Heavier separation, less frequent intersections with roads and driveways, and less complexity appear to contribute to reduced risk in protected bike lanes. Future research should systematically examine the characteristics that reduce risk in protected lanes to guide design. Planners should minimize conflict points when choosing where to place protected bike lanes and should implement countermeasures to increase visibility at these locations when they are unavoidable.

What are the effects of cycling infrastructure interventions on cyclist collisions and injuries?

What are the effects of cycling infrastructure interventions on cyclist collisions and injuries?

Head protection with cyclist helmet in impact against vehicle A-pillar

ABSTRACTIn vehicle–cyclist collisions, the head of cyclists impacts frequently against the windshield and the A-pillar of the vehicle. Accident analyses show that the head injury risk is high in impacts with stiff A-pillars. In this research, the head protection with cyclist helmets in impacts against the A-pillar was investigated from experiments and finite element (FE) analyses. The headform impact tests were conducted using the pedestrian headform with and without a cyclist helmet. In the A-pillar impact at 35 km/h, the HIC reduced substantially from without helmet (4815) to with helmet (3000) though it was far above the injury acceptance level (1000). The head protection by helmets in impacts against A-pillars might be limited because of high impact velocities of the head and A-pillar stiffness. The FE simulations of the headform impacts indicated that the helmet liner deformed locally and bottomed-out in the A-pillar impacts, whereas the A-pillar deformation was small. In the FE analysis of a human head model with a capping helmet, the skull fracture did not occur but the brain strain was large. From a simple model of the head acceleration in the A-pillar impact, it was shown that the helmet liner has little effect on the HIC, whereas a deforming A-pillar with a force level of 5–7 kN could reduce the HIC of the head with helmets less than 1500.

Evaluating the impact of bike network indicators on cyclist safety using macro-level collision prediction models

Many cities worldwide are recognizing the important role that cycling plays in creating green and livable communities. However, vulnerable road users such as cyclists are usually subjected to an elevated level of injury risk which discourages many road users to cycle. This paper studies cyclist-vehicle collisions at 134 traffic analysis zones in the city of Vancouver to assess the impact of bike network structure on cyclist safety. Several network indicators were developed using Graph theory and their effect on cyclist safety was investigated. The indicators included measures of connectivity, directness, and topography of the bike network. The study developed several macro-level (zonal) collision prediction models that explicitly incorporated bike network indicators as explanatory variables. As well, the models incorporated the actual cyclist exposure (bike kilometers travelled) as opposed to relying on proxies such as population or bike network length. The macro-level collision prediction models were developed using generalized linear regression and full Bayesian techniques, with and without spatial effects. The models showed that cyclist collisions were positively associated with bike and vehicle exposure. The exponents of the exposure variables were less than one which supports the “safety in numbers” hypothesis. Moreover, the models showed positive associations between cyclist collisions and the bike network connectivity and linearity indicators. In contrast, negative associations were found between cyclist collisions and the bike network continuity and topography indicators. The spatial effects were statistically significant in all of the developed models.

Field Testing of a Cyclist Collision Avoidance System for Heavy Goods Vehicles

This paper focuses on preventing collisions between cyclists and heavy goods vehicles (HGVs). A collision avoidance system, which is designed to avoid side-to-side collisions between HGVs and cyclists, is proposed. The cyclist's motion relative to the HGV is measured with an array of ultrasonic sensors. The detected distances from ultrasonic sensors are processed in real time to construct a smooth trajectory for the cyclist. The controller assumes constant acceleration and constant yaw rate for both the HGV and the cyclist and extrapolates the relative motion forward in time. The HGV's brakes are engaged if a collision is predicted. A prototype system was built and fitted onto a test truck. The proposed collision avoidance system was tested in real time and proved to be effective within certain speed ranges.

Cycling infrastructure for reducing cycling injuries in cyclists.

Cycling infrastructure for reducing cycling injuries in cyclists.

Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: Methodology and application

Pedestrians and cyclists are amongst the most vulnerable road users. Pedestrian and cyclist collisions involving motor-vehicles result in high injury and fatality rates for these two modes. Data for pedestrian and cyclist activity at intersections such as volumes, speeds, and space–time trajectories are essential in the field of transportation in general, and road safety in particular. However, automated data collection for these two road user types remains a challenge. Due to the constant change of orientation and appearance of pedestrians and cyclists, detecting and tracking them using video sensors is a difficult task. This is perhaps one of the main reasons why automated data collection methods are more advanced for motorized traffic. This paper presents a method based on Histogram of Oriented Gradients to extract features of an image box containing the tracked object and Support Vector Machine to classify moving objects in crowded traffic scenes. Moving objects are classified into three categories: pedestrians, cyclists, and motor vehicles. The proposed methodology is composed of three steps: (i) detecting and tracking each moving object in video data, (ii) classifying each object according to its appearance in each frame, and (iii) computing the probability of belonging to each class based on both object appearance and speed. For the last step, Bayes’ rule is used to fuse appearance and speed in order to predict the object class. Using video datasets collected in different intersections, the methodology was built and tested. The developed methodology achieved an overall classification accuracy of greater than 88%. However, the classification accuracy varies across modes and is highest for vehicles and lower for pedestrians and cyclists. The applicability of the proposed methodology is illustrated using a simple case study to analyze cyclist–vehicle conflicts at intersections with and without bicycle facilities.

Association Between Neighbourhood Marginalization and Pedestrian and Cyclist Collisions in Toronto Intersections

Pedestrian and cyclist collisions comprise a significant proportion of preventable injury. In urban settings, collision rates have been linked to various socio-demographic factors. We sought to determine whether neighbourhood marginalization affects pedestrian and cyclist collisions in the Greater Toronto Area. For 114 intersections, pedestrian and cyclist collisions were extracted from the Toronto Traffic Data Centre database. We used a geographic information system approach to determine census Dissemination Areas and an associated Ontario Marginalization Index (ON-Marg) for each intersection. We performed a logistic regression to examine the associations between the four ON-Marg dimensions (residential instability, material deprivation, dependency, ethnic concentration) and pedestrian and cyclist collisions. The odds of sustaining a collision were independently associated with residential instability for both pedestrians (OR 1.84, 95% CI 1.21-2.84, p=0.006) and cyclists (OR 2.04, 95% CI 1.34-3.16, p=0.001). Higher overall collision rates (both pedestrian and cyclist) were associated with both ethnic concentration (OR 1.56, 95% CI 1.05-2.37, p=0.033) and residential instability (OR 2.16, 95% CI 1.43-3.38, p=0.001). Material deprivation and dependency were not significant risk factors for intersection collisions in this model. Collisions involving pedestrians and cyclists are more common in areas of increased residential instability and ethnic concentration in Toronto. Intersections in neighbourhoods with these characteristics could be targeted for strategies to reduce pedestrian and cyclist injury risk in urban settings.

The effectiveness of helmets in bicycle collisions with motor vehicles: A case–control study

There has been an ongoing debate in Australia and internationally regarding the effectiveness of bicycle helmets in preventing head injury. This study aims to examine the effectiveness of bicycle helmets in preventing head injury amongst cyclists in crashes involving motor vehicles, and to assess the impact of ‘risky cycling behaviour’ among helmeted and unhelmeted cyclists. This analysis involved a retrospective, case–control study using linked police-reported road crash, hospital admission and mortality data in New South Wales (NSW), Australia during 2001–2009.The study population was cyclist casualties who were involved in a collision with a motor vehicle. Cases were those that sustained a head injury and were admitted to hospital. Controls were those admitted to hospital who did not sustain a head injury, or those not admitted to hospital. Standard multiple variable logistic regression modelling was conducted, with multinomial outcomes of injury severity.There were 6745 cyclist collisions with motor vehicles where helmet use was known. Helmet use was associated with reduced risk of head injury in bicycle collisions with motor vehicles of up to 74%, and the more severe the injury considered, the greater the reduction. This was also found to be true for particular head injuries such as skull fractures, intracranial injury and open head wounds. Around one half of children and adolescents less than 19 years were not wearing a helmet, an issue that needs to be addressed in light of the demonstrated effectiveness of helmets. Non-helmeted cyclists were more likely to display risky riding behaviour, however, were less likely to cycle in risky areas; the net result of which was that they were more likely to be involved in more severe crashes.

