The effect of crash characteristics on cyclist injuries: An analysis of Virginia automobile-bicycle crash data

Robartes, Erin; Chen, T. Donna

doi:10.1016/j.aap.2017.04.020

Cited by 57 publications

(30 citation statements)

References 18 publications

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“…Several environmental conditions related variables are found to affect bicyclist injury severity significantly, as shown in Table 5. Consistent with previous research, bad weather can result in more dangerous cycling [17,19,23]. However, the impact of environmental conditions on the probability of cycling crashes that occurred at the various intersections is quite dissimilar.…”

Section: Environmental Conditionssupporting

confidence: 88%

“…Cycling on the curved road segments, rural roads, and high-speed roads could raise the risk of severe injury [16]. Also, intoxicated bicyclists and automobile drivers, vans, SUVs, light-duty trucks, and roads with a grade or a curve are more likely to be involved in severe crashes [17]. Regarding the bicycle lanes, Morrison et al [18] insisted that setting up exclusive bicycle lanes was a solution to improve the safety of bicyclists, which reduces the crash risk between bicycles and vehicles and raises the perception of cycling safety, thereby attracting more people to use bikes.…”

Section: Introductionmentioning

confidence: 99%

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Determinants of Bicyclist Injury Severity Resulting from Crashes at Roundabouts, Crossroads, and T-Junctions

Shen

Wang

Zheng

et al. 2020

Journal of Advanced Transportation

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This study explores the contributing factors that influence bicyclist injury severity at three types of intersection: roundabouts, crossroads, and T-junctions. Using bicycle-involved crash data in the UK over nine years (from 2009 to 2017), the bicyclist injury severity (with three severity levels: fatal injury, serious injury, and slight injury) was estimated using the generalized ordered logit (GOL) model and partial proportional odds (PPO) model. The marginal effects of each explanatory variable were computed to investigate the impacts on bicyclist injury severity occurring probabilities. A wide range of variables potentially affecting injury severity was considered, including bicyclist characteristics, intersection characteristics, environmental conditions, bicyclist movement and location preceding the crash, and types of collisions. Our findings show that the PPO model outperforms the GOL model for analyzing the factors that affect the bicyclist injury severity at intersections. The factors that affect cycling safety at various intersections show enormous differences. Specifically, nine variables have significant impacts on bicyclist injury severity at those three types of intersections. And there are only two variables, four variables, and eleven variables that have significant impact on bicyclist injury severity at roundabouts, crossroads, and T-junctions, respectively. The findings of this study can help decision makers better understand the spatial heterogeneity of the factors that influence the bicyclist injury severity at various intersections.

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Section: Environmental Conditionssupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Determinants of Bicyclist Injury Severity Resulting from Crashes at Roundabouts, Crossroads, and T-Junctions

Shen

Wang

Zheng

et al. 2020

Journal of Advanced Transportation

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“…Important determinants of bicyclist injury severity reported in the literature include: a head on/angle crash [ 23 – 26 ], speeding-involved [ 23 ], truck involved [ 23 – 25 , 27 , 28 ], an elderly cyclist [ 23 , 27 ], driver/cyclist intoxication [ 23 , 25 , 28 ], vertical/horizontal curves [ 25 , 26 , 28 ], night without streetlight [ 23 , 24 , 26 ], higher speed limit [ 24 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Comparison of traffic-injury related hospitalisation between bicyclists and motorcyclists in Taiwan

