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


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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    • "Such strategies can create inattentional blindness (Mack and Rock, 1998), ultimately contributing to 'looked but failed to see errors " in relation to cyclists. Other factors identified as increasing the likelihood of fatal injury of cyclists in collisions with other vehicles are greater vehicle speed, truck involvement, intoxicated driver or cyclist, cyclist age, weather, darkness without streetlights and head-on collision (Kim et al., 2007). Rissel et al. (2002) considered car driver attitudes towards cyclists as an outcome of driver knowledge about road rules. "
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    ABSTRACT: The interaction of car drivers and cyclists is one of the main causes of cycle incidents. The role of attitudes and social norms in shaping car drivers' aggressive behaviour towards cyclists, is not well understood and merits investigation. A sample of 276 drivers completed an online questionnaire concerning their attitudes towards cyclists, attitudes towards risky driving, perception of social norms concerning aggressive driving towards cyclists, and the frequency with which they engage in such aggressive driving behaviours. The results showed that attitudes towards cyclists, as well as social norm perceptions concerning aggressive driving towards cyclists, were associated with aggressive driving towards cyclists. Negative attitudes towards cyclists were more pronounced in non-cyclists than cyclists and their association with aggressive driving behaviour was stronger in cyclists than non-cyclists. The perception of social norms concerning aggressive driving towards cyclists had a stronger association with aggressive driving in non-cyclists than cyclists. Attitudes towards risk taking did not affect aggressive driving towards cyclists. These findings can inform campaigns that aim to improve cyclist and car driver interaction on the roads, making them safer to use for cyclists. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Accident; analysis and prevention 08/2015; 83:162-170. DOI:10.1016/j.aap.2015.07.003 · 1.65 Impact Factor
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    • "Hu et al. explored the related risk factors of injuries caused by e-bike and bicycle crashes in Hefei, China [14]. Joon-Ki et al. explored the factors contributing to the injury severity of bicyclists in bicycle–motor vehicle accidents using a multinomial logit model [15]. In order to study the kinematics involved in cyclist accidents as well as the specific injuries and the points of contact on the vehicles, a real-world accident was reconstructed using mathematical dynamic model (MADYMO) crash simulation software by Carter et al. [16]. "

    • "In addition, higher numbers of light injuries among cyclists are recorded in proximity of parking facilities and public transport stops (e.g., Kim and Kim 2015; Kim et al. 2007; Rifaat et al. 2011). From the traffic condition perspective, both peak and off-peak hours observe fewer crashes (e.g., Hels and Orozova-Bekkevold 2007; Wang and Nihan 2004), but the severity is lower in peak hours because of the reduced speed differential between fast and slow transport modes and higher in off-peak hours because of high vehicle speeds (e.g., Kaplan et al. 2014; Kim et al. 2007; Klop and Khattak 1999). From the individual perspective, cyclists' fragility and intoxication level contribute to more severe consequences among cyclists (Kaplan et al. 2014), and type of maneuvers and vehicles involved play a role in the injury severity outcome (Bíl et al. 2010; Hu et al. 2014; Kaplan et al. 2014). "
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    ABSTRACT: 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.
    Traffic Injury Prevention 02/2015; 16(7). DOI:10.1080/15389588.2014.1003818 · 1.41 Impact Factor
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