Bicycle-motor Vehicle Collisions Research Articles

A correlated random parameters ordered probit approach to analyze the injury severity of bicycle-motor vehicle collisions at intersections

Bicycle-motor vehicle (BMV) accidents hold paramount importance due to their substantial impact on public safety. Specifically, road intersections, being critical conflict points, demand focused attention to reduce BMV crashes effectively and mitigate their severity. The existing research on the severity analysis of these crashes appears to have certain gaps that warrant further contribution. To address the mentioned limitations, this study first integrates multiple pre-collision features of the bicycles and vehicles to classify crash types based on the mechanism of the crashes. Then, the correlated random parameters ordered probit (CRPOP) model is employed to examine the factors influencing injury severity among bicyclists involved in intersection BMV crashes in Pennsylvania from 2013 to 2018. To gain deeper insights, this study conducts a separate analysis of crash data from 3-leg intersections, 4-leg intersections, and their combined scenarios, followed by a comparative examination of the results. The findings revealed that the presented crash typing approach yields new insights regarding injury severity outcomes. Moreover, in addition to exhibiting a comparable statistical performance contrasting to the more restricted models, the CRPOP model identified hidden correlations between three random parameters. Furthermore, the study demonstrated that analyzing combined crash data from the two intersection types obscured certain factors that were found significantly influential in the injury outcomes through analyzing sub-grouped data. Consequently, it is recommended to implement tailored countermeasures for each type of intersection.

Blame attribution analysis of police motor vehicle collision reports involving child bicyclists

BackgroundInjuries resulting from collisions between a bicyclist and driver are preventable and have high economic, personal and societal costs. Studying the language choices used by police officers to describe factors...

Enhancing trauma registries by integrating traffic records and geospatial analysis to improve bicyclist safety.

Trauma registries are used to identify modifiable injury risk factors for trauma prevention efforts. However, these may miss factors useful for prevention of bicycle-automobile collisions, such as vehicle speeds, driver intoxication, street conditions, and neighborhood characteristics. We hypothesize that (GIS) analysis of trauma registry data matched with a traffic accident database could identify risk factors for bicycle-automobile injuries and better inform injury prevention efforts. The trauma registry of a US Level I trauma center was used retrospectively to identify bicycle-motor vehicle collision admissions from January 1, 2010, to December 31, 2018. Data collected included demographics, vitals, injury severity scores, toxicology, helmet use, and mortality.Matching with the Statewide Integrated Traffic Records System was done to provide collision, victim and GIS information. The GIS mapping of collisions was done with census tract data including poverty level scoring. Incident hot spot analysis to identify statistically significant incident clusters was done using the Getis Ord Gi* statistic. Of 25,535 registry admissions, 531 (2.1%) were bicyclists struck by automobiles, 425 (80.0%) were matched to Statewide Integrated Traffic Records System. Younger age (odds ratio [OR], 1.026; 95% confidence interval [CI], 1.013-1.040, p < 0.001), higher census tract poverty level percentage (OR, 0.976; 95% CI, 0.959-0.993, p = 0.007), and high school or less education (OR, 0.60; 95 CI, 0.381-0.968; p = 0.036) were predictive of not wearing a helmet. Higher census tract poverty level percentage (OR, 1.019; 95% CI, 1.004-1.034; p = 0.012) but not educational level was predictive of toxicology positive-bicyclists in automobile collisions. Geographic information systems analysis identified hot spots in the catchment area for toxicology-positive bicyclists and lack of helmet use. Combining trauma registry data and matched traffic accident records data with GIS analysis identifies additional risk factors for bicyclist injury. Trauma centers should champion efforts to prospectively link public traffic accident data to their trauma registries. Prognostic and Epidemiological, level III.