Mapping commuter cycling risk in urban areas

Cycling is becoming an increasingly important transportation option for commuters. Cycling offers exercise opportunities and reduces the burden of motor vehicle travel on society. Mapping the risk of collision between cyclists and motor vehicles in urban areas is important to understanding safe cyclist route opportunities, making informed transportation planning decisions, and exploring patterns of injury epidemiology. To date, many geographic analyses and representations of cyclist risk have not taken the concept of exposure into account. Instead, risk is either expressed as a rate per capita, or as a count of events. Using data associated with the City of Hamilton, Canada, we illustrate a method for mapping commuter cyclist collision risk per distance travelled. This measure can be used to more realistically represent the underlying geography of cycling risk, and provide more geographically and empirically meaningful information to those interested in understanding how cycling safety varies over space.

Cyclist safety on bicycle boulevards and parallel arterial routes in Berkeley, California

This study compares the safety of bicyclists riding on bicycle boulevards to those riding on parallel arterial routes in Berkeley, California. Literature on the impact of motor vehicle traffic characteristics on cyclist safety shows that high motor vehicle speeds and volumes and the presence of heavy vehicles are all detrimental to cyclist safety. This suggests that cyclists may be safer on side streets than on busy arterials. Bicycle boulevards—traffic-calmed side streets signed and improved for cyclist use—purport to offer cyclists a safer alternative to riding on arterials. Police-reported bicycle collision data and manually collected cyclist count data from bicycle boulevards and parallel arterial routes in Berkeley, California from 2003 to 2010 are used to test the hypothesis that Berkeley's bicycle boulevards have lower cyclist collision rates and a lower proportion of bicycle collisions resulting in severe injury. While no significant difference is found in the proportion of collisions that are severe, results show that collision rates on Berkeley's bicycle boulevards are two to eight times lower than those on parallel, adjacent arterial routes. The difference in collision rate is highly statistically significant, unlikely to be caused by any bias in the collision and count data, and cannot be easily explained away by self-selection or safety in numbers. Though the used dataset is limited and the study design is correlational, this study provides some evidence that Berkeley's bicycle boulevards are safer for cyclists than its parallel arterial routes. The results may be suggestive that, more generally, properly implemented bicycle boulevards can provide cyclists with a safer alternative to riding on arterials.

Disaggregate Exposure Measures and Injury Frequency Models of Cyclist Safety at Signalized Intersections

This paper proposes a new approach to represent cyclist risk exposure. This approach considers disaggregate motor vehicle and cyclist flows and develops cyclist injury frequency models. Three definitions of risk exposure were used in this research, including aggregated flows, motor vehicle flows aggregated by movement type, and potential conflicts between motor vehicles and cyclists. As an application environment, a large sample of signalized intersections on the island of Montreal, Quebec, Canada, was used, along with data that comprised disaggregate motor vehicle and cyclist flows. Several negative binomial models were fitted to the data. This study showed that cyclist collisions were sensitive to changes in both cyclist and motor vehicle flows. A 10% increase in bicycle flow was associated with a 4.4% increase in the frequency of cyclist injuries. A 10% increase in the total number of motor vehicles that passed through the intersection would result in a 3.4% increase in cyclist injury occurrence. When motor vehicle flows were considered on the basis of movement type, right-turn movements had a great effect on injury occurrence. Similar results, which identified right turns as having the greatest effect on cyclist injuries, were produced when the impact of potential conflicts was determined. The number of bus stops in the proximity of the intersection increased cyclist injury occurrence. Some geometric design factors, such as the presence of a median, parking entrance, and the number of intersection legs, were tested. The effect, however, was found to be statistically nonsignificant.