Exposure measurement in bicycle safety analysis: A review of the literature

Vanparijs, Jef; Panis, Luc Int; Meeusen, Romain; Geus, Bas de

doi:10.1016/j.aap.2015.08.007

Cited by 141 publications

(85 citation statements)

References 54 publications

(131 reference statements)

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“…Strategies to prevent bicycling-related crashes, fatalities, and injuries include improved environmental infrastructure (Vanparijs, Int Panis, Meeusen, & de Geus, 2015), following traffic laws, respecting rules for sharing the road, and using proper turn signals for both motorists and bicyclists (National Center for Statistics and Analysis, 2015). However, in the event of a crash, bicycle helmets are effective for the prevention of serious head injuries and deaths (Amoros et al, 2012; Bambach et al, 2013; Boufous et al, 2012; Cripton et al, 2014; McNally & Whitehead, 2013; Persaud et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Bicycle helmet use among persons 5 years and older in the United States, 2012

Jewett

Beck

Taylor

et al. 2016

Journal of Safety Research

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Introduction In 2013, injuries to bicyclists accounted for 925 fatalities and 493,884 nonfatal, emergency department-treated injuries in the United States. Bicyclist deaths increased by 19% from 2010 to 2013. The greatest risk of death and disability to bicyclists is head injuries. The objective of this study was to provide estimates of prevalence and associated factors of bicycle riding and helmet use among children and adults in the United States. Method CDC analyzed self-reported data from the 2012 Summer ConsumerStyles survey. Adult respondents (18+ years) were asked about bicycle riding and helmet use in the last 30 days for themselves and their children (5 to 17 years). For bicycle riders, CDC estimated the prevalence of helmet use and conducted multivariable regression analyses to identify factors associated with helmet use. Results Among adults, 21% rode bicycles within the past 30 days and 29% always wore helmets. Respondents reported that, of the 61% of children who rode bicycles within the past 30 days, 42% always wore helmets. Children were more likely to always wear helmets (90%) when their adult respondents always wore helmets than when their adult respondents did not always wear helmets (38%). Children who lived in states with a child bicycle helmet law were more likely to always wear helmets (47%) than those in states without a law (39%). Conclusions Despite the fact that bicycle helmets are highly effective at reducing the risk for head injuries, including severe brain injuries and death, less than half of children and adults always wore bicycle helmets while riding. Practical application States and communities should consider interventions that improve the safety of riding such as policies to promote helmet use, modeling of helmet wearing by adults, and focusing on high risk groups, including Hispanic cyclists, occasional riders, adults, and children ages 10 to 14.

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Section: Discussionmentioning

confidence: 99%

Bicycle helmet use among persons 5 years and older in the United States, 2012

Jewett

Beck

Taylor

et al. 2016

Journal of Safety Research

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“…Most importantly, bicycle volume data can help forecast bicycle crash risk, which in turn inform road-safety improvements. Bicycle crash risk is often calculated by the number of bicycle collisions divided by a denominator indicating bike volume such as the number of bicycle trips, the bicycle miles traveled, or the number of bicyclists (Vanparijs, Panis, Meeusen, & de Geus, 2015). Generating these preferred denominators, mostly unavailable now, will help researchers accurately measure the bicycle crash risk.…”

Section: Built Environment Determinants Of Bicycle Volume: a Longitudmentioning

confidence: 99%

Built environment determinants of bicycle volume: A longitudinal analysis

Chen

Zhou

Sun

2017

JTLU

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Abstract:This study examines determinants of bicycle volume in the built environment with a five-year bicycle count dataset from Seattle, Washington. A generalized linear mixed model (GLMM) is used to capture the bicycle volume change over time while controlling for temporal autocorrelations. The GLMM assumes that bicycle count follows a Poisson distribution. The model results show that (1) the variables of non-winter seasons, peak hours, and weekends are positively associated with the increase of bicycle counts over time; (2) bicycle counts are fewer in steep areas; (3) bicycle counts are greater in zones with more mixed land use, a higher percentage of water bodies, or a greater percentage of workplaces; (4) the increment of bicycle infrastructure is positively associated with the increase of bicycle volume; and (5) bicycling is more popular in neighborhoods with a greater percentage of whites and younger adults. It concludes that areas with a smaller slope variation, a higher employment density, and a shorter distance to water bodies encourage bicycling. This conclusion suggests that to best boost bicycling, decision-makers should consider building more bicycle facilities in flat areas and integrating the facilities with employment densification and open-space creation and planning.

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“…Zernetsch et al (2016) collected data at a single intersection for path prediction of a starting cyclists, and Hubert et al (2017) used the same data to find indicators of cyclist starting behavior. Some studies have used naturalistic data to detect and classify critical vehicle-cyclist interactions at intersections (Sayed et al 2013;Vanparijs et al 2015;Cara and de Gelder 2015), while others use simulations to study bicycle motion at intersections Zhang et al 2017).…”

Section: Context Cues For Vru Behaviorsmentioning

confidence: 99%

Context-Based Path Prediction for Targets with Switching Dynamics

et al. 2018

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Anticipating future situations from streaming sensor data is a key perception challenge for mobile robotics and automated vehicles. We address the problem of predicting the path of objects with multiple dynamic modes. The dynamics of such targets can be described by a Switching Linear Dynamical System (SLDS). However, predictions from this probabilistic model cannot anticipate when a change in dynamic mode will occur. We propose to extract various types of cues with computer vision to provide context on the target's behavior, and incorporate these in a Dynamic Bayesian Network (DBN). The DBN extends the SLDS by conditioning the mode transition probabilities on additional context states. We describe efficient online inference in this DBN for probabilistic path prediction, accounting for uncertainty in both measurements and target behavior. Our approach is illustrated on two scenarios in the Intelligent Vehicles domain concerning pedestrians and cyclists, so-called Vulnerable Road Users (VRUs). Here, context cues include the static environment of the VRU, its dynamic environment, and its observed actions. Experiments using stereo vision data from a moving vehicle demonstrate that the proposed approach results in more accurate path prediction than SLDS at the relevant short time horizon (1 s). It slightly outperforms a computationally more demanding state-of-the-art method.

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Exposure measurement in bicycle safety analysis: A review of the literature

Cited by 141 publications

References 54 publications

Bicycle helmet use among persons 5 years and older in the United States, 2012

Bicycle helmet use among persons 5 years and older in the United States, 2012

Built environment determinants of bicycle volume: A longitudinal analysis

Context-Based Path Prediction for Targets with Switching Dynamics

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