Predictive modelling of injury severity in bicycle–motor vehicle collisions utilizing learning vector quantization: a case study of Britain’s cycling capital

Cambridge is truly known as Britain’s cycling capital but this has all been achieved without any real cycling infrastructure. Therefore, safety concern is the persistent barrier to cycling in Cambridge and its reputation is on the decline. Thus in response to the small number of the literatures on prediction models for cyclist related injuries, this study presents a modelling technique that applies learning vector quantization network to predict injury severity of cyclist. By discovering the potentially relationships between the injury levels and the factors that contribute to their generation, the model predicts the likelihood of the injury into two classes: slight and killed/seriously injured. The findings display that the effect of junction actions are almost double. Following this, T/staggered junction on an unclassified bend was discovered as UK's cycling capital’s “collision hotspots”. Subsequently, absence of a sufficient number of crossing facilities for cyclists had the largest effect. All other things being held near equal; rush hours during the weekdays, vehicle manoeuvre due to a poor turn and parked vehicle, going ahead bend manoeuvre by cyclist, limited modern protected bike lanes, wet road surface owing to inclement weather, vehicle blind spot and driving too close, visual distraction caused by adverse lighting condition and dazzle of sun, and junction control commonly at situations of give-way or uncontrolled intersections. The study then ends by maximising the overall predictive accuracy through contacting the most sensitive predictors into the model. Consequently, a professional road safety education needs to be delivered so as to crack down the human failure as a main contributory factor.

Identifying motorist characteristics associated with youth bicycle–motor vehicle collisions

Objective: The objective of this study was to identify driver characteristics associated with youth bicycle–motor vehicle collisions in Alberta, Canada.Methods: Edmonton and Calgary police collision report data from the years 2010–2014 were used. From these data, motor vehicle collisions involving youth (<18 years old) were identified (cases). The controls were drivers who, over the same period, were involved in separate motor vehicle–only collisions but deemed not at fault using an automated culpability analysis. Control selection used the quasi-induced exposure method, assuming that not-at-fault drivers in collisions are representative of the typical driver (source population). Descriptive statistics, including proportions, medians, and interquartile ranges (as appropriate) were used to describe the characteristics of the case and control drivers. Purposeful variable selection techniques were used to inform multivariable logistic regression models and results are presented as adjusted odds ratios (aORs) and 95% confidence intervals (CIs).Results: Four hundred twenty-three drivers involved in youth bicycle–motor vehicle collisions were identified, as were 243,927 not-at-fault control drivers. Drivers >54 years old had higher odds of involvement in youth bicycle–motor vehicle collisions than drivers between 25 and 39 years old (aOR = 1.37; 95% CI, 1.03, 1.82). Compared to driving between 3:01 p.m. and 6:00 p.m., driving between 12:01 a.m. and 6:00 a.m. (aOR = 0.27; 95% CI, 0.11, 0.66), between 6:01 a.m. and 9:00 a.m. (aOR = 0.61; 95% CI, 0.44, 0.85), or between 9:01 a.m. and 12:00 p.m. (aOR = 0.26; 95% CI, 0.16, 0.41) had lower odds of bicyclist collision, whereas driving between 6:01 p.m. and 12:00 a.m. had higher odds (aOR = 1.34; 95% CI, 1.01, 1.79). Driving a truck/van had lower odds of bicyclist collision compared to driving a passenger car (aOR = 0.67; 95% CI, 0.48, 0.94).Conclusions: Culpability analysis is typically applied to motorists to identify transient exposures; however, this study used culpability analysis to select control drivers who could be compared with drivers involved in youth bicycle–motor vehicle collisions. This study highlights motorist characteristics in youth bicycle–motor vehicle collisions. In doing so, we hope to inform primary prevention strategies for motorists and the environment that will reduce collisions.

Child and adolescent bicycling injuries involving motor vehicle collisions

BackgroundBicycle-related injuries are among the most common recreational injuries for children in Canada; moreover, bicycle-motor vehicle collisions often result in serious injuries. This study seeks to examine environmental, motorist, and bicyclist characteristics of bicycle-motor vehicle collisions that resulted in police reported severe injuries in youth (< 18 years old) bicyclists, in Alberta, Canada.FindingsUsing Calgary and Edmonton police collision reports, 423 youth bicycle-motor vehicle collisions were identified from 2010 to 2014. Forty-three (10.2%) of these collisions resulted in major/fatal (severe) injuries. These severe injury cases were compared with the 380 youth bicycle-motor vehicle collisions resulting in minor or no injury (controls) using classification tree and logistic regression analyses. There were no driver or bicyclist characteristics with a significant effect on the odds of severe injury to youth bicyclists; however, lower odds were found on each of: divided roads with no barrier (aOR = 0.36; 95% CI: 0.13–0.97) or during peak traffic time (aOR = 0.44; 95% CI: 0.16–0.99).ConclusionPersonal and environment characteristics should be considered in future research and interventions focused on reducing severe youth bicycle-motor vehicle collision injuries.

