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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    ABSTRACT: Regular cycling plays an important role in increasing physical activity levels but raises safety concerns for many people. While cyclists bear a higher risk of injury than most other types of road users, the risk differs geographically. Auckland, New Zealand's largest urban region, has a higher injury risk than the rest of the country. This paper identified underlying factors at individual, neighbourhood and environmental levels and assessed their relative contribution to this risk differential. The Taupo Bicycle Study involved 2590 adult cyclists recruited in 2006 and followed over a median period of 4.6 years through linkage to four national databases. The Auckland participants were compared with others in terms of baseline characteristics, crash outcomes and perceptions about environmental determinants of cycling. Cox regression modelling for repeated events was performed with multivariate adjustments. Of the 2554 participants whose addresses could be mapped, 919 (36%) resided in Auckland. The Auckland participants were less likely to be Maori but more likely to be socioeconomically advantaged and reside in an urban area. They were less likely to cycle for commuting and off-road but more likely to cycle in the dark and in a bunch, use a road bike and use lights in the dark. They had a higher risk of on-road crashes (hazard ratio: 1.47; 95% CI: 1.22, 1.76), of which 53% (95% CI: 20%, 72%) was explained by baseline differences, particularly related to cycling off-road, in the dark and in a bunch and residing in urban areas. They were more concerned about traffic volume, speed and drivers' behaviour. The excess crash risk in Auckland was explained by cycling patterns, urban residence and factors associated with the region's car-dominated transport environment.
    Environmental Health 12/2013; 12(1):106. · 2.71 Impact Factor
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    ABSTRACT: Scientific literature lacks a model which combines exposure to risk, risk, and the relationship between them. This paper presents a conceptual road safety framework comprising mutually interacting factors for exposure to risk resulting from travel behaviour (volumes, modal split, and distribution of traffic over time and space) and for risk (crash and injury risk). The framework's three determinants for travel behaviour are locations of activities; resistances (generalized transport costs); needs, opportunities, and abilities. Crash and injury risks are modelled by the three ‘safety pillars’: infrastructure, road users and the vehicles they use. Creating a link in the framework between risk and exposure is important because of the ‘non-linear relationship’ between them, i.e. risk tends to decrease as exposure increases. Furthermore, ‘perceived’ risk (a type of travel resistance) plays a role in mode choice, i.e. the perception that a certain type of vehicle is unsafe can be a deterrent to its use. This paper uses theories to explain how the elements in the model interact. Cycling is an area where governments typically have goals for both mobility and safety. To exemplify application of the model, the paper uses the framework to link research on cycling (safety) to land use and infrastructure. The model's value lies in its ability to identify potential consequences of measures and policies for both exposure and risk. This is important from a scientific perspective and for policy makers who often have objectives for both mobility and safety.
    Accident Analysis & Prevention 04/2013; · 1.87 Impact Factor
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    ABSTRACT: This thesis is focused on the question how the road environment (road design and network characteristics) affects road safety for cyclists through effects on risk and exposure to risk. This question is relevant because government agencies in many countries aim to improve road infrastructure safety for cyclists to decrease the substantial health burden due to cyclist injuries. This concerns both collisions with motor vehicles which regularly result in fatal injuries as well as single-bicycle crashes (falls or obstacle collisions) in which many cyclists incur serious injuries. The research questions formulated in this thesis address three main topics. The first subject is about how the road environment affects travel behaviour and exposure. The second is about its effect on crash risk (injury risk is only marginally addressed). The third topic is the relationship between exposure and risk, because both may affect one another. One of the innovative aspects of this thesis is that it contains studies related to all three topics (exposure, risk and their relationship), aiming to increase the knowledge of how the road environment contributes to or helps to prevent bicycle crashes. Most research is restricted to one of the three issues. Chapter 1 describes a conceptual framework which combines exposure to risk, risk, and the relationship between them. The framework’s three determinants for travel behaviour are locations of activities; resistances (generalized transport costs); needs, opportunities, and abilities. Crash and injury consequences are modelled by the three ‘safety pillars’: infrastructure, road users and the vehicles they use. The framework’s link between risk and exposure is important because of the ‘non-linear relationship’ between these two, i.e. risk tends to decrease as exposure increases. Finally, the framework has a link from (perceived) risk to resistance because perceived risk plays a role in travel behaviour, e.g. a road user may prefer driving over cycling because cars are perceived to be safer. The remainder of this summary is organized according to the three research topics. The road environment may encourage or discourage cycling which affects exposure to risk. It depends on the relationship between exposure and risk to what extent the number of road traffic casualties is affected. Chapters 2 and 3 focus on the following research question: How does a modal shift from short car trips to cycling affect road safety? To answer this question, Crash Prediction Models (CPMs) were developed forDutch municipalities. Models were also developed for single-bicycle crashes which was not done before. As single-bicycle crashes are under-reported by the police, the study also included another data source: self-reported crashes from a questionnaire study. It was found that cyclists are less likely to be involved in a severe single-bicycle crash in municipalities with a high amount of cycling. The volumes of cyclists and motor vehicles before and after a hypothetical modal shift were entered into the CPMs to estimate the road safety effects. The results suggest that, under conditions such as in Dutch municipalities, transferring short trips made by cars to bicycles does not change the number of fatalities, but increases the number of serious road injuries. The rise in the number of serious road injuries is due to high numbers of severe single-bicycle crashes. The effect of