2018

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ObjectivesBicyclists and motorcyclists contribute substantially to the morbidity and mortality rates of road crash casualties. The objective of the study was to investigate the crash characteristics of bicyclist and motorcyclist casualties presented to hospitals in Taiwan resulting from crashes.MethodsBy using linked data from The National Traffic Crash Dataset and the National Health Insurance Database between 2003 and 2012, logistic regression models were used to examine the determinants of hospitalisation among motorcyclist and bicyclist casualties. The examined variables include demographic characteristics, road and weather conditions, and vehicle characteristics.ResultsA total of 1,998,606 two-wheelers were enrolled in the study, of whom 216,600 were hospitalised: 203,623 were motorcyclists and 12,964 were bicyclists. Bicyclists were more likely to be hospitalised than motorcyclists were (14.0% vs. 10.7%). The pooled logistic regression model shows that bicyclists had higher odds of hospitalisation than motorcyclists (adjusted odds ratio [AOR] = 1.11, 95% confident interval [CI] = 1.08–1.14). In the motorcyclist and bicyclist models, helmet non-use appears to be a determinant of hospitalisation for motorcyclists (AOR = 1.14, CI = 1.12–1.16), although insignificant for cyclists (AOR = 1.03, CI = 0.94–1.12). Other important determinants of hospitalisation for motorcyclists and cyclists include female riders, elderly riders, rural roadways, unlicensed riding (for motorcyclists only), curved roadways, defective roadways, alcohol consumption (only for motorcyclists), and single-vehicle crashes (for motorcyclists only).ConclusionsThe result that bicyclists had an increased probability of being hospitalised than motorcyclists is particularly noteworthy, because there have recently been much more users of bikesharing systems in metropolitan cities where cycle helmets are not provided. We further found that helmet non-use was also a risk factor for motorcyclists, but insignificant for cyclists, possibly due to lower helmet utilization rates among bicyclists. Our findings regarding the increased hospitalisation percentage emphasize the importance of helmet use.

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“…Cycling is an active mode of transportation with well-established health and economic benefits, so much so that authorities are constantly developing cycling-promoting specificstrategies with the aim of increasing cycling participation [1][2][3]. However, the safety of cyclists in traffic remains a major concern [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…*: statistically significant differences between groups; CI = 95% Confidence Interval for the Mean; 2,3 : statistically significant differences with group 2 (adults) or 3 (aging adults), respectively. ECBQ: External Rater Cycling Behavior Questionnaire (CBQ);4 Brown-Forsythe (robust mean comparison test score).…”

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confidence: 99%

“Not as Safe as I Believed”: Differences in Perceived and Self-Reported Cycling Behavior between Riders and Non-Riders

et al. 2021

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Cycling behavior remains a key issue for explaining several traffic causalities occurring every day. However, recent studies have shown how the assessment of the own safety-related behaviors on the road may substantially differ from how third parties assess them. Thus, the aim of this study was to evaluate the differences between cyclists’ self-reported behavior and the proxy-reported behavior that other (non-cyclist) road users perceive from bike riders. For this purpose, this study used data from two samples: (i) 1064 cyclists (M = 32.83 years) answering the Cycling Behavior Questionnaire—CBQ, and (ii) 1070 non-cyclists (M = 30.83 years) answering an adapted version of the CBQ for external raters—ECBQ. The results show how the self-reported and proxy-reported behaviors of cyclists greatly differ in terms of all behavioral factors composing the CBQ model, i.e., traffic violations, riding errors, and positive behaviors. Also, external raters (non-cyclists) are those targeting significantly riskier behaviors than those self-reported by cyclists. These discrepancies between perceived behaviors may give rise to conflicting viewpoints on the interaction between bicycle riders and other road users. Therefore, this study underscores the importance of behavioral awareness, providing highlights for future studies on the behavioral interaction between cyclists and other road users. Results can be used to improve the road safety of all road users by giving indications on self-and proxy-perceived safety-related behaviors and visibility of protective riding habits.

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The effect of crash characteristics on cyclist injuries: An analysis of Virginia automobile-bicycle crash data

Cited by 57 publications

References 18 publications

Determinants of Bicyclist Injury Severity Resulting from Crashes at Roundabouts, Crossroads, and T-Junctions

Determinants of Bicyclist Injury Severity Resulting from Crashes at Roundabouts, Crossroads, and T-Junctions

Comparison of traffic-injury related hospitalisation between bicyclists and motorcyclists in Taiwan

“Not as Safe as I Believed”: Differences in Perceived and Self-Reported Cycling Behavior between Riders and Non-Riders

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