Bike lanes next to on-street parallel parking

For decades it has been the conventional wisdom that crashes involving bicyclists and opening car doors are rare. This belief is based on motor vehicle crash reports, but these reports generally exclude this crash type by definition. More complete sources show that dooring crashes are one of the most common causes of urban bicycle-motor vehicle collisions, accounting for 12%–27% of the total.This paper reviews all available studies of bicyclist position in bike lanes adjacent to on-street parking. With bike lanes meeting current minimum standards, almost all bicyclists were observed riding within range of opening doors. However, when an additional three or four feet is provided between the bike lane and parked cars, hardly any bicyclists are observed in the door zone.All of the design guides recently developed in North America for separated bike lanes include a buffer to account for the door zone when the bike lane is placed between on-street parallel parking and the curb. However, only the Ontario design guide has a similar requirement for standard bike lanes. The buffer requirement for standard bike lanes adjacent to on-street parking should be incorporated into all design guidance.When there is not room for this necessary buffer, an alternative is to place a shared lane marking in the center of the travel lane, which encourages bicyclists to ride outside the door zone. Increasing the number of bicyclists who ride outside of the door zone may require lowering speed limits and repealing laws that create a presumption that bicyclists must always keep to the right of the travel lane.

Bicycle-related injuries among children treated in US emergency departments, 2006-2015

ObjectiveOne of the leading causes of non-fatal injury among children is bicycling. Past studies indicate that helmets are protective against bicycle-related injuries and involvement of motor vehicles is associated with severe injuries, but research utilizing a nationally representative data set for this population and focusing on these risk factors does not exist. The objective of this study was to describe the epidemiology of bicycle-related injuries among children treated in hospital emergency departments (EDs) in the United States (US). MethodsA retrospective analysis was conducted with data from the National Electronic Injury Surveillance System for children 5–17 years of age who were treated in US EDs from 2006 through 2015 for a bicycle-related injury. Helmet use and motor vehicle involvement were two variables that were created and coded using keyword searches of the case narratives. Rates of injuries over time were described. Multivariate logistic regression along with 95% confidence intervals (CIs) were used to contrast types of injuries sustained among injured helmet users with non-users. ResultsAn estimated 2 219 742 (95% CI: 1 871 120-2 568 363) children 5–17 years of age were treated in US EDs for bicycle-related injuries over the 10-year study period, an average of 608 injuries per day. Most injuries (45.7%) involved children 10–14 years of age. The rate of bicycle-related injuries significantly decreased from 447.4 per 100 000 children in 2006 to 321.1 per 100 000 children in 2015 (P < 0.001). Helmet use at the time of injury was significantly associated with lower likelihood of head and neck injuries (OR: 0.52 [95% CI: 0.40-0.59]) and hospitalizations (OR: 0.71 [95% CI: 0.54-0.94]), but there was no significant change in the rate of injury among helmet users over the study period (P = 0.224). Motor vehicle involvement increased the odds of bicycle-related traumatic brain injuries (TBIs) (OR: 1.98 [95% CI: 1.49–2.64]) as well as injury-related hospitalizations (OR: 4.04 [95% CI: 3.33–4.89]). ConclusionsDespite decreasing injury rates, bicycling remains an important source of injury for children. Helmet use has demonstrated significant protective effects for TBIs, head and neck injuries, and hospitalizations. Motor vehicle involvement increased the risk of hospitalization. More efforts are needed to promote use of helmets and to reduce the possibility of bicycle-motor vehicle collisions to prevent bicycle-related injuries among children.

