Bicyclist injury severities in bicycle-motor vehicle accidents

Washington University in St. Louis, Department of Civil Engineering, Campus Box 1130, One Brookings Drive, St. Louis, MO 63130-4899, USA.
Accident Analysis & Prevention (Impact Factor: 1.87). 04/2007; 39(2):238-51. DOI: 10.1016/j.aap.2006.07.002
Source: PubMed

ABSTRACT This research explores the factors contributing to the injury severity of bicyclists in bicycle-motor vehicle accidents using a multinomial logit model. The model predicts the probability of four injury severity outcomes: fatal, incapacitating, non-incapacitating, and possible or no injury. The analysis is based on police-reported accident data between 1997 and 2002 from North Carolina, USA. The results show several factors which more than double the probability of a bicyclist suffering a fatal injury in an accident, all other things being kept constant. Notably, inclement weather, darkness with no streetlights, a.m. peak (06:00 a.m. to 09:59 a.m.), head-on collision, speeding-involved, vehicle speeds above 48.3 km/h (30 mph), truck involved, intoxicated driver, bicyclist age 55 or over, and intoxicated bicyclist. The largest effect is caused when estimated vehicle speed prior to impact is greater than 80.5 km/h (50 mph), where the probability of fatal injury increases more than 16-fold. Speed also shows a threshold effect at 32.2 km/h (20 mph), which supports the commonly used 30km/h speed limit in residential neighborhoods. The results also imply that bicyclist fault is more closely correlated with greater bicyclist injury severity than driver fault.

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    • "A cycle network is only as good as its weakest features and these are often the junctions (ETSC, 1999). In North Carolina, USA, 50.2% of bicycle–motor vehicle accidents occurred at intersections (Kim et al., 2007). Riders' red-light infringement is a type of highly dangerous behavior occurring at intersections. "
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    ABSTRACT: Hazard-based duration models are proposed to investigate riders’ waiting times, violation hazards and associated risk factors.•Most riders are prone to terminate waiting duration and run against the red light over the waiting time elapsed.•Rider type, gender, waiting position, conformity tendency and traffic volume have significant effects on waiting times and violation hazards.•E-bikers are more sensitive to the external risk factors such as other riders’ crossing behavior and crossing traffic volume than cyclists.•The finding of this paper can explain when and why cyclists and e-bikers run against the red light at intersections.
    Accident Analysis & Prevention 01/2015; 74. DOI:10.1016/j.aap.2014.10.014 · 1.87 Impact Factor
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    • "It is therefore difficult to underpin a hypothesis on injury severity. User characteristics such as age are related to injury severity (Kim et al. 2007) and need to be controlled for to conclude whether differences are related due to bicycle type. "
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    ABSTRACT: Use of electrically assisted bicycles with a maximum speed of 25 km/h is rapidly increasing. This growth has been particularly rapid in the Netherlands, yet very little research has been conducted to assess the road safety implications. This case–control study compares the likelihood of crashes for which treatment at an emergency department is needed and injury consequences for electric bicycles to classic bicycles in the Netherlands among users of 16 years and older. Data were gathered through a survey of victims treated at emergency departments. Additionally, a survey of cyclists without any known crash experience, drawn from a panel of the Dutch population acted as a control sample. Logistic regression analysis is used to compare the risk of crashes with electric and classical bicycles requiring treatment at an emergency department. Among the victims treated at an emergency department we compared those being hospitalized to those being send home after the treatment at the emergency department to compare the injury consequences between electric and classical bicycle victims. The results suggest that, after controlling for age, gender and amount of bicycle use, electric bicycle users are more likely to be involved in a crash that requires treatment at an emergency department due to a crash. Crashes with electric bicycles are about equally severe as crashes with classic bicycles. We advise further research to develop policies to minimize the risk and maximize the health benefits for users of electric bicycles.
    Accident Analysis & Prevention 09/2014; 73:174-180. DOI:10.1016/j.aap.2014.09.010 · 1.87 Impact Factor
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    • "Comprehensive intervention programs for drivers and child bicyclists, to improve traffic safety behaviors and bicycle riding skills, are other avenues for crash reduction; although further evaluation of such programs is needed to generate impact beyond knowledge change by assessing riding skills, driving behaviors, and injury and crash rates post intervention (Rivara and Metrik, 1998; Richmond et al., 2013; Ellis, 2014). Additionally, one of the known ways to reduce injuries and injury severity is speed reduction of motorized transport, of particular interest for non-intersection collisions (Kim et al., 2007; World Health Organization, 2014). Speed reduction is also pertinent to crashes involving children 10 and younger, rather than safety training alone; given their heightened impulsivity and eyesight that is not fully developed to be able to judge speeds over 20 mph (Wang et al., 2011). "
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    ABSTRACT: Purpose To identify how person, crash, environment, and population characteristics differ between bicycle-motor vehicle crashes that occur at intersections and non-intersections. Methods The Iowa Department of Transportation crash database for the years 2001 through 2011 was used to identify bicycle-motor vehicle (BMV) crashes and associated person, crash, and environment characteristics. Population-level data were drawn from the 2010 U.S. Census and the 2010 American Community Survey. Descriptive statistics, GIS mapping, and multivariable logistic regression were used to examine factors associated with crash risk and crash location. Results Compared to intersections, non-intersection BMV crashes had higher odds of involving young bicyclists (<10 years old; OR: 1.8, 95%CI: 1.2–2.6), location outside city limits (OR: 5.7, 95%CI: 3.9–8.3), with driver vision obscured (OR: 1.5, 95% CI: 1.2–1.8), reduced lighting on roadway (OR: 1.9, 95% CI: 1.5–2.4), and lower odds when the bicyclist (OR: 0.4, 95% CI: 0.3–0.6) or motorist (OR: 0.6, 95% CI: 0.4–0.8) failed to yield right of way. Conclusions Environmental factors, as well as developmental (age) and behavioral factors of bicycle-motor vehicle crashes vary by location (intersection/non-intersection). Results from this study can be used to tailor and target multiple intervention approaches, such as making infrastructure changes, increasing safety behavior among both motorists and bicyclists, and identifying which age groups and locations would most benefit from intervention.
    09/2014; 2(2). DOI:10.1016/j.jth.2014.08.006
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