The unintended impact of helmet use on bicyclists' risk-taking behaviors

被引:9
|
作者
Kang, Lei [1 ]
Vij, Akshay [2 ]
Hubbard, Alan [3 ]
Shaw, David [4 ]
机构
[1] Univ Calif Berkeley, Dept Civil & Environm Engn, 107 McLaughlin Hall, Berkeley, CA 94720 USA
[2] Univ South Australia, Inst Choice, Level 13,140 Arthur St, Sydney, NSW 2060, Australia
[3] Univ Calif Berkeley, Sch Publ Hlth, Biostat Div, 113B Haviland Hall, Berkeley, CA 94720 USA
[4] Sees Consulting & Testing, Fresno, CA 93729 USA
关键词
Risk compensation effects; Bicycle helmet; Risk-taking behavior; Targeted maximum likelihood estimation; Data adaptive estimation; SEAT-BELT LAWS; CAUSAL INFERENCE; HEAD-INJURIES; PEDESTRIAN PERCEPTIONS; COMPENSATION THEORY; LEGISLATION; CYCLISTS; CHILDREN; COLLISIONS; LEVEL;
D O I
10.1016/j.jsr.2021.08.014
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
Introduction: Safety is a critical factor in promoting sustainable urban non-motorized travel modes like bicycles. Helmets have shown to be effective in reducing injury severity in bicycle crashes, however, their effects on bicyclists' behaviors still requires deeper understanding, especially amid the emerging trend of using shared bicycles. Risk compensation effects suggest that bicyclists may offset perceived gains in safety from wearing a helmet by increasing risk-taking behaviors. A better understanding of these compensation effects can be useful in assessing various bicycle safety related programs. Method: Using a sample of 131 bicyclists from the San Francisco Bay area, this research studies how bicyclists respond with respect to risk-taking behaviors under various urban-street conditions, as a function of helmet use. Study participants were each shown 12 videos, shot in Berkeley, California, from the perspective of a bicyclist riding behind another bicyclist. A fractional factorial experiment design was used to systematically vary contextual attributes (e.g., speed, bike lane facilities, on-street parking, passing vehicles) across the videos. After each video, participants were asked to indicate if they would overtake the bicyclist in the video. With the help of data adaptive estimation techniques, targeted maximum likelihood estimation (TMLE) was applied to estimate the average risk difference between helmeted users and non-users, controlling for self-selection effects. Individual-based nonparametric bootstrap was performed to assess the uncertainty associated with the estimator. Results: Our findings suggest, on average, individuals more likely to wear a helmet are 15.6% more likely to undertake a risky overtaking maneuver. Practical Applications: This study doesn't try to oppose mandatory helmet laws, but rather serves as a cautionary warning that road safety programs may need to consider strategies in which unintended impact of bicycle helmet use can be mitigated. Moreover, our findings also provide additional evaluation component when it comes to the cost-benefit assessment of helmet-related laws. (c) 2021 National Safety Council and Elsevier Ltd. All rights reserved.
引用
收藏
页码:135 / 147
页数:13
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