Characteristics and clinical outcomes of head-injured cyclists with and without helmets in urban and rural areas of Taiwan: A 15-year study

ABSTRACTObjective: Bicycle riding is increasingly popular in Taiwan, but the number of cyclists injured and cyclists' death rates are both increasing. The aim of this study was to investigate the different characteristics and clinical outcomes of traffic accident–related head injuries among cyclists in urban and rural areas.Methods: Records of 812 patients (533 urban and 279 rural) admitted to 27 hospitals in Taipei City and Hualien County as the result of a traumatic head injury while bicycling between 1998 and 2013 were retrieved for study. Demographics, details about the accident, protective helmet use, and clinical outcomes were then subjected to analysis.Results: Urban victims were more likely to be injured during morning and early evening rush hours and rural victims during the day; most urban victims were between 19 and 34 years of age and injured in the slow lane; rural victims tended to be younger or older and were injured in the fast lane (all P ≤.001). Riders who wore a helmet were less likely to suffer loss of consciousness (odds ratio [OR] = 0.31), amnesia (OR = 0.069), neurological disorders (OR = 0.205), or facial fractures (OR = 0.369). Older age, more severe head injuries, and bicycle–motor vehicle collisions influenced the severity of symptoms on admission and the residual effects at discharge.Conclusions: Differences in the characteristics of injuries in urban and rural areas and the utilization of protective helmets may help government authorities adopt appropriate policies to promote safer and more enjoyable cycling.

Analyzing the severity of bicycle-motor vehicle collision using spatial mixed logit models: A City of Edmonton case study

The City of Edmonton is developing a city-wide cycling network through the Complete Streets project (inclusively) and the On-Street Bike Route project (exclusively). The Complete Streets project develops new roadway design guidelines that consider a specific corridor’s function and users, and provides appropriate transportation infrastructure, including traffic lights, signage, and turning lanes. The On-Street Bike Route project designs and installs cycling infrastructure (facilities), including multiuse trails, bike lanes, and shared-use lanes. The present study aims to gain a better understanding of the factors contributing to severe bicycle-motor vehicle (BMV) collisions. The study investigates a total of 571 BMV collisions (424 intersection-related and 147 midblock-related BMV collisions) that occurred between 2006 and 2009 to provide a baseline understanding of Edmonton’s cycling safety concerns, prior to the installation of on-street cycling facilities. Spatial mixed logit models were fitted to the data. The categories of covariates included corridor design, human, temporal, and environmental factors. The results did not show any common factors contributing to BMV collision severity at intersections or midblock sections. Significant factors affecting the BMV intersection collision severity included the interaction between roadway and approach-control type, the existence of partial crosswalks and bike signs, and the cyclist’s gender and age. Alternatively, factors affecting the probability of BMV midblock collision severity included roadway classification, on-street parking allocations, and the driver’s age.

Bicycle Helmet Laws Are Associated with a Lower Fatality Rate from Bicycle–Motor Vehicle Collisions

To assess the association between bicycle helmet legislation and bicycle-related deaths sustained by children involved in bicycle-motor vehicle collisions. We conducted a cross-sectional study of all bicyclists aged 0-16 years included in the Fatality Analysis Reporting System who died between January 1999 and December 2010. We compared fatality rates in age-specific state populations between states with helmet laws and those without helmet laws. We used a clustered Poisson multivariate regression model to adjust for factors previously associated with rates of motor vehicle fatalities: elderly driver licensure laws, legal blood alcohol limit (<0.08% vs ≥ 0.08%), and household income. A total of 1612 bicycle-related fatalities sustained by children aged <16 years were evaluated. There were no statistically significant differences in median household income, the proportion of states with elderly licensure laws, or the proportion of states with a blood alcohol limit of >0.08% between states with helmet laws and those without helmet laws. The mean unadjusted fatality rate was lower in states with helmet laws (2.0/1,000,000 vs 2.5/1,000,000; P = .03). After adjusting for potential confounding factors, lower fatality rates persisted in states with mandatory helmet laws (adjusted incidence rate ratio, 0.84; 95% CI, 0.70-0.98). Bicycle helmet safety laws are associated with a lower incidence of fatalities in child cyclists involved in bicycle-motor vehicle collisions.