a modal shift is dependent on the age of the population in which the shift is concentrated, i.e. more favourable for young and less favourable for older drivers. Furthermore, the results suggest that it may be possible to influence the effect of a modal shift by measures specifically affecting cyclists’ risk. Chapter 4, 5, and 6 are focused on road design and crash risk. Chapters 4 describes a study conducted to answer question 3a: How is the design of unsignalized priority intersections related to bicycle–motor vehicle crashes? In this study, the safety of cyclists at unsignalized priority intersections within built-up areas is investigated. Failure-to-yield crashes recorded at unsignalized intersections were classified into two types based on the movements of the involved motorists and cyclists: • type I: through bicycle related collisions where the cyclist has right of way (i.e. bicycle on the priority road); • type II: through motor vehicle related collisions where the motorist has right of way (i.e. motorist on the priority road). The probability of each crash type was related to its relative flows and to independent variables using negative binomial regression. The results show that more type I crashes occur at intersections with two-way bicycle tracks, well-marked, and reddish coloured bicycle crossings. Type I crashes are negatively related to the presence of raised bicycle crossings (e.g. on a speed hump) and other speed reducing measures. The accident probability is also decreased at intersections where the cycle track approaches are deflected between 2 and 5m away from the main carriageway. No significant relationships are found between type II crashes and design factors such as the presence of a raised median. Chapter 5 focuses on research question 3b: What single-bicycle crash types can be distinguished and can these be related to infrastructure?A literature search showed that only a few studies addressed single-bicycle crashes (i.e. a fall or obstacle collision). These studies and theories were used to develop a draft categorization of single-bicycle crash types. The typology was tested using a survey among bicycle crash victims treated at Emergency Care Departments. The results indicate that about half of all single-bicycle crashes are related to infrastructure: the cyclist collided with an obstacle (1ai), rode off the road (1aii), the bicycle skidded due to a slippery road surface (1bi), or the rider was unable to stabilize the bicycle or stay on the bike because of an uneven road surface (1bii). The first two categories happen due to the cyclist inadvertently taking a dangerous riding line, while the last two happen under more direct influence of the road surface conditions. Crash types related to the cyclist are loss of control at low speed (2a), due to forces on the front wheel (2b), or poor or risky riding behaviour (2c). Bicycle defects (3) contribute to a small group of crashes. Finally, some cyclists fall because of an external force such as a gust of wind (4). Question 3c is about the role of visibility of infrastructure in single-bicycle crashes: What do cyclists need to see to avoid single-bicycle crashes?This question is addressed in Chapter 6. To study the role of visual characteristics of the infrastructure, such as pavement markings, in single-bicycle crashes, a study in two steps was conducted. In Study 1, a questionnaire study was conducted among bicycle crash victims. Logistic regression was used to study the relationship between the crashes and age, light condition, alcohol use, gaze direction and familiarity with the crash scene. In Study 2, the image degrading and edge detection method (IDED-method) was used to investigate the visual characteristics of 21 of the crash scenes. The results of the studies indicate that crashes, in which the cyclist collided with a bollard or road narrowing or rode off the road, were related to the visual characteristics of bicycle facilities. Chapter 7 focuses on network characteristics and cycling safety. It addresses the first research question: How does network-level separation of vehicular and cycle traffic (unbundling) in urban networks affect road safety? This is related to the distribution of traffic over space, one of the elements of travel behaviour. Bicycle-motor vehicle crashes are concentrated along distributor roads where cyclists are exposed to greater volumes of high-speed motorists than they would experience on access roads. This study examined the road safety impact of unbundling vehicular and cycle traffic in Dutch urban networks. Unbundling is operationalized as the degree to which cyclists use access roads and grade-separated intersections to cross distributor roads. The effect on the share of cycling in the modal split is also assessed as unbundling measures may affect the competitiveness of cycling compared to driving. The analyses were conducted using data of all Dutch municipalities with more than 50,000 inhabitants. Negative binomial regression was used to analyse the effect on the number of police-reported cyclist deaths and in-patients in bicycle-motor vehicle crashes. A mediation model was tested, with Structural Equation Modelling hypothesizing that unbundling corresponds positively with the cycling modal share via the length of car trips divided by those by bicycle. The results of this study suggest that unbundling improves cycling safety, and increases the share of cycling in the modal split (as a result of improved competitiveness of cycling in terms of trip length). Chapter 8 discussed the main findings of the research conducted throughout the thesis and considered the implications. It can be concluded that cycling safety is affected by the road design. For instance, the studies described in Chapters 4, 5, and 6 indicate that the design of bicycle tracks and intersections affect the likelihood of BMV and single-bicycle crashes. Chapter 7 indicates that network characteristicsare related to the likelihood of BMV crashes due its effect on the distribution of vehicular and cycle traffic over the network. This affects cyclists’ exposure to high-speed vehicular traffic. The road environment may encourage or discourage cycling. For example, the study described in Chapter 7 suggest that the measures taken for unbundling correspond positively with the modal share of cycling because trips become relatively shorter by bicycle then by car. In Chapters 2 and 3 it is estimated that under conditions such as in Dutch municipalities, transferring short trips made by cars to bicycles does not change the number of fatalities, but increases the number of serious road injuries. Chapter 8 discusses a number of uncertainties regarding the latter conclusion. A more favourable road safety impact can be expected if the modal shift would be induced by instance network-level separation or other safety-related measures than if it were induced by factors unrelated to safety (e.g. an increased gasoline price). The chapterdiscusseschallengesforfuture research.
    12/2013, Degree: PhD, Supervisor: Prof. ir. F.C.M. Wegman


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