Identifying and Assessing Perceived Cycling Safety Components
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2023/12/01
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Description:Perceived safety is recognized throughout the mode choice literature as a key barrier to cycling, yet its constructs are poorly understood. Although commonly understood to relate to crash and injury risk and sometimes vulnerability to crime, health impact assessments identify numerous other pathways through which cycling can negatively impact health. This study leverages a nationally representative survey of U.S. adults in 2022 to assess a set of eleven factors as potential components of perceived cycling safety. We use principal component analysis to identify components of perceived cycling safety and then employ principal component regression to assess these components in relation to predicting unsafe cycling perception. We identify five key dimensions of perceived safety. Specifically, we found that perceived bicycling safety can be encompassed in the following components: (1) contaminant exposure, (2) injurious collision risk, (3) street conditions, (4) weather conditions, and (5) crime risk. In evaluating each identified component, we found that injurious collision risk and street conditions were the most predictive of considering cycling as unsafe. We further develop an understanding of how differences in cycling behavior, such as using cycling for commuting purposes, may contribute to differences in how cycling safety components coalesce into perceived safety. [Description provided by NIOSH]
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ISSN:2313-576X
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Pages in Document:75
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Volume:9
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Issue:4
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NIOSHTIC Number:nn:20069502
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Citation:Safety 2023 Dec; 9(4):75
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Contact Point Address:Michelle Duren, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA
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Email:mduren3@jhmi.edu
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Federal Fiscal Year:2024
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Performing Organization:Johns Hopkins University
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Peer Reviewed:True
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Start Date:20050701
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Source Full Name:Safety
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End Date:20280630
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Main Document Checksum:urn:sha-512:ed3ab71ccc8422172db7ce09dcd0ed2d55f32de90c8491bb70fabb718c3be1f1c8c0edc77117398654842b9e752dcda8636af2ff8d3c9b5b46ca14d004d3400c
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