Poisson Multilevel Methodology of Bicycle Levels of Service for Road Networks

Methodologies for bicycle levels of service have been developed for urban and suburban road segments, as well as for rural road segments. Today, however, the utilitarian (commuter) cyclist requires access to urban, suburban, and rural environs to travel safely between home and work. To complement bicycle level-of-service methodologies that incorporate mental stressors in road segments, this study developed a bicycle level-of-service methodology that incorporates physical stressors in road networks with the greatest influence on the frequency and the severity of cycling injuries from bicycle–motor vehicle collisions. To do so, the methodology integrates output from two different statistical models. To model the frequency of bicycle–motor vehicle collisions on all road segments (n = 65) in a regional road network, a Poisson model was used. To model the severity of all bicycle–motor vehicle collisions (n = 25) on the road segments in a regional road network, a multilevel model was used. To complete the Poisson multilevel bicycle level-of-service methodology, the outputs from the Poisson model of the frequency of bicycle–motor vehicle collisions and from the multilevel model of the severity of bicycle-motor vehicle collisions were used as the inputs in a geographic information system. Through analysis of the study area road network, samples of utilitarian cycling routes that minimize distance alone and that maximize safety alone were selected to quantify the benefits and the costs to utilitarian cyclists in terms of time and safety for both selections.

Profile of pediatric bicycle injuries.

Bicycle injury data from local communities are important for developing injury prevention and control programs. This study represents the efforts of one community trauma center to describe bicycle injuries. We conducted a retrospective analysis of bicycle injury data from hospital charts, emergency medical services reports, and medical examiner reports. The review encompassed a 4-year period. The study sample included 211 trauma alert patients, ages 1 through 15 years, who were treated for bicycle-related injuries at our level II pediatric trauma center. Bicycle injuries accounted for 18% of all pediatric trauma alert patients. The mean age of injured children was 10 years, and 79% were males. Bicycle-motor vehicle collisions caused 84% of injuries. Only 3 children (1.4%) wore bicycle helmets. Resulting injuries included external wounds (86%), head injuries (47%), fractures (29%), and internal organs (9%). Six children died. Bicycle injuries are a significant cause of mortality and morbidity for children in our community. Use of safety helmets by child bicyclists is inadequate. The data from this study can be used as a baseline in testing the effectiveness of local and state interventions, including new legislation mandating helmet use by children in our state.

Police Reporting of Pedestrians and Bicyclists Treated in Hospital Emergency Rooms

Drawing on a study of injured pedestrians and bicyclists treated at hospital emergency rooms in California, New York, and North Carolina, an attempt was made to match the emergency room cases to state motor vehicle crash data, and to analyze the factors associated with the occurrence of a match. The purpose is to (1) provide additional information on the likely level of underreporting of pedestrian– and bicycle–motor vehicle collisions on state crash files and (2) examine whether cases that are reported differ in any systematic way from those that are not reported. A total of 388 pedestrian– and 255 bicycle–motor vehicle cases were available for inclusion in the analysis, with 56 percent of the pedestrian and 48 percent of the bicycle cases matched to state crash files. After adjusting for other potential confounders, pedestrians who were hospitalized or killed were 1.3 times more likely to be reported on the state crash files than were pedestrians receiving emergency room treatment only; those struck in the roadway were 1.7 times more likely to be reported than those struck in nonroadway locations; and those ages 15 to 24 were 70 percent less likely to be reported than pedestrians of all other ages. In contrast, the only factor found to be associated with police reporting of bicycle–motor vehicle crashes was the severity level of the injury: bicyclists who were hospitalized or killed were 1.4 times more likely to be reported on the state crash files than were bicyclists receiving emergency room treatment only.

Trends in bicycle-related injuries in Norway, 1990-1993. Does prevention yield effects?

Abstract PROBLEM Numerous activities to promote bicycle safety have been implemented in Norway. The overall effect of these activities has not been evaluated. method Information about cases of bicycle-related injuries occurring in 1990-1993 was obtained from prospective registration of all injuries treated by hospitals and emergency clinics in four urban centers in NORWAY. Incidence was calculated per number of cyclists, based on data obtained from the 1992 National Bicycle Survey. Temporal changes in injury severity and in age-specific incidence (per bicycle riders) of all injuries, upper-head injuries, other injuries, injuries due to bicycle-motor vehicle collision, and injuries leading to inpatient treatment were analyzed by logistic regression analysis. RESULTS 3,893 injuries occurred in the study population in 1990-1993. The average annual incidence was 33.1 per 10,000 population aged 4 years and higher, or 47.9 per 10,000 bicyclists. No significant secular trends were found for any of the analyzed p...

Modeling Fault among Bicyclists and Drivers Involved in Collisions in Hawaii, 1986–1991

Subsequent to a review of trends in collisions between bicyclists and motorists in Hawaii during the period 1986 to 1991, characteristics of bicyclists and drivers involved in crashes are compared. On the basis of police-reported crash data it can be concluded that bicyclists tend to be young, male, and, not surprisingly, more likely to be seriously injured than motorists in bicycle–motor-vehicle collisions. Bicyclists are much less likely to be attributed with inattention than motorists, and slightly less likely to be attributed with misjudgment or alcohol or drug use than motorists. Bicyclists, however, are much more likely than motorists to disregard traffic controls or go the wrong way on a street just before becoming involved in a collision. Motorists are more likely than bicyclists to fail to yield, to engage in improper overtaking, or to follow too closely before becoming involved in a collision. The largest proportion of bicycle collisions occurs during the period 3:30 to 6:30 p.m. Other temporal and locational features of bicycle collisions are described. Among the most significant findings presented is that motorists in Hawaii are much more likely to be classified at fault than bicyclists. Whereas motorists are at fault in approximately 83.5 percent of incidents, bicyclists are at fault in only 16.5 percent of incidents. A logistic model is developed and used to explain the likelihood of motorists being at fault in collisions with bicyclists. Covariates that increase the likelihood of motorist fault include motorist age (squared), bicyclist age, bicyclist helmet use, and motorist turning actions. Variables that decrease the likelihood of motorist fault include motorist age, bicyclist age (squared), bicyclist alcohol use, bicyclists making turning actions, and rural locations.

Evaluation of Potentially Preventable Deaths Among Pedestrian and Bicyclist Fatalities

The prehospital, emergency department, and in-hospital care of 84 patients who died following a pedestrian- or bicycle-motor vehicle collision in a four-county area was retrospectively reviewed using a systematic, detailed scoring system. The purpose of the study was to examine the effectiveness of an advanced paramedic-regionalized trauma care system and to examine the usefulness of a systematic evaluation tool in identifying preventable and possibly preventable deaths. Among the 84 deaths, one was judged to be preventable and 18 possibly preventable. The one preventable death occurred in the emergency department, while the possibly preventable deaths more commonly occurred after 48 hours and were due to central nervous system injury, sepsis, and multiple organ failure. Prolonged prehospital and emergency department time and failure to establish an airway were the most common problems identified as contributing to fatal outcome. The use of explicit criteria was demonstrated to be an effective addition to the analysis of trauma care systems.

Factors in the initiation of bicycle-motor vehicle collisions.

In this study, 888 injury-producing bicycle-motor vehicle collisions were examined. On the basis of the movements of the vehicles involved, the bicyclist or the bicycle or both was probably responsible for the initiation of more than three fourths of the collisions. Bicyclist age was strongly related to probable responsibility for the collision. Through age 12, nine out of ten bicyclists were probably responsible for the collision; above age 12, probable responsibility decreased in proportion to age; and only 34% of the bicyclists aged 25 years or older were probably responsible. Collisions involving bicyclists in various age groups differed considerably in where, when, and how they occurred. The movements of the vehicles primarily involved in the initiation of the collisions were evaluated in terms of counter-measure planning.